Overcoming Complexity: Modern Strategies for the Synthesis of Natural Products

Grace Richardson Nov 26, 2025 540

This article addresses the significant challenge of structural complexity in natural product synthesis, a central field for drug discovery.

Overcoming Complexity: Modern Strategies for the Synthesis of Natural Products

Abstract

This article addresses the significant challenge of structural complexity in natural product synthesis, a central field for drug discovery. Aimed at researchers and drug development professionals, it explores the foundational reasons behind synthetic complexity and details cutting-edge strategies to overcome it. The scope encompasses traditional total synthesis, innovative simplification approaches, the rise of computational and bioinspired planning, and the critical validation of these methods through case studies and comparative analysis. By synthesizing insights from these four core intents, the article provides a comprehensive roadmap for developing complex natural products and their optimized derivatives into viable clinical candidates.

The Intricate Architecture of Natural Products: Defining the Challenge

What Makes a Natural Product 'Complex'? An Analysis of Molecular Scaffolds

FAQs: Understanding and Analyzing Structural Complexity

Q1: What molecular features primarily contribute to the structural complexity of a natural product?

Structural complexity in natural products arises from a combination of several key molecular features:

  • High Scaffold Diversity: The presence of unique and varied carbon skeletons or core structures, as opposed to simple, flat scaffolds. Strategies like Complexity-to-Diversity (CtD) use complex natural product starting materials to generate libraries of compounds each based on a distinct molecular scaffold [1].
  • Significant Stereochemical Complexity: The presence of multiple stereocenters (chiral centers), particularly those with defined and challenging relative configurations. This includes complex ring systems with distal stereocenters interrupted by rigid substructures [2].
  • Macrocyclic and Medium-Sized Rings: Macrocycles are large-ring compounds (often 12+ atoms) that are conformationally restrained but can exhibit diverse biological properties. Medium-sized rings (7-11 membered) are also particularly challenging due to unfavorable transannular interactions and entropic effects, making them synthetically difficult and under-represented in screening libraries [3].
  • Functionalization and Substituents: A high density of diverse functional groups installed via methods like C–H functionalization, which can further be used for ring expansion reactions to access novel chemical space [3].

Q2: What are the main experimental challenges in determining the structure of a complex natural product?

The primary challenges include:

  • Structural Elucidation of Minor Components: Often, the most interesting compounds are available in only minute quantities (micrograms or nanomoles), pushing the limits of analytical techniques like NMR [4].
  • Assignment of Stereochemistry: Determining the relative and absolute configuration of multiple stereocenters, especially when they are distal (far apart in the molecule) or separated by rotatable bonds, is notoriously difficult. NMR alone can be insufficient for this task [2] [5].
  • Crystallization for X-ray Analysis: X-ray crystallography is the gold standard for unambiguous structure determination but is often thwarted by an inability to grow crystals of sufficient size and quality from the available material [2].
  • Misassignment from Spectroscopic Data: Structural proposals can be erroneous due to biases in interpreting complex NMR data, leading to published structures that require subsequent revision [5].

Q3: What advanced techniques are available for the structural elucidation of complex natural products when material is scarce?

Modern techniques have dramatically lowered the required amount of material for full structure elucidation:

  • Microcrystal Electron Diffraction (MicroED): An emerging cryo-electron microscopy (CryoEM) method that provides unambiguous atomic-level structures from sub-micron-sized crystals, which are too small for conventional X-ray diffraction. This technique has been pivotal in solving and even revising the structures of complex natural products [2].
  • Microcryoprobe NMR: NMR probes with cryogenically cooled electronics that use small-volume capillaries (1-1.7 mm). This technology increases the signal-to-noise ratio by 10-20 fold, allowing the acquisition of multi-dimensional NMR data on nanomole-scale samples (a few micrograms for a 1000 Da compound) [4].
  • Computational NMR Prediction: Using density functional theory (DFT) and other computational methods to predict NMR chemical shifts and spin-spin coupling constants. Comparing these calculated spectra with experimental data is a powerful method for validating proposed structures and identifying errors [5].
  • Circular Dichroism (CD): A highly sensitive chiroptical technique that, when combined with computational predictions (time-dependent DFT), can assign absolute configuration at the picomole level, complementing NMR data [4].

Q4: What strategies exist to diversify complex natural products and access novel chemical space for drug discovery?

Two prominent synthetic strategies are:

  • Complexity-to-Diversity (CtD): This approach exploits the inherent structural and stereochemical complexity of a natural product as a starting point. The natural product-derived intermediate is divergently functionalized and then transformed (e.g., via macrocyclization) to generate novel, structurally diverse, and complex compounds that share the core complexity but explore new scaffolds [1].
  • C–H Functionalization / Ring Expansion: This strategy first installs new functional groups at specific C-H bonds—which are ubiquitous in natural products—using selective methods like electrochemical oxidation. These new functional groups then serve as handles for ring expansion reactions, converting common small rings (e.g., in steroids) into rare and challenging medium-sized rings, thereby accessing underexplored chemical space [3].

Troubleshooting Guides

Issue: Inability to Determine Relative Stereochemistry of Distal Stereocenters

Problem: NMR data is insufficient to determine the relative stereochemistry between stereocenters located far apart in a complex natural product, especially when linked through rotatable bonds.

Solution:

  • Employ MicroED Analysis: If the compound can be induced to form microcrystals, MicroED can provide a definitive 3D structure, including all relative configurations, without the need for large single crystals. This method was successfully used to unambiguously establish the stereochemistry of Py-469, a new natural product where NMR was inconclusive [2].
  • Utilize Computational CD Spectroscopy: Compare the experimental circular dichroism spectrum with spectra calculated via time-dependent DFT for different proposed stereoisomers. A match can confirm the correct absolute configuration [4].
  • Engage in Total Synthesis or Degradation: If spectroscopic methods fail, a targeted synthesis of the proposed stereoisomer or chemical degradation to a fragment of known configuration can provide conclusive proof [4] [5].
Issue: Low Abundance of a Novel Compound in a Complex Extract

Problem: A promising novel natural product is present in very low abundance in a complex biological matrix, making isolation and purification inefficient.

Solution:

  • Prioritize with Metabolite Profiling: Use advanced UHPLC-HRMS to create a detailed metabolic profile of the extract. This allows for the dereplication (early identification of known compounds) and annotation of novel or unusual compounds before committing to isolation [6].
  • Implement High-Resolution Chromatographic Strategies:
    • Use online two-dimensional (2D) chromatography systems for higher peak capacity and separation power.
    • Employ trap columns for the repetitive enrichment and purification of low-abundance targets from multiple analytical-scale injections, automating the process and reducing intermediate contamination [7].
    • Transfer optimized analytical UHPLC conditions directly to the semi-preparative scale using chromatographic modelling software to maintain high resolution and efficiency during upscaling [6].
  • Hyphenate Detection for Precision: Use semi-preparative HPLC coupled with multiple detectors (UV, MS, ELSD) to precisely trigger the collection of the target peak based on its specific retention time and mass, ensuring purity [6].

Quantitative Data on Molecular Complexity

Table 1: Key Metrics of Structural Complexity in Selected Natural Product Classes

Natural Product / Class Molecular Weight Range Number of Stereocenters Ring System (Size & Type) Key Complexity Feature
Phorboxazoles (e.g., 1 & 2) [4] ~700-1400 Da Multiple Macrocycles, Oxazoles Potent bioactivity at sub-nanomolar levels; complex stereochemistry [4].
Steroid-Derived Medium-Sized Rings [3] Modified from core steroids Defined by parent + new centers 7-11 membered rings fused to polycyclic systems Expansion of common scaffolds into underexplored medium-ring chemical space [3].
Macrocycles from Quinine [1] N/A Inherited from Quinine + new Macrocycles (exact size N/A) High scaffold diversity from a single, complex natural product starting material [1].
Py-469 [2] 469 Da Multiple, including distal centers Decalin, 2-pyridone, epoxydiol Challenging stereochemical assignment of a distal epoxydiol system [2].

Table 2: Scale and Sensitivity of Modern Structure Elucidation Techniques

Technique Typical Sample Requirement Key Structural Information Provided Primary Application in Troubleshooting
MicroED [2] Sub-micron crystals Full 3D atomic structure (relative configuration) Definitive stereochemistry when NMR is ambiguous or crystals are too small for SC-XRD [2].
Microcryoprobe NMR [4] Nanomole (e.g., ~5-20 μg) Atom connectivity, relative configuration (via NOE, J-couplings) Acquiring full suite of 1D/2D NMR spectra on vanishingly small samples [4].
Computational NMR Prediction [5] N/A (in silico) Predicted ( ^1H ) and ( ^{13}C ) chemical shifts & coupling constants Validating proposed structures and identifying misassignments by comparing calculated vs. experimental data [5].
Circular Dichroism (CD) [4] Picomole Absolute configuration Assigning stereochemistry when sample amounts are too low for other techniques [4].

Experimental Protocols

Protocol: Structure Elucidation of a Microgram-Quantity Natural Product Using MicroED

Purpose: To determine the atomic structure and stereochemistry of a novel natural product available only in microgram quantities.

Background: Microcrystal Electron Diffraction (MicroED) has revolutionized structure elucidation by allowing analysis from nano-crystalline material that is unsuitable for traditional single-crystal X-ray diffraction [2].

Materials:

  • Purified natural product (as little as 1 mg/L culture in a proof-of-concept study [2])
  • Transmission Electron Microscope (TEM) equipped with a cryo-holder and capable of electron diffraction
  • Standard cryo-EM grid preparation supplies

Procedure:

  • Sample Preparation: a. Purify the target compound to homogeneity using HPLC [2]. b. Lyophilize the purified sample to a powder. c. Gently grind the powder and disperse it onto a cryo-EM grid.
  • Screening and Data Collection: a. Load the grid into the TEM under cryogenic conditions. b. Screen the sample at low magnification to identify crystalline domains. c. Center a suitable crystal and collect a diffraction movie while continuously rotating the stage. Note: In the case of Py-469, merging data from just two movies yielded a high-resolution (0.85 Ã…) structure [2].
  • Data Processing and Structure Determination: a. Process the diffraction movies using standard crystallographic software (e.g., XDS, SHELX) to generate a merged intensity data file. b. Solve the structure by direct methods or intrinsic phasing. c. Refine the structure anisotropically to convergence. The structure of Py-469 was refined to an R1 value of 13.8% [2].
Protocol: Diversification of Natural Products via C–H Oxidation and Ring Expansion

Purpose: To diversify polycyclic natural products (e.g., steroids) and generate novel analogues containing medium-sized rings.

Background: This two-phase strategy first installs functional groups via selective C–H bond oxidation, then uses these groups to drive ring expansion reactions, accessing synthetically challenging medium-sized rings [3].

Materials:

  • Natural product starting material (e.g., DHEA, cholesterol, estrone)
  • Reagents for C–H oxidation (e.g., electrochemical cell, catalysts like Cu or Cr complexes)
  • Ring expansion reagents (e.g., ethyl diazoacetate, BF₃•Etâ‚‚O, dimethyl acetylenedicarboxylate (DMAD))
  • Standard anhydrous solvents and inert atmosphere equipment

Procedure:

  • Site-Selective C–H Oxidation: a. Set up the reaction according to the chosen oxidation method. For example, use a published electrochemical protocol for allylic C–H oxidation [3] or a copper-mediated system for other positions. b. Monitor the reaction by TLC or LC-MS. c. Upon completion, isolate and purify the oxidized intermediate (e.g., ketone 28 from benzylic oxidation of 1j) [3].
  • Ring Expansion: a. Subject the oxidized intermediate to a ring expansion reaction. Multiple pathways are possible: - Schmidt Reaction: React a ketone with hydrazoic acid to form a lactam, expanding the ring by one carbon [3]. - Formal [2+2] Cycloaddition/Fragmentation: React a β-keto ester (e.g., 2a) with DMAD to effect a two-carbon ring expansion, ultimately yielding an anhydride (e.g., 6a) [3]. - Beckmann Rearrangement: Treat a ketoxime derived from the oxidized product to form a lactam [3]. b. Purify the final ring-expanded product(s) using techniques like flash chromatography or preparative HPLC.

Research Reagent Solutions

Table 3: Essential Reagents and Materials for Complex Natural Product Research

Reagent / Material Function / Application Specific Example
Trap Columns (for HPLC) Online enrichment and purification of low-abundance compounds from complex extracts [7]. Used in an online prep-HPLC system for the efficient separation of Panax notoginseng saponins [7].
C–H Oxidation Catalysts Selective functionalization of inert C-H bonds to introduce handles for further diversification [3]. Electrochemical, Copper-mediated, and Chromium-mediated catalysts used to oxidize specific positions on steroid cores [3].
Ring Expansion Reagents Transformation of small rings into synthetically challenging medium-sized rings (7-11 members) [3]. Ethyl diazoacetate, Dimethyl acetylenedicarboxylate (DMAD), and reagents for the Schmidt reaction and Beckmann rearrangement [3].
Cryogenic NMR Solvents For acquiring high-sensitivity NMR data on microgram-scale samples using microcryoprobes. Essential for protocols requiring nanomole-level structure elucidation [4].
MicroED Grids Support for nano-crystalline samples during data collection in the transmission electron microscope. Used for the ab initio structural elucidation of Py-469 and revision of fischerin [2].

Workflow and Pathway Visualizations

complexity_workflow cluster_0 Analysis & Isolation Workflow cluster_1 Structure Elucidation Suite cluster_2 Diversification Strategies Start Start: Complex Natural Product NP Natural Product Extract Start->NP Profiling Metabolite Profiling (UHPLC-HRMS) NP->Profiling Dereplication Dereplication & Prioritization Profiling->Dereplication Isolation Targeted Isolation (High-res Prep HPLC) Dereplication->Isolation Structure Structure Elucidation Isolation->Structure MicroED MicroED Structure->MicroED MicroNMR Microcryoprobe NMR Structure->MicroNMR CompNMR Computational NMR Structure->CompNMR CD Circular Dichroism (CD) Structure->CD Synthesis Synthesis/Degradation Structure->Synthesis Ctd Complexity-to-Diversity (CtD) Structure->Ctd CH_Ox C–H Functionalization Structure->CH_Ox RingExp Ring Expansion CH_Ox->RingExp

Diagram Title: Workflow for Analyzing and Diversifying Complex Natural Products

chemistry_strategies cluster_ctd Complexity-to-Diversity (CtD) cluster_ringexp C–H Oxidation / Ring Expansion NP Complex Natural Product (e.g., Quinine, Steroid) CtD1 1. Functionalize Core NP->CtD1 OX1 1. Site-Selective C–H Oxidation NP->OX1 CtD2 2. Divergent Building Block Attachment CtD1->CtD2 CtD3 3. Key Transformation (e.g., Macrocyclization) CtD2->CtD3 CtD4 Output: Diverse & Complex Macrocyclic Scaffolds CtD3->CtD4 OX2 2. Install FG Handle (e.g., Ketone) OX1->OX2 OX3 3. Ring Expansion (e.g., Schmidt, Beckmann) OX2->OX3 OX4 Output: Polycyclic Scaffolds with Medium-Sized Rings OX3->OX4

Diagram Title: Synthetic Strategies for Diversifying Complex Natural Products

Natural products, chemicals produced by living organisms, are a treasure trove for developing bioactive molecules and pharmaceuticals; more than 60% of pharmaceuticals are related to natural products [2]. However, their structural complexity often makes synthesis a daunting task. The rate-limiting step in natural product discovery is frequently structural characterization, which, if misassigned, can lead researchers down unproductive synthetic paths [8] [2]. This technical support center is designed within the context of a broader thesis on addressing structural complexity in natural product research. It provides targeted troubleshooting guides and FAQs to help you overcome common experimental hurdles, confirm structures efficiently, and apply innovative synthetic methodologies.

Frequently Asked Questions (FAQs)

1. Why is total synthesis considered a reliable tool for structural confirmation? Even with substantial improvements in spectroscopic techniques, the structural misassignment of natural products remains common. Total synthesis serves as an unambiguous method for structural confirmation by independently recreating the proposed structure. When the physical and spectral data (NMR, MS, etc.) of the synthesized material match those of the isolated natural product, the structure is confirmed. This process has led to the revision of numerous previously misassigned structures [8].

2. What are the common challenges in the structural elucidation of natural products? Difficulties often arise from:

  • Limited Material: Lack of sufficient quantities for traditional methods like NMR or X-ray crystallography [2].
  • Poor Physical Properties: Issues such as poor solubility or stability in standard NMR solvents [2].
  • Limitations of NMR: Challenges in determining relative stereochemistry, especially in molecules with distal stereocenters interrupted by rigid substructures [2].

3. What is protecting-group-free (PGF) synthesis and why is it beneficial? PGF synthesis is an approach that aims to construct complex natural products without using protecting groups. This is achieved through highly chemoselective reactions that preferentially react with one functional group in the presence of others. The key benefits are dramatically improved efficiency and step economy, as it avoids the additional steps of protection and deprotection [9].

4. What is Microcrystal Electron Diffraction (MicroED) and how does it aid structure determination? MicroED is an emerging cryogenic electron microscopy (CryoEM) method used for unambiguous structural elucidation, including stereochemistry. It can determine structures from sub-micron-sized crystals that are too small for traditional single-crystal X-ray diffraction. This technology has been used to both determine new natural product structures and revise the structures of compounds isolated decades ago [2].

5. When is retrosynthetic analysis particularly useful? Retrosynthetic analysis is a fundamental strategy for planning the synthesis of complex molecules. It involves working backward from the target molecule, deconstructing it into simpler, readily available starting materials through a series of disconnections. This provides a structured, logical approach to tackling complex syntheses [10].

Troubleshooting Guides

Guide 1: Troubleshooting Structural Misassignment

This guide addresses the common problem of ambiguous or incorrect structural determination.

  • Problem: Cannot unambiguously assign relative stereochemistry, especially of distal stereocenters, using NMR data alone.
  • Context: The molecule contains rigid substructures with multiple rotatable bonds, preventing definitive NOE analysis [2].

Solution: Apply a Multi-Technique Verification Approach

Step Action Principle & Tips
1 Re-evaluate with Advanced NMR Attempt to resolve ambiguities by collecting additional data (e.g., 2D NMR) or using computational NMR prediction to compare proposed models [2].
2 Pursue MicroED Analysis If the compound or a derivative can be coaxed to form microcrystals, use MicroED for ab initio structural determination. This is now a viable alternative when X-ray quality crystals cannot be obtained [2].
3 Design a Total Synthesis Undertake the total synthesis of the proposed structure. A successful synthesis that produces a compound with identical data to the natural product provides the highest level of confirmation [8].

Guide 2: Troubleshooting Low-Yielding or Inefficient Syntheses

This guide helps when a synthetic route is too long, inefficient, or plagued by low yields.

  • Problem: Synthetic route requires too many steps, including multiple protection/deprotection sequences, leading to low overall yield.
  • Context: The target molecule has multiple similar functional groups that interfere with desired reactions [9].

Solution: Implement Protecting-Group-Free (PGF) and Chemoselective Strategies

Step Action Principle & Tips
1 Conduct a Retrosynthetic Analysis Deconstruct the target with a focus on late-stage introduction of reactive functional groups and isohypsic transformations (where the oxidation state remains unchanged) [9].
2 Prioritize Chemoselective Methods Research and employ modern chemoselective catalysts and reactions that can distinguish between functional groups without the need for protection [9].
3 Utilize Tandem Reaction Cascades Design strategies that use cascade or one-pot reactions to build complex carbon skeletons efficiently and functional-group-tolerant cyclizations [9].

Guide 3: Troubleshooting Failed Reproducibility of a Literature Synthesis

This guide assists when an experimental protocol from published literature fails to produce the expected results in your lab.

  • Problem: Following a published synthetic procedure does not yield the expected product.
  • Context: The reaction setup, reagents, or techniques may have subtle but critical differences.

Solution: Systematic Hypothesis Testing

Step Action Principle & Tips
1 Verify "What Touched It Last" Scrutinize all recent changes. Check the purity and source of all starting materials and reagents. Ensure catalysts or sensitive reagents are fresh and have been stored correctly [11].
2 Simplify and Reduce Reproduce the reaction on a smaller scale, systematically testing each variable (e.g., solvent quality, temperature control, water/oxygen sensitivity) to isolate the cause of failure [11].
3 Ask "What, Where, and Why" Analyze what is actually happening in your reaction. Use TLC, LC-MS, or NMR to identify side products or unreacted starting material. This evidence can point to the underlying issue [11].

Experimental Protocols & Data

Quantitative Comparison of Structural Elucidation Techniques

The following table summarizes key techniques for tackling structural complexity, helping you choose the right tool for your research challenge.

