Sunday fillet lipinski

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  • Figure 2 | The distribution of new drugs discovered between 1999 and 2008, according to the discovery strategy. The graph illustrates the number of new molecular entities (NMEs) in each category. Phenotypic screening was the most successful approach for first-in-class drugs, whereas target-based screening was the most successful for follower drugs during the period of this analysis. The total number of medicines that were discovered via phenotypic assays was similar for first-in-class and follower drugs — 28 and 30, respectively — whereas the total number of medicines that were discovered via target-based screening was nearly five times higher for follower drugs versus first-in-class drugs (83 to 17, respectively). Nature drug discovery july 2011
  • Sunday fillet lipinski

    1. 1. THANK YOU!Funding for this conference was made possible in part by Cooperative Agreement U13AG031125-05 from the National Institute on Aging. The views expressed in written conference materials or publications and by speakers andmoderators do not necessarily reflect the official policies of the Department of Health and Human Services; nor does mention by trade names, commercial practices, or organizations imply endorsement by the U.S. Government.
    2. 2. Thank You!MEETING SPONSORS BRONZE SPONSORS
    3. 3. SCIENTIFIC ADVISORY COMMITTEE SCIENTIFIC ADVISORY COMMITTEEKurt R. Brunden, PhD, University of PennsylvaniaNeil S. Buckholtz, PhD, National Institute on AgingRebecca Farkas, PhD, National Institute of Neurological Disorders and StrokeHoward Fillit, MD, Alzheimer’s Drug Discovery FoundationBrian Fiske, PhD, Michael J. Fox Foundation for Parkinson’s ResearchMark Frasier, PhD, Michael J. Fox Foundation for Parkinson’s ResearchAbram Goldfinger, MBA, New York UniversityLorenzo Refolo, PhD, National Institute on AgingSuzana Petanceska, PhD, National Institute on AgingDiana Shineman, PhD, Alzheimer’s Drug Discovery FoundationEdward G. Spack, PhD, Fast Forward, LLCD. Martin Watterson, PhD, Northwestern University
    4. 4. ADDF Staff• Diana Shineman, PhD – Assistant Director, Scientific Affairs• Rachel Lane, PhD – Scientific Program Manager• Filomena Machleder – Assistant Director, Institutional Partnerships• Natalie Romatz – Partnerships Assistant, Institutional Partnerships• Niyati Thakker – Grants Assistant• World Events Forum – Conference Secretariat
    5. 5. NOTES Please remember to complete and submit the meeting survey! CME Certificates available at the Registration DeskA webcast of the conference will be available soon on our website: www.alzdiscovery.org
    6. 6. SAVE THE DATE!13th International Conference on Alzheimer’s Drug Discovery September 10-11, 2012 • Jersey City, NJ across from NYC on the Hudson River
    7. 7. Goals of the Meeting• Knowledge: – The principles and practice of drug discovery, with a focus on the unique aspects for neurodegenerative diseases• Network: – >190 attendees from 20 countries, ~40% from industry – Exchange ideas, foster alliances, partnerships and collaborations
    8. 8. Neurodegenerative Diseases Affect >22 Million Worldwide Some symptomatic agents, few disease modifying drugs Multiple Huntington’s, 30, sclerosis, 400,00 000 ALS, 30,000 0• WHO estimatesneurodegenerative disorders will Parkinson’sbe the major unmet disease, 1,000,00 0medical need of the 21stcentury,• surpassing cancer as the Alzheimer’s disease, 5,000,00worlds’ second leading cause of 0death by the year 2040
    9. 9. Drug Discovery is a Vital Stage in Drug Development When Innovation is Created Proof Safety and Proof Innovation of Mechanism Proof of Concept of Efficacy ANIMAL BIOLOGY STUDIES and AND CHEMISTRY HUMAN STUDIES PHARMACOLOGY10,000 to 1 FDA>1 million Approvedchemicals Drug Developing a Drug is Risky, Takes 12-15 years and Costs Over $1.2B
    10. 10. Opportunity and Challenges for Success:A Perspective On The Origin of FDA Approved Drugs 20,000 human genes ~50M compounds in Chem Abstracts; 100,000 proteins 1040-10100 possible small molecules ~10,000 approved drugs Most are variants on formulation and delivery Many anti-microbials Less than 500 distinct chemical entities Targeting ~266 human genome derived proteins Less than 50 unique chemical scaffolds From: T. Bartfai and GV Lees, Drug Discovery from Bedside to Wall Street, 2006; Le Couteur, et al 2011
    11. 11. How a Biologist Thinks About Drug Discovery: Many Targets for Neurodegeneration?• Deposits of Misfolded Protein – Β-Amyloid, tau, α-synuclein, TDP-43, poly-Q aggregates• Oxidative stress• Inflammation• Mitochondrial dysfunction• Synaptic and neuronal cell dysfunction• Vascular ischemia and damage• Other novel mechanisms (eg. epigenetics)
    12. 12. How a Chemist Thinks About Targets for Drug Discovery: Success Rates of Target Types• Target types – GPCR (small ligand) High – Enzyme (small ligand) – Ion channel – Nuclear receptor – Protease Success – Enzyme (large ligand) – GPCR (large ligand) – Cytotoxic (other) – Protein kinase – Protein-protein Low
