Making Lead

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Making Lead
Discovery
less Complex?
Mike Hann, Andrew Leach Gavin Harper.
Discovery Research
GlaxoSmithKline Medicines Research Centre
Gunnels Wood Rd
Stevenage
SG1 2NY
email MMH1203_at_gsk.com
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Subtitle Knowledge of Molecular Recognition
versus the gambling game that we play in using
HTS and libraries to discover new leads and drugs

• Introduction
• A simple model of molecular recognition and its
  implications
• Experimental data
• An extreme example
• Conclusions

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HTS Libraries - have they been successful in
creating the revolution that was needed in the
drug discovery business?

• Despite some successes, the high throughput
  synthesis of libraries and the resulting HTS
  screening paradigms have not delivered the
  results that were initially anticipated.
• Why?
• immaturity of the technology,
• lack of understanding of what the right types of
  molecule to make actually are . (the design
  problem)
• the inability to make the right types of
  molecules with the technology . (the
  synthesis problem)

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The Right Type of Molecules?

• Drug likeness
• Lipinski for oral absorption
• Models (eg Mike Abrahams) for BBB penetratio
• These address the properties required for the
  final candidate drug
• Lead Likeness
• What should we be seeking as good molecules as
  starting points for drug discovery programs?
• Are they different to our current design
  perspectives?
• A theoretical analysis of why they need to be
  different to drug like molecules
• Some practical data

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A very simple model of Molecular Recognition

• Define a linear pattern of s and -s to
  represent the recognition features of a binding
  site
• these are generic descriptors of recognition
  (shape, charge, etc)
• Vary the Length ( Complexity) of this linear
  Binding site as s and -s
• Vary the Length ( Complexity) of this linear
  Ligand up to that of the Binding site
• Calculate probabilities of number of matches as
  ligand complexity varies.
• Example for binding site of 12 features and
  ligand of 4 features

Ligand mode 1 -
Ligand mode 2 -
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Probabilities of ligands of varying complexity
(i.e. number of features) matching a binding
site of complexity 12
Example from last slide
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The effect of potency (binding site 12 ligand
complexity lt/12)

• P (useful event) P(measure binding) x P(ligand
  matches)

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Limitations of the model

• Linear representation of complex events
• No chance for mismatches - ie harsh model
• No flexibility
• only and - considered
• But the characteristics of any model will be the
  same
• Real data to support this hypothesis!!

P (useful event) P(measure binding) x P(ligand
matches)
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Leads vs Drugs

• Data taken from W. Sneaders book Drug
  Prototypes and their exploitation
• Converted to Daylight Database and then profiled
  with ADEPT
• 470 drug case histories in the following plots

Leads are less complex than drugs!!
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Change in MW on going from Lead to Drug for 470
drugs
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In terms of numbers
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AZ data
AstraZeneca Lead Drug difference DMW 89 DCMR
2.3 DCLogP 1.16 DLogD7.4 0.97 DRNG
DHBA 1 DRTB 2 DHBD 0.2

• Astra Zeneca data similar using hand picked data
  from literature
• AZ increases typically even larger (but DHBD
  still ca. zero)
• RSC/SCI Medchem conference Cambridge 2001. MW
  increase ca. 70-90 depending on starting
  definitions

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ADEPT plots for WDI a variety of GW libraries
Library compounds are often far too complex to be
found as leads !!

• Molecules in libraries are still even more
  complex than WDI drugs, let alone Sneader Leads

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Why is this?
A Catch 22 problem

• We are dealing with probabilities so increasing
  the number of samples assayed will increase the
  number of hits (HTS).
• We have been increasing the number of samples by
  making big libraries (combichem)
• And to make big libraries we have to have many
  points of diversity
• Which leads to greater complexity
• Which decreases the probability of a given
  molecule being a hit!!!

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Concentration as the escape route

• Screen less complex molecules to increase our
  probability of finding hits
• Less potent but higher chance of getting on to
  the success landscape.

Opportunity for medicinal chemists to then
optimise by adding back complexity and desirable
ADME properties
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Consequences for screening

• Need for appropriate assay and ligands
• Robust assays and soluble compounds
• The extreme Mulbits (Multiple Bits) approach
• Mulbits are molecules of MW lt 150 and highly
  soluble.
• Screen at up to 1mM
• An example indicating how far this can be taken
• from 5 years ago - Thrombin
• Preselected (in silico) set of basic Mulbits to
  use in a Proflavin displacement assay specific
• known to be be specific for P1 pocket based on
  Xray crystallography.

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Thrombin Mulbit to drug
Save on synthesis of no hope complex molecules
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Related Literature examples of Mulbits type
methods

• Needles method in use at Roche
• .Boehm, H-J. et al Novel Inhibitors of DNA
  Gyrase 3D Structure Based Biased Needle
  Screening, Hit Validation by Biophysical Methods,
  and 3D Guided Optimization. A Promising
  Alternative to Random Screening. J. Med. Chem.,
  2000, 43 (14), 2664 -2674.
• NMR by SAR method in use at Abbott
• Hajduk, P. J. Meadows, R. P. Fesik, S. W..
  Discovering high-affinity ligands for proteins.
  Science, 1997, 278(5337), 497-499.
• Ellman method at Sunesis
• Maly, D. J. Choong, I. C. Ellman, J. A..
  Combinatorial target-guided ligand assembly
  identification of potent subtype-selective c-Src
  inhibitors. Proc. Natl. Acad. Sci. U. S. A.,
  2000, 97(6), 2419-2424.

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Enzyme target - bangs per bucks
Plot of Log Enzyme activity vs MW for
Interesting monomer containing inhibitors
Interesting monomer
mM
MW
nM
Most interesting lead
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Anticipated pKi vs MW for useful lead2drug
identification
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The Lead Continuum
H2L problems ? Lipinski Data zone
Drug-like
Leadlike
HTS screening
Slide adapted from Andy Davis _at_ AZ
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In conclusion
Molecular Complexity and Its Impact on the
Probability of Finding Leads for Drug Discovery
Michael M. Hann, Andrew R. Leach, and Gavin
Harper J. Chem. Inf. Comput. Sci., 41 (3), 856
-864, 2001. Is There a Difference between Leads
and Drugs? A Historical Perspective Tudor I.
Oprea, Andrew M. Davis, Simon J. Teague, and
Paul D. Leeson J. Chem. Inf. Comput. Sci., ASAP
Articles

• Lipinski does not go far enough in directing us
  to leads.
• We have provided a model which explains why
• We have provided some data to support the
  hypothesis
• Everything should be made as simple as possible
  but no simpler. Einstein
• Simple is a relative not absolute term
• where is that optimal peak in the plot for each
  target?
• Simple does not mean easy!!

Thanks to Andrew Leach, Gavin Harper. Darren
Green, Craig Jamieson, Rich Green, Giampa Bravi,
Andy Brewster, Robin Carr, Miles Congreve,Brian
Evans, Albert Jaxa-Chamiec, Duncan Judd, Xiao
Lewell, Mika Lindvall, Steve McKeown, Adrian
Pipe, Nigel Ramsden, Derek Reynolds, Barry Ross,
Nigel Watson, Steve Watson, Malcolm Weir, John
Bradshaw, Colin Grey, Vipal Patel, Sue Bethell,
Charlie Nichols, Chun-wa Chun and Terry
Haley. Andy Davis and Tudor Oprea at AZ