ANATOMY AND PHYSIOLOGY OF REPRODUCTIVE SYSTEM.pptx
Drug Design.ppt
1. Pharm 202
Computer 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?
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
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
0
200
400
600
800
1000
1200
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
F
ill
rate,
Mp
ixels/s
SGI PC cards
* Not counting
custom hardware
or special
configurations
• Fill rates recently growing by
x2 every year
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
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
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
30. Statement of the Director, NIGMS, before the House Appropriations
Subcommittee on Labor, HHS, Education Thursday, February 25, 1999