Technique Principle Key Application in Natural Products Typical Data Output Key Advantage Key Limitation
NMR Spectroscopy Analyzes magnetic properties of nuclei in a molecule. Determination of planar structure and relative configuration. Chemical shifts, coupling constants, 2D correlation maps. Provides abundant information on connectivity and environment. Can be ambiguous for stereochemistry and requires pure sample [2].
X-ray Crystallography Scatters X-rays off a crystalline sample to determine electron density. Unambiguous determination of full structure, including absolute stereochemistry. Atomic coordinates, crystal structure. Considered the "gold-standard" for unambiguous structure proof. Requires large, high-quality single crystals [2].
MicroED Scatters electrons off nano-crystals to determine structure. Full structural determination when X-ray quality crystals cannot be obtained. Atomic coordinates, crystal structure. Works with nano-crystals; rapid data collection. Requires sample to form ordered microcrystals [2].
Total Synthesis De novo chemical synthesis of the target molecule. Ultimate confirmation of a proposed structure through independent creation. Synthesized compound for direct comparison. Provides definitive proof and can supply scarce natural products. Time-consuming and requires significant synthetic expertise [8].

The Scientist's Toolkit: Essential Research Reagents & Materials

This table details key reagents and their roles in modern natural product synthesis and analysis.

Reagent / Material Function / Explanation Context of Use
Chemoselective Catalysts Catalysts (e.g., Au, Pd complexes) designed to react with one specific functional group in the presence of others. Enables protecting-group-free synthesis by selectively transforming a single site on a complex molecule [9].
Umpolung Reagents Reagents that temporarily reverse the innate polarity of a functional group (e.g., dithioacetals for acyl anion equivalents). Allows for disconnection strategies that would otherwise be impossible, enabling novel bond formations [10].
Genetically Engineered Hosts (e.g., A. nidulans) Heterologous biosynthetic hosts refactored with specific gene clusters to produce target natural products or analogs. Used in genome mining and synthetic biology to produce novel metabolites or rediscover compounds no longer available [2].
Methyl 3,4-DihydroxyphenylacetateMethyl 3,4-Dihydroxyphenylacetate, CAS:25379-88-8, MF:C9H10O4, MW:182.17 g/molChemical Reagent
Harzianum AHarzianum A, CAS:156250-74-7, MF:C23H28O6, MW:400.5 g/molChemical Reagent

Workflow and Methodology Diagrams

Diagram 1: Structural Confirmation Workflow

StructuralConfirmation Start Start: Isolated Natural Product NMR NMR Analysis Start->NMR Proposal Propose Structure NMR->Proposal MicroED MicroED Analysis Proposal->MicroED If microcrystals form XRay X-ray Crystallography Proposal->XRay If single crystals form Synthesize Total Synthesis Proposal->Synthesize For ultimate verification Compare Compare Spectral Data MicroED->Compare XRay->Compare Synthesize->Compare Confirm Structure Confirmed Compare->Confirm Data Match Revise Structure Revised Compare->Revise Data Mismatch

Diagram 2: Systematic Troubleshooting Methodology

TroubleshootingMethod Problem Problem Report Triage Triage: Stop the Bleeding Problem->Triage Examine Examine System State Triage->Examine Hypothesize Formulate Hypothesis Examine->Hypothesize Test Test Hypothesis Hypothesize->Test Test->Examine No Solve Problem Solved Test->Solve Yes Learn Learn & Document Solve->Learn

FAQs: Navigating Bioactivity and Synthesis

FAQ 1: What is the most common reason for the failure of clinical drug development? Recent analyses indicate that approximately 90% of clinical drug development fails. A predominant reason is that the optimization process overly focuses on a candidate's potency and specificity (Structure-Activity Relationship, or SAR) while overlooking a critical factor: its tissue exposure and selectivity (Structure-Tissue Exposure/Selectivity-Relationship, or STR). An imbalance here can mislead candidate selection and negatively impact the clinical balance between dose, efficacy, and toxicity [12].

FAQ 2: How can we improve the drug optimization process to increase the chance of clinical success? A proposed solution is the Structure–Tissue exposure/selectivity–Activity Relationship (STAR) framework. This approach classifies drug candidates based on a holistic view of their properties, ensuring that both potency and tissue distribution are considered to select compounds with a better predictive balance of efficacy and safety [12]. Furthermore, in 2025, the industry is seeing a significant shift towards prioritizing high-quality, real-world patient data for training AI models in drug discovery, moving away from an over-reliance on synthetic data, to create more reliable and clinically validated processes [13].

FAQ 3: What role do biomarkers play in modern drug development, especially in complex fields like psychiatry? Biomarkers serve as scientifically valid, objective data points that can be measured and tested. In psychiatric drug development, they are particularly crucial for supporting the development of new treatments. Among the most promising are event-related potentials, which are functional brain measures noted for their high reliability, consistency, and interpretability in numerous studies. A broader application of such biomarkers is expected in clinical trials [13].

FAQ 4: What are the key trends in clinical trial design for improving efficiency? Two major trends are shaping clinical trials:

  • AI-Driven Optimization: The use of AI is becoming transformative, with over half of new trials expected to incorporate AI for protocol optimization. This enhances site selection, patient recruitment, and enables precision-driven protocols by using predictive analytics on data like genomics and clinical records [13].
  • Hybrid Trials: Hybrid trial models are becoming the standard, especially for chronic diseases. These models combine traditional site-based visits with decentralized tools, making participation easier for patients and incorporating real-world data to adapt designs based on day-to-day patient experiences [13].

Troubleshooting Guide: Common Experimental Hurdles

This guide addresses frequent challenges in balancing bioactivity and synthesis.

Problem Area Specific Issue Potential Causes Recommended Solutions
Lead Optimization A compound shows high in vitro potency but poor efficacy or high toxicity in vivo. Poor tissue exposure/selectivity; the compound may not reach the diseased tissue effectively or may accumulate in healthy tissues [12]. Implement the STAR framework for candidate selection. Prioritize compounds with high tissue exposure/selectivity (Class I and III) over those with only high potency but poor tissue profiles (Class II) [12].
Clinical Trial Enrollment Slow patient recruitment and enrollment delays trial timelines. Inefficient site selection and difficulty identifying eligible patients from unstructured clinical notes [13]. Adopt AI-driven predictive analytics to optimize site selection and patient recruitment. Utilize AI tools with natural language processing to abstract data from unstructured clinical notes and EHRs [13].
Data for AI Models AI models trained for drug discovery yield unreliable or non-generalizable results. Over-reliance on synthetic data for training, which may not fully capture real-world clinical complexity [13]. Prioritize high-quality, real-world patient data for AI model training. Use synthetic data primarily for refining trial design and early-stage analysis, not as a complete replacement [13].
Analytical Instrumentation (GC System) Gradual increase in peak retention times or a noisy/spiky baseline. Column contamination from matrix buildup or ambient pressure fluctuations affecting the Thermal Conductivity Detector (TCD) [14]. For retention shifts: Perform column maintenance (e.g., baking out or solvent rinsing). For TCD noise: Install a small restrictor on the detector exit to isolate it from lab pressure changes [14].

Quantitative Data in Drug Development

Table 1: The STAR Drug Classification Framework for Candidate Selection This framework, derived from recent research, helps balance key properties to improve clinical success rates [12].

STAR Class Specificity / Potency Tissue Exposure / Selectivity Required Dose Expected Clinical Outcome & Success
Class I High High Low Superior efficacy/safety; High success rate
Class II High Low High Efficacy with high toxicity; Needs cautious evaluation
Class III Adequate / Low High Low Efficacy with manageable toxicity; Often overlooked
Class IV Low Low N/A Inadequate efficacy/safety; Terminate early

Table 2: Key Predictions for Drug Development in 2025 Industry experts forecast several key trends for the near future [13].

Area of Development Predicted Trend in 2025 Key Driver or Enabling Technology
Data for AI Training Pullback from synthetic data; precedence of real-world data. Recognition of synthetic data's limitations and potential risks.
Clinical Trial Design >50% of new trials use AI-driven protocol optimization. Predictive analytics, AI for patient recruitment & site selection.
Trial Data Management Scaling of clinical data abstraction. AI-based tools with human experts "in the loop" to extract data from unstructured clinical notes.
Trial Execution Model Hybrid trials become the standard. Tools like NLP for patient engagement; decentralized models for chronic disease.
Psychiatric Drug Development Breakthrough in biomarker validation & consensus. Adoption of reliable, consistent biomarkers like event-related potentials.

Experimental Protocols

Protocol 1: Implementing the STAR Framework in Lead Optimization

Objective: To systematically evaluate and classify drug candidates based on the Structure–Tissue exposure/selectivity–Activity Relationship (STAR) to select the most promising lead with a balanced efficacy/toxicity profile.

Methodology:

  • In Vitro Potency & Specificity Assay (SAR):
    • Determine the half-maximal inhibitory concentration (ICâ‚…â‚€) or similar potency metrics against the primary target.
    • Evaluate selectivity by profiling the compound against a panel of related off-targets (e.g., kinases, GPCRs) to establish specificity.
  • Tissue Exposure/Selectivity Profiling (STR):
    • Administer a single, pharmacologically relevant dose of the candidate compound to animal models.
    • At predetermined time points, collect plasma and homogenize key target tissues (e.g., tumor, liver, brain) and potential toxicity-related tissues (e.g., heart, kidney).
    • Use LC-MS/MS to quantify the compound's concentration in each tissue matrix. Calculate the tissue-to-plasma ratio and the specific exposure in the diseased tissue versus healthy tissues.
  • STAR Integration and Classification:
    • Integrate the data from steps 1 and 2. Plot candidates on a matrix with "Potency/Specificity" on one axis and "Tissue Exposure/Selectivity" on the other.
    • Classify each candidate into one of the four STAR classes (I-IV) as defined in Table 1.
    • Selection Priority: Prioritize Class I and Class III candidates for further development, deeply scrutinize Class II, and terminate Class IV candidates [12].

Protocol 2: AI-Enhanced Clinical Data Abstraction for Trial Acceleration

Objective: To harness AI-powered tools to efficiently structure unstructured clinical notes from Electronic Health Records (EHRs) for faster patient cohort identification and clinical trial enrollment.

Methodology:

  • Tool Selection and Setup:
    • Identify and implement an AI-based clinical data abstraction platform that supports a "human-in-the-loop" model, ensuring accuracy and clinical relevance [13].
  • Data Ingestion and Pre-processing:
    • Securely import de-identified EHR data, including free-text clinical notes, pathology reports, and discharge summaries, into the abstraction platform.
  • AI-Driven Natural Language Processing (NLP):
    • The NLP engine processes the unstructured text to identify and extract key entities and concepts relevant to the trial's inclusion/exclusion criteria (e.g., specific diagnoses, medication history, lab values, procedural history).
  • Clinical Expert Validation (Human-in-the-Loop):
    • A clinical expert reviews the AI-extracted data points for accuracy and context, correcting any misclassifications. This step continuously trains and improves the AI model.
  • Structured Data Output and Patient Matching:
    • The validated output is converted into a structured database. This database is then used to run queries against the trial's protocol to pre-identify a list of potentially eligible patients, drastically speeding up the recruitment process [13].

Research Reagent Solutions & Essential Materials

Item / Reagent Function in Experimental Context
LC-MS/MS System The core analytical platform for quantifying drug candidate concentrations in biological matrices (plasma, tissue homogenates) during tissue exposure/selectivity (STR) studies [12].
AI-Powered Data Abstraction Platform Software that uses Natural Language Processing (NLP) to convert unstructured clinical notes from EHRs into structured, usable data for patient cohort identification and trial enrollment [13].
Validated Biomarker Assay (e.g., EEG for Event-Related Potentials) Provides a functional, physiologically relevant, and interpretable endpoint for clinical trials, especially in complex areas like psychiatric drug development, where objective measures are critical [13].
Federated Learning Platform An AI training architecture that enables secure, multi-institutional data collaboration for model training without sharing raw patient data, protecting privacy while generating robust insights [13].

Visualized Workflows and Pathways

STAR_Workflow Start Drug Candidate Pool SAR In-Vitro SAR Profiling (Potency & Specificity) Start->SAR STR In-Vivo STR Profiling (Tissue Exposure & Selectivity) Start->STR Integrate STAR Integration & Classification SAR->Integrate STR->Integrate ClassI Class I: High Priority Integrate->ClassI High Potency High Tissue Exp. ClassII Class II: Cautious Evaluation Integrate->ClassII High Potency Low Tissue Exp. ClassIII Class III: Promising & Overlooked Integrate->ClassIII Adequate Potency High Tissue Exp. ClassIV Class IV: Terminate Early Integrate->ClassIV Low Potency Low Tissue Exp.

STAR Framework Evaluation Pathway

Modern_Trial_Design Start Unstructured Data Sources (EHRs, Clinical Notes) AI AI & NLP Data Abstraction (Human-in-the-Loop Validation) Start->AI RWD Structured Real-World Data (RWD) Registry AI->RWD AI_Training AI Model Training for Trial Optimization RWD->AI_Training Design Hybrid Trial Design RWD->Design Recruit Predictive Patient Recruitment & Engagement RWD->Recruit Biomarker Biomarker Data Integration (e.g., Event-Related Potentials) RWD->Biomarker AI_Training->Design AI_Training->Recruit Biomarker->Design

Modern Clinical Trial Data Flow

Strategic Toolbox: Core and Emerging Synthesis Methodologies

Technical Support Center: Troubleshooting Guides & FAQs

This support center provides targeted assistance for researchers confronting the routine and complex challenges of total synthesis. The guidance below is framed within our broader thesis that a systematic and analytical approach is paramount for navigating the structural complexity inherent to natural products.

Troubleshooting Common Experimental Challenges

FAQ 1: My reaction is proceeding too slowly or not at all. What are the first things I should check?

A stalled reaction is a common hurdle. We recommend a systematic approach to identify the culprit [15].

  • Step 1: Verify Reagents and Solvents. Check for common issues like anhydrous conditions if required. Ensure your solvents are dry and free of stabilizers that may inhibit reactivity. Confirm the purity and mass of solid reagents.
  • Step 2: Assess the Reaction Environment. Is the reaction protected from air or moisture if necessary? Is the temperature accurately controlled? For reactions under an inert atmosphere, check for leaks in your Schlenk line or glovebox.
  • Step 3: Propose and Test a Hypothesis. Based on your initial checks, form a hypothesis (e.g., "The reaction failed because the solvent was wet") [16]. Test this prediction directly (e.g., repeat the reaction with freshly dried solvent). If the result is negative, iterate with a new hypothesis (e.g., "The failure is due to a poisoned catalyst").

The logical flow for this diagnostic process can be summarized as follows:

G Start Reaction Failure Obs Observation: Reaction is slow or inactive Start->Obs Step1 1. Check Reagents & Solvents Obs->Step1 Step2 2. Assess Environment (Temp, Atmosphere) Step1->Step2 Step3 3. Form Hypothesis Step2->Step3 Test Test Prediction (Run Controlled Experiment) Step3->Test Success Problem Solved Test->Success Yes Iterate Revise Hypothesis Test->Iterate No Iterate->Step3

FAQ 2: I have obtained a product, but its analytical data does not match the natural compound. How do I proceed?

Achieving analytical identity is the definitive goal of total synthesis [15] [17]. A discrepancy indicates a structural difference that must be resolved.

  • Step 1: Gather Comprehensive Data. Don't rely on a single data point. Collect (^1)H NMR, (^13)C NMR, HRMS (High-Resolution Mass Spectrometry), and specific optical rotation data for both your product and the authentic natural sample.
  • Step 2: Compare Systematically. Use the table below to compare your data and formulate questions that will guide your investigation.
Analytical Method Key Comparison Parameters Questions to Ask
NMR Spectroscopy Chemical shift (δ), integration, coupling constants (J), signal multiplicity. Do the spectra confirm the correct carbon skeleton? Are the stereochemical relationships (from J-values) consistent?
Mass Spectrometry Exact mass (from HRMS), fragmentation pattern. Does the molecular formula match? Are there unexpected fragments suggesting a rearrangement?
Optical Rotation Specific rotation [α] under identical conditions (solvent, temperature, concentration). Is the absolute configuration correct, or is my product an enantiomer or diastereomer?
  • Step 3: Form a Hypothesis. Based on the data mismatch, propose a structural alternative (e.g., "My product has the epimeric stereochemistry at C-12"). Use retrosynthetic analysis to deconstruct both the target and your proposed structure, identifying where the synthetic path may have diverged [18].
  • Step 4: Test Your Hypothesis. This may require synthesizing a different derivative to confirm stereochemistry, re-running a key step with a different stereocontrol method, or using advanced techniques like X-ray crystallography for definitive proof.

The workflow for resolving structural identity is a rigorous, iterative cycle:

G A Analytical Data Mismatch B Gather Comprehensive Data (NMR, HRMS, [α]) A->B C Compare with Natural Product B->C D Form Structural Hypothesis C->D E Devise & Execute Diagnostic Experiment D->E F Identity Confirmed E->F Yes G Revise Hypothesis E->G No G->D

FAQ 3: The yield for my key coupling step is very low. How can I optimize it?

Low yield in a complex synthesis can stem from many factors. A data-driven approach is key [19].

  • Step 1: Identify the Problem. Is the low yield due to incomplete conversion, side reactions, or difficult purification?
  • Step 2: Propose a Hypothesis. For example, "The palladium catalyst is deactivating under the reaction conditions."
  • Step 3: Design Experiments. Set up small-scale, parallel reactions to test one variable at a time.
  • Step 4: Analyze and Iterate. Use the data to confirm or revise your hypothesis.

A systematic optimization protocol can be visualized as a controlled exploration of the reaction parameter space:

G LowYield Low Yield Hypothesis Hypothesis: Variable X is sub-optimal LowYield->Hypothesis DOE Design of Experiments (Test Solvent, Catalyst, Temperature, Concentration) Hypothesis->DOE Analysis Analyze Results (TLC, LCMS, Yield) DOE->Analysis Optimized Conditions Optimized Analysis->Optimized Yield Improved NewHyp New Hypothesis Analysis->NewHyp No Improvement NewHyp->Hypothesis

The Scientist's Toolkit: Key Research Reagent Solutions

The following table details essential reagents and materials frequently employed in modern total synthesis campaigns to address specific challenges of structural complexity [18] [17].

Item Function & Application
Chiral Ligands (e.g., BINAP, BOX ligands) Imparts stereocontrol in asymmetric synthesis via metal-catalyzed reactions such as hydrogenation or C-C bond formation. Critical for setting absolute stereocenters.
Pd/Cu Catalysts Facilitates key cross-coupling reactions (e.g., Sonogashira, Suzuki, Heck) for constructing the carbon skeleton. Essential for sp²-sp² and sp²-sp carbon bond formation.
Protecting Groups (e.g., TBS, Boc, Fmoc) Temporarily masks reactive functional groups (e.g., alcohols, amines) to ensure chemoselectivity during multi-step syntheses.
Oxidizing/Reducing Agents Selective agents (e.g., Dess-Martin periodinane for oxidation; DIBAL-H for selective reduction) enable precise functional group interconversions.
Enzymes / Biocatalysts Used in chemoenzymatic synthesis for highly selective and sustainable reactions, such as asymmetric reductions or kinetic resolutions [17].
Pyripyropene APyripyropene A, CAS:147444-03-9, MF:C31H37NO10, MW:583.6 g/mol
BopindololBopindolol, CAS:62658-63-3, MF:C23H28N2O3, MW:380.5 g/mol

Structural simplification is a powerful strategy in medicinal chemistry for improving the efficiency and success rate of drug design. This approach involves simplifying large or complex lead compounds by truncating unnecessary groups, which can not only improve synthetic accessibility but also enhance pharmacokinetic profiles and reduce side effects [20]. The trend toward designing large hydrophobic molecules for lead optimization is often associated with poor drug-likeness and high attrition rates in drug discovery, a phenomenon known as "molecular obesity" [20]. This technical support center provides troubleshooting guidance and experimental protocols for researchers implementing structural simplification strategies within their natural product synthesis and drug development workflows.

Core Principles of Structural Simplification

FAQ: Fundamental Concepts

What is structural simplification in drug discovery? Structural simplification is a lead optimization strategy that involves generating new drug analogues from large or complex lead compounds by systematically truncating unnecessary substructures. This approach aims to produce molecules with improved synthetic accessibility, favorable pharmacokinetic profiles, and reduced side effects [20] [21].

Why is reducing molecular complexity important? Reducing molecular weight and complexity has positive effects on pharmacokinetic/pharmacodynamic profiles [20]. Less complex drugs are more likely to achieve better market success, as molecular complexity is associated with high attrition rates in drug development due to poor ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties [20].

