==The Drug Discovery Pipeline==
- Target Identification and Validation - Before designing a drug, you need to find a target
What makes a good target?
- Disease relevance - genetic evidence that the target is causally linked to disease
- Druggability - Can a small molecule, biologic, or RNA therapy actually target it?
- Selectivity potential - Can you hit this target without affecting other members that would cause toxicity?
- Biomarker availability - Can you measure target engagement in a patient?
- Expression pattern - Is it expressed in a specific part of the body but nowhere else? E.g only expressed in disease tissue.
Validation Approaches
- Genetic: siRNA/shRNA knockdown, CRISPR knockout, patient genetics
- Pharmacological: tool compounds that inhibit target
- Disease models: animal knockouts, organoids, patient-derived cells
- Hit discovery - HTS, virtual screening, fragment-based design
- High throughput screening (HTS) - physically test thousands to millions of compounds against a target in automated assays
- Produces “hits” - compounds that show activity above a threshold
- Virtual screening - computationally dock large libraries against a target structure
- Fragment-based design - screen very small molecules that bind weakly but efficiently
- Find two fragments that bind adjacent sites, and link them into a single large molecule with high affinity
- Lead optimization - ADMET properties and medicinal chemistry
- Lead optimization is the iterative process of taking a hit compound and making it better across multiple axes simultaneously
- A central challenge of drug discovery
- Absorption
- Distribution
- Metabolism
- Excretion
- Toxicity
- Preclinical → IND → Phase I/II/III
- Preclinical is in-vitro and in-vivo testing
- IND (investigational New Drug) Filing
- Submit to FDA to request permission to begin human trials
- Phase I - Safety
- First-in-human tests, typically healthy volunteers
- Goal: is it safe?
- Phase II - Efficacy signals
- Hundreds of patients with target disease
- Goal: does it work? Whats the right dose? What are common side effects?
- Phase III - Confirmation
- Thousands of patients, randomized controlled trial against standard of care or placebo
- Goal: statistically confirm efficacy and characterize safety at scale
==Therapeutic Modalities==
mRNA therapeutics
- mRNA delivers instructions to the cell to produce a desired protein
- cell’s ribosomes read message and make proteins - no DNA involved and transient
Anatomy of mRNA 5’ Cap - 5’ UTR - Start Codon - CDS (Coding sequence) - Stop Codon - 3’ UTR - Poly-A Tail
- Therapeutic mRNA uses pseudouridine modification - dramatically reduce innate immune activation
- not optional: unmodified mRNA is too immunogenic and too poorly translated for most therapeutic uses
- LNP (Lipid Nanoparticle) delivery
siRNA (Small Interfering RNA) ASOs (Antisense Oligonucleotides) Aptamers SELEX (Systematic Evolution of Ligands by Exponential enrichment) Antibodies
==Computational Drug Design==
- Structure-based vs Ligand-based Drug Design
- Structure-based requires a 3D structure of the target - design molecules that fit geometrically and chemically
- Ligand-based does not require target structure - only a set of known molecules - what 3D features do all actives share?
- Molecular Docking
- Computationally predict how a small molecule (ligand) binds to a target (receptor) - finds the lowest-energy binding pose and estimates binding affinity
- Typically scoring functions
- Force-field based
- Empirical
- Knowledge-based
- ML-based
- Molecular dynamics
- Simulate the physical motion of atoms over time by numerically integrating Newton’s equation of motion - captures dynamic behavior of molecules that docking misses
- Captures flexibility, solvation, entropic effects, binding kinetics, allosteric effects
- Free energy perturbation
- thermodynamic method to calculate the binding free energy difference between two related compounds. Gold standard for affinity prediction in lead optimization.
- ADMET prediction methods
==Sequence Optimization for Therapeutics== Codon optimization - The degeneracy problem: Most amino acids are encoded by multiple codons (synonyms). Which codon you choose doesn’t change the protein sequence, but it dramatically affects:
- Translational speed: Rare codons slow ribosome elongation (lack of cognate tRNA). Sometimes this is good (folding time), but generally slows expression
- Translational accuracy: Rare codons increase misincorporation rate
- mRNA stability: Codon usage affects mRNA folding, which affects degradation rate
- Immunogenicity: CpG dinucleotides in DNA and dsRNA structures in mRNA can trigger innate immune sensors