1. Computer aided drug design
Lecture 12
Structural Bioinformatics
Dr. Avraham Samson
81-871

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?

4. 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

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

8. Drug Discovery Processes - II
Hit to Lead
Chemistry
- physical
properties
-in vitro
metabolism
Secondary
Evaluation
- Mechanism
Of Action
- Dose Response
Initial Hit
Compounds
Initial Synthetic
Evaluation
- analytics
- first analogs
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

15. Chembank database

16. Patchdock

17. From the Computer Perspective

18. 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

19. Growth of pixel fill rates
SGI
PC cards
* Not counting custom hardware or special configurations
Mention Moore’s Law.
Fill rates recently growing by x2 every year
Data source: Product literature

20. Comparing Growth Rates

21. From the Target Perspective

23. Bioinformatics - A Revolution
Biological Experiment Data Information Knowledge Discovery
Collect Characterize Compare Model Infer
Complexity
Technology
Data
Higher-life 1
1000
100000
Computing
Power
Organ
Brain
Mapping
Cardiac
Modeling
Cellular
Model Metaboloic
Pathway of E.coli
Sub-cellular
106
102
Neuronal
Modeling 1
# People/Web Site
Assembly
Virus
Structure
Ribosome
Genetic
Circuits
Structure
Human
Genome
Project
Yeast
Genome
E.Coli
Genome
C.Elegans
Genome
1 Small
Genome/Mo.
Sequencing
Technology
ESTs
Gene Chips
Human
Genome
Sequence 90 95 00 05
Year

24. 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

25. Example - http://arabidopsis.sdsc.edu

26. 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