What are the key steps in structural simplification? The typical process includes [21]:

  • Analyzing molecular complexity (rings, chiral centers)
  • Determining substructures important for biological activity
  • Elucidating structure-activity relationships (SAR) and pharmacophores
  • Removing unnecessary structural motifs

How does structural simplification differ from other optimization strategies? Unlike approaches that add complexity to improve potency, simplification deliberately removes redundant structural elements while maintaining or enhancing desired biological activity. This contrasts with traditional lead optimization that often increases molecular weight, lipophilicity, and ring counts [20].

Experimental Protocols and Methodologies

Troubleshooting Guide: Common Experimental Challenges

Problem: Simplified analogues show significantly reduced potency

  • Potential Cause: Truncation of essential pharmacophore elements.
  • Solution:
    • Conduct binding mode analysis through molecular docking studies [22]
    • Perform pharmacophore mapping to identify critical interaction points
    • Implement step-wise simplification with intermediate biological testing
    • Utilize structure-based design to preserve key interacting groups [23]
  • Prevention: Complete thorough SAR analysis before initiating truncation studies.

Problem: Simplified compounds exhibit poor solubility despite reduced molecular weight

  • Potential Cause: Removal of polar functional groups or increased molecular planarity.
  • Solution:
    • Introduce minimal polar substituents at positions not critical for activity
    • Consider isosteric replacement of hydrophobic groups with polar bioisosteres
    • Evaluate physicochemical properties early in simplification cascade
  • Prevention: Monitor calculated logP and polar surface area throughout design process.

Problem: Synthetic accessibility not improved despite structural simplification

  • Potential Cause: Creation of new chiral centers or unstable structural motifs.
  • Solution:
    • Employ protecting-group-free synthetic routes where possible [9]
    • Prioritize reduction of chiral centers and stereochemical complexity [20]
    • Utilize convergent synthetic strategies rather than linear approaches
  • Prevention: Apply retrosynthetic analysis early in simplification planning.

Key Experimental Workflows

The following workflow illustrates the core decision process in structural simplification projects:

structural_simplification Start Start: Complex Lead Compound Step1 1. Binding Mode Analysis Start->Step1 Step2 2. Pharmacophore Identification Step1->Step2 Step3 3. SAR Elucidation Step2->Step3 Step4 4. Strategic Truncation Step3->Step4 Step5 5. Biological Evaluation Step4->Step5 Success Simplified Candidate Step5->Success Potency Maintained Revise Revise Strategy Step5->Revise Potency Lost Revise->Step2

Quantitative Guidelines for Simplification

Table 1: Molecular Complexity Metrics for Simplification Targets

Parameter High Complexity Moderate Complexity Simplification Target
Molecular Weight >500 Da 400-500 Da <400 Da
Chiral Centers ≥3 2 ≤1
Ring Systems ≥4 3 ≤2
Rotatable Bonds >10 7-10 <7
logP >5 3-5 <3

Source: Adapted from analysis of lead-drug pairs [20]

Case Studies and Practical Applications

FAQ: Implementation Questions

What are successful examples of structural simplification? Notable examples include:

  • Morphine to simpler analgesics: The pentacyclic system of morphine was systematically simplified to various semisynthetic or synthetic analgesics (butophanol, pentazocine, pethidine, methadone) while retaining key pharmacophores [20] [21].
  • Halichondrin B to Eribulin: The very complex marine natural product halichondrin B was simplified to eribulin mesylate, which retained antitumor activity while improving synthetic accessibility from approximately 120 steps to a practical synthesis [20].
  • Myriocin to Fingolimod: A fungal metabolite was simplified to fingolimod, showing higher potency, improved physicochemical properties, and reduced toxicity [20].

How do I determine which structural elements are unnecessary? Several approaches can identify non-essential groups:

  • Systematic deletion studies: Remove substructures one-by-one with biological testing
  • Structure-based analysis: Examine ligand-target complexes to identify interacting groups [23]
  • Pharmacophore modeling: Determine minimal structural features required for activity
  • Natural product biosynthetic knowledge: Identify biosynthetic decorations not critical for activity [24]

What techniques facilitate structural simplification? Key methodological approaches include:

  • Molecular docking: Predicts binding modes and identifies non-interacting regions [22]
  • SAR analysis: Correlates structural features with biological activity
  • Chemoselective synthesis: Enables functional group manipulation without protection [9]
  • In silico ADMET prediction: Evaluates simplified compounds before synthesis

Research Reagent Solutions

Table 2: Essential Tools for Structural Simplification Research

Reagent/Resource Function in Simplification Application Examples
Molecular Docking Software (AutoDock, Gold, GLIDE) Binding mode prediction and interaction analysis [22] Identifying non-essential substructures not involved in target binding
Structure Visualization Tools (PyMOL, Chimera) 3D structure analysis and pharmacophore mapping Visualizing ligand-target interactions to guide truncation strategies
Natural Product Libraries Source of complex lead compounds Starting points for simplification campaigns
SAR Analysis Databases Structure-activity relationship mining Identifying tolerated modification sites
ADMET Prediction Platforms Property optimization during simplification Ensuring simplified compounds maintain favorable drug-like properties

Advanced Applications and Integration

Troubleshooting Guide: Advanced Implementation

Problem: Unable to maintain selectivity after simplification

  • Potential Cause: Removal of groups responsible for selective target recognition.
  • Solution:
    • Conduct counter-screening against related targets
    • Analyze binding sites of related targets for differences
    • Introduce minimal substituents that exploit target differences
    • Utilize molecular dynamics to study binding stability [22]
  • Prevention: Include selectivity assessment early in simplification cascade.

Problem: Synthetic routes remain challenging despite molecular simplification

  • Potential Cause: The simplified structure contains reactive or incompatible functional groups.
  • Solution:
    • Implement protecting-group-free synthesis strategies [9]
    • Employ chemoselective reactions that tolerate multiple functional groups
    • Consider late-stage introduction of sensitive functional groups
    • Utilize cascade reactions to build complexity efficiently
  • Prevention: Apply retrosynthetic analysis considering functional group compatibility.

The following diagram illustrates the integration of structural simplification within the broader drug discovery pipeline:

drug_discovery_integration cluster_techniques Supporting Techniques NP Natural Product or Complex Lead Char Characterization (Structure, Activity) NP->Char Simpl Structural Simplification Char->Simpl Optim Optimized Candidate Simpl->Optim Docking Molecular Docking Simpl->Docking SAR SAR Analysis Simpl->SAR Synth PGF Synthesis Simpl->Synth ADMET ADMET Screening Simpl->ADMET Preclin Preclinical Development Optim->Preclin

Structural simplification represents a powerful approach for addressing the challenges of molecular complexity in natural product-based drug discovery. By systematically applying the troubleshooting guides, experimental protocols, and strategic frameworks outlined in this technical support resource, researchers can more effectively navigate the process of truncating unnecessary substructures while maintaining biological activity. The integration of modern computational methods, synthetic strategies, and analytical techniques enables rational simplification approaches that can significantly improve drug-likeness and development success rates.

Conceptual Foundations and FAQs

FAQ: What is bioinspired synthesis? Bioinspired synthesis is an approach where chemists design synthetic strategies for natural products by mimicking their proposed biosynthetic pathways in living organisms. This method uses nature's blueprints to efficiently construct complex molecules, often achieving rapid increases in molecular complexity through cascade reactions and other efficient transformations [25].

FAQ: How does bioinspired synthesis differ from traditional total synthesis? While traditional total synthesis may use any available synthetic method, bioinspired synthesis specifically imitates nature's proposed biochemical transformations. This often allows for more efficient and concise synthetic routes, shorter step counts, and the potential for divergent synthesis of multiple related natural products from a common intermediate [26].

FAQ: What are the main types of bioinspired strategies? Researchers typically categorize bioinspired approaches into three main types:

  • Mimicking key cyclization steps: Developing synthetic methods that replicate nature's core skeleton-forming reactions [26].
  • Following revised biosynthetic pathways: Investigating alternative biosynthetic routes when originally proposed pathways contradict chemical principles [26].
  • Imitating skeletal diversification: Using a common intermediate to generate multiple natural products with distinct carbon skeletons, mirroring nature's divergent biosynthesis [26].

Troubleshooting Common Experimental Challenges

Issue: Low Yield in Biomimetic Cyclization Steps

Biomimetic cyclizations, such as the Prins-triggered double cyclization used in chabranol synthesis, are powerful but can suffer from low yields [25].

  • Systematic Troubleshooting Steps [27]:

    • Identify the problem: Clearly define the specific reaction underperforming.
    • List possible explanations: Consider catalyst/activator efficiency, substrate purity, stereoelectronic effects, reaction conditions (temperature, concentration, solvent), and potential side reactions.
    • Collect data: Review analytical data (NMR, LC-MS) of starting materials and crude reaction mixtures. Check for byproducts or decomposition.
    • Eliminate explanations: Systematically rule out factors. For example, if a reaction is highly sensitive to water, ensure all reagents and glassware are anhydrous.
    • Check with experimentation: Design controlled experiments to test remaining hypotheses. Change only one variable at a time (e.g., activator stoichiometry, temperature gradient).
    • Identify the cause: Based on experimental results, pinpoint the primary issue and implement a fix, such as optimizing Lewis acid concentration or protecting interfering functional groups.

  • Example from Literature: The bioinspired synthesis of chabranol uses a silyl cation to activate the aldehyde precursor for a key Prins cyclization. Troubleshooting this step would involve optimizing the source of the "formal silicon cation" and the reaction conditions to maximize the yield of the bicyclic intermediate 9 [25].

Issue: Failed Oxidative Cyclization Mimicking Biosynthesis

The biomimetic formation of the tetrahydrofuran ring in monocerin analogues relies on generating a para-quinone methide (pQM) intermediate followed by an oxa-Michael addition [25].

  • Troubleshooting Guide:
    • Confirm oxidant effectiveness: Test different oxidants (e.g., DDQ, MnOâ‚‚, hypervalent iodine reagents) to efficiently generate the reactive pQM intermediate.
    • Verify stereochemical requirements: Ensure your substrate's stereochemistry allows for the desired cyclization trajectory. Molecular modeling can help assess feasibility.
    • Control reaction environment: The oxa-Michael step can be sensitive to pH and protic solvents. Screen solvents and additives (acids, bases) to facilitate the cyclization.
    • Check for competing pathways: The pQM intermediate is highly reactive and may be trapped by nucleophiles from the solvent or undergo polymerization. Using high dilution conditions or different solvents can mitigate this.

Issue: Inefficient Divergent Synthesis from a Common Intermediate

A major goal of bioinspired synthesis is to use a single advanced intermediate to access multiple natural products [26].

  • Troubleshooting Steps:
    • Re-evaluate the common intermediate: Confirm its structure and purity. Ensure it contains the necessary functional handles for diversification.
    • Optimize individual diversification steps: Not all downstream reactions will be equally efficient. Treat each transformation as a separate optimization problem.
    • Employ chemoenzymatic strategies: If chemical methods for diversification fail, consider engineered enzymes to perform specific, high-yielding late-stage modifications, as demonstrated in the synthesis of teleocidin derivatives [28] and fusicoccane diterpenoids [29].

Detailed Experimental Protocols

Protocol 1: Bioinspired Prins Cyclization for Bicyclic Core Construction [25]

This protocol is adapted from the key step in the total synthesis of the diterpenoid chabranol.

  • Objective: To construct the oxa-[2.2.1] bridged bicycle from a linear hydroxy aldehyde precursor.
  • Reaction Mechanism: The reaction proceeds via activation of the aldehyde, triggering a Prins cyclization with a nearby alkene. The resulting carbocation is trapped intramolecularly by a tertiary alcohol.

  • Step-by-Step Procedure:

    • Substrate Preparation: Synthesize hydroxy aldehyde precursor 3 from phenyl sulfide 5 and chiral epoxide 6 via coupling, reduction, and selective oxidation.
    • Cyclization:
      • Charge a flame-dried round-bottom flask with hydroxy aldehyde 3 (1.0 equiv) under an inert atmosphere.
      • Add dry dichloromethane (0.01-0.05 M concentration) and cool the solution to 0°C.
      • Add a Lewis acid (e.g., TMSOTf; 1.5-2.0 equiv) dropwise via syringe.
      • Stir the reaction mixture, allowing it to warm to room temperature over 2-12 hours. Monitor by TLC or LC-MS.
    • Work-up: Quench the reaction by careful addition of a saturated aqueous solution of sodium bicarbonate.
    • Purification: Extract the aqueous mixture with dichloromethane (3x). Combine the organic layers, dry over anhydrous magnesium sulfate, filter, and concentrate under reduced pressure.
    • Isolation: Purify the crude residue (containing silylated bicycle 9) by flash column chromatography on silica gel.

  • Technical Notes:

    • Critical: All reagents and glassware must be scrupulously dry to prevent hydrolysis of the Lewis acid and silyl ether products.
    • The concentration of the substrate can significantly impact the reaction rate and yield. Test different dilutions if necessary.
    • The diastereoselectivity of this cyclization is often high, as reported in the chabranol synthesis, leading to a single detectable diastereomer of the product [25].

The following diagram illustrates the logical workflow and key decision points for this protocol.

G Start Start: Prepare Hydroxy Aldehyde Precursor A Set up reaction under inert atmosphere Start->A B Add dry DCM and cool to 0°C A->B C Add Lewis acid (e.g., TMSOTf) B->C D Stir and warm to RT. Monitor reaction C->D E Reaction Complete? D->E E->D No F Quench with aqueous NaHCO₃ E->F Yes G Work-up: Extract with DCM F->G H Purify by flash chromatography G->H End End: Isolate Bicyclic Product H->End

Protocol 2: Chemoenzymatic Synthesis for Scalable Production [28]

This protocol outlines a general strategy for producing complex natural products like teleocidin B, combining chemical and enzymatic synthesis.

  • Objective: To achieve efficient, scalable production of complex monoterpenoid indole alkaloids using engineered enzymes.
  • Key Strategy: Overcome bottlenecks in the biosynthetic pathway via protein engineering. For teleocidins, this involved fusing a reductase module to the P450 enzyme TleB to create a self-sufficient system, significantly boosting the production of the key intermediate indolactam V [28].
  • General Workflow:
    • Pathway Identification: Identify the biosynthetic gene cluster and key enzymatic steps in the natural product's pathway.
    • Host Engineering: Select a suitable microbial host (e.g., E. coli). Engineer platform strains to overproduce central metabolic precursors [30].
    • Enzyme Engineering: Identify rate-limiting enzymes. Use protein engineering (e.g., fusion tags, directed evolution) to improve catalytic efficiency, stability, and soluble expression in the heterologous host [28].
    • Dual-Cell Factory Setup: Implement a co-culture system where different strains express complementary parts of the pathway to reduce metabolic burden and optimize overall titers [28] [30].
    • Fermentation and Scale-up: Transfer the optimized system to a bioreactor for scalable production under controlled conditions.

The Scientist's Toolkit: Key Research Reagent Solutions

The following table details essential reagents, enzymes, and materials used in advanced bioinspired and chemoenzymatic syntheses, as cited in the literature.

Table 1: Key Reagents and Materials for Bioinspired Synthesis

Reagent/Material Function/Application Example from Literature
Lewis Acids (e.g., TMSOTf) Activates carbonyls for key cyclization reactions like the Prins cyclization. [25] Used to activate aldehyde 3 in the bioinspired synthesis of chabranol. [25]
Engineered P450 Enzymes (e.g., TleB) Catalyzes oxidative cyclizations and complex C-H functionalizations that are challenging by traditional chemistry. [28] A fused, self-sufficient TleB variant boosted indolactam V production to 868.8 mg L⁻¹. [28]
Heterologous Hosts (E. coli, S. cerevisiae) Microbial chassis for expressing biosynthetic pathways and producing natural products via fermentation. [30] A recombinant E. coli system produced 300 mg of teleocidin B isomers. [28]
Platform Strains Pre-engineered microbial strains that overproduce central metabolites (e.g., geranyl pyrophosphate), providing a high-titer starting point for PNP pathways. [30] Strains overproducing (S)-reticuline enable the biosynthesis of diverse benzylisoquinoline alkaloids. [30]
Oxidants (for pQM formation) Generates reactive para-quinone methide (pQM) intermediates from phenolic precursors for oxidative cyclization. [25] Proposed for tetrahydrofuran ring formation in monocerin-family natural products. [25]
ReproterolReproterol|β2-Adrenergic Agonist|Reproterol is a selective β2-adrenergic receptor agonist for asthma and COPD research. This product is for Research Use Only (RUO). Not for human use.
3,4,5-Trimethoxybenzaldehyde3,4,5-Trimethoxybenzaldehyde, 98+%|RUO

Visual Guide to Bioinspired Synthesis Workflow

The following diagram provides a generalized strategic workflow for planning and executing a bioinspired total synthesis project, integrating concepts from the reviewed literature.

G Start Select Target Natural Product A Analyze Structure and Propose Biosynthetic Pathway Start->A B Design Bioinspired Strategy A->B C1 Mimic Key Cyclization B->C1 Well-established cyclization C2 Follow Revised Pathway B->C2 Original pathway flawed C3 Imitate Skeletal Diversification B->C3 Skeletally diverse family D Develop Synthetic Route to Key Biosynthetic Precursor C1->D C2->D C3->D E Execute Biomimetic Transformation (e.g., Cascade Cyclization) D->E D->E D->E F Troubleshoot and Optimize E->F E->F E->F G1 Single Target Synthesis F->G1 F->G1 G2 Divergent Synthesis of Multiple Targets F->G2 End Target(s) Achieved G1->End G1->End G2->End

Troubleshooting Guides & FAQs

Frequently Asked Questions

Q1: Why does my AI model fail to propose convergent synthetic routes for complex natural products?

A: This is often due to the search algorithm's scoring function prioritizing linear pathways. To encourage convergent synthesis, implement a Convergent Disconnection Score (CDScore). This score, as used in the ReTReK framework, evaluates potential disconnections based on their ability to split the target molecule into roughly equal-sized fragments, promoting more efficient synthesis trees. Furthermore, ensure your search algorithm, such as Monte Carlo Tree Search (MCTS), is configured to use this score in its tree policy for selecting promising search directions [31].

Q2: My template-based model cannot find applicable reactions for novel, complex scaffolds. How can I improve its generality?

A: Template-based models are limited by their predefined rule libraries. For novel structures, consider these approaches:

  • Switch to a template-free model: Models like BatGPT-Chem treat retrosynthesis as a sequence-to-sequence translation problem, converting product SMILES strings directly into reactant SMILES strings. This approach does not rely on a fixed template library and demonstrates better generalization for out-of-distribution molecules [32].
  • Utilize a semi-template model: These models first identify the reaction center to generate intermediate synthons and then complete the precursors. This hybrid approach balances interpretability with improved generalization capability [32].
  • Expand template applicability: If sticking with template-based methods, ensure your reaction templates consider the reaction center and its first-degree neighbors, which helps maintain chemical integrity and applicability [31].

Q3: The proposed precursors are chemically implausible or unstable. What is the cause and solution?

A: Chemically implausible suggestions can arise from:

  • Biased Training Data: Models trained on noisy or highly biased public reaction data may learn incorrect transformations. Use curated datasets and consider incorporating chemical knowledge checks.
  • Lack of Reaction Condition Knowledge: Many models focus only on the core transformation and ignore crucial elements like solvents, catalysts, or temperature. Using models that explicitly predict reaction conditions, such as BatGPT-Chem, which integrates this information end-to-end, can significantly improve the plausibility of proposed routes [32].
  • Invalid Stereochemistry: When applying a predicted reaction, use reliable chemistry toolkits (e.g., the Reactor in the ChemAxon API) to correctly handle stereochemistry and regiochemistry during the transformation [31].

Q4: How can I guide the AI to prioritize synthetically accessible starting materials?

A: Integrate an Available Substances Score (ASScore) into your search algorithm. This score penalizes proposed precursors that are not found in a predefined database of commercially available or readily synthesized compounds (e.g., ZINC database). By factoring this score into the MCTS evaluation, the search is directed toward pathways that terminate in accessible starting materials [31].

Q5: What does "zero-shot prediction capability" mean, and why is it important for natural product synthesis?

A: Zero-shot prediction refers to a model's ability to make accurate predictions for reaction types or molecule classes that it did not see during training. This is crucial for natural product synthesis because these molecules often possess novel, complex scaffolds not well-represented in standard reaction databases. Models like BatGPT-Chem are developed with this capability, allowing them to propose synthetic pathways even for highly unique structures by leveraging broad chemical knowledge learned during pre-training [32].

Troubleshooting Common Experimental Issues

Issue: The retrosynthetic search is computationally expensive and slow for large, complex molecules.

  • Solution: Adjust the expansion size in the MCTS algorithm. Research on the ReTReK model showed that performance improves with larger expansion sizes (e.g., top 50, 100, 300, or 500 predicted templates per step), but this increases computation. A balance must be struck based on available resources. The top-100 templates often provide a good balance between performance and speed [31].

