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Pharm 202Computer Aided Drug Design
Phil [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
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
0
200
400
600
800
1000
1200
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
Fill
ra
te, M
pix
els
/s
SGI PC cards
* Not counting custom hardware or special configurations
• Fill rates recently growing by x2 every year
Comparing Growth Rates
0
5
10
15
20
25
30
35
40
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Inc
rea
se
fa
cto
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