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 1

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 2

DDND 2012 Lipinski keynote 3

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

DDND 2012 Lipinski keynote 4

….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

DDND 2012 Lipinski keynote 5

50 years of medicinal chemistry

DDND 2012 Lipinski keynote 6

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.

DDND 2012 Lipinski keynote 7

Nanomolar is not necessary

DDND 2012 Lipinski keynote 8

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.

DDND 2012 Lipinski keynote 9

Phenotypic screening

• Finally government is paying attention

• NIH new institute TRND

• 25% of assays are reserved for phenotypic screening

DDND 2012 Lipinski keynote 10

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

11DDND 2012 Lipinski keynote

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 12

Do drug structure networks map on biology networks?

DDND 2012 Lipinski keynote 13

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.”

DDND 2012 Lipinski keynote 15

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

DDND 2012 Lipinski keynote 17

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 18

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 19