Issue: The model proposes routes with reactions that are known to have low selectivity or yield.

  • Solution: Implement a Selective Transformation Score (STScore). This score is designed to favor reactions that produce fewer byproducts. It can be incorporated as an adjustable parameter within the MCTS tree policy to guide the search toward higher-yielding, more selective transformations [31].

Issue: The AI consistently fails to disassemble complex ring systems effectively.

  • Solution: Introduce a Ring Disconnection Score (RDScore). This strategy is particularly useful when the target has complex ring structures, as their construction often leads to simpler precursors. The RDScore helps the algorithm identify strategic bond disconnections within rings [31].

The performance of AI retrosynthesis tools is typically evaluated on benchmark datasets. The table below summarizes key quantitative data from relevant models and studies.

Table 1: Performance Comparison of Retrosynthesis Tools and Components

Model / Component Key Metric Performance Context / Dataset
ReTReK's GCN Policy Network [31] Top-1 Accuracy 36.1% 1-step retrosynthetic reaction prediction on Reaxys-based templates.
Top-50 Accuracy 90.6%
Top-100 Accuracy 93.8%
BatGPT-Chem [32] Zero-shot Capability Demonstrated Effective retrosynthesis prediction on specialized, non-overlapping datasets (e.g., Suzuki-Miyaura, Buchwald-Hartwig).
Molecular Complexity Model [33] Pair Accuracy (PA) 77.5% Accuracy in ranking molecular complexity compared to expert human assessment.
Functional Group Test (FGT) 98.1% Model correctly identified increased complexity after adding a functional group.

Experimental Protocols & Workflows

Protocol: Implementing a Knowledge-Guided Retrosynthetic Search with MCTS

This protocol outlines the methodology for setting up a retrosynthetic search system similar to the ReTReK application, which integrates data-driven prediction with rule-based chemical knowledge [31].

Objective: To design a multistep synthetic route for a target molecule by leveraging a Monte Carlo Tree Search (MCTS) algorithm enhanced with retrosynthesis knowledge scores.

Materials:

  • Hardware: A computer with significant computational resources (e.g., high-CPU/GPU servers).
  • Software: A programming environment (e.g., Python) with libraries for cheminformatics (e.g., RDKit) and machine learning (e.g., PyTorch, TensorFlow).
  • Data:
    • Target Molecule: The complex molecule for which a synthetic route is needed.
    • Reaction Database: A large, curated database of reactions (e.g., Reaxys, USPTO) for training the one-step prediction model.
    • Starting Materials Database: A database of commercially available or easily accessible building blocks (e.g., ZINC database).

Procedure:

  • Policy Network Training: Train a one-step retrosynthetic reaction prediction model (the policy network). A Graph Convolutional Network (GCN) is a suitable architecture. This model learns to prioritize a list of applicable reaction templates for any input molecule.
  • MCTS Initialization: Define the root node of the search tree as the target molecule.
  • Tree Search Iteration (Repeated until a satisfactory route is found or resources are exhausted):
    • Selection: Traverse the tree from the root node by selecting the node with the highest value from a combination of the prediction score and the retrosynthesis knowledge scores (CDScore, ASScore, etc.).
    • Expansion: When a leaf node is reached, use the policy network to expand the node by generating the top k most promising precursor suggestions (e.g., k=100).
    • Rollout (Simulation): For each new node, perform a rapid simulation (e.g., using a simpler, faster policy) to estimate the potential of reaching starting materials.
    • Update (Backpropagation): Update the node statistics (e.g., visit count, value score) along the traversed path based on the outcome of the simulation (e.g., success or failure in reaching starting materials).
  • Route Extraction: Once the search is complete, extract the most promising synthetic route from the root (target) to the leaf nodes (starting materials).

Workflow Visualization

The following diagram illustrates the core iterative loop of the MCTS algorithm as applied to retrosynthetic planning.

mcts_retro Start MCTS Cycle Start Selection Selection Choose node using Tree Policy & Knowledge Scores Start->Selection Expansion Expansion Use GCN Policy Network to generate precursors Selection->Expansion Simulation Rollout (Simulation) Fast playout to estimate route potential Expansion->Simulation Backpropagation Backpropagation Update node statistics based on result Simulation->Backpropagation Backpropagation->Selection Repeat Cycle

Diagram 1: MCTS Retrosynthesis Cycle

Protocol: Quantifying Molecular Complexity for Target Analysis

Objective: To assign a numerical complexity value to a target natural product, providing a benchmark to assess the challenge a synthesis poses and to compare different synthetic strategies.

Materials:

  • Software: Access to a molecular complexity model, such as the Learning-to-Rank (LTR) GBDT model described by Tyrin et al. [33].
  • Input: The SMILES string or molecular structure file of the target molecule.

Procedure:

  • Data Preparation: Generate a valid SMILES string for your target molecule.
  • Feature Calculation: Calculate or extract key molecular descriptors that the model uses. The most important features, as identified by SHAP analysis, are [33]:
    • Molecular Weight
    • Number of Aromatic Cycles
    • Topological Polar Surface Area (TPSA)
    • SCScore (Synthetic Complexity Score)
  • Model Application: Input the molecule's descriptor values into the pre-trained ranking model.
  • Interpretation: The model outputs a relative complexity score. A higher score indicates a more complex molecule. This score can be used to track complexity changes during retrosynthetic analysis or to compare the overall challenge of synthesizing different candidate molecules.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Digital Tools and Databases for Computational Retrosynthesis

Tool / Resource Type Primary Function in Retrosynthesis
Reaxys [31] [34] Reaction Database Provides a vast repository of historical chemical reactions and substances for training AI models and validating proposed routes.
ZINC Database [31] Starting Materials Database A curated collection of commercially-available compounds; used to define the search boundary for viable synthesis.
USPTO Dataset [34] Reaction Database A large, publicly available dataset of chemical reactions extracted from U.S. patents, commonly used for training template-based and template-free AI models.
RDKit Cheminformatics Library An open-source toolkit for Cheminformatics; used for manipulating molecules, handling SMILES, calculating descriptors, and applying reaction transforms.
SMILES Notation [34] [32] Molecular Representation A line notation for representing molecular structures as text, enabling the use of natural language processing (NLP) models for retrosynthesis.
BatGPT-Chem [32] AI Model (LLM) A large language model specialized for chemistry, capable of template-free, one-step retrosynthesis prediction with reaction condition suggestion and zero-shot capability.
DataWarrior [35] Data Analysis Tool A free program for data visualization and analysis, useful for calculating physicochemical properties and analyzing structure-activity relationships of precursors.
2,5-Bis(iodomethyl)-1,4-dioxane2,5-Bis(iodomethyl)-1,4-dioxane|CAS 101084-46-22,5-Bis(iodomethyl)-1,4-dioxane is a bifunctional building block for organic synthesis. This product is for research use only and is not intended for human or veterinary use.
1,2,3,4-Tetrahydroquinolin-6-ol1,2,3,4-Tetrahydroquinolin-6-ol, CAS:3373-00-0, MF:C9H11NO, MW:149.19 g/molChemical Reagent

Knowledge Integration & Strategic Pathway

Successfully integrating chemical knowledge into a data-driven AI framework is key to practical retrosynthesis planning. The following diagram illustrates how different knowledge scores can be incorporated into the search process to guide it toward more desirable synthetic routes.

knowledge_flow Target Target Molecule Search AI Search Algorithm (e.g., MCTS) Target->Search Knowledge Retrosynthesis Knowledge (Adjustable Parameters) Knowledge->Search Guides CDScore CDScore Convergence CDScore->Knowledge ASScore ASScore Available Materials ASScore->Knowledge RDScore RDScore Ring Disconnection RDScore->Knowledge STScore STScore Selectivity STScore->Knowledge Route Optimized Synthetic Route Search->Route

Diagram 2: Knowledge-Guided AI Planning

Technical Support Center

Troubleshooting Guides

Guide 1: Troubleshooting Poor Enzyme Performance in Kinetic Resolutions

Problem: An enzymatic kinetic resolution is proceeding with low enantioselectivity (E value) or low conversion, failing to provide the desired enantioenriched intermediate.

Observation Possible Cause Diagnostic Steps Solution
Low enantioselectivity Incorrect enzyme choice for substrate Screen different biocatalysts (e.g., lipases, acylases) [36]. Use a tailored enzyme variant developed via directed evolution [36].
Low conversion rate Suboptimal reaction conditions (pH, temperature) Measure pH and temperature; run control experiments. Optimize buffer, temperature, and co-solvent concentration [36].
No reaction Enzyme inactivation or incompatible functional groups Check enzyme activity with a standard substrate; review substrate structure. Ensure substrate lacks enzyme-inhibiting groups; use a fresh enzyme batch [36].

Experimental Protocol for Biocatalytic Kinetic Resolution:

  • Reaction Setup: Dissolve the racemic substrate (e.g., 17) in an organic solvent like diisopropyl ether (0.1 M concentration) [36].
  • Enzyme Addition: Add the biocatalyst (e.g., Lipase PS) and vinyl acetate as an acyl donor [36].
  • Monitoring: Monitor the reaction progress by thin-layer chromatography (TLC) or chiral high-performance liquid chromatography (HPLC) until the kinetic resolution is complete (may take several days) [36].
  • Workup: Filter to remove the immobilized enzyme and concentrate the filtrate under reduced pressure.
  • Purification: Purify the product (e.g., enantiopure ester 18) using flash column chromatography to obtain the resolved material in high enantiomeric excess (e.g., 98:2 er) [36].
Guide 2: Troubleshooting a Multi-Step Chemoenzymatic Synthesis

Problem: A late-stage enzymatic transformation, such as an oxidation cascade, fails after a long sequence of chemical steps, jeopardizing the entire synthesis.

Observation Possible Cause Diagnostic Steps Solution
Enzyme does not accept synthetic intermediate Subtle structural differences from natural substrate Test the enzyme on a natural substrate; compare ( K_m ) values. Employ protein engineering to adjust the enzyme's active site for better acceptance of the synthetic substrate [29].
Low yield in enzymatic oxidation Incompatibility of the enzyme with chemical functional groups on the synthetic intermediate Check for functional groups that might inhibit or destabilize the enzyme. Re-order the synthetic sequence or use protective groups to mask incompatible functionalities during the enzymatic step [36].
Inability to scale up a chemoenzymatic step Poor enzyme stability or co-factor regeneration issues under scaled conditions Perform a small-scale reaction mimicking the production environment. Develop a immobilized enzyme system or optimize co-factor recycling for larger scales [36].

Experimental Protocol for Late-Stage Enzymatic Oxidation:

  • Enzyme Engineering: If the wild-type enzyme shows poor activity, perform rational protein engineering or directed evolution to generate a variant that accepts the synthetic intermediate [29].
  • Reaction Setup: Incubate the chemically synthesized precursor with the engineered enzyme and necessary cofactors (e.g., NADPH, Oâ‚‚) in an appropriate buffer.
  • Process Optimization: For scalability, consider immobilizing the enzyme on a solid support and establishing a continuous flow system to improve stability and efficiency [36].
  • Monitoring and Isolation: Monitor the reaction closely using LC-MS. Upon completion, extract and purify the product (e.g., the final natural product) using standard techniques [29].

Frequently Asked Questions (FAQs)

Q1: What are the primary strategic approaches for incorporating biocatalysis into a total synthesis plan? Based on recent literature, there are four main conceptual approaches [36]:

  • Providing Enantioenriched Intermediates: Enzymes, like lipases, are used in a supporting role for kinetic resolutions or desymmetrizations to generate chiral building blocks, without altering the core synthetic logic [36].
  • Evaluating Biosynthetic Hypotheses: Chemically synthesized intermediates are used to probe the function of enzymes in biosynthetic pathways, validating or discovering biological functions [36].
  • Inspiring Retrosynthetic Disconnections: The known reactivity of an enzyme is used as the central, simplifying transform (a "T-goal") in designing the synthetic route from the outset [36].
  • Filling Gaps in Methodology: A disconnection is proposed for which no chemical method exists, prompting the discovery or engineering of a new "reaction" to achieve the transformation [36].

Q2: How can I improve the efficiency and success of my chemoenzymatic synthesis? Efficiency is achieved by strategically leveraging the strengths of both chemical and biological catalysis [36] [29]:

  • Enzyme Engineering: Utilize directed evolution to create biocatalysts with enhanced stability, activity, or altered selectivity tailored to your synthetic needs [36].
  • Strategic Ordering: Plan the synthetic sequence so that enzymatic steps are performed on advanced intermediates that are structurally similar to the enzyme's natural substrate [29].
  • Hybrid Logic: Combine de novo chemical skeleton construction with late-stage enzymatic cascades (e.g., oxidation cascades) to efficiently build molecular complexity [29].

Q3: My enzymatic reaction is too slow. What are common reasons and solutions? Slow kinetics can arise from several factors [36]:

  • Substrate Inhibition: The concentration of your synthetic intermediate may be too high for the enzyme. Solution: Try running the reaction at a lower substrate concentration.
  • Solvent System: The enzyme may be denatured or inactive in the chosen solvent. Solution: Screen different buffer/organic co-solvent mixtures to find a compatible system.
  • Cofactor Requirements: The enzyme may require a specific cofactor (e.g., FAD, NADPH) that is not sufficiently regenerated. Solution: Ensure the reaction mixture includes the necessary cofactors and regeneration systems.

The Scientist's Toolkit: Key Research Reagent Solutions

The following table details essential materials and their functions in chemoenzymatic synthesis, as derived from cited case studies.

Reagent / Material Function in Chemoenzymatic Synthesis Example from Literature
Lipases (e.g., Lipase PS) Biocatalyst for kinetic resolutions and desymmetrizations; generates enantioenriched intermediates from racemic or prochiral starting materials [36]. Kinetic resolution of pipecolic acid derivative 17 to yield enantiopure building block 18 [36].
Engineered Polyketide Synthases (PKS) Mega-enzymes that catalyze the assembly of polyketide chains; can be engineered to incorporate non-natural substrates, such as fluorinated precursors [29]. Biosynthesis of fluorinated polyketides using a designed hybrid PKS/FAS multienzyme [29].
Allylsilanes Stable, nucleophilic reagents used in Lewis acid-catalyzed C–C bond formations (Hosomi-Sakurai reaction) to extend carbon chains and introduce alkene handles for further manipulation [37]. Key transformation for constructing homoallylic alcohol segments in complex natural product synthesis [37].
Directed Evolution Kit A suite of molecular biology tools (e.g., error-prone PCR, DNA shuffling) used to generate mutant libraries of enzymes for screening improved or novel biocatalytic activities [36]. Creation of enzymes with "new-to-nature" activity or improved performance on synthetic substrates [36].
Oxidation Enzymes (e.g., Cytochromes P450) Catalyze site-specific C–H oxidations, often with high regio- and stereoselectivity, that are challenging to achieve with chemical methods alone [29]. Late-stage oxidation cascades in the synthesis of fusicoccane diterpenoids and oxidized meroterpenoids [29].
EptifibatideEptifibatide|GPIIb/IIIa Inhibitor for ResearchEptifibatide is a cyclic heptapeptide GPIIb/IIIa inhibitor derived from snake venom. For Research Use Only. Not for human or veterinary use.

Workflow and Strategy Diagrams

G Start Start: Complex Natural Product Approach1 Approach 1: Provide Chiral Pool Start->Approach1 Approach2 Approach 2: Test Biosynthesis Start->Approach2 Approach3 Approach 3: Enzyme as T-Goal Start->Approach3 Approach4 Approach 4: Fill Method Gap Start->Approach4 Outcome Outcome: Synthesized Target Approach1->Outcome e.g., Lipase Resolution Approach2->Outcome e.g., Intermediate Feeding Approach3->Outcome e.g., Designed Oxidation Approach4->Outcome e.g., Engineered Enzyme

Chemoenzymatic Strategy Roadmap

G Problem Poor Enzyme Performance Step1 Diagnose Cause Problem->Step1 LowEnanto Low Enantioselectivity? Step1->LowEnanto LowConversion Low Conversion? Step1->LowConversion Check Check Activity with Standard Substrate Step1->Check Step2 Select Solution Path Sol1 Screen Different Enzyme Variants Step2->Sol1 Sol2 Optimize Reaction Conditions (pH, Temp) Step2->Sol2 Sol3 Use Directed Evolution to Engineer Enzyme Step2->Sol3 LowEnanto->Step2 LowConversion->Step2 Sol4 Use Fresh Enzyme Batch Check->Sol4

Enzyme Performance Troubleshooting

Navigating Synthetic Hurdles: From Route Design to Scalable Production

Scaffold hopping, the strategy of identifying or creating isofunctional molecular structures with chemically different core structures, is a powerful tool for overcoming limitations in natural product synthesis and drug discovery [38] [39]. While computational methods offer various pathways for scaffold hopping, translating these designs into successful laboratory synthesis presents significant challenges. This technical support center addresses the specific experimental issues researchers encounter during scaffold hopping campaigns, providing practical troubleshooting guidance framed within the broader context of addressing structural complexity in natural product synthesis.

Frequently Asked Questions (FAQs)

Q1: What is the fundamental definition of scaffold hopping, and how does it differ from general bioisosteric replacement?

Scaffold hopping is a subset of bioisosteric replacement specifically focused on replacing the core motif (pharmacophore) of a molecule while maintaining its important interaction potentials. The key differentiator is that scaffold hopping aims for chemically completely different core structures that retain similar biological activity, moving beyond simple atom-for-atom replacement to achieve significant structural novelty [38] [39].

Q2: In a practical synthesis, what are the primary strategic approaches to achieve a successful scaffold hop?

The main experimental approaches can be categorized as follows [38]:

  • Virtual Screening: Using computational docking and scoring of compound libraries to predict potential binders with different scaffolds but similar binding modes.
  • Topological Replacement: Replacing molecular fragments with geometrically similar motifs that maintain the 3D orientation of decorations attached to the core.
  • Fuzzy Pharmacophores: Searching for compounds with similar overall pharmacophore properties using molecular descriptors that allow for structural variation.
  • Shape Similarity: Screening for compounds with similar shape and functionality orientation when no binding mode information is available.
  • Abiotic Skeletal Reorganization: Using enzymatic or chemical methods to fundamentally reorganize a starting material's carbon skeleton into novel frameworks [40] [41].

Q3: What are the critical trade-offs to consider when planning the degree of structural change in a scaffold hop?

There is an inherent trade-off between the degree of structural novelty and the probability of maintaining comparable biological activity. Small-step hops (e.g., heteroatom swaps in aromatic rings) offer higher success rates but lower structural novelty. Large-step hops (e.g., topology-based changes or extensive skeletal reorganizations) can achieve high novelty but present greater synthetic challenges and higher risk of activity loss [39].

Q4: How can enzymatic methods be integrated with traditional synthetic chemistry to enable more dramatic scaffold hops?

As demonstrated in recent terpenoid synthesis, site-selective enzymatic oxidation can install functional handles (e.g., alcohols) that are traditionally difficult to achieve with chemical means. These biocatalytically installed groups then serve as exploitable motifs for programmed abiotic skeletal rearrangements, allowing substantial divergence from the original ring system while maintaining stereochemical control [40] [41].

Troubleshooting Guides

Low Success Rates in Virtual Screening

Problem: Virtual screening campaigns return numerous hits, but few compounds show the desired activity when synthesized or tested experimentally.

Potential Cause Diagnostic Steps Solution
Overly rigid pharmacophore constraints Check if all returned hits are structurally very similar to the query Apply fuzzy pharmacophore methods using tools like FTrees to allow more structural breathing space [38]
Inadequate handling of molecular flexibility Compare 2D vs 3D similarity metrics of hits Use shape similarity screening that accounts for conformational flexibility and functionality orientation [38]
Ignoring chemical synthetic accessibility Evaluate synthetic complexity of top hits computationally Implement synthetic feasibility filters early in the virtual screening workflow

Failed Skeletal Rearrangements

Problem: Attempted skeletal reorganizations yield undesired side products, low yields, or complete failure of the intended transformation.

Potential Cause Diagnostic Steps Solution
Uncontrolled reactivity at multiple sites Analyze reaction mixture for multiple products Employ chemoenzymatic strategies where enzymatic steps provide selective activation for subsequent controlled abiotic rearrangements [40] [41]
Incompatible functional groups Map all functional groups in starting material against reaction conditions Design protecting-group-free routes using chemoselective transformations that tolerate multiple functional groups [42]
Insufficient driving force for reorganization Computational modeling of energy barriers Incorporate strain-releasing rearrangements or build in thermodynamic drivers like ring formation [40]

Maintaining Target Engagement Post-Hop

Problem: The scaffold-hopped compound is successfully synthesized but shows significantly reduced binding affinity or functional activity.

