Sunday (2) lipinski


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Sunday (2) lipinski

  1. 1. Where is drug discovery going? Christopher A. Lipinski Scientific Advisor, Melior Discovery [email_address] DDND 2012 Lipinski keynote
  2. 2. Outline <ul><li>Academic targets and the translational gap </li></ul><ul><ul><li>is it just a missing resource issue? </li></ul></ul><ul><li>Chemistry & attrition - worse with time </li></ul><ul><ul><li>reductionism , genomics, HTS to blame? </li></ul></ul><ul><li>Screening diverse compounds </li></ul><ul><ul><li>the worst way to discover a drug </li></ul></ul><ul><ul><li>novelty drive comes from patents and not science </li></ul></ul><ul><li>Biology and chemistry networks analysis </li></ul><ul><ul><li>chemistry due diligence on leads is essential </li></ul></ul><ul><li>What to look for </li></ul>DDND 2012 Lipinski keynote
  3. 3. Drivers for discovery changes <ul><li>Chemistry , 65% successful predictivity </li></ul><ul><ul><li>rules and filters, eg. phys chem, structural </li></ul></ul><ul><ul><li>ADME predictivity worsens outside of RO5 space </li></ul></ul><ul><li>Safety , 50% successful predictivity </li></ul><ul><li>Efficacy , 10% successful predictivity </li></ul><ul><li>Tackle efficacy using academic collaborations </li></ul><ul><ul><li>systems biology still too new to save us </li></ul></ul><ul><ul><li>target quality is most likely from rich biology </li></ul></ul>DDND 2012 Lipinski keynote
  4. 4. Death Valley California DDND 2012 Lipinski keynote
  5. 5. Translational valley of death DDND 2012 Lipinski keynote &quot;curing disease is a byproduct of the [NIH] system and not a goal,&quot; says FasterCures' Simon. Most scientists don't want to and don't have the skills to translate a discovery into a treatment; researchers at a dedicated center would try to do that full-time.
  6. 6. Death valley, politically correct causes? <ul><li>Academics lack drug discovery skills </li></ul><ul><li>Requires industry / academic collaboration </li></ul><ul><ul><li>eg. medicinal chemists are mostly in industry </li></ul></ul><ul><li>No access to ADMET, drug met, pharm sci etc. </li></ul><ul><ul><li>critical disciplines not in academia </li></ul></ul><ul><li>No access to preclinical – clinical interface skills </li></ul><ul><ul><li>eg. analytical, process chemistry, formulation </li></ul></ul><ul><li>No access to early development skills </li></ul><ul><ul><li>eg. toxicology, biomarkers, project management </li></ul></ul>DDND 2012 Lipinski keynote
  7. 7. Death valley, politically incorrect causes? <ul><li>Assumption - academic ideas on new targets are of high quality </li></ul><ul><li>WRONG </li></ul><ul><li>Bayer analysis of validation of academic targets </li></ul><ul><li>50 % of academic targets are wrong </li></ul><ul><li>25% of academic targets are partially flawed </li></ul><ul><li>Translational death valley exists (in part) because of poor quality academic target identification </li></ul>DDND 2012 Lipinski keynote
  8. 8. Why the academic target problem <ul><li>Culprit is primarily the pressure to publish to support both grant applications and career development </li></ul><ul><li>A people problem </li></ul><ul><li>A government problem </li></ul><ul><li>Exacerbated by hypothesis driven research </li></ul><ul><li>The positive: infrastructure collaboration </li></ul>DDND 2012 Lipinski keynote
  9. 9. Bayer observation in NRDD DDND 2012 Lipinski keynote
  10. 10. Has drug discovery gone wrong? <ul><li>Prevailing mantra: identify a mechanism and discover a selective ligand for a single target </li></ul><ul><li>Counter responses: </li></ul><ul><ul><li>Phenotypic screening </li></ul></ul><ul><ul><li>Drug repurposing </li></ul></ul><ul><ul><li>Multi targeted drug discovery </li></ul></ul><ul><ul><li>In-vivo screening </li></ul></ul><ul><ul><li>Non target non mechanism screening </li></ul></ul>DDND 2012 Lipinski keynote
  11. 11. Genomics – Chemistry parallel <ul><li>Genome sequence deciphered in 2000 </li></ul><ul><li>Automated chemistry starts in 1992 </li></ul><ul><li>Misapplied, both impeded drug discovery </li></ul><ul><ul><li>“ The DNA reductionist viewpoint of the molecular genetics community has set drug discovery back by 10-15 years” Craig Venter quote </li></ul></ul><ul><ul><li>“ In 1992-1997 if you had stored combinatorial chemistry libraries in giant garbage dumpsters you would have much improved drug discovery productivity” Chris Lipinski quote </li></ul></ul>DDND 2012 Lipinski keynote
  12. 12. Genomics / HTS science madness <ul><li>Collaborations to mine genomic targets </li></ul><ul><li>Massive HTS campaigns to discover ligands </li></ul><ul><li>500 different targets, a million data points </li></ul><ul><li>“ a wish to screen 100,000 compounds per day in a drug discovery factory and a wish to make a drug for each target” </li></ul><ul><li>Drug discovery and development using chemical genomics. A. Sehgal, Curr Opin in Drug Disc & Dev (2002), 5(4), 526-531. </li></ul><ul><li>The drug discovery factory : an inevitable evolutionary consequence of high throughput parallel processing. R. Archer, Nat Biotech (1999), 17(9), 834. </li></ul><ul><li>  </li></ul>DDND 2012 Lipinski keynote
  13. 13. Genomics financial madness DDND 2012 Lipinski keynote 1% success, NPV $34M, Decision Resources March 29, 2004
  14. 14. Target-based drug discovery: Slide thanks to Andrew Reaume, Melior Discovery DDND 2012 Lipinski keynote E1 E5 R2 R3 R4 R5 R6 R1 E2 E3 E4 E7 E6 DP 1 DP 2 D1 D2
  15. 15. … .the real picture R8 DP 5 Slide thanks to Andrew Reaume, Melior Discovery DDND 2012 Lipinski keynote E10 E9 E8 E1 E5 R2 R3 R4 R5 R6 R1 E2 E3 E4 E7 E6 DP 1 DP 2 R7 R9 R10 R11 R12 DP 3 DP 4 E7 E8 D1 D2
  16. 16. 50 years of medicinal chemistry DDND 2012 Lipinski keynote What Do Medicinal Chemists Actually Make? A 50-Year Retrospective Pat Walters et al. J Med Chem 2011
  17. 17. Attrition rates by phase The Productivity Crisis in Pharmaceutical R&D, Fabio Pammolli, Laura Magazzini and Massimo Riccaboni, Nature Reviews Drug Discovery 2011 (10) 428-438. DDND 2012 Lipinski keynote
  18. 18. Nanomolar is not necessary DDND 2012 Lipinski keynote Mean po dose is 47 mg Mean pXC 50 is 7.3 (IC 50 5 x 10 -8 ) Gleeson, M. Paul; Hersey, Anne; Montanari, Dino; Overington, John. Probing the links between in vitro potency, ADMET and physicochemical parameters. Nature Reviews Drug Discovery (2011), 10(3), 197-208.