    13. 13. Why A Biological Network Approach to Drug Discovery is Needed: Signaling in the Synapse is Complex
    14. 14. How Were New Drugs Discovered?Phenotypic Screening Vs. Target-based Screening Swinney, et al, Nature Reviews Drug Discovery, July, 2011
    15. 15. Case Studies: Routes to Drug Discoverybeta-secretase inhibitors gamma-secretase inhibitors Inhibitor Development Rational design approach Screening approach Assay development generation of protein High throughput screen (500,000 cpds.) Identification of hits Crystal Com puter Structure Model Selection of leads Focused Medicinal Chem istry Potency Medicinal Chemistry Specificity PK Test for in v o activ iv ity
    16. 16. Improving Success Rates? Drug Discovery in Academia• Drug discovery is the interface between basic research and clinical development• Requires extensive resources and collaboration between teams of investigators• Increasingly requires partnerships between pharma, biotechs, non-profits, and government, especially for neurodegenerative diseases
    17. 17. Drug Discovery and Development Requires Multidisciplinary Teams of Scientists Clinical Trialists Clinical Development IND enabling studies: ADMET, Pharamaceutical Scientists formulation and scale-up chemistry Animal Trialists In vivo Testing and Biomarker Development Preclinical Proof of MechanismMedicinal Chemistry, Pharmacology Lead Identification and optimization Assay Development High Throughput Structure Based Chemical Libraries Screening Chemistry Computational Chemistry Basic Neurobiology Target identification
    18. 18. Feeding the Pipeline: The Alzheimer’s Drug Discovery Foundation The ADDF has granted over $55 million to >370Alzheimer’s drug discovery programs in academic centers and biotechnology companies in 20 countries ADDF funding has resulted in >$2 billion in follow-on commitments, and several novel drugs entering clinical trials www.AlzDiscovery.org
    19. 19. Drug Discovery: The “Valley of Death”?Or “Welcome to An Amazing Journey”!
    20. 20. Where is drug discovery going? Christopher A. Lipinski Scientific Advisor, Melior Discovery clipinski@meliordiscovery.com DDND 2012 Lipinski keynote 20
    21. 21. Outline• Academic targets and the translational gap –is it just a missing resource issue?• Chemistry & attrition - worse with time –reductionism , genomics, HTS to blame?• Screening diverse compounds –the worst way to discover a drug –novelty drive comes from patents and not science• Biology and chemistry networks analysis –chemistry due diligence on leads is essential• What to look for DDND 2012 Lipinski keynote 21
    22. 22. Drivers for discovery changes• Chemistry, 65% successful predictivity • rules and filters, eg. phys chem, structural • ADME predictivity worsens outside of RO5 space• Safety, 50% successful predictivity• Efficacy, 10% successful predictivity• Tackle efficacy using academic collaborations • systems biology still too new to save us • target quality is most likely from rich biology DDND 2012 Lipinski keynote 22
    23. 23. Death Valley California DDND 2012 Lipinski keynote 23
    24. 24. Translational valley of death"curing disease is a byproduct of the [NIH] system and not a goal," saysFasterCures Simon. Most scientists dont want to and dont have the skills totranslate a discovery into a treatment; researchers at a dedicated center wouldtry to do that full-time. DDND 2012 Lipinski keynote 24
    25. 25. Death valley, politically correct causes?• Academics lack drug discovery skills• Requires industry / academic collaboration • eg. medicinal chemists are mostly in industry• No access to ADMET, drug met, pharm sci etc. • critical disciplines not in academia• No access to preclinical – clinical interface skills • eg. analytical, process chemistry, formulation• No access to early development skills • eg. toxicology, biomarkers, project management DDND 2012 Lipinski keynote 25
    26. 26. Death valley, politically incorrect causes?• Assumption - academic ideas on new targets are of high quality WRONG• Bayer analysis of validation of academic targets• 50 % of academic targets are wrong• 25% of academic targets are partially flawed• Translational death valley exists (in part) because of poor quality academic target identification DDND 2012 Lipinski keynote 26
    27. 27. Why the academic target problem• Culprit is primarily the pressure to publish to support both grant applications and career development• A people problem• A government problem• Exacerbated by hypothesis driven research• The positive: infrastructure collaboration DDND 2012 Lipinski keynote 27
    28. 28. Bayer observation in NRDD DDND 2012 Lipinski keynote 28
    29. 29. Has drug discovery gone wrong?• Prevailing mantra: identify a mechanism and discover a selective ligand for a single target• Counter responses: • Phenotypic screening • Drug repurposing • Multi targeted drug discovery • In-vivo screening • Non target non mechanism screening DDND 2012 Lipinski keynote 29