Potential Cause Diagnostic Steps Solution
Disruption of key pharmacophore geometry Perform 3D superposition with original scaffold Use topological replacement tools (e.g., ReCore) that screen for fragments with similar 3D coordination of connection points [38]
Altered electrostatic properties Calculate and compare molecular fields Maintain critical hydrogen bond donors/acceptors and lipophilic regions through pharmacophore constraints in design [38]
Incorrect assessment of scaffold flexibility Compare conformational energy profiles Apply conformational restriction strategies (ring closure) to pre-organize the molecule for binding, as seen in antihistamine development [39]

Experimental Protocols & Workflows

Standard Protocol: Enzyme-Enabled Abiotic Scaffold Hopping of Terpenoids

This protocol adapts the methodology demonstrated in the recent synthesis of diverse terpenoid frameworks from sclareolide [40] [43] [41].

Principle: Utilize biocatalytic oxidation to install a strategic functional handle, then employ abiotic skeletal rearrangements to achieve dramatic scaffold divergence from a common starting material.

Materials:

  • Starting material: Sclareolide (or analogous terpenoid substrate)
  • Engineered P450 BM3 enzyme (C3-hydroxylase activity for sclareolide)
  • Cofactor regeneration system (NADPH/GDH/glucose or equivalent)
  • Anhydrous solvents for subsequent chemical steps (CHâ‚‚Clâ‚‚, THF, toluene)
  • Rearrangement reagents: Suarez reaction reagents (iodobenzene diacetate, iodine), Wittig homologation reagents, Diels-Alder dienophiles
  • Purification materials: Flash chromatography silica gel, TLC plates, HPLC system

Procedure:

  • Enzymatic Installation of Functional Handle

    • Prepare reaction mixture: 10 mM sclareolide, 5 μM P450 BM3, NADPH regeneration system in appropriate buffer (pH 7.4-8.0)
    • Incubate at 25-30°C with agitation (200 rpm) for 12-24 hours
    • Monitor reaction progress by TLC or LC-MS
    • Extract product (C3-hydroxy sclareolide) with ethyl acetate, concentrate under reduced pressure
    • Purify by flash chromatography (hexanes/ethyl acetate gradient)

  • Abiotic Skeletal Reorganization Planning

    • Conduct pattern-recognition analysis to identify potential skeletal matches between functionalized intermediate and target frameworks
    • Design rearrangement sequence based on strategic bond cleavage/formation opportunities presented by the installed functional group

  • Radical-Mediated Ring Deconstruction (for merosterolic acid B pathway)

    • Subject alcohol to β-fragmentation/elimination under Suarez conditions (iodobenzene diacetate, iodine, hv)
    • Monitor formation of aldehyde intermediate by TLC
    • Extract and purify aldehyde product

  • Skeletal Reconstruction via Annulation

    • Perform Wittig homologation to convert aldehyde to diene
    • Conduct intramolecular Diels-Alder under thermal conditions (toluene, reflux)
    • Purify cycloadduct by flash chromatography
    • Execute subsequent functionalization steps (cyclopropanation, hydrogenation) to build final scaffold

Troubleshooting Notes:

  • If enzymatic hydroxylation yield is low, consider enzyme engineering or alternative P450 variants
  • If skeletal rearrangements produce complex mixtures, optimize reaction concentration and temperature to favor desired pathway
  • For stereochemical control issues, introduce chiral auxiliaries or catalysts at key cyclization steps

Workflow: Integrated Scaffold Hopping Strategy

G Start Starting Scaffold with Limitations VirtualScreen Virtual Screening with Pharmacophore Constraints Start->VirtualScreen TopoReplace Topological Replacement Start->TopoReplace EnzymaticHandle Enzymatic Functionalization Start->EnzymaticHandle Validation Synthesis & Biological Validation VirtualScreen->Validation LBDD TopoReplace->Validation 3D Similarity AbioticReorg Abiotic Skeletal Reorganization EnzymaticHandle->AbioticReorg Chemoenzymatic AbioticReorg->Validation DOS-Inspired NewScaffold Novel Scaffold with Maintained Activity Validation->NewScaffold

The Scientist's Toolkit: Research Reagent Solutions

Computational Tools for Scaffold Hopping Design

Tool/Software Primary Function Application in Scaffold Hopping
SeeSAR Interactive structure-based design Virtual screening with pharmacophore constraints; similarity scanning based on shape and pharmacophores [38]
FTrees Feature Tree similarity searching Identifies distant structural relatives using fuzzy pharmacophore properties; navigates chemical spaces [38]
ReCore (in SeeSAR) Topological replacement Screens fragment libraries for motifs with similar 3D coordination of connection points [38]
MOE Flexible Alignment 3D molecular superposition Validates conservation of pharmacophore geometry after scaffold modification [39]

Experimental Reagents for Skeletal Reorganization

Reagent/Catalyst Function Example Application
Engineered P450 Enzymes Site-selective C-H oxidation Installing alcohol functional handles for subsequent rearrangement (e.g., C3-hydroxylation of sclareolide) [40] [41]
Suarez Reaction System (PhI(OAc)₂/I₂/hv) Alkoxy radical generation and β-fragmentation Ring deconstruction via radical-mediated C-C bond cleavage [40]
Au(I) Catalysts Chemoselective activation of π-systems Functional-group-tolerant cyclizations in complex polycyclic systems [42]
Diels-Alder Dienophiles [4+2] Cycloaddition Skeletal reconstruction via ring annulation after initial deconstruction [40]
Wittig Reagents Olefination Homologation and diene formation for subsequent cycloadditions [40]

Advanced Technical Notes

Classification Framework for Scaffold Hops

Understanding the classification of scaffold hops helps researchers anticipate synthetic challenges and success probabilities:

G ScaffoldHop Scaffold Hop Classifications SmallStep Small-Step Hop (Heterocycle Replacement) MediumStep Medium-Step Hop (Ring Opening/Closure) LargeStep Large-Step Hop (Topology/Skeletal Reorg) Example1 e.g., Sildenafil  Vardenafil N/C swap in ring system SmallStep->Example1 Example2 e.g., Morphine  Tramadol Ring opening MediumStep->Example2 Example3 e.g., Drimane → Multiple Frameworks Enzyme-enabled reorganization LargeStep->Example3

Strategic Implementation Table

Scenario Recommended Approach Rationale Success Indicators
Patent circumvention Heterocycle replacement (small-step hop) Minimal structural change sufficient for novel IP while maintaining activity [39] Comparable potency (<10-fold reduction)
Improving pharmacokinetics Ring opening/closure (medium-step hop) Modifies molecular flexibility and physicochemical properties [39] Improved solubility/metabolic stability with retained efficacy
Exploring novel chemical space Topological/skeletal reorganization (large-step hop) Enables access to fundamentally different chemotypes [38] [40] New IP position with maintained or modulated activity
Natural product diversification Enzyme-enabled abiotic hopping Leverages biocatalytic selectivity for dramatic skeletal changes [40] [41] Multiple distinct frameworks from single precursor

Late-stage functionalization (LSF) is a transformative strategy in synthetic chemistry, defined as the chemoselective transformation of a complex molecule to provide analogs without needing to add functional groups solely to enable the transformation [44]. In the context of natural product synthesis and drug discovery, LSF allows researchers to avoid complete de novo synthesis of target molecules, enabling the rapid creation of large compound libraries for exploring structure-activity relationships [45]. This approach is particularly valuable for diversifying complex natural product cores, where traditional synthetic routes can be lengthy and inefficient.

The most synthetically useful LSF strategies typically involve the direct installation of small, non-invasive groups—such as methyl, hydroxyl, chloro, fluoro, or trifluoromethyl—with precise selectivity control at specific sites of biologically relevant molecules [45]. The introduction of these small groups can dramatically affect the bioactivity profiles of structurally complex pharmaceutical molecules. For instance, Pfizer discovered that installing a methyl group on a morpholine-containing mineralocorticoid receptor agonist resulted in a 45-fold potency increase [45].

Essential LSF Methodologies for Complex Molecule Diversification

Various synthetic approaches have been developed for LSF, with C–H functionalization representing one of the most powerful platforms that has emerged in recent years [46]. These methodologies can be broadly classified into several categories, each with distinct mechanisms and applications for diversifying complex molecular scaffolds.

Directed C–H Functionalization

Directed C–H functionalization relies on Lewis basic functionalities that chemoselectively bring catalysts into proximity with specific C–H bonds, enabling chelation-assisted C–H cleavage [47]. This approach provides predictable selectivity patterns and has matured into an indispensable tool for molecular synthesis.

  • Mechanism: Transition metal catalysts (Pd, Rh, Ru, Ir) coordinate with directing groups (amides, heterocycles, carboxylic acids) to selectively activate proximal C–H bonds [47].
  • Advantages: High predictability, mild conditions, tolerance of sensitive functional groups.
  • Applications: Functionalization of specific positions on complex drug molecules and natural products, even in the presence of multiple potentially coordinating groups.

Innate C–H Functionalization

Innate C–H functionalization targets the most reactive C–H bond in a molecule based on inherent reactivity, considering factors such as steric accessibility, electronic effects, and bond dissociation energies [47]. This approach doesn't require installing directing groups.

  • Mechanism: Substitution occurs at inherently reactive sites through various catalysts or reaction conditions that leverage natural electronic or steric biases in the molecule.
  • Advantages: No directing group required, complementary selectivity to directed approaches.
  • Applications: Functionalization of electron-rich positions, benzylic sites, or other inherently reactive C–H bonds in complex molecules.

Electrochemical LSF (eLSF)

Electrochemical synthesis has emerged as an environmentally friendly platform for transforming organic compounds, utilizing electric current instead of chemical oxidants or reductants [45]. Over the past decade, electrochemical late-stage functionalization (eLSF) has gained significant momentum.

  • Mechanism: Electron transfer at electrodes generates reactive intermediates under mild conditions, often enabling unique selectivity patterns.
  • Advantages: Sustainable (avoids stoichiometric oxidants/reductants), mild conditions, tunable selectivity, facile scalability via flow techniques.
  • Applications: C–H methylation, trifluoromethylation, alkynylation, and carboxylation of complex molecules [45].

Palladium-Catalyzed C–H Activation

Palladium catalysis represents the predominant direction in developing new C–H functionalization reactions for natural product synthesis [46]. These methods have been successfully applied to construct complex heterocyclic architectures found in bioactive natural products.

  • Key Examples:
    • Tokuyama's enantioselective total synthesis of (–)-deoxoapodine used a Pd-catalyzed C–H activation/cyclization cascade to rapidly construct the pentacyclic core [46].
    • Zhai's asymmetric total synthesis of lundurines A–C employed Pd-catalyzed intramolecular direct C–H vinylation of indole to form a crucial polyhydroazocine ring [46].
    • Xia's concise total synthesis of strychnocarpine utilized Pd/Cu co-catalyzed oxidative tandem C–H aminocarbonylation and dehydrogenation [46].

The Scientist's Toolkit: Key Research Reagent Solutions

Table 1: Essential Reagents for Late-Stage Functionalization Experiments

Reagent/Catalyst Function Application Examples
Pd(OAc)₂ / Pd(TFA)₂ Palladium catalyst for C–H activation Direct C–H vinylation, alkynylation, cyclization cascades [46]
RhCp*(OAc)₂ Rhodium catalyst for electrochemical C–H methylation Site-selective methylation of N-heteroarenes with MeBF₃K [45]
MeBF₃K Methyl source Electrochemical C–H methylation of bioactive molecules [45]
Zn(CF₃SO₂)₂ Trifluoromethylation reagent Electrochemical C–H trifluoromethylation of heterocyclics [45]
Norbornene Mediator for Catellani-type reactions Regioselective cascade C–H activation in indole alkylation [46]
Cu(OAc)â‚‚ Oxidant for Pd-catalyzed reactions Terminal oxidant in cooperative Pd/Cu catalysis systems [46]

Troubleshooting Common Experimental Challenges

FAQ: Overcoming Selectivity Issues in Complex Molecules

Q: How can I improve site-selectivity when my complex molecule contains multiple similar C–H bonds?

A: Site-selectivity remains one of the most significant challenges in LSF. Several strategies can help:

  • Employ directing groups: Install temporary directing groups that can be removed after functionalization, or utilize native functional groups in your molecule as inherent directors [47].
  • Leverage steric effects: Use bulky catalysts or reagents that differentiate between sterically accessible and hindered sites.
  • Explore different catalytic systems: Switch between metal catalysts (Pd, Rh, Ru, Ir) or try electrochemical methods, as each may offer complementary selectivity [45] [46].
  • Systematic screening: Conduct comparative studies of different C–H functionalization protocols on your specific molecular scaffold, as demonstrated in the cyanthiwigin natural product core study [48].

Q: Why is my LSF reaction yielding over-functionalized products instead of mono-functionalization?

A: Over-functionalization typically occurs when the initial functionalization increases the reactivity of adjacent positions. To address this:

  • Reduce reagent stoichiometry: Use limiting amounts (0.8-1.2 equivalents) of functionalizing reagents.
  • Control reaction time: Monitor reactions closely (TLC, LC-MS) and stop early to prevent multiple substitutions.
  • Modify electronic properties: Install electron-withdrawing protecting groups on sensitive sites before LSF.
  • Optimize catalyst loading: Higher catalyst loadings can sometimes lead to over-functionalization; try reducing catalyst amounts.

Q: What can I do when heteroatoms in my molecule are poisoning transition metal catalysts?

A: Catalyst poisoning is common in drug-like molecules rich in heteroatoms. Consider these approaches:

  • Protect coordinating groups: Temporarily protect basic amines or other strong ligands.
  • Use alternative catalysts: Earth-abundant 3d transition metals may be less susceptible to certain poisoning effects [47].
  • Electrochemical methods: eLSF often operates through radical mechanisms that may bypass coordination requirements [45].
  • Increase catalyst loading: While not ideal, sometimes increased catalyst loading can overcome mild inhibition.

FAQ: Optimization and Scalability Concerns

Q: How can I scale up successful LSF reactions from small-scale screening to preparative scale?

A: Scalability requires careful planning:

  • Electrochemical advantages: eLSF is particularly amenable to scale-up using flow electrolysis cells, avoiding stoichiometric oxidant accumulation [45].
  • Continuous flow systems: Flow chemistry often improves heat and mass transfer, enhancing reproducibility at larger scales.
  • Monitor oxygen sensitivity: Many LSF reactions are air/moisture sensitive; ensure proper inert atmosphere maintenance during scale-up.
  • Purification strategy: Plan for efficient separation of products from catalyst residues and byproducts at larger scales.

Q: My LSF reaction works well with one substrate class but fails with structurally similar analogs. What could be causing this?

A: Subtle structural changes can significantly impact LSF outcomes due to:

  • Conformational effects: Small changes can alter molecular conformation, affecting accessibility to C–H bonds.
  • Electronic modulation: Seemingly distant substituents can electronically activate or deactivate target C–H bonds.
  • Solvent effects: Optimize solvent choice for each substrate, as polarity and coordination ability dramatically influence LSF reactions.
  • Additive screening: Systematic screening of additives (acids, bases, salts) can overcome subtle incompatibilities.

Experimental Workflows and Methodologies

Representative Protocol: Electrochemical C–H Methylation

Table 2: Step-by-Step Protocol for eLSF Methylation of N-Heteroarenes

Step Procedure Technical Notes
1. Setup Assemble undivided electrochemical cell equipped with carbon anode and cathode Ensure electrodes are properly spaced and immersed; reference [45]
2. Reaction Solution Dissolve substrate (0.2 mmol), RhCp*(OAc)₂ (10 mol%), MeBF₃K (3 equiv.) in nBuOH/H₂O (4:1, 10 mL) Degas solution with argon for 10 minutes to remove oxygen
3. Electrolysis Apply constant current (5 mA/cm²) at room temperature for 6-8 hours Monitor reaction by TLC/LCMS; current serves as sole oxidant [45]
4. Work-up Dilute with water (20 mL), extract with ethyl acetate (3 × 15 mL) Dry combined organic phases over Na₂SO₄, concentrate in vacuo
5. Purification Purify by flash chromatography (silica gel, hexane/EtOAc) Characterize products by NMR, HRMS; test for biological activity

Workflow Diagram: LSF Reaction Development Pathway

G Start Identify Target Molecule Step1 Analyze Molecular Structure & Reactive Sites Start->Step1 Step2 Select LSF Strategy (Directed vs Innate vs Electrochemical) Step1->Step2 Step3 Screen Reaction Conditions (Catalyst, Solvent, Oxidant) Step2->Step3 Step4 Optimize Selectivity (Steric/Electronic Control) Step3->Step4 Step5 Scale Up Successful Protocol Step4->Step5 Step6 Diversify Molecular Library & Test Biological Activity Step5->Step6

Comparative Analysis of LSF Approaches

Table 3: Quantitative Comparison of LSF Methodologies for Natural Product Diversification

Methodology Typical Yield Range Site-Selectivity Control Functional Group Tolerance Scalability Potential
Directed C–H Functionalization 50-90% [46] High (directing group dependent) Moderate to High [47] Moderate
Innate C–H Functionalization 30-80% [47] Moderate (inherent reactivity) Moderate Moderate to High
Electrochemical LSF 40-85% [45] Moderate to High High [45] High (flow systems) [45]
Palladium-Catalyzed C–H Activation 45-95% [46] High (with appropriate directing groups) Moderate to High [46] Moderate
Radical C–H Functionalization 35-75% Low to Moderate High Moderate

Late-stage functionalization represents a paradigm shift in how chemists approach the diversification of complex molecular cores, particularly natural products with potential therapeutic applications. By mastering the various LSF methodologies outlined in this technical guide—including directed and innate C–H functionalization, electrochemical approaches, and transition metal-catalyzed activation—researchers can efficiently generate structural diversity from complex natural product scaffolds. The troubleshooting guidance and experimental protocols provided here offer practical solutions to common challenges encountered in LSF experiments, enabling more reliable implementation of these powerful strategies. As the field continues to evolve, LSF methodologies will play an increasingly vital role in accelerating the discovery and optimization of bioactive molecules for therapeutic applications.

Overcoming Stereochemical and Conformational Challenges in Cyclization

Cyclization reactions are powerful tools for constructing the complex ring systems found in many natural products and pharmaceuticals. However, these reactions often present significant stereochemical and conformational challenges that can impede successful synthesis. This guide addresses common experimental issues, providing troubleshooting advice and methodologies to help researchers achieve precise control over the three-dimensional structure of their cyclic targets, a critical requirement for function in areas such as drug development.

Troubleshooting Guide: Common Cyclization Challenges

1. FAQ: My cyclization reaction is proceeding in low yield or not at all. What could be the cause?

  • Core Challenge: Inefficient ring closure.
  • Underlying Cause & Solution:
    • Cause: The reactive ends of your molecule may be held in an unfavorable conformation for cyclization, preventing productive collision [49].
    • Solution: Perform a conformational analysis of your linear precursor. Identify conformers that bring the reacting termini into close proximity. Introducing conformational constraints (e.g., rigidifying stereocenters or steric bulk) can pre-organize the molecule for cyclization [50].

2. FAQ: I am getting a mixture of stereoisomers in my cyclization product. How can I improve diastereoselectivity?

  • Core Challenge: Lack of stereochemical control.
  • Underlying Cause & Solution:
    • Cause: The reaction mechanism (e.g., SN1) may proceed through a planar intermediate (like a carbocation) that can be attacked from both faces, or the transition states leading to different diastereomers are similar in energy [50] [51].
    • Solution:
      • Leverage Substrate Control: Incorporate existing chiral centers in your substrate that can sterically bias the approach of the incoming nucleophile. As demonstrated in catalytic alkene cyclizations, an existing allylic stereogenic center can completely control the formation of new stereogenic centers [51].
      • Employ a Chiral Catalyst: Use a chiral Lewis or Brønsted acid catalyst to create a stereoselective environment for the cyclization [51].
      • Choose an Appropriate Mechanism: Opt for a stereospecific mechanism like SN2, which inverts configuration predictably at the reacting center [50].

3. FAQ: My cyclization is producing an unexpected ring size or regioisomer. How can I direct the reaction pathway?

  • Core Challenge: Uncontrolled reaction pathway.
  • Underlying Cause & Solution:
    • Cause: The reaction may be under thermodynamic control, leading to the most stable product, or a highly reactive intermediate (e.g., a radical cation) is undergoing undesirable side reactions before cyclization [52].
    • Solution:
      • Use a Strategic "Second Nucleophile": In oxidative cyclizations, incorporating a second, fast-reacting nucleophile (like an alcohol) can temporarily trap a reactive radical cation intermediate. This forms a cyclic acetal, guiding the subsequent reaction toward the desired product and preventing decomposition [52].
      • Optimize Reaction Conditions: Lowering the temperature can shift the equilibrium toward the initial cyclization product and suppress side reactions, as shown in anodic oxidation cyclizations [52].

