sunday (2) lipinski
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Where is drug discovery going?
Christopher A. LipinskiScientific Advisor, Melior Discovery
1DDND 2012 Lipinski keynote
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 for2DDND 2012 Lipinski keynote
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
3DDND 2012 Lipinski keynote
Death Valley California
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Translational valley of death
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"curing disease is a byproduct of the [NIH] system and not a goal," 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.
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
6DDND 2012 Lipinski keynote
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
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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
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Bayer observation in NRDD
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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
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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
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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.
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Genomics financial madness
1% success, NPV $34M, Decision Resources March 29, 2004
Target-based drug discovery:
E1 E5
R2R3
R4R5
R6R1
E2
E3 E4 E7
E6
DP 1 DP 2
D1D2
Slide thanks to Andrew Reaume, Melior Discovery
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….the real picture
R8
DP 5
E10
E9
E8E1 E5
R2R3
R4R5
R6R1
E2
E3 E4 E7
E6
DP 1 DP 2
R7
R9 R10
R11R12
DP 3DP 4
E7
E8
D1D2
Slide thanks to Andrew Reaume, Melior Discovery
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50 years of medicinal chemistry
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What Do Medicinal Chemists Actually Make? A 50-Year Retrospective Pat Walters et al. J Med Chem 2011
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.
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Nanomolar is not necessary
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Mean 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 the links between in vitro potency, ADMET and physicochemical parameters. Nature Reviews Drug Discovery (2011), 10(3), 197-208.
Phenotypic screening advantageThe 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.
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Phenotypic screening
• Finally government is paying attention
• NIH new institute TRND
• 25% of assays are reserved for phenotypic screening
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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
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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
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Do drug structure networks map on biology networks?
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Chemistry drug class network
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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.”
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What is going on?
• Old maxim: Similar biology implies similar chemistry
• If strictly true biology and chemistry networks should coincide
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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
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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
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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
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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
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