This document summarizes a keynote presentation on the state and future of drug discovery. The presentation discusses: 1) The "valley of death" between academic research and clinical development due to a lack of translational skills and partially flawed academic targets; 2) How genomics and high-throughput screening have set back drug discovery by prioritizing a reductionist approach; and 3) How phenotypic screening and considering biological networks may improve success rates over the traditional target-based approach. The keynote argues for improved target validation and considering the relationships between biological and chemical networks.

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 and
moderators 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. Thank You!
MEETING SPONSORS
BRONZE SPONSORS

3. SCIENTIFIC ADVISORY COMMITTEE
SCIENTIFIC ADVISORY COMMITTEE
Kurt R. Brunden, PhD, University of Pennsylvania
Neil S. Buckholtz, PhD, National Institute on Aging
Rebecca Farkas, PhD, National Institute of Neurological Disorders and Stroke
Howard Fillit, MD, Alzheimer’s Drug Discovery Foundation
Brian Fiske, PhD, Michael J. Fox Foundation for Parkinson’s Research
Mark Frasier, PhD, Michael J. Fox Foundation for Parkinson’s Research
Abram Goldfinger, MBA, New York University
Lorenzo Refolo, PhD, National Institute on Aging
Suzana Petanceska, PhD, National Institute on Aging
Diana Shineman, PhD, Alzheimer’s Drug Discovery Foundation
Edward G. Spack, PhD, Fast Forward, LLC
D. Martin Watterson, PhD, Northwestern University

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. NOTES
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CME Certificates available at the Registration Desk
A webcast of the conference will be available soon on our
website:
www.alzdiscovery.org

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. 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. 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 estimates
neurodegenerative disorders will Parkinson’s
be the major unmet disease, 1,000,00
0
medical need of the 21st
century,
• surpassing cancer as the Alzheimer’s
disease, 5,000,00
worlds’ second leading cause of 0
death by the year 2040

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
PHARMACOLOGY
10,000 to 1 FDA
>1 million Approved
chemicals Drug
Developing a Drug is Risky,
Takes 12-15 years and Costs Over $1.2B

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. 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. 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. Why A Biological Network Approach to Drug Discovery is
Needed: Signaling in the Synapse is Complex

14. How Were New Drugs Discovered?
Phenotypic Screening Vs. Target-based
Screening
Swinney, et al, Nature Reviews Drug Discovery, July, 2011

15. Case Studies: Routes to Drug Discovery
beta-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. 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. 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 Mechanism
Medicinal Chemistry, Pharmacology Lead Identification and optimization
Assay Development High Throughput Structure Based
Chemical Libraries Screening Chemistry
Computational Chemistry
Basic Neurobiology Target identification

18. Feeding the Pipeline:
The Alzheimer’s Drug Discovery Foundation
The ADDF has granted over $55 million to >370
Alzheimer’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. Drug Discovery: The “Valley of Death”?
Or “Welcome to An Amazing Journey”!

20. Where is drug discovery going?
Christopher A. Lipinski
Scientific Advisor, Melior Discovery
clipinski@meliordiscovery.com
DDND 2012 Lipinski keynote 20

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. 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. Death Valley California
DDND 2012 Lipinski keynote 23

24. Translational valley of death
"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.
DDND 2012 Lipinski keynote 24

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. 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. 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. Bayer observation in NRDD
DDND 2012 Lipinski keynote 28

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. 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. 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. Genomics financial madness
1% success, NPV $34M, Decision Resources March 29, 2004
DDND 2012 Lipinski keynote 32

33. Target-based drug discovery:
D
D 2
1 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. ….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 E6
R8 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. 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. 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 36

37. Nanomolar is not necessary
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.
DDND 2012 Lipinski keynote 37

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. 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. 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. 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. Do drug structure networks map on
biology networks?
DDND 2012 Lipinski keynote 42

43. Chemistry drug class network
DDND 2012 Lipinski keynote 43

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. 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. 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. 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. 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. 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