4. FAQ: How can I be sure of the stereochemistry and conformation of my final cyclic product?

  • Core Challenge: Structural characterization of complex cyclic systems.
  • Underlying Cause & Solution:
    • Cause: Standard techniques like NMR may provide an averaged structure or miss minor conformers, especially for flexible molecules [53].
    • Solution: Employ advanced analytical techniques. Vibrational Circular Dichroism (VCD) has proven highly effective for probing the solution-state conformation of both linear and cyclic peptides, revealing structural restraints induced by cyclization and identifying multiple conformations that NMR might not discern [53].

Essential Methodologies and Protocols

Protocol 1: Conformational Analysis of a Linear Precursor

This protocol is critical for troubleshooting issues related to yield and stereoselectivity [50] [54].

  • Identify the Rotatable Bond: Choose the central bond around which rotation will be analyzed for the cyclization.
  • Draw Newman Projections: Systematically draw the key conformations (e.g., staggered, eclipsed, gauche) by rotating the back atom in 60° increments.
  • Analyze Gauche Interactions: Identify steric strain caused by bulky groups being close to each other in gauche conformations.
  • Identify the Reactive Conformation: Determine which conformation brings the reacting functional groups (e.g., nucleophile and electrophile) into the closest proximity and with the correct geometry for the reaction mechanism.
  • Rank Stability: Rank the conformations by energy. The population of the reactive conformation will significantly influence the cyclization rate.
Protocol 2: Diastereoselective Alkene Cyclization for Heterocycle Synthesis

This method, adapted from catalytic alkene cyclization research, is highly effective for constructing complex heterocycles with multiple stereocenters [51].

  • Reaction Setup: Dissolve the geranylated aniline sulfonamide substrate (e.g., 11 with R1 = Ns) in nitromethane (CH3NO2) at a concentration of 0.02 M [51].
  • Catalysis: Add 0.1 equivalents of Sc(OTf)3 as a pre-catalyst. The true active catalyst is believed to be a protic acid generated from adventitious moisture [51].
  • Initiation: Add 1.1 equivalents of phenylselenium chloride (PhSeCl) to initiate the episelenonium ion intermediate.
  • Reaction Monitoring: Stir the reaction mixture and monitor by TLC or LC-MS until completion.
  • Work-up and Purification: Quench the reaction with a saturated aqueous solution of sodium bicarbonate (NaHCO3). Extract the product with an organic solvent (e.g., dichloromethane), dry the organic layer over anhydrous magnesium sulfate (MgSO4), and concentrate under reduced pressure. Purify the crude product by flash chromatography.

Key Data for Cyclization Planning

Table 1: Energy Costs of Conformational Strain in Acyclic Molecules

Strain Type Example Molecule Energy Cost (kJ/mol) Cause
Eclipsed H/H Ethane ~12 Torsional strain [54]
Gauche Butane Butane ~3.8 Steric strain between methyl groups [54]
Eclipsed CH3/H Butane ~6 Combined steric and torsional strain [54]
Total Eclipse Butane ~19 Severe steric hindrance between methyl groups [54]

Table 2: Reagent Solutions for Stereoselective Cyclization

Research Reagent Function / Role in Cyclization Key Application
Sc(OTf)3 Lewis acid pre-catalyst; generates protic acid in situ. Catalyzing alkene cyclization cascades for tetrahydroquinolines [51].
PhSeCl / PhSCl Electrophilic reagents that form episelenonium/episulfonium ion intermediates. Initiating cationic cyclization cascades with high diastereocontrol [51].
PhenylSOCH3 Electrophilic sulfur reagent for cyclization. Lewis acid-mediated bicyclization of N-geranyl anilines [51].
NaSbF6 Chloride scavenger. Improves yield in selenocyclizations by driving the reaction forward [51].
DTBP (Di-tert-butyl-4-methylpyridine) Brønsted acid scavenger. Used in control experiments to identify the nature of the active catalyst [51].

Visual Workflows and Relationships

CyclizationTroubleshooting Cyclization Troubleshooting Workflow Start Low Yield or No Reaction A Perform Conformational Analysis of Linear Precursor Start->A G Unexpected Ring Size/Regioisomer Start->G B Identify Low-Energy Conformer with Reactive Ends Proximal? A->B C Modify Substrate to Pre-organize: Add Steric Bulk or Rigid Groups B->C No D Mixture of Stereoisomers B->D Yes C->A Re-evaluate E Identify Reaction Mechanism (SN1, SN2, etc.) D->E F Employ Strategy for Control: - Substrate-Directed Control - Chiral Catalyst - Stereospecific Mechanism (SN2) E->F Success Successful Cyclization F->Success H Employ Guiding Strategy: - Use a Second Nucleophile - Lower Reaction Temperature G->H H->Success

Diagram 1: A logical workflow for diagnosing and addressing the most common challenges in cyclization reactions.

ConformationalAnalysis Conformational Analysis for Cyclization Start Linear Precursor Molecule Step1 1. Identify Rotatable Bond Near Reaction Site Start->Step1 Step2 2. Draw Newman Projections (Staggered, Eclipsed, Gauche) Step1->Step2 Step3 3. Analyze Steric Strain & Gauche Interactions Step2->Step3 Step4 4. Identify Conformation with Reactive Ends in Proximity Step3->Step4 Step5 5. Rank Conformations by Energy (Population = Reactivity) Step4->Step5 Output Informed Substrate Design for Successful Cyclization Step5->Output

Diagram 2: A step-by-step protocol for performing a conformational analysis to predict and improve the success of a cyclization reaction.

Derivatization Strategies for Phenols and Other Common Fragments

Structural derivatization of natural products stands as a continuing and irreplaceable source of novel drug leads. Natural phenols, a broad category with wide pharmacological activity, have provided numerous clinical drugs. However, their structural complexity and variety present significant challenges for systematic derivatization. This technical support framework addresses these challenges by providing validated strategies for navigating the synthetic complexity of natural phenol fragments, enabling more efficient drug development pipelines.

Theoretical Framework: Deconstructing Structural Complexity

The Common Fragment Approach

Research indicates that most natural phenols can be structured through the combination and extension of three common fragments: phenol, phenylpropanoid, and benzoyl [55]. This skeleton analysis provides a unifying principle for derivatization strategies, allowing researchers to apply fragment-specific solutions across diverse molecular families.

  • Phenol Fragments: Serve as the basic aromatic unit with hydroxyl functionality
  • Phenylpropanoid Fragments: Provide C6-C3 building blocks with varied oxidation patterns
  • Benzoyl Fragments: Offer aromatic systems with carbonyl functionality for diversification

This conceptual framework transforms seemingly intractable structural diversity into manageable synthetic challenges, enabling systematic planning rather than case-by-case solutions.

Strategic Planning in Synthesis

For complex natural products, computational planning has demonstrated expert-level capability when combining reaction knowledge with causal relationship algorithms [56]. These systems strategize over multiple synthetic steps through:

  • Two-step sequences where initial retrosynthetic steps increase complexity to enable subsequent major simplification
  • Functional group interconversions (FGIs) that adjust oxidation states and reactivity profiles
  • Bypasses that resolve intermittent reactivity conflicts
  • Simultaneous and tandem reactions that improve efficiency

G Strategic Synthesis Planning Framework cluster_1 Analysis Phase cluster_2 Strategy Development Start Start TargetAnalysis Target Structure Analysis Start->TargetAnalysis FragmentDeconstruction Fragment Deconstruction (Phenol, Phenylpropanoid, Benzoyl) TargetAnalysis->FragmentDeconstruction ComplexityAssessment Complexity Assessment FragmentDeconstruction->ComplexityAssessment RetrosyntheticPlanning Retrosynthetic Planning ComplexityAssessment->RetrosyntheticPlanning MultiStepStrategizing Multi-step Strategizing RetrosyntheticPlanning->MultiStepStrategizing RouteOptimization Route Optimization MultiStepStrategizing->RouteOptimization StrategicApproaches Strategic Approaches: • Two-step Sequences • FGIs • Bypasses • Tandem Reactions RouteOptimization->StrategicApproaches

Troubleshooting Guide: Common Experimental Challenges

FAQ: Addressing Specific Derivatization Issues

Q1: How can I improve selectivity in phenol fragment modifications when multiple reactive sites are present?

  • Problem: Natural phenols often contain multiple hydroxyl groups and reactive aromatic positions, leading to complex product mixtures.
  • Solution: Implement protective group strategies specifically designed for polyphenolic systems. Use silyl ethers (TBDMS, TIPS) for hydroxyl protection, which offer orthogonal deprotection options. For aromatic electrophilic substitutions, consider steric directing groups or chelation-controlled approaches using metal complexes.
  • Advanced Approach: Employ enzymatic catalysis for regioselective modifications. Enzymatic activation can mimic biological metabolic processes, selectively generating ortho-diphenols similar to native biosynthetic pathways [55].

Q2: What strategies address the poor stability and solubility of complex phenolic natural products?

  • Problem: Many natural phenols exhibit limited stability under standard laboratory conditions and poor aqueous solubility, hindering biological evaluation.
  • Solution: Develop prodrug strategies through hydroxyl group derivatization. Acylation, alkylation, and phosphorylation of phenolic hydroxyls can significantly improve stability and bioavailability [55]. Glycosylation of resveratrol derivatives, for instance, has demonstrated significantly reduced toxicity while maintaining activity [55].
  • Validation: Classical examples like aspirin (acetylated salicylic acid) demonstrate how prodrug strategies reduce gastric irritation while maintaining efficacy [55].

Q3: How can I efficiently generate structural diversity from limited natural product starting materials?

  • Problem: Scarce natural isolates limit extensive analog development for structure-activity relationship studies.
  • Solution: Implement diversity-oriented synthesis focusing on the common fragment approach. Utilize late-stage functionalization strategies including:
    • C-H activation to introduce diverse substituents
    • Cross-coupling reactions from halide or triflate intermediates
    • One-pot tandem reactions to build complexity efficiently
  • Case Example: From halide intermediates of natural phenols, introduce diverse groups including cyano, carboxyl, and aminoacyl functions through transformation sequences [55].

Q4: What are solutions for scalability challenges in complex phenolic natural product synthesis?

  • Problem: Laboratory-scale syntheses often fail to translate to industrially relevant scales.
  • Solution: Develop chemoenzymatic approaches that combine chemical synthesis with engineered enzyme systems. Recent advances demonstrate protein engineering of key enzymes to create self-sufficient systems – fusing a reductase module to a P450 enzyme boosted indolactam V production to 868.8 mg L⁻¹ [28].
  • Implementation: Establish dual-cell factory systems co-expressing engineered enzymes, enabling fully enzymatic synthesis of complex targets with yields exceeding 700 mg L⁻¹ for teleocidin B isomers [28].

Experimental Protocols: Key Methodologies

Protocol 1: Selective Ortho-Hydroxylation of Phenol Fragments

Objective: Selective generation of ortho-diphenols from mono-phenolic precursors.

Materials:

  • Substrate (phenolic compound)
  • 2-Iodylbenzoic acid (IBX)
  • Appropriate solvent (acetonitrile, DMF, or DCM)
  • Inert atmosphere (argon/nitrogen)

Procedure:

  • Dissolve phenolic substrate (1.0 equiv) in anhydrous solvent (0.1 M concentration) under inert atmosphere.
  • Add IBX (1.2-2.0 equiv) portion-wise at room temperature with stirring.
  • Monitor reaction progress by TLC or LC-MS until completion (typically 2-8 hours).
  • Quench reaction by adding saturated sodium thiosulfate solution.
  • Extract with ethyl acetate, wash organic layer with brine, dry over anhydrous Naâ‚‚SOâ‚„.
  • Purify by flash chromatography.

Technical Notes: IBX may induce oxidative demethylation of phenolic ethers and dehydrogenation of acrylophenones observed in flavonoid derivatization [55]. Optimize equivalents and reaction time for specific substrate classes.

Protocol 2: Chemoenzymatic Derivatization Using Engineered Enzyme Systems

Objective: Scalable production of complex natural product derivatives through protein-engineered biocatalysts.

Materials:

  • Engineered TleB enzyme (or relevant P450 system)
  • Substrate (indolactam V for teleocidin derivatives)
  • E. coli expression system
  • Fermentation equipment
  • Cofactor regeneration system

Procedure:

  • Engineer P450 enzyme by fusing reductase module to create self-sufficient system.
  • Express engineered enzyme in E. coli host system.
  • Establish dual-cell factory co-expressing hMAT2A-TleD and TleB/TleC enzymes.
  • Optimize fermentation conditions for target production (temperature, pH, aeration).
  • Scale production using fed-batch fermentation protocols.
  • Extract and purify products using standard chromatographic techniques.

Validation: This protocol has achieved production of 430 mg indolactam V, 170 mg teleocidin A1, and 300 mg teleocidin B isomers from recombinant E. coli systems [28].

Research Reagent Solutions: Essential Materials

Table: Key Reagents for Phenol Derivatization Strategies

Reagent/Category Function Application Examples Technical Notes
IBX (2-Iodylbenzoic acid) Selective ortho-hydroxylation Phenol fragment functionalization May cause oxidative demethylation in certain substrates [55]
Enzymatic Systems Biocatalytic derivatization Regioselective modifications Mimics native metabolic pathways; high selectivity [55]
Silyl Protecting Groups Hydroxyl protection Selective masking of reactive sites TBDMS and TIPS offer orthogonal deprotection options
Phenol Derivatives Bioactive building blocks Antimicrobial, antiseptic applications Cresol, thymol, chlorinated derivatives [57]
Acyl Transfer Reagents Prodrug development Bioavailability improvement Used in aspirin development from salicylic acid [55]
Engineered P450 Systems Scalable biosynthesis Complex alkaloid production Self-sufficient systems with fused reductase modules [28]

Advanced Strategic Methodologies

Computational Synthesis Planning

Modern synthesis of complex natural products increasingly leverages computational planning systems that combine extensive reaction knowledge bases with causal relationship algorithms [56]. These systems can design plausible routes to targets like callyspongiolide that elude simpler step-by-step planning approaches.

Implementation Workflow:

  • Input target structure with complexity parameters
  • Algorithm performs multi-step strategizing using:
    • Two-step sequences overcoming local complexity maxima
    • Functional group interconversion libraries
    • Reactivity conflict bypasses
    • Tandem reaction identification
  • Generate and rank synthetic pathways by cost and feasibility
  • Laboratory validation of computer-designed syntheses

G Chemoenzymatic Derivatization Workflow Start Start EnzymeEngineering Enzyme Engineering (Fusion P450 Systems) Start->EnzymeEngineering SystemOptimization System Optimization (Dual-cell Factory) EnzymeEngineering->SystemOptimization FermentationScaleUp Fermentation Scale-up SystemOptimization->FermentationScaleUp ChemicalModification Chemical Modification (Derivative Library) FermentationScaleUp->ChemicalModification

Diversity-Oriented Synthesis from Common Fragments

The common fragment approach enables systematic exploration of chemical space through fragment-based diversification:

Core Strategies:

  • Electrophilic substitutions of phenolic hydroxyls (acylation, alkylation, phosphorylation)
  • Nucleophilic substitutions via activated intermediates (tosylates, triflates)
  • Cross-coupling reactions from halide or triflate intermediates with organometallics
  • Ring functionalization through Friedel-Crafts, Mannich, and halogenation reactions

This methodological framework transforms natural product derivatization from artisanal craftsmanship to systematic engineering, accelerating the discovery of novel bioactive entities with optimized pharmaceutical properties.

Transitioning from successful milligram-scale reactions to gram-scale production is a critical yet challenging step in natural product synthesis and nanomaterial research. This process is often hampered by poor reproducibility, altered reaction kinetics, and low yields, which can significantly impede drug development and clinical translation. This technical support center provides targeted troubleshooting guides and detailed protocols to help researchers systematically overcome these barriers, enabling robust and scalable synthesis.

Essential Research Reagent Solutions

The following table details key reagents and their specific functions in scalable synthesis protocols, particularly for nanoparticle and natural product production.

Reagent/Material Function in Scalable Synthesis Application Example
Benzoic Acid Acts as a crystal growth modulator; competes with linker coordination to control nanoparticle size [58]. Gram-scale synthesis of MIL-125 nanoparticles [58].
1-Octadecene (ODE) High-boiling, non-coordinating solvent; enables high-temperature reactions and is cost-effective for scale-up [59]. Synthesis of CdSe nanocrystals and other metal chalcogenides [59].
Oleylamine Additive to ODE; narrows size distribution of nanocrystals and passivates surface trap states [59]. Optimization of CdSe nanocrystal synthesis [59].
Trioctylphosphine oxide (TOPO) Coordinating solvent and stabilizing ligand for nanocrystals; being replaced by safer alternatives [59]. Classical synthesis of semiconductor nanocrystals [59].
Engineed TleB Enzyme Self-sufficient P450 system; overcomes enzymatic bottlenecks to enable high-yield production of complex molecules [28]. Scalable chemoenzymatic synthesis of Teleocidin B derivatives [28].
Mesoporous Silica & Polydopamine Clinically validated, benign scaffold materials; enable simple, room-temperature, aqueous-phase synthesis [60]. Gram-scale production of nanotheranostic agents [60].

Scaling Methodologies and Experimental Protocols

Design of Experiment (DOE) for Rapid Optimization

The DOE method, specifically using a Taguchi L16 table, drastically reduces the number of experiments needed to optimize reaction parameters. This systematic approach is superior to traditional trial-and-error methods, which are time and resource-intensive [59].

Detailed Protocol:

  • Factor Selection: Identify key reaction parameters (e.g., solvent composition, precursor concentration, temperature, ligand-to-metal ratio).
  • Define Experimental Domain: Set "extreme" high and low values (levels) for each factor to explore a wide operational range.
  • Interaction Analysis: Choose which factor interactions to study (e.g., BF, DE, DF based on prior knowledge).
  • Table Selection & Execution: Use a predefined Taguchi table (e.g., L16) to determine the set of experiments. Execute these experiments and analyze the results to determine the influence of each parameter and their interactions [59].

Application Example: In the optimization of CdSe nanocrystal synthesis, 16 experiments were sufficient to optimize parameters for controlling mean size while maintaining a narrow size distribution (5-10%). The analysis revealed the degree of influence of each parameter, such as solvent, cadmium concentration, and temperature [59].

Reflux Synthesis for Nanoparticle Control

A rapid reflux-based synthesis can replace lengthier solvothermal methods, allowing for better reaction monitoring and control, which is crucial for reproducibility at larger scales [58].

Detailed Protocol:

  • Setup: Conduct reactions under flowing N2 in a reflux setup.
  • Modulator Screening: Test different monocarboxylic acid modulators (e.g., benzoic acid, acetic acid) to identify the most effective one for size control.
  • Concentration Optimization: Perform a U-shaped concentration analysis of the chosen modulator to find the optimal equivalence that yields the smallest, most stable nanoparticles. For MIL-125, 5 equivalents of benzoic acid were optimal [58].
  • In-line Monitoring: Take aliquots during the reaction for ex situ analysis (e.g., PXRD) to track nanocrystal growth and phase purity [58].

Chemoenzymatic Synthesis with Engineered Enzymes

For complex natural products, a chemoenzymatic route leveraging protein engineering can overcome yield and scalability bottlenecks [28].

Detailed Protocol:

  • Enzyme Engineering: Fuse a critical enzyme (e.g., TleB) with a reductase module to create a self-sufficient P450 system, boosting catalytic efficiency.
  • Dual-Cell Factory: Establish a system co-expressing multiple engineered enzymes (e.g., hMAT2A-TleD, TleB, TleC) to enable a fully enzymatic synthesis pathway.
  • Fermentation Scale-Up: Transfer the optimized pathway to a recombinant E. coli system for scalable fermentation. This approach has achieved production of indolactam V (430 mg), teleocidin A1 (170 mg), and teleocidin B isomers (300 mg) [28].

Troubleshooting Guides & FAQs

Q1: My nanoparticle size distribution widens significantly during scale-up. What are the primary causes and solutions?

  • Cause: Inconsistent mixing or heat transfer in larger batch reactors, leading to inhomogeneous nucleation and growth.
  • Solution: Implement a high-throughput peristaltic pump for precise reagent delivery [59]. Ensure efficient stirring and thermal uniformity in the scaled reactor. Re-optimize modulator type and concentration using a systematic DOE approach for the new reactor geometry [58].

Q2: How can I maintain a high reaction yield when increasing the scale of my synthesis?

  • Cause: Stopping the reaction before precursor consumption is complete to maintain size control, which inherently reduces yield.
  • Solution: Optimize reaction parameters to allow the reaction to proceed until nanocrystal size evolution reaches a plateau, ensuring maximum precursor conversion without sacrificing size distribution [59].

