💊Medicinal Chemistry Unit 7 – Drug design strategies

Key Concepts in Drug Design

• Drug design involves the rational design and synthesis of molecules that can interact with specific biological targets to elicit a desired therapeutic effect
• Involves a multidisciplinary approach that integrates knowledge from chemistry, biology, pharmacology, and computational sciences
• Aims to develop molecules with high affinity, selectivity, and potency for the target while minimizing off-target effects and toxicity
• Considers the pharmacokinetic properties (ADME) of the drug to ensure optimal bioavailability, distribution, metabolism, and excretion
• Utilizes various strategies such as structure-based drug design (SBDD), ligand-based drug design (LBDD), and fragment-based drug discovery (FBDD)
  • SBDD relies on the three-dimensional structure of the target protein to design complementary ligands
  • LBDD uses the knowledge of known ligands to identify common pharmacophoric features and guide the design of new molecules
  • FBDD involves screening small molecular fragments to identify weak binders that can be optimized into potent lead compounds
• Employs computational tools and techniques such as molecular docking, virtual screening, and quantitative structure-activity relationship (QSAR) analysis to accelerate the drug discovery process
• Iterative process that involves multiple rounds of design, synthesis, and biological evaluation to optimize the lead compounds

Target Identification and Validation

• Target identification involves identifying a specific biological molecule (protein, enzyme, receptor, or nucleic acid) that plays a crucial role in the disease pathology and can be modulated by a drug to achieve a therapeutic effect
• Targets can be identified through various approaches such as:
  • Genetic association studies that link specific genes to disease susceptibility
  • Expression profiling studies that identify differentially expressed genes or proteins in disease states
  • Phenotypic screening that identifies compounds with desired biological effects without prior knowledge of the target
• Target validation is the process of confirming that modulation of the identified target will have a therapeutic benefit with minimal adverse effects
• Involves various experimental techniques such as:
  • Genetic knockdown or knockout studies to assess the effect of target modulation on disease phenotype
  • Pharmacological studies using selective agonists, antagonists, or inhibitors to evaluate the therapeutic potential and safety of target modulation
  • Animal models that recapitulate the human disease to assess the efficacy and toxicity of target modulation in vivo
• Considers the druggability of the target, which refers to the likelihood of developing a small molecule or biologic that can effectively modulate the target's activity
• Assesses the selectivity of the target to minimize off-target effects and potential toxicity
• Evaluates the potential for drug resistance and identifies strategies to mitigate its development

Structure-Activity Relationships (SAR)

• SAR is the study of how the chemical structure of a molecule relates to its biological activity
• Involves systematic modification of a lead compound to identify the key structural features that contribute to its activity and selectivity
• Modifications can include changes to the scaffold, substituents, stereochemistry, and physicochemical properties
• Aims to improve the potency, selectivity, and pharmacokinetic properties of the lead compound while minimizing toxicity
• Utilizes various techniques such as:
  • Bioisosteric replacement, which involves replacing a functional group with another that has similar electronic and steric properties
  • Homologation, which involves adding or removing methylene groups to explore the effect of chain length on activity
  • Constraint of conformational flexibility, which involves introducing rings or double bonds to restrict the conformational space and improve binding affinity
• Generates SAR tables that summarize the activity data for a series of analogs and help identify trends and structure-activity relationships
• Guides the design of focused libraries that explore the SAR around a promising lead compound
• Can be augmented by computational approaches such as 3D-QSAR and pharmacophore modeling to predict the activity of untested compounds

Pharmacophore Modeling

• Pharmacophore modeling involves identifying the essential structural features of a ligand that are responsible for its biological activity
• A pharmacophore is an abstract three-dimensional arrangement of chemical features (hydrogen bond donors/acceptors, hydrophobic regions, aromatic rings, etc.) that are necessary for optimal interaction with a specific biological target
• Can be derived from a set of known active ligands (ligand-based pharmacophore) or from the structure of the target protein (structure-based pharmacophore)
• Ligand-based pharmacophore modeling involves superimposing a set of active ligands to identify common chemical features and their spatial arrangement
  • Conformational flexibility of the ligands is considered, and multiple conformations are generated and aligned to identify the bioactive conformation
  • Features are identified based on their chemical properties and their frequency of occurrence in the active ligands
• Structure-based pharmacophore modeling involves analyzing the binding site of the target protein to identify key interaction points and generate a pharmacophore model
  • Utilizes the knowledge of the protein-ligand complex structure obtained from X-ray crystallography or homology modeling
  • Identifies key amino acid residues in the binding site that interact with the ligand and maps their chemical features onto the pharmacophore model
• Pharmacophore models can be used for virtual screening of large compound libraries to identify potential hits that match the pharmacophoric features
• Can also guide the design of new molecules that incorporate the essential pharmacophoric features and optimize their spatial arrangement

Molecular Docking and Virtual Screening

• Molecular docking is a computational technique that predicts the preferred orientation and binding mode of a ligand within the binding site of a target protein
• Involves two main components: a search algorithm that generates possible ligand conformations and orientations within the binding site, and a scoring function that evaluates the strength of the protein-ligand interactions
• Search algorithms can be classified into systematic methods (exhaustive search, conformational sampling) and stochastic methods (genetic algorithms, Monte Carlo simulations)
• Scoring functions can be based on force fields (molecular mechanics), empirical data (regression-based), or knowledge-based potentials
• Docking can be performed in a rigid or flexible manner, considering the conformational flexibility of the ligand and/or the protein
• Virtual screening is the application of molecular docking to large compound libraries to identify potential hits that can bind to the target protein
  • Can be structure-based (docking-based) or ligand-based (pharmacophore-based, QSAR-based)
  • Enables the rapid and cost-effective exploration of chemical space to prioritize compounds for experimental testing
• Docking and virtual screening results are evaluated based on various metrics such as docking score, ligand efficiency, and interaction fingerprints
• Hits identified from virtual screening are further validated through experimental assays to confirm their activity and selectivity

