1. Structural Bioinformatics in Drug Discovery
Melissa Passino

2. Structural Bioinformatics
What is SBI?
“Structural bioinformatics is a subset of bioinformatics concerned with the use of biological structures – proteins, DNA, RNA, ligands etc. and complexes thereof to further our understanding of biological systems.”

3. SBI in Drug Design and Discovery
SBI can be used to examine:
drug targets (usually proteins)
binding of ligands ↓
“rational” drug design
(benefits = saved time and $$$)

4. Traditional Methods of Drug Discovery
natural
(plant-derived) treatment for illness/ailments ↓
isolation of active compound
(small, organic)

5. manipulation of structure to get better drug
synthesis
of compound ↓
manipulation of structure to get better drug
(greater efficacy,
fewer side effects)
Aspirin

6. Modern Methods of Drug Discovery
NEW and IMPROVED!
What’s different?
Drug discovery process begins
with a disease (rather than a treatment)
Use disease model to pinpoint relevant genetic/biological components (i.e. possible drug targets)

7. discovery of a “lead” molecule
Modern Drug Discovery
disease → genetic/biological target ↓
discovery of a “lead” molecule
- design assay to measure function of target
- use assay to look for modulators of target’s function
high throughput screen (HTS)
- to identify “hits” (compounds with binding in low nM to low μM range)

8. Modern Drug Discovery small molecule hits ↓
manipulate structure to increase potency
i.e. decrease Ki to low nM affinity
*optimization of lead molecule into candidate drug*
fulfillment of required pharmacological properties:
potency, absorption, bioavailability, metabolism, safety
clinical trials

9. Interesting facts...
Over 90% of drugs entering clinical trials fail to make it to market
The average cost to bring a new drug to market is estimated at $770 million

10. Impact of Structural Bioinformatics on Drug Discovery
Fig 1 & 2
Fauman et al.
Speeds up key steps in DD process by combining aspects of bioinformatics, structural biology, and structure-based drug design

11. Identifying Targets: The “Druggable Genome”

12. human genome
polysaccharides
nucleic acids
proteins
lipids
Problems with toxicity, specificity, and difficulty in creating potent inhibitors eliminate the first 3 categories...

13. proteins with binding site
human genome
polysaccharides
nucleic acids
proteins
lipids
proteins with binding site
“druggable genome” = subset of genes which express proteins capable of binding small drug-like molecules

14. Relating druggable targets to disease...
Analysis of pharm industry reveals:
Over 400 proteins used as drug targets
Sequence analysis of these proteins shows that most targets fall within a few major gene families (GPCRs, kinases, proteases and peptidases)
Fig. 3, Fauman et al.

15. Is this a “druggable” target?
Assessing Target Druggability
Once a target is defined for your disease of interest, SBI can help answer the question:
Is this a “druggable” target?
Does it have sequence/domains similar to known targets?
Does the target have a site where a drug can bind, and with appropriate affinity?

16. Other roles for SBI in drug discovery
Binding pocket modeling
Lead identification
Similarity with known proteins or ligands
Chemical library design / combinatorial chemistry
Virtual screening
*Lead optimization*
Binding
ADMET

17. SBI in cancer therapy: MMPIs

18. Used to treat “major” cancers: lung, GI, prostate
Inability to control metastasis is the leading cause of death in patients with cancer (Zucker et al. Oncogene. 2000, 19, )
Matrix metalloproteinase inhibitors (MMPIs) are a newer class of cancer therapeutics
can prevent metastasis (but not cytotoxic); may also play role in blocking tumor angiogenesis (growth inhibition)
Used to treat “major” cancers: lung, GI, prostate

19. What is an MMP? Family of over 20 structurally related proteinases
Principal substrates:
protein components of extracellular matrix (collagen, fibronectin, laminin, proteoglycan core protein)
Functions:
Breakdown of connective tissue; tissue remodeling
Role in cancer:
Increased levels/activity of MMPs in area surrounding tumor

20. Brown PD. Breast Cancer Res Treat 1998, 52, 125-136.

21. Whittaker et al. Chem. Rev. 1999, 99, 2735-2776

22. Whittaker et al. Chem. Rev. 1999, 99, 2735-2776
MMP-1,3,8
MMP-2
MMP-7
MMP-10 to 13,19,20
MMP-9
Whittaker et al. Chem. Rev. 1999, 99,
MMP-14
to 17

23. Whittaker et al. Chem. Rev. 1999, 99, 2735-2776
MMP catalysis
“metallo” in MMP = zinc
→ catalytic domain contains 2 zinc atoms
Whittaker et al. Chem. Rev. 1999, 99,

24. Brown PD. Breast Cancer Res Treat 1998, 52, 125-136.
Peptidic inhibitors
Structure based design
based on natural substrate collagen
zinc binding group
Poor Ki values, not very selective (inhibit other MPs)
Brown PD. Breast Cancer Res Treat 1998, 52,

25. Peptidic hydroxamate inhibitors
Specificity for MMPs over other MPs
Better binding (low nM Ki)
But poor oral bioavailability

26. A (not very) long time ago, in a town (not too) far away…
…lived a company named Agouron…
…and this company had a dream, a dream to design a nonpeptidic hydroxamate inhibitor of MMPs…

27. ...so they made some special crystals…
used x-ray crystallography/3D structure of recombinant human MMPs bound to various inhibitors ↓
to determine key a.a. residues, ligand substituents needed for binding
Gelatinase A

28. …and used the magic of structural bioinformatics to design many, many nonpeptidic hydroxylates.
oral bioavailabity
anti-metastasis
anti-growth Ki
repeat…

29. Results… AG3340 “Prinomastat” Good oral bioavailability
Selective for specific MMPs
may implicate their roles in certain cancers

30. Prinomastat
Evidence showing prevention of lung cancer metastasis in rat and mice models
Clinical trials
→ non small cell lung cancer
→ hormone refractory prostate cancer
…stopped at Phase 3 (Aug 2000) because did not show effects against late stage metastasis

31. Morals of the story…
SBI can be used as basis for lead discovery and optimization
MMPs are good targets for chemotherapy to help control metastasis…
…but MMPIs must be combined with other cytotoxic drugs to get maximum benefits, and used at earliest stage possible