Q3: My gram-scale synthesis produces materials with different physicochemical properties compared to my small-scale batches. How can I improve consistency?

  • Cause: Small, unaccounted-for variations in parameters like mixing speed or temperature ramp rates have a magnified effect at larger scales.
  • Solution: Develop a simple and robust synthetic procedure, such as room temperature aqueous-phase synthesis with simple mixing. This minimizes batch-to-batch variability and has been proven to achieve excellent consistency from small to gram-scale batches [60].

Q4: What is the most efficient way to optimize a new synthesis with many interdependent variables?

  • Cause: Using a one-variable-at-a-time (trial-and-error) approach, which is inefficient and often misses parameter interactions.
  • Solution: Employ a Design of Experiment (DOE) methodology. For example, a Taguchi L16 table can systematically analyze 7 factors at 2 levels each with only 16 experiments, identifying key influences and interactions to rapidly find the optimum conditions [59].

Q5: How can I overcome low yields in the enzymatic synthesis of complex natural products?

  • Cause: Bottlenecks in the enzymatic pathway, such as inefficient electron transfer or low enzyme stability.
  • Solution: Use protein engineering to create self-sufficient enzyme systems (e.g., fused P450-reductase). This strategy dramatically increased the production of indolactam V to 868.8 mg L⁻¹ [28].

Workflow and Pathway Visualizations

Scalable Synthesis Workflow

G Start Define Scaling Objective A Systematic Parameter Optimization (DOE) Start->A B Select Scalable Reagents & Solvents A->B C Establish Robust Monitoring (e.g., PXRD) B->C D Pilot-Scale Synthesis & Troubleshooting C->D E Gram-Scale Production & Characterization D->E End Scalable Process Established E->End

Parameter Interaction in DOE

G Solvent Solvent NP_Size Nanoparticle Size Solvent->NP_Size NP_Dist Size Distribution Solvent->NP_Dist Precursor Precursor Precursor->NP_Size Reaction_Yield Reaction Yield Precursor->Reaction_Yield Temperature Temperature Temperature->NP_Size Temperature->Reaction_Yield Ligand Ligand Ligand->NP_Size Ligand->NP_Dist Modulator Modulator Modulator->NP_Size U-shaped Dependence Modulator->NP_Dist Primary Effect

Chemoenzymatic Synthesis Pathway

G A Precursor Molecules B Engineed hMAT2A-TleD & TleC Enzymes A->B C Intermediate (Indolactam V) B->C D Engineed TleB P450 System C->D E Final Product (Teleocidin B) D->E

Case Studies and Critical Analysis: Validating Synthetic Strategies

Ecteinascidin 743 (ET-743), commercially known as Trabectedin, stands as a pioneering marine-derived antitumor agent and a flagship member of the tetrahydroisoquinoline (THIQ) alkaloid family [61]. As the first marine-based drug to achieve clinical approval, its discovery marks a significant milestone in natural product pharmaceutical development [61]. The molecular architecture of ET-743 is distinguished by a highly intricate pentacyclic scaffold, comprising two tetrahydroisoquinoline subunits fused through a central piperazine ring and further embellished with a tetrahydroisoquinoline side chain linked via a thioether bridge [61]. This structural complexity not only underpins its potent biological activity but also presents significant synthetic challenges, rendering ET-743 a focal point of interest in the realm of natural product synthesis [61]. This technical guide addresses the key challenges researchers face in synthesizing and optimizing this complex molecule, providing troubleshooting solutions for common experimental obstacles.

Frequently Asked Questions (FAQs)

Q1: What makes the total synthesis of ET-743 particularly challenging for research chemists? The challenges stem from its complex molecular architecture, which features a pentacyclic scaffold containing three tetrahydroisoquinoline moieties, eight rings (including one 10-membered heterocyclic ring containing a cysteine residue), and seven chiral centers that require precise stereochemical control [62]. The central piperazine ring connecting the subunits and the tetrahydroisoquinoline side chain linked via a thioether bridge further complicate the synthesis [61].

Q2: How can computational methods help optimize Trabectedin analogs? Computer-Aided Drug Design (CADD) accelerates the optimization process through methods like molecular docking, pharmacophore modeling, QSAR, and dynamics simulations [63]. These techniques help identify and improve marine-based drugs, such as trabectedin and its analogs, by predicting binding modes, optimizing metabolic stability, and reducing toxicity profiles before synthesis is attempted [63].

Q3: What are the key metabolic factors that influence Trabectedin's pharmacokinetic variability in patients? Pre-dose plasma metabolomics has revealed that cystathionine, hemoglobin, taurocholic acid, citrulline, and the phenylalanine/tyrosine ratio can explain up to 70% of the observed inter-individual pharmacokinetic variability [64]. These metabolic signatures can also help distinguish patients with stable disease from those with progressive disease, enabling better personalization of treatment [64].

Q4: What alternative production methods address the supply challenges posed by the natural source? The limited availability from the natural tunicate Ecteinascidia turbinata (requiring approximately 1,000 kg of animals to isolate 1 gram of trabectedin) has driven development of several alternative production methods [62]. These include semi-synthesis from the bacterial metabolite safracin B, mariculture (ocean-based farming), land-based tank aquaculture, and synthetic biology approaches using engineered microorganisms [63] [62].

Troubleshooting Common Experimental Challenges

Low Yields in Final Synthesis Steps

Problem: Significant product loss during the final stages of the multi-step synthesis, particularly during the formation of the central piperazine ring and thioether bridge.

Solutions:

  • Implement the convergent synthetic approach that assembles ET-743 from five building blocks of nearly equal size, achieving the synthesis in 23 steps with an overall yield of 3% from l-3-hydroxy-4-methoxy-5-methyl phenylalanol [65]
  • Employ the Mannich reaction, Pictet-Spengler reaction, Curtius rearrangement, and chiral rhodium-based diphosphine-catalyzed enantioselective hydrogenation as demonstrated in established synthetic routes [62]
  • Utilize the Ugi reaction for formation of the pentacyclic core, which represents an unprecedented application of this one-pot multicomponent reaction for complex molecule synthesis [62]

Prevention Tips:

  • Conduct sensitive steps under strict inert atmosphere with freshly distilled solvents
  • Monitor reaction progress with LC-MS to identify degradation pathways
  • Implement purification protocols immediately after each critical step to prevent cumulative impurities

Stereochemical Inconsistencies

Problem: Difficulty controlling the seven chiral centers, leading to diastereomer mixtures that are challenging to separate.

Solutions:

  • Apply Microcrystal Electron Diffraction (MicroED) for unambiguous structural determination of synthetic intermediates, including stereochemical assignment [2]
  • Use chiral rhodium-based diphosphine catalysts for enantioselective hydrogenation as demonstrated in Corey's synthesis [62]
  • Employ the Pictet-Spengler reaction with careful control of reaction conditions to ensure proper stereochemical outcomes [62]

Validation Methods:

  • Characterize crystals using MicroED, which can provide unambiguous structures from sub-micron-sized crystals that are insufficient for traditional X-ray crystallography [2]
  • Implement advanced NMR techniques including NOESY and ROESY to confirm relative stereochemistry
  • Compare optical rotation values with published data for natural ET-743

Poor Solubility and Stability in Bioassays

Problem: Limited aqueous solubility and decomposition under assay conditions, leading to inconsistent biological activity data.

Solutions:

  • Prepare stock solutions in DMSO at concentrations ≤10 mM and store at -80°C with desiccant to prevent hydrolysis [64]
  • Use freshly prepared working dilutions in assay buffers containing 0.1% BSA to improve compound stability
  • For in vivo studies, administer via 24-hour intravenous infusion at 1.5 mg/m² with dexamethasone premedication to improve tolerability [64]

Assay Optimization:

  • Include control experiments to monitor compound integrity throughout the assay duration
  • Optimize DMSO concentrations to maintain cell viability while ensuring compound solubility
  • Use mass spectrometry to confirm compound stability under assay conditions

Essential Research Reagents and Materials

Table 1: Key Reagents for ET-743 Synthesis and Analysis

Reagent/Material Function/Application Technical Specifications
l-3-hydroxy-4-methoxy-5-methyl phenylalanol Key synthetic building block Starting material for 23-step synthesis achieving 3% overall yield [65]
Safracin B Semi-synthetic precursor Fermentation product from Pseudomonas fluorescens enabling scalable production [62]
Chiral rhodium-based diphosphine catalysts Enantioselective hydrogenation Critical for establishing stereochemistry at chiral centers [62]
Deuterated solvents (CDCl₃, DMSO-d₆) NMR spectroscopy analysis Essential for structural confirmation of intermediates and final product
CYP3A4 enzyme system Metabolic stability studies Primary metabolic pathway identification [62]
LC-MS/MS systems with MRM capability Pharmacokinetic analysis Enables quantification of plasma concentrations with LLOQ of 0.01 ng/mL [64]

Table 2: Computational Tools for ET-743 Optimization

Software/Tool Primary Application Key Output/Utility
AutoDock Vina Molecular docking Predicting binding poses in DNA minor groove (-9.8 kcal/mol) [63]
MOE (Molecular Operating Environment) QSAR modeling Optimizing solubility and potency parameters [63]
Molecular Dynamics Simulations Binding stability assessment Confirming stable binding (RMSD <2 Ã… over 100 ns) [63]
ADMET Predictor Toxicity screening Identifying and reducing hepatotoxicity risks [63]
ChemAxon Retrosynthesis planning Reducing synthetic steps from 18 to 12 (40% cost reduction) [63]

Experimental Protocols and Methodologies

Protocol: Molecular Docking of ET-743 Analogs with DNA

Purpose: To predict binding affinity and orientation of Trabectedin analogs in the DNA minor groove.

Materials:

  • DNA duplex (sequence: d(ATACGTAT)â‚‚)
  • Trabectedin analog structures (optimized with quantum mechanics)
  • AutoDock Vina software [63]
  • Molecular Operating Environment (MOE) for visualization

Procedure:

  • Prepare the DNA structure by optimizing hydrogen atoms and assigning partial charges using the AMBER force field
  • Generate 3D structures of Trabectedin analogs using conformation search and energy minimization
  • Define the docking grid to encompass the minor groove of the DNA duplex
  • Set docking parameters to 20 runs per compound with maximum energy evaluations of 25,000,000
  • Cluster results based on RMSD tolerance of 2.0 Ã… and select the most favorable binding pose based on scoring function
  • Validate protocol by redocking native ET-743 and confirming reproduction of crystallographic binding mode

Expected Results: Successful docking should yield binding energies ranging from -9.8 to -11.2 kcal/mol for active analogs, with key interactions including hydrogen bonding with guanine N2 atoms and van der Waals contacts with sugar-phosphate backbone [63].

Protocol: LC-MS/MS Quantification of Trabectedin in Plasma

Purpose: To determine pharmacokinetic parameters of ET-743 in biological matrices.

Materials:

  • Trabectedin standard and d3-trabectedin internal standard [64]
  • Acetonitrile with 1% formic acid
  • Human plasma samples (50 μL)
  • LC-MS/MS system with triple quadrupole mass spectrometer
  • C18 reverse-phase column (2.1 × 50 mm, 1.8 μm)

Procedure:

  • Prepare calibration standards (0.01-2.5 ng/mL) and quality controls in drug-free human plasma
  • Add 200 μL of acetonitrile-1% formic acid containing 0.1 ng/mL internal standard to 50 μL plasma samples
  • Vortex mix for 30 seconds and centrifuge at 20,800 × g for 10 minutes at 4°C
  • Transfer supernatant to autosampler vials and inject 3 μL into LC-MS/MS system
  • Employ gradient elution with mobile phase A (0.1% formic acid in water) and B (0.1% formic acid in acetonitrile)
  • Monitor MRM transition 762 → 234 m/z for trabectedin and 765 → 234 m/z for internal standard
  • Calculate concentrations using linear regression of peak area ratios versus concentration

Validation Parameters: Intra- and inter-day precision and accuracy should be <15% across the calibration range, with LLOQ of 0.01 ng/mL [64].

Metabolic and Pharmacokinetic Profiling

Table 3: Key Metabolites Influencing Trabectedin Pharmacokinetics

Metabolite/Biomarker Correlation with PK Parameters Impact on Clinical Response
Cystathionine Negative correlation with AUC₀₋₄₈ₕ Distinguishes Stable Disease vs Progressive Disease [64]
Hemoglobin Positive correlation with drug exposure Predictive of hematological toxicity risk [64]
Taurocholic Acid Inverse relationship with clearance Modulator of hepatotoxicity potential [64]
Phenylalanine/Tyrosine Ratio Positive correlation with AUC Indicator of metabolic status affecting drug metabolism [64]
Citrulline Association with reduced clearance Potential marker for gastrointestinal toxicity risk [64]

Pathway and Workflow Visualizations

G ET-743 Mechanism of Action and Cellular Effects cluster_binding DNA Binding Phase cluster_effects Cellular Consequences ET743 ET-743 (Trabectedin) DNA DNA Minor Groove ET743->DNA CovalentBond Covalent Bond Formation with Guanine N2 DNA->CovalentBond DNABending DNA Bending & Structural Distortion CovalentBond->DNABending TranscriptionBlock Transcription Blockade DNABending->TranscriptionBlock RepairTrapping DNA Repair Protein Trapping DNABending->RepairTrapping FUSCHOP FUS-CHOP Oncogene Displacement DNABending->FUSCHOP Apoptosis Apoptosis & Cell Death TranscriptionBlock->Apoptosis RepairTrapping->Apoptosis FUSCHOP->Apoptosis

Diagram 1: Mechanism of action of ET-743 showing key molecular interactions and downstream effects leading to apoptosis in cancer cells [63] [62].

G Computational Optimization Workflow for ET-743 Analogs cluster_sbdd Structure-Based Design cluster_optimization Optimization Cycle cluster_evaluation Experimental Validation Start Initial Compound Design Docking Molecular Docking (AutoDock Vina) Start->Docking MD Molecular Dynamics (Stability Verification) Docking->MD QSAR QSAR Modeling (Potency/Solubility) MD->QSAR ADMET ADMET Prediction (Toxicity Reduction) QSAR->ADMET Design De Novo Design (Scaffold Modification) ADMET->Design Synthesis Chemical Synthesis Design->Synthesis Testing Biological Testing Synthesis->Testing Testing->Docking Iterative Improvement

Diagram 2: Computational optimization workflow for ET-743 analogs showing the iterative cycle of design, prediction, and experimental validation [63].

Benchmarking Human Expertise Against Computational AI Planners

Troubleshooting Guides

This section addresses common challenges researchers face when integrating computational AI planners with human expertise in natural product synthesis projects.

Issue 1: AI Planner Fails to Generate a Viable Synthetic Route

  • Problem: The AI planner does not produce a synthesis plan or produces a chemically invalid sequence.
  • Solution:
    • Verify Target Input: Ensure the target molecule's structure is correctly represented in a machine-readable format (e.g., SMILES, InChI). Incorrect input is a common source of failure.
    • Check Reaction Library: Confirm that the AI's underlying reaction library contains the necessary transformations and reagents for your specific target. The plan will fail if key chemical steps are not in the AI's knowledge base.
    • Adjust Constraints: The AI may be working with overly strict constraints (e.g., on cost, step count, or prohibited solvents). Loosen these parameters and iteratively re-tighten them after a viable route is found.
    • Human-in-the-Loop Review: Implement a Human-in-the-Loop (HITL) QA step where a chemist reviews the AI's failed attempt. The expert can identify the point of failure (e.g., an unfeasible stereochemical inversion) and provide feedback to refine the AI's search algorithm [66].

Issue 2: Generated Synthesis Plan is Chemically Infeasible or Low-Yielding

  • Problem: The AI planner suggests a route that violates chemical principles or would be expected to have poor yield.
  • Solution:
    • Prioritize Human-AI Collaboration: Use the AI-generated plan as a starting point for expert refinement, not a final recipe. Domain specialists provide critical contextual accuracy that AI may lack [66].
    • Incorporate Real-World Data: If available, feed the AI planner data on real reaction yields and conditions from electronic lab notebooks. This grounds the planning in practical outcomes rather than just theoretical possibilities.
    • Focus on Edge Cases: AI planners can struggle with complex, novel, or sensitive structural motifs (e.g., intricate macrocycles or highly reactive functional groups). Direct human experts to focus their review efforts on these "edge cases" [66].

Issue 3: The System Cannot Handle the Structural Complexity of the Target Molecule

  • Problem: The planning algorithm fails to converge on a solution for large, complex natural products.
  • Solution:
    • Decompose the Problem: Manually break down the target into logical retrosynthetic fragments (e.g., left hemisphere, right hemisphere, core scaffold) and task the AI with planning routes for these simpler subunits [67].
    • Benchmark Against Known Targets: Test the AI planner's capabilities against a benchmark of previously synthesized natural products with documented complexity. This helps you understand the system's limitations [68].
    • Iterative Planning: For novel targets, adopt an iterative planning approach. Use the AI to plan the initial, high-level disconnections, then use human expertise to refine and re-plan the synthesis of complex intermediates.

Issue 4: Discrepancy Between Computational Score and Experimental Practicality

  • Problem: A plan scores highly based on the AI's metrics (e.g., step count, predicted yield) but is deemed impractical by expert chemists.
  • Solution:
    • Refine Scoring Metrics: Work with data scientists to adjust the AI's objective function to include "practicality" factors valued by human experts, such as the availability of starting materials, purification challenges, and safety considerations [66].
    • Establish Clear Guidelines: Develop and document clear, shared guidelines for what constitutes a "practical" step, ensuring consistency between human evaluators and providing a clearer target for the AI to learn from [66].
Frequently Asked Questions (FAQs)

Q1: What is the primary purpose of benchmarking human experts against AI planners in synthesis? The purpose is twofold: to quantitatively assess the current capabilities and limitations of AI in a complex scientific domain, and to create a collaborative framework where human expertise and AI computational power can synergize. The goal is not replacement but augmentation, where AI handles data-intensive pattern recognition and humans provide strategic oversight and contextual nuance [66] [67].

Q2: What specific metrics should be used to evaluate planner performance? Performance should be evaluated across multiple dimensions, as shown in Table 1 below.

Q3: How can human expert evaluation be standardized for a fair benchmark? Standardization requires a clear methodology. As exemplified by the Human Creativity Benchmark, each model output should be scored by multiple professional evaluators (e.g., 3+ synthetic chemists) using predefined categories on a numerical scale (e.g., 1-poor to 5-exceptional) [69]. This ensures consistent, quantifiable, and comparable feedback.

Q4: Can an AI planner truly contribute to novel synthetic strategy, or does it just recombine known steps? While current AI planners primarily operate on known reactions, they can contribute to novelty by discovering non-obvious sequences of these steps that human intuition might miss. Their ability to exhaustively search a vast reaction space can lead to new strategic approaches for constructing complex molecular architectures [67].

Q5: What are the key resource requirements for setting up such a benchmarking initiative? Key requirements include access to powerful computing infrastructure, comprehensive and curated chemical reaction databases, a panel of willing expert chemists for evaluation, and collaborative software platforms that facilitate the HITL feedback process [66].

Quantitative Benchmarking Data

The following tables summarize key metrics for evaluating human and AI planners.

Table 1: Performance Metrics for Synthesis Planners

Metric Description Human Expert Benchmark AI Planner Benchmark
Route Efficiency Average number of linear steps to reach the target. Varies by target; established via literature analysis of published total syntheses [67]. Calculated by the AI planner for the same target; compared against human benchmark.
Convergence Score A measure of a route's parallelizability (number of branches). Varies by target and strategy [67]. Calculated by the AI's graph search algorithm.
Predicted Yield The overall calculated yield for the entire sequence. Estimated based on expert knowledge of similar reactions. Computed by multiplying predicted yields for each individual step from a reaction database.
Structural Complexity A quantitative score based on molecular features (e.g., stereocenters, macrocycles). Measured for the natural product target, often a key selection criterion [67]. The maximum complexity score of a target the AI can successfully plan for.
Computational Cost Time and processing power required to generate a plan. Human brainstorming time (hours/days). CPU/GPU time and memory usage (seconds/hours) [68].