Lead Optimization Strategies

• Lead optimization is the process of improving the potency, selectivity, and pharmacokinetic properties of a lead compound to develop a clinical candidate
• Involves iterative rounds of design, synthesis, and biological evaluation to optimize the lead compound
• Strategies for lead optimization include:
  • SAR exploration to identify key structural features that contribute to activity and selectivity
  • Bioisosteric replacement to improve potency, selectivity, or pharmacokinetic properties
  • Scaffold hopping to explore different chemical scaffolds that maintain the key pharmacophoric features
  • Prodrug design to improve solubility, permeability, or stability
  • Conjugation with targeting moieties (antibodies, peptides) for targeted drug delivery
• Utilizes various computational tools such as QSAR, molecular dynamics simulations, and ADMET prediction to guide the optimization process
• Considers the physicochemical properties of the lead compound (molecular weight, lipophilicity, hydrogen bond donors/acceptors) and their impact on drug-like properties
• Balances the improvement in potency and selectivity with the maintenance of favorable pharmacokinetic and safety profiles
• Aims to develop a clinical candidate with optimal efficacy, safety, and pharmaceutical properties for further development

ADME Considerations

• ADME (Absorption, Distribution, Metabolism, Excretion) properties of a drug candidate are critical determinants of its in vivo performance and clinical success
• Absorption refers to the ability of the drug to cross biological membranes and enter the systemic circulation
  • Influenced by factors such as solubility, permeability, and efflux transporters
  • Can be predicted using in vitro assays (Caco-2, PAMPA) and in silico models (rule of five, QSAR)
• Distribution refers to the extent and pattern of drug dissemination into various tissues and organs
  • Influenced by factors such as plasma protein binding, tissue affinity, and blood-brain barrier penetration
  • Can be predicted using in vitro assays (plasma protein binding, tissue distribution studies) and in silico models (QSAR, PBPK)
• Metabolism refers to the biochemical modification of the drug by enzymes (cytochrome P450, UGTs) to facilitate its elimination
  • Can lead to the formation of active, inactive, or toxic metabolites
  • Can be predicted using in vitro assays (liver microsomes, hepatocytes) and in silico models (QSAR, structure-based modeling)
• Excretion refers to the elimination of the drug and its metabolites from the body
  • Occurs primarily through renal (urine) and hepatic (bile) routes
  • Can be predicted using in vitro assays (hepatocyte stability, renal clearance) and in silico models (QSAR, PBPK)
• ADME properties are optimized during lead optimization to ensure adequate bioavailability, target tissue exposure, and elimination
• Poor ADME properties can lead to suboptimal efficacy, toxicity, or drug-drug interactions, and are a major cause of attrition in drug development

Drug Delivery Systems

• Drug delivery systems are designed to improve the pharmacokinetic and pharmacodynamic properties of drugs by controlling their release, distribution, and targeting
• Conventional drug delivery systems include:
  • Oral formulations (tablets, capsules) for systemic delivery
  • Parenteral formulations (injections, infusions) for rapid onset or long-acting effects
  • Topical formulations (creams, ointments) for local delivery to the skin or mucous membranes
• Advanced drug delivery systems aim to overcome the limitations of conventional formulations and enhance the therapeutic efficacy and safety of drugs
• Examples of advanced drug delivery systems include:
  • Controlled release formulations (matrix, reservoir) that provide sustained or pulsatile drug release over an extended period
  • Targeted drug delivery systems (antibody-drug conjugates, nanoparticles) that selectively deliver drugs to specific cells or tissues
  • Transdermal patches that allow for continuous drug delivery through the skin
  • Inhalation devices (metered-dose inhalers, dry powder inhalers) for pulmonary delivery of drugs
  • Implantable devices (pumps, stents) for localized drug delivery to specific organs or tissues
• Drug delivery systems are designed based on the physicochemical properties of the drug (solubility, stability), the desired pharmacokinetic profile, and the target tissue or organ
• Utilize various materials (polymers, lipids) and technologies (microencapsulation, nanoparticle synthesis) to achieve controlled release and targeting
• Can improve patient compliance, reduce side effects, and enhance the therapeutic index of drugs

Emerging Trends in Drug Discovery

• Drug discovery is an evolving field that constantly adapts to new scientific advances and technological innovations
• Some of the emerging trends in drug discovery include:
  • Precision medicine: tailoring drug therapy based on individual patient characteristics (genetic profile, biomarkers) to optimize efficacy and safety
  • Immunotherapy: harnessing the power of the immune system to fight diseases such as cancer and autoimmune disorders
  • Gene therapy: delivering functional genes to replace or correct defective genes in genetic disorders
  • RNA therapeutics: targeting disease-causing RNA with antisense oligonucleotides, siRNA, or miRNA
  • Cell therapy: using living cells (stem cells, engineered cells) to replace or regenerate damaged tissues and organs
  • Microbiome-based therapy: modulating the gut microbiome to treat diseases such as inflammatory bowel disease, obesity, and neurological disorders
• Emerging technologies such as CRISPR-Cas9 gene editing, organ-on-a-chip, and 3D bioprinting are enabling new approaches to drug discovery and development
• Artificial intelligence (AI) and machine learning (ML) are being increasingly applied to various aspects of drug discovery, from target identification to lead optimization and clinical trial design
• Open innovation models that involve collaboration between academia, industry, and government are facilitating the sharing of knowledge and resources to accelerate drug discovery
• Regulatory agencies are adapting to the changing landscape of drug discovery by providing guidance on the development of novel therapeutic modalities and streamlining the approval process for breakthrough therapies