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Foundations in Drug Discovery

Course Description

This course introduces the interdisciplinary process of modern drug discovery, covering small molecules and biologics. Students learn target identification, lead optimization, preclinical testing, and translational research, while exploring ethical, regulatory, and commercial factors. Real-world examples and collaboration prepare students for multidisciplinary therapeutic development.

Athena Title

Foundations in Drug Discovery

Semester Course Offered

Offered fall

Grading System

A - F (Traditional)

Student learning Outcomes

  • Students will develop a comprehensive understanding of the key stages and concepts in the drug discovery pipeline.
  • Students will analyze the role of various scientific disciplines in drug discovery, including chemistry, biology, and pharmacology.
  • Students will evaluate different approaches for target identification and validation by examining a range of strategies used in modern drug discovery.
  • Students will understand the general process for lead compound discovery, optimization, and characterization.
  • Students will understand the general the principles of preclinical testing and translational research.
  • Students will provide students with practical knowledge of ethical, regulatory, and commercial issues in drug discovery.
  • Students will familiarize students with the transformative impact of Artificial Intelligence (AI) and Machine Learning on accelerating R&D across the entire drug discovery and development spectrum.

Topical Outline

  • Module 1: Overview of the drug discovery process • Historical overview of drug discovery and development. • The drug discovery and development timeline: From target identification to post-market surveillance. • Key disciplines: Interplay between biology, medicinal chemistry, pharmacology, toxicology, and regulatory affairs. • Drug modalities: Small molecules, biologics (antibodies, peptides, gene/cell therapy), and novel therapeutics (RNA-based drugs). • The Target Product Profile (TPP) and Target Candidate Profile (TCP): Defining the desired characteristics of a new drug (TPP) and the required attributes of a drug candidate molecule (TCP) as it enters preclinical development. • Industry landscape: Developing a new therapy from academic research, biotech startups, pharmaceutical companies, and contract research organizations (CROs). • AI-driven drug discovery: leveraging Artificial Intelligence (AI) and Machine Learning (ML) to accelerate pharmaceutical research and development (R&D). By analyzing massive, complex datasets from genomics and proteomics to clinical trial results and scientific literature, AI dramatically accelerates decision-making across the entire pipeline.
  • Module 2: Target identification and validation • Disease biology: Understanding the molecular, cellular, and genetic mechanisms responsible for a particular disease to elucidate the specific biological alterations, at the cellular, tissue, or organ level, that give rise to disease states. A comprehensive understanding of these mechanisms is essential for identifying drug or biological targets for novel therapeutic strategies. • Target selection criteria: understanding essentiality vs vulnerability, druggability or modulation by small molecules or biologics to reduce investment risks. • Target validation: Discussion of genetic, biochemical, and computational approaches to validate drug and biological targets as candidates for therapeutic intervention.
  • Module 3: Screening technologies, hit discovery and lead optimization • Assay Development and High-Throughput Screening (HTS): Techniques for assay development and screening. Primary vs. secondary assays, hit definition, and quality control. Phenotypic screening vs. target-based screening, emerging technologies and automation in screening processes. • Chemical libraries (natural vs synthetic compounds): Source of chemical libraries, quality, structural heterogeneity, molecular assortment, chemical breadth for identification of initial “hits.” • Hit-to-Lead (H2L) and Lead Optimization (LO): The process of turning a screened 'hit' into a developable 'lead' compound. • Structure-Activity Relationship (SAR): Principles of how chemical structure and its physicochemical properties influences biological activity. Overview of the medicinal chemistry strategies, and iterative compound modification for improving the potency, selectivity, and pharmacokinetic properties of hit/lead compounds. • Principles of Drug Design: Bioisosterism, prodrugs, and strategies for improving potency and selectivity. • AI-driven drug discovery: Virtual ligand screening (VLS), target-based drug design, molecular docking, and structural biology.
  • Module 4: In vitro and in vivo pharmacology, preclinical and translational research • Pharmacology principles: Introduction to experimental models used to assess drug efficacy, toxicity (safety assessments, on/off-target such hERG, genotoxicity (Ames test), and organ-specific toxicity), and mechanism of action. Cell-based and animal studies that inform compound selection for further development. • Pharmacokinetics (PK): Principles of absorption, distribution, metabolism, excretion, and toxicity (ADMET) in drug development. In vivo and in vitro PK models, to determine PK parameters including half-life, clearance, and bioavailability. • Pharmacodynamics (PD): In vivo efficacy, target engagement, and the PK/PD relationship including changes on PK profiles when the disease comes into play. • Candidate Selection: The final decision process for advancing a molecule to clinical development, including risk assessment.
  • Module 5: Ethical, regulatory, and commercial aspects Discussion of the ethical considerations in drug discovery, intellectual property, and the regulatory landscape. Students will also examine the commercial challenges and opportunities associated with bringing new drugs to market. • Intellectual property (IP) and patent law: Basic concepts of patents, exclusivity, and freedom to operate. • Regulatory frameworks: Overview of the roles of regulatory bodies (e.g., FDA, EMA) and the IND/NDA process. • Good Practices (GxP): Good Laboratory Practice (GLP), Good Manufacturing Practice (GMP), and Good Clinical Practice (GCP). • Translational Medicine: Bridging preclinical data to human studies. • Ethics in drug discovery and testing: human subjects protection, trials involving children, research integrity and public trust. • Economic drivers: Cost, risk, commercial challenges, opportunities and the market for pharmaceuticals.
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