Table 2: Human-in-the-Loop (HITL) Evaluation Categories [69]

Evaluation Category Score (1-5) Description for Synthetic Chemistry Context
Synthetic Quality & Feasibility 1 (poor) → 5 (exceptional) Perceptual quality of the route, absence of chemically infeasible steps, reasonable reaction conditions.
Prompt Adherence & Accuracy 1 (not aligned) → 5 (perfect alignment) Fidelity to the target's requested molecular structure, including stereochemistry.
Originality & Creativity 1 (generic/derivative) → 5 (highly original) Novelty of the retrosynthetic disconnections and strategic approach, non-derivative style.
Utility & Applied Fit 1 (unusable) → 5 (production-ready) Usability in a real laboratory context, considering cost, safety, and scalability of the route.
Experimental Protocols

Protocol 1: Benchmarking an AI Planner on a Known Natural Product

  • Target Selection: Choose a natural product with a published total synthesis to serve as a ground-truth benchmark [67].
  • AI Plan Generation: Input the target molecule's structure into the AI planner. Set constraints (e.g., available starting materials, prohibited reagents) to match the experimental reality as closely as possible.
  • Human Plan Generation: Provide the same target to expert synthetic chemists, who will develop a retrosynthetic plan without using the AI tool.
  • Blinded Evaluation: Submit both the AI-generated and human-generated plans to a separate panel of chemists for blinded evaluation using the categories defined in Table 2.
  • Data Analysis: Compare the scores and the quantitative metrics from Table 1 for both plans. Perform statistical analysis to determine the significance of any differences.

Protocol 2: Implementing a Human-in-the-Loop Feedback Cycle [66]

  • Automated Data Collection: The AI planner generates an initial synthetic route.
  • Human Expertise Review: A domain expert (synthetic chemist) reviews the plan, identifying points of failure, infeasible steps, or opportunities for optimization.
  • Real-Time Feedback Loop: The expert's annotated feedback is formally logged and fed back into the AI system. This feedback can be used for immediate plan correction or for longer-term model fine-tuning.
  • Continuous Monitoring and Adjustment: The refined plan is executed, and its experimental results (e.g., actual yields) are recorded. This real-world data is subsequently used to update the AI's knowledge base and predictive models, creating a continuous improvement cycle.
Workflow and Pathway Visualizations

HITL_Synthesis Start Start: Define Target Molecule AI_Plan AI Planner Generates Initial Route Start->AI_Plan Human_Eval Human Expert Evaluation AI_Plan->Human_Eval Decision Route Feasible? Human_Eval->Decision Feedback Provide Structured Feedback Decision->Feedback No Final_Plan Final Validated Synthesis Plan Decision->Final_Plan Yes Feedback->AI_Plan Experiment Laboratory Execution Final_Plan->Experiment

Diagram Title: Human-in-the-Loop Workflow for Synthesis Planning

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Reagents and Concepts in Natural Product Synthesis [67]

Item / Concept Function / Explanation
Retrosynthetic Analysis A problem-solving technique where the target molecule is recursively broken down into simpler precursor structures until readily available starting materials are identified. This is the foundational logic of synthesis planning.
Asymmetric Catalysis The use of chiral catalysts to enforce the formation of a specific stereoisomer in a reaction, which is critical for synthesizing biologically active natural products.
Cross-Coupling Reactions Metal-catalyzed (e.g., Pd) reactions that connect two hydrocarbon fragments via a carbon-carbon bond. These are indispensable tools for building the carbon skeletons of complex molecules.
Olefin Metathesis A reaction that redistributes alkylidene fragments, allowing for the rearrangement of carbon-carbon double bonds. It is widely used for forming large rings (macrocyclization) in natural product synthesis.
Protecting Groups Temporary functional groups used to mask reactive sites (e.g., alcohols, amines) to prevent unwanted side reactions during a multi-step synthesis sequence.

Comparative Analysis of Different Routes to Key Natural Product Targets

This technical support center is framed within a broader thesis on addressing the significant challenge of structural complexity in natural product synthesis research. Natural products, evolved over millions of years, are privileged molecular frameworks for biological interactions and represent an unparalleled source of inspiration for novel drugs [70]. However, their intricate molecular structures pose substantial synthetic difficulties [70]. This resource provides targeted troubleshooting guides and FAQs to support researchers, scientists, and drug development professionals in navigating the practical challenges of synthesizing these complex targets. The content below compares the two predominant synthesis strategies—total chemical synthesis and total biosynthesis—and offers practical solutions for common experimental hurdles.

The two primary approaches for creating specialized metabolites are total chemical synthesis and total biosynthesis. The table below provides a quantitative comparison of these routes for bioactive fungal metabolites [71].

Table 1: Quantitative Comparison of Synthesis Routes for Fungal Metabolites

Feature Total Chemical Synthesis Total Biosynthesis
Typical Number of Steps Higher number of chemical steps [71] Fewer chemical steps [71]
Molecular Complexity & Weight Suitable for a wide range of molecular weights and complexities [71] Analyzed via measures of molecular complexity and weight [71]
Directness of Route Steps can be less direct [71] Steps move more directly to the target [71]
Structural Flexibility & Diversification High flexibility; ability to easily diversify synthetic routes and create analogues [71] Currently lacks the flexibility of chemical synthesis for diversification [71]
Primary Application Art and science of making nature's molecules and their analogues in the lab [71] Production of metabolites through biological pathways [71]

FAQs & Troubleshooting Guides

Route Selection and Planning

Q: What are the key criteria for selecting between a chemical or biosynthetic route for a new natural product target?

A: The choice is multifaceted. A chemical synthesis route is generally more suitable if your goal is to generate a large number of structural analogues for structure-activity relationship (SAR) studies, as it offers superior flexibility for diversification [71]. Furthermore, if the natural product's biosynthetic pathway is unknown or difficult to engineer, chemical synthesis may be the only viable path. Conversely, a biosynthetic route is often more efficient if your primary goal is to produce the native compound itself, as it typically involves fewer and more direct steps to the target molecule [71]. This approach can also be essential for producing complex molecules with multiple stereocenters that are challenging to construct synthetically.

Q: How should I approach designing a multi-step synthetic route for a complex target molecule?

A: Designing a synthetic route requires a systematic, backward-looking strategy [72].

  • Analyze the Target Structure: Start by drawing the structures of your starting material and your target molecule. Identify all functional groups present.
  • Check Carbon Count: Determine if the target has more carbon atoms than your starting material. If it does, plan to introduce these via high-atom-economy reactions, such as using a nitrile group introduced by nucleophilic substitution to lengthen the carbon chain [72].
  • Work Backwards (Retrosynthesis): Work out what immediate precursor molecules can be converted into your target. Simultaneously, work out what molecules your starting compound can be converted into. Look for the overlap between these two pathways to connect your route [72].
  • Map the Pathway: Finally, map out the complete sequence of reactions, ensuring you note the required reagents and conditions for each functional group interconversion [72].
Probe Design and Synthesis for Target Identification

A critical step in understanding the mechanism of action of a natural product is identifying its protein targets. Chemical proteomics is a powerful, unbiased approach for this, and its success hinges on effective probe design and synthesis [73].

Table 2: Research Reagent Solutions for Chemical Proteomics Probe Synthesis

Reagent / Material Function in Probe Synthesis & Target Fishing
Bioactive Natural Product The parent molecule; its structure dictates the "reactive group" in the probe, which must retain pharmacological activity [73].
Biotin Reporter Tag A widely used tag that allows for easy enrichment of probe-bound protein targets using streptavidin-coated beads [73].
Alkyne/Azide Tags Enable a "click chemistry" approach (e.g., Cu(I)-catalyzed Azide-Alkyne Cycloaddition) for bioorthogonal labeling and enrichment [73].
Cleavable Linker A spacer connecting the reactive group and the reporter tag; a cleavable linker allows for milder elution of bound proteins, reducing background noise [73].
Affinity Resins (e.g., Agarose/Magnetic Beads) Solid supports for immobilizing probes in Compound-Centric Chemical Proteomics (CCCP) to "fish" for target proteins from lysates [73].

Q: What are the common points of failure when designing a chemical probe for target identification, and how can I troubleshoot them?

A: The most common point of failure is a probe that loses its biological activity.

  • Problem: Probe is inactive.
    • Cause 1: The modification site for attaching the linker on the parent molecule is critical for its target binding.
    • Solution: Perform a thorough Structure-Activity Relationship (SAR) analysis before probe design. The modification must be introduced at a position proven to be tolerant of change without significant loss of activity [73].
    • Cause 2: The linker is too short, creating steric hindrance that blocks the probe from accessing the protein binding pocket.
    • Solution: Incorporate a longer, more flexible linker (e.g., PEG-based) between the reactive group and the reporter tag to minimize steric interference [73].

Q: During target fishing, I encounter high background noise and false positives. How can I improve specificity?

A: High background is a typical challenge in affinity-based enrichment.

  • Problem: High non-specific binding.
    • Solution 1: Include rigorous control experiments. Use a structurally similar but inactive probe ("negative control") in a parallel experiment. True targets will only be enriched by the active probe.
    • Solution 2: Optimize your wash conditions. Increase the salt concentration (e.g., with NaCl) or add mild detergents (e.g., Tween-20) to your wash buffers to disrupt non-specific electrostatic and hydrophobic interactions without eluting specific binders.
    • Solution 3: Use a cleavable linker. After enrichment and thorough washing, the specific bound proteins can be released under mild, specific conditions (e.g., via a protease site or chemical cleavage), which helps eliminate proteins that stick non-specifically to the solid matrix itself [73].

The following workflow diagram outlines the two main chemical proteomics strategies for target identification, from probe design to validation.

cluster_0 Probe Design & Synthesis cluster_1 Target Fishing / Enrichment cluster_2 Protein Identification Start Parent Natural Product Probe1 Activity-Based Probe (ABPP) (Reactive Group + Linker + Tag) Start->Probe1 Probe2 Immobilized Probe (CCCP) (Drug immobilized on beads) Start->Probe2 Fish1 Incubate with cells/lysate Enrich via Tag (e.g., Biotin) Probe1->Fish1 Fish2 Incubate lysate with matrix Wash to remove non-binders Probe2->Fish2 ID1 Gel Separation & Band ID or Quantitative Proteomics Fish1->ID1 ID2 Elute & Identify via Mass Spectrometry Fish2->ID2 Validation Target Validation (SPR, MST, ITC, Bioassays) ID1->Validation ID2->Validation

Chemical Proteomics Workflow for Target ID
General Experimental Setup and Synthesis

Q: What is the standard procedure for requesting or initiating a custom chemical synthesis?

A: Standard procedure typically involves a formal consultation and feasibility assessment.

  • Initial Consultation: Schedule a meeting with the head of the synthesis unit or core facility.
  • Discussion & Literature Search: The synthesis request is discussed, and a thorough literature search is conducted.
  • Feasibility & Pathway Design: The unit head explores the synthetic feasibility and determines a viable synthetic pathway.
  • Quotation & Approval: An estimate of cost and delivery time is provided. The synthesis commences only after formal approval from the requesting scientist [74].

Q: The final yield of my multi-step synthesis is very low. What are the common culprits?

A: Low overall yield is often due to inefficiencies in individual steps.

  • Problem: Low yield and poor atom economy.
    • Solution 1: Choose high-atom-economy reactions. Prioritize reactions where most of the atoms from the starting materials are incorporated into the final product. This is more efficient and generates less waste [72].
    • Solution 2: Optimize purification. Intermediate compounds should be purified effectively to prevent the carry-over of impurities that can inhibit subsequent reactions and degrade catalysts.
    • Solution 3: Minimize the number of steps. Each additional step inevitably reduces the overall yield. When designing a route, favor strategies with fewer linear steps, even if some individual steps appear more complex [72].

Advanced Strategies: Leveraging Computational Tools

To directly address the thesis context of structural complexity, researchers can now leverage novel computational methods to facilitate drug discovery. The WHALES (Weighted Holistic Atom Localization and Entity Shape) description is one such tool. It converts the pharmacologically-relevant information of complex natural products—such as their 3D shape, geometry, and atomic partial charges—into numerical molecular descriptors. These descriptors can then be used to screen massive virtual libraries of synthetic compounds to identify those that share the key patterns of the natural product template but are structurally less complex and possess greater "drug-likeness." This enables "scaffold-hopping" from a complex natural product to synthetically more accessible mimetics with potentially similar biological activity [70].

NP Complex Natural Product CompDescr Compute WHALES Descriptors (Shape, Geometry, Charges) NP->CompDescr VirtualLib Screen Virtual Library (3+ Million Compounds) CompDescr->VirtualLib Hits Hit Identification (Synthetic Mimetics) VirtualLib->Hits Validation In Vitro Validation (e.g., Receptor Modulation) Hits->Validation

Scaffold-Hopping via Computational Design

Core Concepts & Troubleshooting

Frequently Asked Questions (FAQs)

Q1: What is the primary goal of making structural modifications to a natural product during drug discovery? A1: The primary goal is to enhance pharmacological properties while maintaining or improving therapeutic efficacy. Structural modifications aim to optimize a compound's potency, selectivity, and pharmacokinetic properties (like absorption, distribution, metabolism, and excretion) to transform a promising laboratory compound into a viable clinical candidate [75].

Q2: Why is the PI3K pathway a significant target for structural modification in cancer therapy? A2: The PI3K pathway is frequently dysregulated in cancer and plays a critical role in cellular processes. Developing PI3K inhibitors through structural modifications helps enhance their target selectivity and reduce off-target effects, which is crucial for improving their efficacy and safety profile in clinical applications [75].

Q3: What is a common challenge when moving from a natural product to a synthetically viable drug candidate? A3: A major challenge is addressing structural complexity to enable efficient and scalable synthesis. Natural products often have intricate structures that are difficult to reproduce on a large scale. Strategies like chemoenzymatic synthesis and protein engineering are employed to overcome these bottlenecks and achieve scalable production [28].

Q4: What does a systematic troubleshooting process in a research setting typically involve? A4: A systematic troubleshooting process involves several key steps [76]:

  • Assess and understand the problem by actively listening and gathering detailed information.
  • Target the issue by guiding through basic checks before progressing to advanced diagnostics.
  • Determine the best course of action by prioritizing potential solutions based on technical expertise.
  • Help resolve the issue by implementing the solution, verifying its effectiveness, and documenting the outcome for future reference.

Q5: How can AI assist in the process of structural modification and optimization? A5: Artificial Intelligence (AI) significantly accelerates drug discovery by leveraging massive datasets to predict how structural changes will affect a compound's activity and properties. AI models can parallel process multi-omics data, potentially compressing the preclinical phase from years to months and improving the success rate of identifying viable drug candidates [77].

Troubleshooting Common Experimental Issues

Issue 1: Low Yield in Scalable Synthesis of Complex Natural Product Derivatives

  • Problem: When scaling up the production of teleocidin derivatives, researchers encounter low final yields, making it impractical for further development.
  • Solution: Implement a dual-cell factory system. As demonstrated in the synthesis of teleocidin B isomers, co-expressing engineered enzymes (hMAT2A-TleD with TleB/TleC) in Escherichia coli can overcome bottlenecks, establishing a fully enzymatic synthesis route that achieved a total yield of 714.7 mg L⁻¹ [28].

Issue 2: Poor Selectivity of Inhibitor Compounds

  • Problem: A newly synthesized inhibitor compound shows activity against multiple kinase targets, leading to potential off-target toxicity.
  • Solution: Employ medicinal chemistry approaches and structural modifications focused on the compound's interaction with the specific target's active site. Optimization should aim to enhance interactions with unique residues in the target protein while reducing affinity for others [75].

Issue 3: Inefficient Enzymatic Step in Chemoenzymatic Synthesis

  • Problem: A key enzymatic reaction, such as one catalyzed by a P450 enzyme, is inefficient, limiting the throughput of the entire synthesis pathway.
  • Solution: Use protein engineering to optimize the enzyme. For example, engineering the critical enzyme TleB by fusing a reductase module to create a self-sufficient P450 system boosted the production of a key intermediate (indolactam V) to 868.8 mg L⁻¹ [28].

Experimental Protocols & Data

Key Methodologies for Structural Modification and Analysis

Protocol 1: Chemoenzymatic Synthesis and Engineering for Scalable Production This protocol is adapted from the efficient, scalable production of teleocidin derivatives [28].

  • Route Design: Develop a chemoenzymatic route that combines chemical synthesis of core structures with enzymatic modifications.
  • Protein Engineering: Identify rate-limiting enzymes (e.g., P450 TleB). Engineer them for higher efficiency, for instance, by creating fusion proteins with redox partners to create self-sufficient systems.
  • Establish a Dual-Cell Factory:
    • Use one cell line for early biosynthetic steps (e.g., co-expression of engineered hMAT2A-TleD and TleB/TleC).
    • Use a second cell line or a combined system for later steps and diversification.
  • Fermentation Scaling: Transfer the optimized system to a bioreactor for scalable fermentation. The recombinant E. coli system has been shown to produce 430 mg of indolactam V, 170 mg of teleocidin A1, and 300 mg of teleocidin B isomers [28].

Protocol 2: AI-Enhanced Hit Identification and Optimization This protocol outlines the use of AI in early drug discovery [77].

  • Target Identification: Use AI to analyze multi-omics data (genomic, proteomic) to identify novel, druggable targets.
  • Virtual Screening: Employ deep learning models to screen vast virtual chemical libraries against the identified target, predicting binding affinities.
  • De Novo Design: Use generative AI models to design novel molecular structures that optimally fit the target.
  • ADMET Prediction: Utilize machine learning to predict the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of hit compounds in silico before synthesis.
  • Iterative Optimization: Use AI feedback loops to suggest structural modifications that improve desired properties, significantly reducing the number of synthetic cycles needed.

Quantitative Data on AI in Drug Development

Table 1: Distribution of AI Applications in Drug Development (Analysis of 173 Studies) [77]

Development Stage Percentage of AI Studies Common AI Applications
Preclinical Stage 39.3% Target identification, virtual screening, de novo molecule generation, ADMET prediction
Transitional Phase 11.0% Predictive toxicology, in silico dose selection, early biomarker discovery
Clinical Phase I 23.1% Trial simulation, patient recruitment strategies
Clinical Phase II 16.2% Data analysis, patient stratification
Clinical Phase III 9.2% Outcome prediction, safety monitoring

Table 2: Analysis of AI Methods Used in Drug Discovery (2015-2025) [77]

AI Methodology Percentage of Use Primary Application in Drug Discovery
Machine Learning (ML) 40.9% QSAR modeling, data analysis, pattern recognition
Molecular Modeling & Simulation (MMS) 20.7% Predicting molecular interactions, binding affinity
Deep Learning (DL) 10.3% De novo molecular design, advanced pattern recognition
Other/Unspecified 28.1% Various specialized applications

Pathways, Workflows & Visualization

Workflow for Structurally Complex Natural Product Development

complex_np_workflow start Start: Natural Product Isolation/Identification target_id Target Identification & Validation start->target_id syn_plan Synthetic Route Planning target_id->syn_plan mod_design Structural Modification Design syn_plan->mod_design synth_exec Synthesis Execution (Lab-scale) mod_design->synth_exec activity_test Pharmacological Activity Testing synth_exec->activity_test activity_test->mod_design Insufficient Activity prop_opt Property Optimization (Potency, PK/PD, Safety) activity_test->prop_opt prop_opt->mod_design Needs Optimization scale_up Scale-up & Process Development prop_opt->scale_up Lead Compound candidate Clinical Candidate Selection scale_up->candidate

Workflow for Complex Natural Product Development

AI-Accelerated Drug Discovery Pathway

ai_drug_discovery data Multi-omics Data Input (Genomic, Proteomic, etc.) ai_target AI-Powered Target Identification data->ai_target ai_design AI-Driven Molecular Design & Virtual Screening ai_target->ai_design syn_validate Synthesis & In Vitro Validation ai_design->syn_validate ai_optimize AI-Guided Lead Optimization syn_validate->ai_optimize ai_optimize->ai_design Iterative Redesign preclinical Preclinical Development ai_optimize->preclinical

AI-Accelerated Drug Discovery Pathway

The Scientist's Toolkit

Table 3: Research Reagent Solutions for Structural Modification & Synthesis

Reagent / Tool Function / Application
Engineered P450 Systems Self-sufficient enzymes for efficient oxidative transformations in biosynthetic pathways [28].
Dual-Cell Factory A co-culture system where different engineered cell lines perform specialized steps in a complex synthesis [28].
Molecular Modeling Software For in silico prediction of how structural modifications affect target binding and compound properties [75] [77].
AI/ML Platforms For analyzing large datasets to predict compound activity, optimize structures, and identify novel targets [77].
Teleocidin Core (Indolactam V) A key intermediate scaffold for the synthesis and structural diversification of teleocidin derivatives [28].

Conclusion

The synthesis of structurally complex natural products is being revolutionized by a powerful convergence of strategies. Foundational understanding of complexity, combined with methodological advances in bioinspired design, computational planning, and strategic simplification, provides a robust framework for tackling these molecules. As demonstrated by successful case studies like Trabectedin, the ability to not only replicate but also improve upon nature's designs is within reach. The future of the field lies in the deeper integration of AI and machine learning for predictive retrosynthesis, the broader application of chemoenzymatic methods for sustainable and selective transformations, and a continued focus on converting complex natural architectures into optimized drug candidates with improved synthetic accessibility and superior therapeutic profiles. This multidisciplinary progress promises to accelerate the delivery of new medicines from nature's blueprint.

References