Drug discovery Study Guide

📖 Core Concepts Drug discovery – systematic process that moves from hit identification → lead optimization → pre‑clinical → clinical → market approval. Target – a cellular or molecular structure whose modulation can alter disease pathology (e.g., GPCRs, protein kinases). Forward (phenotypic) pharmacology – screens whole cells/organisms for a desired phenotype without knowing the molecular target. Reverse (target‑based) pharmacology – screens large libraries against an isolated, cloned target (often from genome data). High‑Throughput Screening (HTS) – automated testing of thousands‑to‑millions of compounds for activity against a chosen target. Structure‑Activity Relationship (SAR) optimization – medicinal‑chemistry cycle that tweaks a hit to improve potency, selectivity, ADME, and drug‑likeness. Drug‑likeness metrics – Lipinski’s Rule of Five, ligand efficiency (LE), lipophilic efficiency (LipE). Model‑Informed Drug Discovery (MIDD) – early integration of PK/PD, disease‑model, and statistical models to guide decisions; requires rigorous documentation and validation. Natural products – chemically diverse secondary metabolites from plants, microbes, or marine organisms; a major source of novel scaffolds. --- 📌 Must Remember Four classic steps: target identification → screening (HTS or phenotypic) → hit‑to‑lead (SAR) → lead optimization. Forward vs. Reverse: phenotypic screens discover what works; target‑based screens discover how it works. Rule of Five (Lipinski, 1997) – a compound is more likely orally active when: \[ \begin{aligned} \text{MW} &\le 500\ \text{Da} \\ \log P &\le 5 \\ \text{H‑bond donors} &\le 5 \\ \text{H‑bond acceptors} &\le 10 \end{aligned} \] PAINS (pan‑assay interference compounds) are removed early because they give false positives across many assays. Lead selection: choose a lead and a backup after SAR and ADME profiling. MIDD documentation must state model purpose, data sources, assumptions, validation, and uncertainty analysis. Regulatory pathway: successful pre‑clinical → clinical trials → New Drug Application (NDA) for US market approval. Natural‑product contribution: >50 % of innovative small‑molecule drugs (1981‑2014) derived from or inspired by natural products. --- 🔄 Key Processes | Process | Step‑by‑step outline | |---------|----------------------| | HTS workflow | 1. Define assay & target.<br>2. Assemble library (synthetic + natural).<br>3. Miniaturize & automate assay.<br>4. Run primary screen → hit list.<br>5. Counter‑screen & PAINS removal.<br>6. Confirm hits in dose‑response.<br>7. Progress to secondary (cell/animal) assays. | | SAR optimization | 1. Analyze hit structure.<br>2. Design analog series (add, remove, replace groups).<br>3. Synthesize & test potency.<br>4. Evaluate selectivity & ADME (solubility, permeability, metabolic stability).<br>5. Calculate LE = \(-\log(\text{IC}{50})/ \text{Heavy atoms}\).<br>6. Iterate until potency, drug‑likeness, and safety thresholds met. | | Lead selection & backup | 1. Rank optimized compounds by potency, ADME, and safety.<br>2. Choose top candidate as lead.<br>3. Select a chemically distinct backup with comparable profile.<br>4. Document rationale for both. | | MIDD integration | 1. Build PK/PD model using in‑vitro & animal data.<br>2. Validate model predictions against independent data.<br>3. Use model to simulate dosing, drug‑drug interactions, and trial outcomes.<br>4. Update model iteratively as clinical data emerge. | | Fragment‑Based Lead Discovery (FBLD) | 1. Screen low‑MW fragments (≤300 Da).<br>2. Identify weak binders via X‑ray or NMR.<br>3. Grow or link fragments guided by structural data.<br>4. Optimize to higher affinity while monitoring LE. | --- 🔍 Key Comparisons Phenotypic vs. Target‑Based Screening Phenotypic: discovers functional effect first; may uncover novel biology. Target‑Based: requires known target; high‑throughput but can miss pathway context. Established vs. New Targets Established: abundant literature, validated assays, lower risk. New: limited data, higher novelty, higher scientific risk. Natural Products vs. Combinatorial Libraries Natural: higher molecular weight, more stereocenters, greater structural rigidity. Combinatorial: narrower chemical space, often more polar, less diverse. Lipinski Rule of Five vs. Ligand Efficiency (LE) / Lipophilic Efficiency (LipE) Rule of Five: binary filter on