Pharm 202 Computer Aided Drug Design

1. Pharm 202Computer Aided Drug Design Phil Bourne bourne@sdsc.edu http://www.sdsc.edu/pb -> Courses -> Pharm 202 Several slides are taken from UC Berkley Chem 195

2. Perspective • Principles of drug discovery (brief) • Computer driven drug discovery • Data driven drug discovery • Modern target identification and selection • Modern lead identification Overall strong structural bioinformatics emphasis

3. What is a drug? • Defined composition with a pharmacological effect • Regulated by the Food and Drug Administration (FDA) • What is the process of Drug Discovery and Development?

4. Drugs and the Discovery Process • Small Molecules • Natural products • fermentation broths • plant extracts • animal fluids (e.g., snake venoms) • Synthetic Medicinal Chemicals • Project medicinal chemistry derived • Combinatorial chemistry derived • Biologicals • Natural products (isolation) • Recombinant products • Chimeric or novel recombinant products

5. Discovery vs. Development • Discovery includes: Concept, mechanism, assay, screening, hit identification, lead demonstration, lead optimization • Discovery also includes In Vivo proof of concept in animals and concomitant demonstration of a therapeutic index • Development begins when the decision is made to put a molecule into phase I clinical trials

6. Discovery and Development • The time from conception to approval of a new drug is typically 10-15 years • The vast majority of molecules fail along the way • The estimated cost to bring to market a successful drug is now $800 million!! (Dimasi, 2000)

7. Drug Discovery Processes Today Physiological Hypothesis Primary Assays Biochemical Cellular Pharmacological Physiological Molecular Biological Hypothesis (Genomics) Initial Hit Compounds Screening + Sources of Molecules Natural Products Synthetic Chemicals Combichem Biologicals Chemical Hypothesis

8. Drug Discovery Processes - II Hit to Lead Chemistry - physical properties -in vitro metabolism Secondary Evaluation - Mechanism Of Action - Dose Response Initial Hit Compounds Initial Synthetic Evaluation - analytics - first analogs First In Vivo Tests - PK, efficacy, toxicity

9. Drug Discovery Processes - III Lead Optimization Potency Selectivity Physical Properties PK Metabolism Oral Bioavailability Synthetic Ease Scalability Pharmacology Multiple In Vivo Models Chronic Dosing Preliminary Tox Development Candidate (and Backups)

10. Drug Discovery Disciplines • Medicine • Physiology/pathology • Pharmacology • Molecular/cellular biology • Automation/robotics • Medicinal, analytical,and combinatorial chemistry • Structural and computational chemistries • Bioinformatics

11. Drug Discovery Program Rationales • Unmet Medical Need • Me Too! - Market - ($$$s) • Drugs in search of indications • Side-effects often lead to new indications • Indications in search of drugs • Mechanism based, hypothesis driven, reductionism

12. Serendipity and Drug Discovery • Often molecules are discovered/synthesized for one indication and then turn out to be useful for others • Tamoxifen (birth control and cancer) • Viagra (hypertension and erectile dysfunction) • Salvarsan (Sleeping sickness and syphilis) • Interferon-a (hairy cell leukemia and Hepatitis C)

13. Issues in Drug Discovery • Hits and Leads - Is it a “Druggable” target? • Resistance • Pharmacodynamics • Delivery - oral and otherwise • Metabolism • Solubility, toxicity • Patentability

14. A Little History of Computer Aided Drug Design • 1960’s - Viz - review the target - drug interaction • 1980’s- Automation - high trhoughput target/drug selection • 1980’s- Databases (information technology) - combinatorial • libraries • 1980’s- Fast computers - docking • 1990’s- Fast computers - genome assembly - genomic based • target selection • 2000’s- Vast information handling - pharmacogenomics

15. From the Computer Perspective

16. Progress About the computer industry… “If the automobile industry had made as much progress in the past fifty years, a car today would cost a hundredth of a cent and go faster than the speed of light.” • Ray Kurzweil, The Age of Spiritual Machines

17. Growth of pixel fill rates SGI PC cards * Not counting custom hardware or special configurations • Fill rates recently growing by x2 everyyear Data source: Product literature

18. Comparing Growth Rates

19. From the Target Perspective

21. Bioinformatics - A Revolution Biological Experiment Data Information KnowledgeDiscovery Collect Characterize Compare Model Infer Complexity Technology Data Higher-life 1 10 100 1000 100000 Computing Power Organ Brain Mapping Cardiac Modeling Cellular Model Metaboloic Pathway of E.coli Sub-cellular 102 106 1 Neuronal Modeling # People/Web Site Ribosome Assembly Virus Structure Genetic Circuits Structure Human Genome Project Yeast Genome E.Coli Genome C.Elegans Genome 1 Small Genome/Mo. Sequencing Technology ESTs Gene Chips Human Genome Sequence 90 95 00 05 Year (C) Copyright Phil Bourne 1998

22. The Accumulation of Knowledge This “molecular scene” for cAMP dependant protein kinase (PKA) depicts years of collective knowledge. Traditionally structure determination has been functional driven As we shall see it is becoming genomically driven

23. History History • Strong sense of • community ownership • We are the current • custodians • The community • watches our every • move • The community • itself is changing

24. Status - Numbers and Complexity (a) myoglobin (b) hemoglobin (c) lysozyme (d) transfer RNA (e) antibodies (f) viruses (g) actin (h) the nucleosome (i) myosin (j) ribosome Courtesy of David Goodsell, TSRI

25. No? • Bioinformatics • Distant • homologs • Domain • recognition • Bioinformatics • Alignments • Protein-protein • interactions • Protein-ligand • interactions • Motif recognition Automation Better sources • Automation • Bioinformatics • Empirical • rules Software integration Decision Support MAD Phasing Automated fitting Anticipated Developments The Structural Genomics Pipeline (X-ray Crystallography) Basic Steps • Crystallomics • Isolation, • Expression, • Purification, • Crystallization Target Selection Data Collection Structure Solution Structure Refinement Functional Annotation Publish

26. Protein sequences structure info sequence info NR, PFAM SCOP, PDB Prediction of : signal peptides (SignalP, PSORT) transmembrane (TMHMM, PSORT) coiled coils (COILS) low complexity regions (SEG) Building FOLDLIB: ------------------------------------ PDB chains SCOP domains PDP domains CE matches PDB vs. SCOP ----------------------------------- 90% sequence non-identical minimum size 25 aa coverage (90%, gaps <30, ends<30) Structural assignment of domains by PSI-BLAST on FOLDLIB-PRF Only sequences w/out A-prediction Structural assignment of domains by 123D on FOLDLIB-PRF Only sequences w/out A-prediction Create PSI-BLAST profiles for FOLDLIB vs. NR Functional assignment by PFAM, NR, PSIPred assignments FOLDLIB-PRF Domain location prediction by sequence Store assigned regions in the DB The Genome Annotation Pipeline

27. Example - http://arabidopsis.sdsc.edu

28. From the Drug Perspective

29. Combinatorial Libraries • Thousands of variations to a fixed template • Good libraries span large areas of chemical and • conformational space - molecular diversity • Diversity in - steric, electrostatic, hydrophobic interactions... • Desire to be as broad as “Merck” compounds from • random screening • Computer aided library design is in its infancy Blaney and Martin - Curr. Op. In Chem. Biol. (1997) 1:54-59