💊Medicinal Chemistry Unit 3 – Drug Discovery and Development

Key Concepts and Terminology

• Pharmacodynamics studies the biochemical and physiological effects of drugs on the body, including mechanisms of drug action and the relationship between drug concentration and effect
• Pharmacokinetics examines how the body affects a drug, including absorption, distribution, metabolism, and excretion (ADME) processes
• Drug targets are the molecular structures (receptors, enzymes, or other biomolecules) that a drug interacts with to produce its pharmacological effect
  • Examples of drug targets include G protein-coupled receptors (GPCRs), ion channels, and enzymes (kinases)
• Hit compounds are molecules that show initial activity against a drug target during high-throughput screening (HTS)
• Lead compounds are hit compounds that have been optimized and show promising pharmacological activity, selectivity, and safety profile
• Structure-activity relationship (SAR) analyzes how changes in a compound's chemical structure affect its biological activity
• Pharmacophore is the essential structural features of a molecule that are responsible for its biological activity

Drug Discovery Process

• Target identification involves identifying a biomolecule (protein, enzyme, or receptor) that plays a crucial role in the disease pathology and can be modulated by a drug
• Target validation confirms the role of the identified target in the disease and assesses its druggability using various techniques (genetic knockdown, chemical probes, or animal models)
• Hit identification screens large libraries of compounds using high-throughput screening (HTS) to identify molecules that show activity against the validated target
• Hit-to-lead optimization improves the potency, selectivity, and pharmacokinetic properties of hit compounds through medicinal chemistry efforts
• Lead optimization further refines lead compounds to improve their efficacy, safety, and drug-like properties
• Preclinical studies assess the safety and efficacy of optimized lead compounds in animal models before proceeding to human clinical trials
• Clinical trials evaluate the safety and efficacy of the drug candidate in humans, progressing from small Phase I studies to larger Phase II and III trials
• Regulatory approval is sought from the FDA or other regulatory agencies based on the results of clinical trials, allowing the drug to be marketed and prescribed to patients

Target Identification and Validation

• Genomics and proteomics help identify potential drug targets by studying the genetic and protein changes associated with a disease
• Functional genomics techniques (gene knockdown, overexpression, or knockout) validate the role of a target in the disease pathology
• Disease models (cell-based assays, animal models, or patient-derived samples) are used to assess the impact of modulating the target on the disease phenotype
• Biomarkers (molecular, cellular, or imaging markers) are identified to monitor target engagement and disease progression during drug development
• Druggability assessment evaluates the likelihood of a target being modulated by a small molecule or biologic drug
  • Factors considered include the target's structure, function, and cellular location
• Safety assessment investigates potential off-target effects and toxicity risks associated with modulating the target
• Competitive landscape analysis examines existing drugs or ongoing research targeting the same or similar targets to identify opportunities and challenges

Lead Compound Identification

• High-throughput screening (HTS) rapidly tests large libraries of compounds (up to millions) against the target using automated assays
  • Libraries can include small molecules, natural products, or fragment-based compounds
• Virtual screening uses computational methods (docking, pharmacophore modeling, or machine learning) to predict and prioritize compounds for testing
• Phenotypic screening identifies compounds that produce a desired phenotypic change in a disease model without prior knowledge of the target
• Fragment-based drug discovery (FBDD) screens smaller molecular fragments to identify weak binders that can be optimized into lead compounds
• Natural product screening explores compounds derived from plants, microorganisms, or marine sources for their potential therapeutic effects
• Structure-based drug design (SBDD) uses the 3D structure of the target to guide the design and optimization of lead compounds
• Ligand-based drug design (LBDD) relies on the structure and properties of known active compounds to guide the search for new leads

Structure-Activity Relationships (SAR)

• SAR studies investigate how changes in a compound's chemical structure affect its biological activity, guiding lead optimization efforts
• Medicinal chemists synthesize analogs of lead compounds by modifying functional groups, substituents, or scaffolds
• Quantitative structure-activity relationship (QSAR) models use mathematical equations to relate chemical structure to biological activity
  • Descriptors (physicochemical, topological, or electronic properties) are calculated for each compound and used as input for the model
• 3D-QSAR methods (CoMFA or CoMSIA) consider the three-dimensional structure of compounds and their interaction with the target
• SAR by NMR uses nuclear magnetic resonance (NMR) spectroscopy to identify chemical shifts associated with binding to the target
• Pharmacophore modeling identifies the essential structural features (hydrogen bond donors/acceptors, hydrophobic regions, or aromatic rings) required for activity
• Structure-activity landscape index (SALI) quantifies the SAR complexity and guides the selection of compounds for further optimization

