Pharm 202Computer Aided Drug Design

Phil Bournebourne@sdsc.edu

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

MolecularBiologicalHypothesis(Genomics)

ChemicalHypothesis

PhysiologicalHypothesis

Primary Assays Biochemical Cellular Pharmacological Physiological

Sources of Molecules Natural Products Synthetic Chemicals Combichem Biologicals

+

Initial HitCompoundsScreening

Drug Discovery Processes - II

Initial HitCompounds

SecondaryEvaluation - Mechanism Of Action - Dose Response

Initial SyntheticEvaluation - analytics - first analogs

Hit to LeadChemistry- physicalproperties-in vitrometabolism

First In VivoTests- PK, efficacy,toxicity

Drug Discovery Processes - III

Lead Optimization

PotencySelectivityPhysical PropertiesPKMetabolismOral BioavailabilitySynthetic EaseScalability

Pharmacology

Multiple In Vivo Models

Chronic Dosing

Preliminary Tox

DevelopmentCandidate(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- (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

Data source: Product literature

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1200

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Fill

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te, M

pix

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SGI PC cards

* Not counting custom hardware or special configurations

• Fill rates recently growing by x2 every year

Comparing Growth Rates

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5

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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Inc

rea

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rProcessor performance growth

Memory bus speed growth

Pixel fill rate growth

From the Target Perspective

Bioinformatics - A Revolution

Biological Experiment Data Information Knowledge Discovery

Collect Characterize Compare Model Infer

Sequence

Structure

Assembly

Sub-cellular

Cellular

Organ

Higher-life

Year90 05

Computing Power

SequencingTechnology

Data1 10 100 1000 100000

95 00

Human Genome Project

E.ColiGenome

C.ElegansGenome 1 Small

Genome/Mo.ESTs

YeastGenome

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

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

HistoryHistory

• Strong sense of community ownership

• We are the current custodians

• The community watches our every move

• The community itself is changing

(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

The Structural Genomics Pipeline(X-ray Crystallography)

Basic Steps

Target Selection

Crystallomics• Isolation,• Expression,• Purification,• Crystallization

DataCollection

StructureSolution

StructureRefinement

Functional Annotation Publish

Anticipated Developments

Bioinformatics• Distant homologs • Domain recognition

AutomationBioinformatics• Empirical rules

AutomationBetter sources

Software integrationDecision Support

MAD Phasing Automated fitting

Bioinformatics• Alignments• Protein-protein interactions• Protein-ligand interactions• Motif recognition

No?

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

Example - http://arabidopsis.sdsc.edu

From the Drug Perspective

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

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