Your SlideShare is downloading. ×
0
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuity

317

Published on

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 …

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.

Published in: Science, Technology, Business
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
317
On Slideshare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
11
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 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
  • 7. Determining Ligand Similarity 7 Keiser, M. J. et al. Nat Biotechnol 2007, 25, 197-206.
  • 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). 11
  • 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.” 12 http://www.nobelprize.org/nobel_prizes/chemistry/laureates/2013/
  • 13. Questions? 13

×