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    Add as FriendComputer Aided Drug Design

    by: Rogers

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    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 Molecular Biological Hypothesis (Genomics) Chemical Hypothesis Physiological Hypothesis Primary Assays Biochemical Cellular Pharmacological Physiological Sources of Molecules Natural Products Synthetic Chemicals Combichem Biologicals + Initial Hit Compounds Screening
    8 : Drug Discovery Processes - II Initial Hit Compounds Secondary Evaluation - Mechanism Of Action - Dose Response Initial Synthetic Evaluation - analytics - first analogs Hit to Lead Chemistry - physical properties -in vitro metabolism 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 Data source: Product literature SGI PC cards * Not counting custom hardware or special configurations Fill rates recently growing by x2 every year
    18 : Comparing Growth Rates
    19 : From the Target Perspective
    20 :
    21 : Bioinformatics - A Revolution Biological Experiment Data Information Knowledge Discovery Collect Characterize Compare Model Infer Sequence Structure Assembly Sub-cellular Cellular Organ Higher-life Year 90 05 Computing Power Sequencing Technology Data 1 10 100 1000 100000 95 00 Human Genome Project E.Coli Genome C.Elegans Genome 1 Small Genome/Mo. ESTs Yeast Genome Gene Chips Virus Structure Ribosome Model Metaboloic Pathway of E.coli Complexity Technology Brain Mapping Genetic Circuits Neuronal Modeling Cardiac Modeling Human Genome # People/Web Site (C) Copyright Phil Bourne 1998 106 102 1
    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 : (a) myoglobin (b) hemoglobin (c) lysozyme (d) transfer RNA (e) antibodies (f) viruses (g) actin (h) the nucleosome (i) myosin (j) ribosome Status - Numbers and Complexity Courtesy of David Goodsell, TSRI
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    26 : Protein sequences Prediction of : signal peptides (SignalP, PSORT) transmembrane (TMHMM, PSORT) coiled coils (COILS) low complexity regions (SEG) Structural assignment of domains by PSI-BLAST on FOLDLIB-PRF Only sequences w/out A-prediction Only sequences w/out A-prediction Structural assignment of domains by 123D on FOLDLIB-PRF Create PSI-BLAST profiles for FOLDLIB vs. NR Store assigned regions in the DB Functional assignment by PFAM, NR, PSIPred assignments SCOP, PDB FOLDLIB-PRF NR, PFAM 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) Domain location prediction by sequence structure info sequence info The Genome Annotation Pipeline
    27 : Example - http://arabidopsis.sdsc.edu
    28 : From the Drug Perspective
    29 : Combinatorial Libraries Blaney and Martin - Curr. Op. In Chem. Biol. (1997) 1:54-59 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
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