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

Report
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)
Primary Assays
Biochemical
Cellular
Pharmacological
Physiological
+
Chemical
Hypothesis
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
Hit to Lead
Chemistry
- physical
properties
-in vitro
metabolism
Initial Synthetic
Evaluation
- analytics
- first analogs
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
1200
F ill rate, Mp ixels/s
1000
800
SGI
600
PC cards
400
200
• Fill rates recently growing by
x2 every year
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
0
* Not counting
custom hardware
or special
configurations
Data source: Product literature
Comparing Growth Rates
40
Processor performance growth
35
Increase factor
30
Memory bus speed growth
Pixel fill rate growth
25
20
15
10
5
0
2001
2002 2003
2004
2005
2006 2007
2008
2009 2010
2011
From the Target Perspective
Bioinformatics - A Revolution
Biological Experiment
Collect
Data
Information
Characterize
Knowledge
Compare
Model
Discovery
Infer
Complexity
Higher-life
Technology
1
Organ
10
Brain
Mapping
Model Metaboloic
Pathway of E.coli
Sub-cellular
Structure
102 Neuronal
Modeling
106
Virus
Structure
Ribosome
Human
Genome
Project
Yeast
E.Coli
C.Elegans
Genome Genome Genome
90
1
# People/Web Site
Genetic
Circuits
ESTs
Sequence
(C) Copyright Phil Bourne 1998
100000 Computing
Power
Cardiac
Modeling
Cellular
Assembly
Data
1000
100
Gene Chips
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
Crystallomics
• Isolation,
Target • Expression,
Data
Selection • Purification, Collection
• Crystallization
Bioinformatics
• Distant
homologs
• Domain
recognition
Automation
Bioinformatics
• Empirical
rules
Automation
Better
sources
Anticipated Developments
Structure
Solution
Structure
Refinement
Software integration
Decision Support
MAD Phasing Automated
fitting
Functional
Annotation
Publish
No?
Bioinformatics
• Alignments
• Protein-protein
interactions
• Protein-ligand
interactions
• Motif recognition
structure info
sequence info
SCOP, PDB
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)
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
Structural assignment of domains by
123D on FOLDLIB-PRF
Create PSI-BLAST profiles
for FOLDLIB vs. NR
Only sequences w/out A-prediction
Functional assignment by PFAM, NR,
PSIPred assignments
FOLDLIB-PRF
Domain location prediction by sequence
The Genome Annotation Pipeline
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

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