  19. 19. Phenotypic screening advantage The majority of small-molecule first-in-class NMEs that were discovered between 1999 and 2008 were first discovered using phenotypic assays (FIG. 2): 28 of the first-in-class NMEs came from phenotypic screening approaches, compared with 17 from target-based approaches. How were new medicines discovered? David C. Swinney and Jason Anthony Nature Reviews Drug Discovery 2011 (10) 507-519. DDND 2012 Lipinski keynote
  20. 20. Phenotypic screening <ul><li>Finally government is paying attention </li></ul><ul><li>NIH new institute TRND </li></ul><ul><li>25% of assays are reserved for phenotypic screening </li></ul>DDND 2012 Lipinski keynote
  21. 21. Chemistry novelty is harmful <ul><li>Patents direct towards chemistry novelty </li></ul><ul><li>Chemistry novelty correlates with decreased drug discovery success </li></ul><ul><li>“ The role of the patent system in promoting pharmaceutical innovation is widely seen as a tremendous success story. This view overlooks a serious shortcoming in the drug patent system: the standards by which drugs are deemed unpatentable under the novelty and non-obviousness requirement bear little relationship to the social value of those drugs or the need for a patent to motivate their development” Benjamin N. Roin, Texas Law Review </li></ul>DDND 2012 Lipinski keynote
  22. 22. Screening diverse compounds is the worst way to discover a drug <ul><li>Every publication I know of argues that biologically active compounds are not uniformly distributed through chemistry space </li></ul>DDND 2012 Lipinski keynote
  23. 23. Do drug structure networks map on biology networks? DDND 2012 Lipinski keynote
  24. 24. Chemistry drug class network DDND 2012 Lipinski keynote
  25. 25. Network comparison conclusions <ul><li>“ A startling result from our initial work on pharmacological networks was the observation that networks based on ligand similarities differed greatly from those based on the sequence identities among their targets.” </li></ul><ul><li>“ Biological targets may be related by their ligands, leading to connections unanticipated by bioinformatics similarities.” </li></ul>DDND 2012 Lipinski keynote
  26. 26. What is going on? <ul><li>Old maxim: Similar biology implies similar chemistry </li></ul><ul><li>If strictly true biology and chemistry networks should coincide </li></ul>DDND 2012 Lipinski keynote
  27. 27. Network comparisons – meaning? <ul><li>“ Structure of the ligand reflects the target” </li></ul><ul><li>Evolution selects target structure to perform a useful biological function </li></ul><ul><li>Useful target structure is retained against a breadth of biology </li></ul><ul><li>Conservation in chemistry binding motifs </li></ul><ul><li>Conservation in motifs where chemistry binding is not evolutionarily desired </li></ul><ul><ul><li>eg. protein – protein interactions </li></ul></ul>DDND 2012 Lipinski keynote
  28. 28. Hit / lead implications <ul><li>You have a screening hit. SAR on the historical chemistry of your hit can be useful even if it comes from a different biology area </li></ul><ul><li>Medicinal chemistry principles outside of your current biology target can be extrapolated to the ligand chemistry (but not biology) of the new target </li></ul><ul><li>Medicinal chemistry due diligence is essential </li></ul>DDND 2012 Lipinski keynote
  29. 29. Changes in drug discovery <ul><li>Questioning of reductionist approach </li></ul><ul><li>A positive development in CNS drug discovery </li></ul><ul><li>Very few CNS agents are found rationally </li></ul><ul><li>Experimental observations in the clinic </li></ul><ul><li>Multiple Sclerosis as a paradigm </li></ul><ul><li>No drugs until disease progression biomarkers </li></ul><ul><li>Multiple MS drugs recently available </li></ul>DDND 2012 Lipinski keynote
  30. 30. What to look for <ul><li>Disease progression biomarkers </li></ul><ul><ul><li>first impact in drug discovery </li></ul></ul><ul><ul><li>later impact when therapy arrives </li></ul></ul><ul><li>Orphanization of disease diagnosis </li></ul><ul><ul><li>new drugs or fitting patients to current drugs? </li></ul></ul><ul><ul><li>challenges to cost structures </li></ul></ul><ul><li>Exploring drug or target combinations </li></ul>DDND 2012 Lipinski keynote