This is a presentation that I gave for my chemistry seminar class last month on using ligand-comparison techniques to predict off-target effects in drug candidates early in the drug discovery pipeline.
Selaginella: features, morphology ,anatomy and reproduction.
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
1. Acceleration of Novel Drug Design via
Prediction of Drug Candidate Promiscuity
Tamas Nagy
Department of Chemistry
Department of Computer Science
University of Kentucky
Lexington, KY, USA 40508
March 26th, 2014
2. De novo drug discovery is difficult
• Despite dramatic increases in expenditure, R&D productivity in the
pharmaceutical industry is down
2
Ashburn, T. T.; Thor, K. B. Nat Rev Drug Discov 2004, 3, 673-683.
3. De novo drug discovery is difficult
• It is a rare case in modern drug discovery that an unmodified natural
product (e.g. taxol) becomes a drug.
• Process is long and fraught with complications
– 10-17 years from start to finish
– <10% overall probability of success
3
Ashburn, T. T.; Thor, K. B. Nat Rev Drug Discov 2004, 3, 673-683.
Jorgensen, W. L. Science 2004, 303, 1813-1818.
4. Increasing success by understanding drug
candidate polypharmacology in silico
• Speed up process via protein-ligand binding studies that can
elucidate the polypharmacology of drug candidates, i.e. their
tendency to bind multiple targets. Eliminate those that may have off-
target effects early.
– E.g. Molecular docking studies
• Limited by crystal structure availability
• Alternative: search for similarity between
ligand and drug structure instead.
4
Jorgensen, W. L. Science 2004, 303, 1813-1818.
Okimoto, N. et al. PLoS Comput Biol 2009, 5, e1000528.
Hopkins, A. L. Nature 2009, 462, 167-168.
5. Determining Ligand Similarity
• The Tanimoto coefficient relates the similarities of two sets A and B:
5
Tc =
χA ∩ χB
χA ∪ χB
Krasowski, M. D. et al. BMC Emerg Med 2009, 9, 5.
Willett, P. et al. J Chem Inf Comput Sci 1998, 38, 983-996.
6. Determining Ligand Similarity
6
Keiser, M. J. et al. Nat Biotechnol 2007, 25, 197-206.
• Comparing the 216 ligands of
Dihydrofolate reductase (DHFR) with:
– Themselves
• 4.7% of ligand pairs had Tc scores between
0.6-1.0
– The 253 ligands of the similar functionality
TS antifolate enzyme
• 1.6% of ligand pairs had Tc 0.6-1.0
– The 1226 ligands of the unrelated
protease thrombin.
• 0% of ligand pairs had Tc 0.6-1.0
8. 8
Keiser, M. J. et al. Nature 2009, 462, 175-181.
Prediction of drug promiscuity via similarity
ensemble approach (SEA)
3,665 drugs tested against 246 protein targets
(~1,000,000 drug-target combinations)
9. Experimental confirmation of predicted drug
promiscuity results
• Radioligand competition
binding assays for select
drugs (30 in total)
– Confirm Prozac’s novel
interaction with β adrenergic
receptors
– Doralese shows higher
affinity (Ki of 18nM) for the
off target D4 receptor than its
actual α1 adrenergic
receptors
9
Keiser, M. J. et al. Nature 2009, 462, 175-181.
O
O
OH
HO
HN
Kalgut
N
N
Fabahistin
N
+
Prantal
N
H
N
N,N-dimethyltryptam
NH
N
O
HN
Doralese
F
10. Novel off-target effects in common, over-the-
counter drugs
10
Keiser, M. J. et al. Nature
2009, 462, 175-181.
11. Conclusions
• Using the SEA method of ligand fingerprinting is an effective manner
of predicting drug promiscuity and likely can be applied to ranking
drug candidates.
– Limits potential side effects that may not show up till human trials
• It is not without its weaknesses
– It compares drugs to ligand sets based on all shared chemical patterns instead of
ones unique to specific binding sites (i.e. pharmacophores).
– Method susceptible to false-positives (7 of 30 drugs were not active with
predicted off-targets).
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12. Digression
• Last year’s Nobel Prize in Chemistry was the first to recognize the
field of computational chemistry.
• Martin Karplus, Michael Levitt, and Arieh Warshel shared the prize
“for the development of multi-scale models for complex chemical
systems.”
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http://www.nobelprize.org/nobel_prizes/chemistry/laureates/2013/
