Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Lipinski Jmrc Lecture1 Nov2008


Published on

"Academic drug discovery: the chemistry challenges of target choice and screening library selection" presented as lecture 1 of a distinguished lectureship for the Jagiellonian Medical Research Centre, Krakow Poland, Nov 6, 2008 and sponsored by the Kosciuszko Foundation NY.

  • Login to see the comments

Lipinski Jmrc Lecture1 Nov2008

  1. 1. Academic drug discovery: the chemistry challenges of target choice and screening library selection Christopher A. Lipinski Scientific Advisor, Melior Discovery [email_address]
  2. 2. Outline <ul><li>Technology – “good targets” </li></ul><ul><ul><li>biology - chemistry perceptions </li></ul></ul><ul><ul><li>why they occur </li></ul></ul><ul><ul><li>solutions </li></ul></ul><ul><li>People issues – training and world view </li></ul><ul><ul><li>biology - chemistry conflicts </li></ul></ul><ul><ul><li>why they occur </li></ul></ul><ul><ul><li>solutions </li></ul></ul><ul><li>Rules and filters </li></ul><ul><li>Target tractability can improve </li></ul><ul><li>My success formula for academic drug discovery </li></ul>
  3. 3. Defining druggability for targets (1) <ul><li>Chemical druggability </li></ul><ul><ul><li>3000 proteins might bind low MWT ligand </li></ul></ul><ul><li>Biologically relevant druggability </li></ul><ul><ul><li>??? Low 100’s (guess at 10% - 300) </li></ul></ul><ul><li>Medicinally relevant druggability </li></ul><ul><ul><li>??? (guess at less than 100) </li></ul></ul>
  4. 4. Historically few “innovator” targets / year 24 New Ligands in 8 Years Across Everybody
  5. 5. Non combichem innovator drugs
  6. 6. Defining druggability for targets (2) <ul><li>Target exists in the literature </li></ul><ul><li>Literature citation to a ligand exists </li></ul><ul><ul><li>6000 “druggable” targets </li></ul></ul><ul><ul><li>ref. Prous Integrity® </li></ul></ul><ul><ul><li>no drug discovery chemistry quality implied </li></ul></ul><ul><ul><li>e.g. nitropropionate ligand for isocitrate lyase </li></ul></ul>
  7. 7. Target Choice <ul><li>A good target has distinctly different meaning to biology and chemistry personnel </li></ul><ul><li>In a biology sense, a good target is a biological pathway that can be intercepted in some way to give a useful therapeutic outcome </li></ul><ul><li>In a chemistry sense, a good target is a biological pathway that can be intercepted in a useful sense by an orally active small organic molecule </li></ul><ul><li>Interplay of the disciplines leads to success </li></ul>
  8. 8. What is a good target <ul><li>Biology/genomics viewpoint </li></ul><ul><ul><li>must be validated </li></ul></ul><ul><li>Chemistry viewpoint </li></ul><ul><ul><li>low MWT ligand can be found </li></ul></ul><ul><li>Both viewpoints must be correct </li></ul><ul><ul><li>non validated </li></ul></ul><ul><ul><ul><li>chemistry succeeds - worthless </li></ul></ul></ul><ul><ul><li>validated </li></ul></ul><ul><ul><ul><li>chemistry fails - worthless </li></ul></ul></ul>
  9. 9. Biology and Chemoinformatics Biology approach Is this target druggable in chemistry?
  10. 10. Space in screening and ligands <ul><li>Mechanistic screening </li></ul><ul><ul><li>narrow target opportunity space </li></ul></ul><ul><li>Phenotypic screening </li></ul><ul><ul><li>broad target opportunity space </li></ul></ul><ul><li>Typical medicinal chemistry compounds </li></ul><ul><ul><li>broad chemistry space </li></ul></ul><ul><li>Natural products and diversity oriented synthesis </li></ul><ul><ul><li>narrow chemistry space </li></ul></ul>
  11. 11. Matching targets and ligand space <ul><li>Natural products and DOS </li></ul><ul><ul><li>phenotypic targets - good </li></ul></ul><ul><ul><li>mechanistic targets – poor </li></ul></ul><ul><li>Typical medicinal chemistry </li></ul><ul><ul><li>phenotypic targets – excellent: BUT </li></ul></ul><ul><ul><ul><li>mechanism deciphering is tough </li></ul></ul></ul><ul><ul><li>Mechanistic targets – good </li></ul></ul><ul><ul><ul><li>mechanism is known so not an issue </li></ul></ul></ul>
