Phil Bourne bourne@sdsc.edu
http://www.sdsc.edu/pb -> Courses -> Pharm 202
Several slides are taken from UC Berkley Chem 195

• 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

• Defined composition with a pharmacological effect
• Regulated by the Food and Drug
Administration (FDA)
• What is the process of Drug Discovery and
Development?

• 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 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

• 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)

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

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

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)

• Medicine
• Physiology/pathology
• Pharmacology
• Molecular/cellular biology
• Automation/robotics
• Medicinal, analytical,and combinatorial chemistry
• Structural and computational chemistries
• Bioinformatics

• 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

• 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)
– Interferona
(hairy cell leukemia and Hepatitis C)

• Hits and Leads - Is it a “Druggable” target?
• Resistance
• Pharmacodynamics
• Delivery - oral and otherwise
• Metabolism
• Solubility, toxicity
• Patentability

• 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

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

1 2 0 0
1 0 0 0
8 0 0
6 0 0
4 0 0
2 0 0
0
SGI PC cards
* Not counting custom hardware or special configurations
• Fill rates recently growing by x2 every year
Data source: Product literature

40
35
30
25
20
Processor performance growth
Memory bus speed growth
Pixel fill rate growth
15
10
5
0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Bioinformatics - A Revolution
Biological Experiment Data Information Knowledge Discovery
Complexity
Collect Characterize Compare Model Infer
Technology
Higher-life 1 10 100 1000
Data
100000
Computing
Power
Organ Brain
Mapping
Cardiac
Modeling
Cellular
Sub-cellular
10 6
Model Metaboloic
Pathway of E.coli
10 2 Neuronal
Modeling
1
# People/Web Site
Assembly
Virus
Structure
Ribosome
Genetic
Circuits
Structure
Sequence
Human
Genome
Project
ESTs
90
Yeast
Genome
E.Coli
Genome
Gene Chips
C.Elegans
Genome
95 00
Year
(C) Copyright Phil Bourne 1998
1 Small
Genome/Mo.
Human
Genome
05
Sequencing
Technology

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

• 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

Basic Steps
Target
Selection
Crystallomics
• Isolation,
• Expression,
• Purification,
• Crystallization
Data
Collection
Structure
Solution
Structure
Refinement
Functional
Annotation
Publish
Bioinformatics
• Distant homologs
• Domain recognition
Automation
Bioinformatics
• Empirical rules
Automation
Better sources
Software integration
Decision Support
MAD Phasing Automated fitting
Bioinformatics
• Alignments
• Protein-protein interactions
• Protein-ligand interactions
• Motif recognition
No?
Anticipated Developments

structure info
SCOP, PDB sequence info
NR, PFAM
Protein sequences
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)
Create PSI-BLAST profiles for FOLDLIB vs. NR
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
Only sequences w/out A-prediction
Functional assignment by PFAM, NR,
PSIPred assignments
FOLDLIB-PRF
The Genome Annotation Pipeline
Domain location prediction by sequence
Store assigned regions in the DB

Example - http://arabidopsis.sdsc.edu

• 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