This track provides a comprehensive introduction to the entire drug discovery and development pipeline, from identifying disease targets to bringing a therapeutic product to market. It covers the historical evolution of drug discovery, key milestones in pharmaceutical innovation, and modern drug modalities such as small molecules, biologics, and gene therapies. Students will learn about the stages of discovery, preclinical development, clinical trials (Phases I-IV), regulatory approval processes, and the associated timelines, costs, and success rates. The course emphasizes the interdisciplinary nature of drug development and the collaboration required across biology, chemistry, pharmacology, and regulatory affairs.

• Timeline, costs, success rates
• Small molecules vs biologics

This track focuses on the scientific principles underlying drug action and target selection. It begins with an exploration of receptor pharmacology, signal transduction pathways, and the classification of drug targets, including enzymes, GPCRs, ion channels, and nucleic acids. Emphasis is placed on understanding disease mechanisms at the molecular and cellular levels to identify and validate potential therapeutic targets. Students will learn various techniques used to assess target engagement and efficacy, such as RNA interference, CRISPR gene editing, and knockout animal models. Real-world case studies will illustrate the process of moving from a biological hypothesis to a validated drug target.

• Target identification and validation
• Disease biology

This track delves into the foundational chemistry that underpins successful drug design and optimization. Students will explore the principles of structure-activity relationships (SAR), functional group modifications, bioisosterism, and stereochemistry in drug molecules. The course addresses physicochemical properties like solubility, permeability, and lipophilicity, which impact a compound's pharmacokinetics and drug-likeness. Topics also include prodrug strategies, metabolic stability, and the role of chemical synthesis in iterative compound design. By the end of the course, students will be able to critically evaluate lead compounds and propose rational modifications to improve their biological activity and drug-like properties.

• ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity)
• Lipinski’s Rule of Five

This track introduces computational tools and techniques used in the modern drug discovery process. Topics include molecular docking, homology modeling, quantitative structure-activity relationship (QSAR) modeling, and pharmacophore modeling. Students will gain hands-on experience with software platforms used for virtual screening of compound libraries, predicting binding affinities, and simulating molecular dynamics. The course also explores the integration of cheminformatics and bioinformatics in predicting ADMET properties and optimizing hit compounds. The use of AI and machine learning for de novo drug design, compound generation, and multi-objective optimization is also highlighted as a frontier in drug discovery.

• Molecular modeling
• Docking, QSAR, AI in drug design

This track provides a deep understanding of how drugs behave in the body (pharmacokinetics) and how they exert their effects (pharmacodynamics). It covers key concepts such as absorption, distribution, metabolism, and excretion (ADME), as well as dose-response relationships, therapeutic windows, and receptor binding dynamics. Students will learn how to design PK/PD studies, analyze time-concentration curves, and apply models to predict efficacy and toxicity. Emphasis is placed on translating in vitro data to in vivo systems and optimizing drug exposure to achieve desired therapeutic outcomes. The course includes case examples from both preclinical and clinical stages.

• Bioavailability, half-life, clearance
• In vitro vs in vivo correlation

This track focuses on identifying and mitigating safety risks during drug development. Students will explore the types of toxicological assessments-acute, sub-chronic, and chronic toxicity-as well as genotoxicity, carcinogenicity, reproductive toxicity, and organ-specific toxicity. The course examines safety pharmacology studies required by regulatory bodies, including cardiovascular, respiratory, and central nervous system assessments. Students will understand how to interpret toxicological data, determine No Observed Adverse Effect Levels (NOAEL), and conduct risk-benefit analyses. Advanced topics include in silico and in vitro predictive toxicology, animal model selection, and emerging tools like organ-on-chip technologies.

• Regulatory requirements
• GLP studies

This track outlines the global regulatory landscape that governs drug approval and commercialization. Students will learn about the roles of major regulatory bodies such as the FDA, EMA, and PMDA, and the documentation required for Investigational New Drug (IND) applications and New Drug Applications (NDA). The course also introduces the principles of Good Laboratory Practice (GLP), Good Manufacturing Practice (GMP), and Good Clinical Practice (GCP). In parallel, it covers intellectual property (IP) strategies, including patents, exclusivity rights, and freedom-to-operate analyses. Students will explore how regulatory science and IP management are critical for securing market access and protecting innovation.

• FDA, EMA processes
• Patents, exclusivity

This track explores how artificial intelligence and big data are revolutionizing drug discovery. Students will examine the use of machine learning algorithms in target identification, virtual screening, lead optimization, and predictive toxicology. The course covers deep learning, natural language processing for literature mining, and generative models for designing novel molecules. Emphasis is placed on the integration of large-scale biological datasets-such as genomics, transcriptomics, and proteomics-with chemical and clinical data to inform decision-making. Real-world examples and case studies will showcase how AI is accelerating timelines and improving success rates across the discovery pipeline.

• Predictive modeling
• Generative models for molecule design

This track introduces the concept of tailoring medical treatments based on individual genetic, environmental, and lifestyle factors. Students will learn about the use of biomarkers, pharmacogenomics, and companion diagnostics in stratifying patients and predicting therapeutic responses. The course explores applications in oncology, rare diseases, and autoimmune disorders, where patient-specific information can guide treatment choices. Topics include clinical trial design for personalized medicine, regulatory considerations, and challenges in implementing precision approaches in healthcare. Emerging technologies such as single-cell sequencing, liquid biopsies, and digital health tools are also discussed in the context of personalized treatment strategies.

• Biomarkers
• Companion diagnostics

Ensuring a drug reaches its target site effectively is essential for therapeutic outcomes. This track delves into novel delivery technologies, including transdermal systems, nanocarriers, biologic drug stabilization, and injectable sustained-release formulations. Strategies to overcome solubility, permeability, and stability challenges are highlighted.

• Drug Solubility, Stability, & Bioavailability Challenges
• Lipid-Based Drug Formulations (Liposomes, Solid Lipid Nanoparticles)

Clinical trials are essential for evaluating the safety and efficacy of new drugs in humans. This track covers trial design, phases of clinical testing (Phase I-IV), patient recruitment, ethical considerations, and regulatory requirements. Special emphasis is placed on data collection, statistical analysis, and trial monitoring to ensure robust results.

• Clinical Trial Phases (I, II, III, IV) & Their Objectives
• Patient Recruitment & Clinical Trial Ethics
• Biomarkers & Companion Diagnostics in Clinical Trials