size & polarity. LE/LipE: quantitative metrics that balance potency with size or lipophilicity (e.g., LipE = pIC₅₀ − log P). Virtual HTS vs. Physical HTS Virtual: dock libraries in silico; cheap, fast, depends on high‑quality structures. Physical: experimental read‑outs; more definitive but resource‑intensive. --- ⚠️ Common Misunderstandings “Rule of Five is absolute.” → Many successful drugs (e.g., natural products, antibiotics) violate one or more criteria. “PAINS are always useless.” → Some PAINS‑like scaffolds can be optimized into genuine actives after careful validation. “Phenotypic screens need no target deconvolution.” – Target identification is essential for mechanistic insight and later optimization. “Model‑informed approaches replace experiments.” – Models guide experiments; they still need empirical data for building and validation. “All natural products are too complex for drug development.” – Modern synthetic and biosynthetic tools can simplify or mimic complex scaffolds. --- 🧠 Mental Models / Intuition Funnel Analogy – Library → HTS → Hits → SAR → Lead → Backup → Candidate. Visualize each stage as a narrowing funnel; the goal is to keep “quality” while reducing quantity. Potency vs. Burden Trade‑off – Think of LE as “bang‑for‑buck”: a weak binder with very low molecular weight can be a better starting point than a strong binder that’s a “big, heavy truck”. Target‑Centric vs. Phenotype‑Centric Maps – Forward screens are “satellite images” (global view), reverse screens are “street‑level maps” (detail). Both are needed to navigate drug discovery. --- 🚩 Exceptions & Edge Cases Natural products often break Lipinski (high MW, many H‑bond donors) yet reach market (e.g., paclitaxel). Use LE/LipE to justify. Biologics & large peptides are outside the small‑molecule Rule of Five framework entirely. Fragment hits have micromolar affinity (weak) but high LE; they are valuable despite low potency. PAINS may be “context‑dependent” – a compound flagged in one assay may be genuine in another assay type. --- 📍 When to Use Which Choose HTS when a well‑validated assay exists and you have a large, diverse library. Choose phenotypic screen when disease biology is poorly understood or you seek first‑in‑class mechanisms. Use virtual docking when high‑resolution target structure is available and you need to triage millions of compounds quickly. Apply SAR cycles after you have at least 3‑5 confirmed hits with acceptable scaffold. Deploy MIDD early (pre‑lead) for targets with known PK/PD relationships or when dose‑range finding is critical. Select natural‑product library when you need chemical diversity beyond what synthetic libraries provide. --- 👀 Patterns to Recognize PAINS pattern – same scaffold repeatedly active across unrelated assays (e.g., quinones, rhodanines). Fragment pattern – weak activity + high LE → good starting point for fragment‑based programs. Lipophilicity‑potency correlation – many hits improve potency simply by adding hydrophobic groups; watch for rising log P without real efficiency gain. Cross‑screening flag – a hit that also hits close paralogs often indicates a promiscuous binder. --- 🗂️ Exam Traps | Distractor | Why it looks right | Why it’s wrong | |------------|-------------------|----------------| | “HTS always produces viable drug candidates.” | HTS yields many false positives; most are discarded. | Only a tiny fraction survive SAR and ADME filtering. | | “If a compound meets Lipinski, it will be orally bioavailable.” | Rule of Five is a useful guideline. | Bioavailability also depends on solubility, permeability, metabolism, and formulation. | | “Phenotypic screens never require target identification.” | Phenotypic assays focus on cellular outcomes. | Target deconvolution is often needed for lead optimization and regulatory filing. | | “PAINS should be eliminated from every library.” | PAINS cause assay interference. | Some PAINS scaffolds can be re‑engineered into legitimate leads after rigorous validation. | | “MIDD removes the need for early‑stage animal studies.” | Models predict human PK/PD. | Models rely on animal data for calibration; they cannot fully replace in‑vivo validation. | | “Natural‑product leads always violate the Rule of Five, so they’re unsuitable.” | Natural products are large and complex. | Many natural‑product‑derived drugs succeed; other metrics (LE, LipE) can justify them. | ---