Fundamental Principles of Medicinal Chemistry: Drug Discovery and Optimization

Drug Discovery Foundations: Hits and Leads

Hits and Leads: Hit identification involves screening for compounds with biological activity. Hit to Lead (H2L) optimization focuses on improving affinity from the micromolar ( $10^{-6}\,M$ ) range to the nanomolar ( $10^{-9}\,M$ ) range.

Synthesis Methods:

Combinatorial Synthesis: An automated solid-phase procedure designed to produce large chemical pools and mixtures quickly.
Parallel Synthesis: Small-scale synthesis of large numbers of compounds simultaneously in solution or solid phase, producing distinct products in each vial.

Computer-Aided Drug Design (CADD): Utilizes X-ray crystallography to determine protein structures and binding sites, enabling de novo drug design and homology modeling.

Key Pharmaceutical Databases

ZINC: A free database containing over $230$ million purchasable compounds in ready-to-dock 3D formats.
ChEMBL: A manually curated database of bioactive molecules focused on drug-like properties, maintained by the European Bioinformatics Institute (EBI).
PubChem: Maintained by NCBI/NIH, containing three primary databases: Compounds ( $111$ million), Substances ( $293$ million), and BioAssay ( $1.25$ million).
BindingDB: Focuses on experimentally determined protein-ligand binding affinities ( $2.2$ million data points).
DrugBank: Combines detailed drug data with comprehensive target and action information for FDA-approved and experimental drugs.

Lead and Drug Criteria

Lipinski’s Rule of Five: Establishes parameters for drug-likeness (e.g., MW < 500, ClogP < 5).
Lead-like Space: Criteria for leads are more stringent than drugs, as optimization typically increases size and hydrophobicity. Suggested lead properties include $MW$ between $100-350\,amu$ and $ClogP$ values of $1-3$ .
Hit Validation: Hits must be active in vitro, lack toxicophores (e.g., hERG toxicity), show clear Structure-Activity Relationships (SAR), and offer patent opportunities.

Principles of Analogue Optimization

Improvement Goals: Optimization aims to increase potency (e.g., Famotidine), enhance selectivity (e.g., Atenolol over Propranolol), improve bioavailability (e.g., Lisinopril), and extend duration of action (e.g., Tiotropium)

Categories of Analogues:

Direct Analogues: Share both chemical structure and pharmacological similarities ("me-too drugs").
Structural Analogues: Share structural similarities but may have different pharmacological profiles.
Functional Analogues: Chemically different compounds that display similar pharmacological properties (e.g., Morphine and Fentanyl).

Molecular Descriptors and Similarity

SAR Definition: The relationship between chemical structure and biological activity, formalized by Crum-Brown and Fraser (1868) as $\Phi = f(C)$ .
Complexity (Dimensionality): Descriptors range from 0D (chemical formula counts) to 4D (grid-based molecular interaction fields or ensembles).
Similarity Concepts: Chemical similarity is based on physicochemical characteristics ( $log\,P$ , $MW$ ), while molecular similarity focuses on structural features (topology, substructures). Calculations usually yield a coefficient from $0$ to $1$ .

Assay Interference and Polypharmacology

PAINS (Pan-Assay INterference compoundS): Substructural features that cause compounds to appear as frequent hitters through non-specific interference rather than target affinity (e.g., Toxoflavins, Isothiazolones, Curcumin).
Polypharmacology: A shift toward "magic shotguns" (single agent, multiple targets) to treat polygenic diseases like cancer and CNS disorders. For example, Aspirin and various kinase inhibitors act on multiple targets.

SOSA and Drug Repurposing

SOSA Approach: Selective Optimization of Side Activities involves screening "old" drugs to identify side activities that can be optimized into the main effect. Examples include deriving $ETA$ receptor antagonists from Sulfathiazole.
Drug Repurposing: Investigating existing drugs for new indications (e.g., Sildenafil for erectile dysfunction). It reduces clinical trial steps and costs.

Fragment-Based Drug Discovery (FBDD)

Characteristics: Screens low molecular weight fragments ( $100-250\,amu$ ) with low affinity ( $50\,\mu M - 1\,mM$ ) using biophysical methods like NMR and X-ray crystallography.

Strategies:

Fragment Evolution: Growing a starting fragment to reach adjacent binding pockets.
Fragment Linking: Joining two fragments that bind to proximal parts of the active site.
Fragment Self-Assembly: Reactions between complementary fragments in the presence of the target.

Homologous Series

General Rule: Increasing the carbon chain ( $CH_2$ groups) often increases activity logarithmically until a "cut-off" is reached.
Activity Patterns: Relationships can be bell-shaped (peaking at specific chain lengths), zig-zag (alternating affinity), or reach a plateau.