  12. 12. Decline of natural products <ul><li>Similar decision across most of Pharma </li></ul><ul><ul><li>pragmatic, based on results </li></ul></ul><ul><ul><li>chemistry complexity is no advantage unless needed </li></ul></ul><ul><li>Decline coincident with: </li></ul><ul><ul><li>rise of automated chemistry </li></ul></ul><ul><ul><li>demise of phenotypic screening </li></ul></ul><ul><ul><li>decline of infectious disease research </li></ul></ul><ul><li>Resurrection: </li></ul><ul><ul><li>watch for reversal of the above three trends </li></ul></ul>
  13. 13. Phenotypic screening leverage example <ul><li>Phenotypic screening </li></ul><ul><ul><li>enhanced target opportunity space </li></ul></ul><ul><li>Melior Discovery runs 35 phenotypic screens </li></ul><ul><ul><li>highly efficient in-vivo mouse assays </li></ul></ul><ul><ul><li>finds activity in type II diabetes models </li></ul></ul><ul><ul><li>compound is a clinically tested Pfizer anti-ulcer drug </li></ul></ul><ul><ul><li>novel mechanism - Lyn kinase activator </li></ul></ul><ul><li>Wildly lucky or predictable in drug repurposing? </li></ul><ul><ul><li>97 mechanisms for type II diabetes in Prous’ Integrity </li></ul></ul><ul><ul><li>36 targets with low MWT ligand </li></ul></ul>
  14. 14. MLR-1023 aka Tolimidone
  15. 15. Not all targets are equal in screening Reproduced with permission from “Targeting signal transduction with large combinatorial collections”, D. S. Auld, D. Diller, K. Ho, Drug Discovery Today, 2002, 7(24) 1206-13. Size of colored graphic = screening success at Pharmacopeia
  16. 16. Distribution Parameters for 7483 INN/USAN Drugs Define the 90% Limits Corresponding to Properties Unfavorable for Oral Drug Absorption.
  17. 17. The “rule of five” mnemonic <ul><li>Poor absorption or permeation are more likely when there are: </li></ul><ul><li>More than 5 H-bond donors. </li></ul><ul><li>The MWT is over 500. </li></ul><ul><li>The CLog P is over 5 (or MLOGP is over 4.15). </li></ul><ul><li>The sum of N’s and O’s is over 10. </li></ul><ul><li>Substrates for transporters and natural products are exceptions. </li></ul>
  18. 18. Targets, ligands and the rule of 5 <ul><li>Beautiful targets and very do-able </li></ul><ul><ul><li>GPCR’s aminergic </li></ul></ul><ul><ul><li>phosphodiesterases </li></ul></ul><ul><ul><li>kinases </li></ul></ul><ul><li>Difficult targets but still do-able </li></ul><ul><ul><li>GPCR’s peptidergic </li></ul></ul><ul><ul><li>proteases </li></ul></ul><ul><li>Hopeless (or nearly so) targets </li></ul><ul><ul><li>protein protein interactions </li></ul></ul><ul><ul><li>phosphatases </li></ul></ul>
  19. 19. Target tractability can change <ul><li>Protein-protein interactions </li></ul><ul><ul><li>hopeless from an HTS screening viewpoint </li></ul></ul><ul><li>Scientific advances </li></ul><ul><ul><li>fragment screening </li></ul></ul><ul><ul><li>SAR by nmr and x-ray </li></ul></ul><ul><ul><li>Bcl-2 family success from Abbott </li></ul></ul>
  20. 20. Protein protein ligand garbage Lepourcelet, Maina; Chen, Ying-Nan P.; France, Dennis S.; Wang, Huisheng; Crews, Phillip; Petersen, Frank; Bruseo, Charles; Wood, Alexander W.; Shivdasani, Ramesh A. Small-molecule antagonists of the oncogenic Tcf/  -catenin protein complex. Cancer Cell (2004), 5(1), 91-102.
  21. 21. Protein protein ligand ABT-737 Bruncko, Milan; Oost, Thorsten K.; Belli, Barbara A.; Ding, Hong; Joseph, Mary K.; Kunzer, Aaron; Martineau, Darlene; McClellan, William J.; Mitten, Michael; Ng, Shi-Chung; Nimmer, Paul M.; Oltersdorf, Tilman; Park, Cheol-Min; Petros, Andrew M.; Shoemaker, Alexander R.; Song, Xiaohong; Wang, Xilu; Wendt, Michael D.; Zhang, Haichao; Fesik, Stephen W.; Rosenberg, Saul H.; Elmore, Steven W. Studies Leading to Potent, Dual Inhibitors of Bcl-2 and Bcl-xL. Journal of Medicinal Chemistry (2007), 50(4), 641-662.
  22. 22. Nice chemistry: topology in DOS libraries