Drug Optimization and Design

• Medicinal chemistry optimizes lead compounds to improve potency, selectivity, and pharmacokinetic properties
  • Strategies include bioisosteric replacement, scaffold hopping, or prodrug design
• ADME optimization improves the absorption, distribution, metabolism, and excretion properties of the compound
  • Solubility, permeability, metabolic stability, and plasma protein binding are key parameters
• Toxicity assessment identifies and minimizes potential safety liabilities (off-target effects, genotoxicity, or cardiotoxicity)
• Formulation development designs the appropriate dosage form (tablet, capsule, or injectable) and excipients for optimal delivery
• Intellectual property (IP) considerations guide the design of novel compounds that can be patented and protected from competition
• Scalability and manufacturability are evaluated to ensure the compound can be synthesized efficiently on a large scale
• Collaborative efforts between medicinal chemists, computational chemists, and pharmacologists drive the optimization process

Preclinical Studies

• In vitro studies assess the compound's activity, selectivity, and safety using cell-based assays or biochemical tests
• In vivo studies evaluate the compound's efficacy, pharmacokinetics, and toxicity in animal models
  • Common models include mice, rats, dogs, or non-human primates
• Pharmacokinetic studies measure the compound's absorption, distribution, metabolism, and excretion (ADME) properties in animals
• Toxicology studies assess the compound's safety profile, including acute and chronic toxicity, genotoxicity, and reproductive toxicity
• Formulation studies optimize the compound's dosage form and route of administration for animal studies
• Investigational New Drug (IND) application is submitted to the FDA, summarizing preclinical data and proposing clinical trial plans
• Good Laboratory Practice (GLP) regulations ensure the quality and integrity of preclinical data

Clinical Trials and FDA Approval

• Phase I trials assess the safety, tolerability, and pharmacokinetics of the drug in a small group of healthy volunteers (20-100)
• Phase II trials evaluate the drug's efficacy, safety, and optimal dose in a larger group of patients with the target disease (100-500)
  • Randomized, controlled trials compare the drug to a placebo or existing treatment
• Phase III trials confirm the drug's efficacy and safety in a large, diverse patient population (1,000-5,000)
  • Multicenter, randomized, double-blind trials are the gold standard
• New Drug Application (NDA) is submitted to the FDA, containing all preclinical and clinical data, manufacturing information, and proposed labeling
• FDA review process involves a thorough evaluation of the NDA by a team of experts, including clinical, non-clinical, and statistical reviewers
  • Advisory committees may be convened to provide additional expertise and recommendations
• Post-marketing surveillance (Phase IV) monitors the drug's safety and effectiveness in the real-world setting after approval
• Accelerated approval pathways (Fast Track, Breakthrough Therapy, or Priority Review) expedite the development and review of drugs for serious or life-threatening conditions

Challenges and Future Trends

• Attrition rates remain high, with many compounds failing in clinical trials due to lack of efficacy or safety concerns
  • Strategies to improve success rates include better target validation, predictive preclinical models, and biomarker-driven trials
• Drug resistance emerges as a major challenge, particularly for antimicrobial and anticancer agents
  • Combination therapies and novel mechanisms of action are being explored to combat resistance
• Precision medicine aims to tailor treatments based on a patient's genetic, molecular, or clinical characteristics
  • Biomarker-guided drug development and companion diagnostics are key enablers
• Biologics (antibodies, proteins, or cell therapies) are gaining prominence for their specificity and potency
  • Challenges include manufacturing complexity, immunogenicity, and delivery
• Artificial intelligence (AI) and machine learning (ML) are being applied to various stages of drug discovery, from target identification to lead optimization
  • Deep learning models can predict compound properties, design new molecules, or analyze clinical trial data
• Collaborative models between academia, industry, and government are fostering innovation and accelerating drug development
  • Public-private partnerships, precompetitive consortia, and open innovation platforms are examples