    30. 30. Genomics – Chemistry parallel• Genome sequence deciphered in 2000• Automated chemistry starts in 1992• Misapplied, both impeded drug discovery • “The DNA reductionist viewpoint of the molecular genetics community has set drug discovery back by 10-15 years” Craig Venter quote • “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 DDND 2012 Lipinski keynote 30
    31. 31. Genomics / HTS science madness• Collaborations to mine genomic targets• Massive HTS campaigns to discover ligands• 500 different targets, a million data points• “a wish to screen 100,000 compounds per day in a drug discovery factory and a wish to make a drug for each target”Drug discovery and development using chemical genomics. A. Sehgal, Curr Opin in Drug Disc & Dev (2002), 5(4), 526-531.The drug discovery factory : an inevitable evolutionary consequence of high throughput parallel processing. R. Archer, Nat Biotech (1999), 17(9), 834. DDND 2012 Lipinski keynote 31
    32. 32. Genomics financial madness1% success, NPV $34M, Decision Resources March 29, 2004 DDND 2012 Lipinski keynote 32
    33. 33. Target-based drug discovery: DD 21 R3 R2 R4 R5 R1 R6 E1 E5 E2 E6 E3 E4 E7 DP 1 DP 2 Slide thanks to Andrew Reaume, Melior Discovery DDND 2012 Lipinski keynote 33
    34. 34. ….the real picture D D 2 1 R3 R9 R2 R10 R4 R5 R1 E7 R6 R7 E1 E8 E5 R11 E9 R12 E2 E8 E6R8 E10 E3 E4 DP 3 E7 DP 4 DP 1 DP 2 DP 5 Slide thanks to Andrew Reaume, Melior Discovery DDND 2012 Lipinski keynote 34
    35. 35. 50 years of medicinal chemistry What Do Medicinal Chemists Actually Make? A 50-Year Retrospective Pat Walters et al. J Med Chem 2011 DDND 2012 Lipinski keynote 35
    36. 36. Attrition rates by phaseThe Productivity Crisis in Pharmaceutical R&D, Fabio Pammolli, Laura Magazziniand Massimo Riccaboni, Nature Reviews Drug Discovery 2011 (10) 428-438. DDND 2012 Lipinski keynote 36
    37. 37. Nanomolar is not necessaryMean po dose is 47 mg Mean pXC50 is 7.3 (IC50 5 x 10-8)Gleeson, M. Paul; Hersey, Anne; Montanari, Dino; Overington, John. Probing thelinks between in vitro potency, ADMET and physicochemical parameters.Nature Reviews Drug Discovery (2011), 10(3), 197-208. DDND 2012 Lipinski keynote 37
    38. 38. 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 38
    39. 39. Phenotypic screening• Finally government is paying attention• NIH new institute TRND• 25% of assays are reserved for phenotypic screening DDND 2012 Lipinski keynote 39
    40. 40. Chemistry novelty is harmful• Patents direct towards chemistry novelty• Chemistry novelty correlates with decreased drug discovery success• “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 DDND 2012 Lipinski keynote 40
    41. 41. Screening diverse compounds is the worst way to discover a drug• Every publication I know of argues that biologically active compounds are not uniformly distributed through chemistry space DDND 2012 Lipinski keynote 41
    42. 42. Do drug structure networks map on biology networks? DDND 2012 Lipinski keynote 42
    43. 43. Chemistry drug class network DDND 2012 Lipinski keynote 43
    44. 44. Network comparison conclusions• “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.”• “Biological targets may be related by their ligands, leading to connections unanticipated by bioinformatics similarities.” DDND 2012 Lipinski keynote 44
    45. 45. What is going on?• Old maxim: Similar biology implies similar chemistry• If strictly true biology and chemistry networks should coincide DDND 2012 Lipinski keynote 45
    46. 46. Network comparisons – meaning?• “Structure of the ligand reflects the target”• Evolution selects target structure to perform a useful biological function• Useful target structure is retained against a breadth of biology• Conservation in chemistry binding motifs• Conservation in motifs where chemistry binding is not evolutionarily desired –eg. protein – protein interactions DDND 2012 Lipinski keynote 46
    47. 47. Hit / lead implications• 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• Medicinal chemistry principles outside of your current biology target can be extrapolated to the ligand chemistry (but not biology) of the new target• Medicinal chemistry due diligence is essential DDND 2012 Lipinski keynote 47
    48. 48. Changes in drug discovery• Questioning of reductionist approach• A positive development in CNS drug discovery• Very few CNS agents are found rationally• Experimental observations in the clinic• Multiple Sclerosis as a paradigm• No drugs until disease progression biomarkers• Multiple MS drugs recently available DDND 2012 Lipinski keynote 48
    49. 49. What to look for• Disease progression biomarkers –first impact in drug discovery –later impact when therapy arrives• Orphanization of disease diagnosis –new drugs or fitting patients to current drugs? –challenges to cost structures• Exploring drug or target combinations DDND 2012 Lipinski keynote 49

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