  23. 23. Bad chemistry: aggregation false positives in HTS assays Remove these types of compounds from any assays These looked good to PHARMACIA screeners
  24. 24. Chemistry “quality” and biology effort > 80% flawed compounds?
  25. 25. The amygdala and emotional memory <ul><li>Pattern recognition is evolutionarily selected </li></ul><ul><li>Chemists are particularly social </li></ul><ul><li>Chemists are superb at pattern recognition </li></ul><ul><li>Structure of “emotionally significant” compounds is locked into the amygdala </li></ul><ul><li>Biologists just do not understand this </li></ul>
  26. 26. Incomprehensible “ Reality” depiction in chemistry is incomprehensible to biology / genomics and vice versa. Chemistry much closer to pathology than to biology / genomics. Graphical pattern recognition is the forte of chemists
  27. 27. Chemistry pattern recognition
  28. 28. Screening - what is the goal? <ul><li>Target validation </li></ul><ul><ul><li>tool like compounds </li></ul></ul><ul><ul><li>relaxed chemistry criteria permissible </li></ul></ul><ul><ul><li>chemical biology </li></ul></ul><ul><li>Drug discovery </li></ul><ul><ul><li>lead-like or drug-like compounds </li></ul></ul><ul><ul><li>strict chemistry criteria necessary </li></ul></ul><ul><ul><li>needs pharma skills </li></ul></ul>
  29. 29. Target validation versus drug discovery <ul><li>Use a chemical tool to probe biology </li></ul><ul><ul><li>relaxed chemistry criteria permissible </li></ul></ul><ul><ul><li>chemical costs go down </li></ul></ul><ul><ul><li>50,000 or fewer compounds in HTS </li></ul></ul><ul><li>Drug discovery </li></ul><ul><ul><li>strict chemistry criteria </li></ul></ul><ul><ul><li>cost as high as $200-400 for 15 mg compound </li></ul></ul><ul><ul><li>500,000 compounds in an HTS </li></ul></ul>
  30. 30. Tools for target validation <ul><li>Selectivity is paramount </li></ul><ul><li>Covalent functionality can be OK </li></ul><ul><li>But in a complex structure </li></ul>
  31. 31. Questioning diversity <ul><li>How many targets are there? </li></ul><ul><li>23,000 genes, 10 6 proteins </li></ul><ul><li>How many MWT 500 cpds in a human? </li></ul><ul><li>200 moles x 6.02 *10 23 = 10 26 </li></ul><ul><li>Diverse compounds = 10 60 </li></ul><ul><li>Compounds / targets = 10 -34 (1 hit / target) </li></ul><ul><li>Compounds / targets = 10 -25 (1 billion hits/ target) </li></ul><ul><li>Truly diverse library should never give a hit </li></ul>
  32. 32. True diversity does not exist <ul><li>HTS does indeed find hits </li></ul><ul><li>True diversity does exist in silico </li></ul><ul><li>True diversity does not exist experimentally </li></ul><ul><ul><li>chemistry success bias </li></ul></ul><ul><ul><li>reagent access bias </li></ul></ul><ul><ul><li>people selection bias </li></ul></ul><ul><li>Involve medicinal chemists in screening library choices </li></ul>
  33. 33. Chemistry space, drug-like and tool-like December 16, 2004 Nature
  34. 34. Sparse activity in chemistry scaffold space Quest for the Rings. In Silico Exploration of Ring Universe to Identify Novel Bioactive Scaffolds, Ertl et al. J. Med Chem., (2006), 49(15), 4568-4573 .
  35. 35. Sparse oral activity in property space Global mapping of pharmacological space. Paolini et al., Nature Biotechnology (2006), 24(7), 805-815.
  36. 36. Success formula for academic drug discovery <ul><li>Leave the biology alone </li></ul><ul><ul><li>in depth biology is the core strength </li></ul></ul><ul><li>Competent medicinal chemistry </li></ul><ul><ul><li>people with industrial experience </li></ul></ul><ul><ul><li>good screening libraries </li></ul></ul><ul><li>Competent screening facility </li></ul><ul><li>Competent project management </li></ul><ul><li>Access to experts as needed </li></ul>
  37. 37. Achnowledgement The financial support of the Kosciuszko Foundation is gratefully acknowledged The Kosciuszko Foundation, An American Center for Polish Culture, Promoting Educational and Cultural Exchanges and Relations Between the United States and Poland since 1925.