Pharm 202 Computer Aided Drug Design

Phil Bourne [email protected]

http://www.sdsc.edu/pb -> Courses -> Pharm 202

Several slides are taken from UC Berkley Chem 195

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

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?

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

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

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)

Drug Discovery Processes Today

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

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

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)

Drug Discovery Disciplines

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

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

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)

Issues in Drug Discovery

• Hits and Leads - Is it a “Druggable” target?

• Resistance • Pharmacodynamics • Delivery - oral and otherwise • Metabolism • Solubility, toxicity • Patentability

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

From the Computer Perspective

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

Growth of pixel fill rates

1 2 0 0 1 0 0 0 8 0 0 6 0 0 4 0 0 2 0 0 0 SGI PC cards

* Not counting custom hardware or special configurations

• Fill rates recently growing by x2 every year

Data source:

Product literature

Comparing Growth Rates

40 35 30 25 20 Processor performance growth Memory bus speed growth Pixel fill rate growth 15 10 5 0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

From the Target Perspective

Bioinformatics - A Revolution

Biological Experiment Data

Information Knowledge Discovery Complexity

Collect

Characterize Compare Model Infer Technology

Higher-life 1 10 100 1000

Data

100000 Computing Power Organ Brain Mapping Cardiac Modeling Cellular Sub-cellular 10 6 Model Metaboloic Pathway of E.coli

10 2 Neuronal Modeling 1 # People/Web Site Assembly Virus Structure Ribosome Genetic Circuits Structure Sequence (C) Copyright Phil Bourne 1998 Human Genome Project ESTs 90 Yeast Genome E.Coli

Genome Gene Chips C.Elegans

Genome 95 00 Year 1 Small Genome/Mo.

Human Genome 05 Sequencing Technology

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

History

History

• Strong sense of

community ownership

• We are the current

custodians

• The community

watches our every move

• The community

itself is changing

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

The Structural Genomics Pipeline (X-ray Crystallography)

Basic Steps

Target Selection Crystallomics

• Isolation, • Expression, • Purification, • Crystallization

Data Collection Structure Solution Structure Refinement Functional Annotation Publish Bioinformatics

• Distant homologs • Domain recognition

Automation Bioinformatics

• Empirical rules

Automation Better sources Software integration Decision Support

MAD Phasing Automated fitting

Bioinformatics

• Alignments • Protein-protein interactions • Protein-ligand interactions • Motif recognition No?

Anticipated Developments

structure info SCOP, PDB sequence info NR, PFAM Protein sequences 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) Create PSI-BLAST profiles for FOLDLIB vs. NR 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 Structural assignment of domains by 123D on FOLDLIB-PRF Only sequences w/out A-prediction Functional assignment by PFAM, NR, PSIPred assignments FOLDLIB-PRF The Genome Annotation Pipeline Domain location prediction by sequence Store assigned regions in the DB

Example - http://arabidopsis.sdsc.edu

From the Drug Perspective

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

Statement of the Director, NIGMS, before the House Appropriations Subcommittee on Labor, HHS, Education Thursday, February 25, 1999