“Making Lead Discovey less Complex?”

Mike Hann, Andrew Leach & Gavin Harper.

Gunnels Wood RdStevenageSG1 2NY

email MMH1203@gsk.com

Discovery Research

GlaxoSmithKline Medicines Research Centre

Introduction

A simple model of molecular recognition and it’s implications

Experimental data

An extreme example

Conclusions

HTS & Libraries - have they been successful at revolutionising the drug discovery business? Despite some successes, it is clear that 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 . (design problem)

– the inability to make the right types of molecules with the technology . (synthesis problem)

The Right Type of Molecules?

Drug likeness

– Lipinski for oral absorption

– Models (eg Mike Abrahams) for BBB penetration

– But all 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?

– A theoretical analysis of why they need to be different to drug like molecules

– Some practical data

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:

Feature Position 1 2 3 4 5 6 7 8 9 10 11 12Binding site features - - + - + - - + - + + +

Ligand mode 1 + + - +

Ligand mode 2 + + - +

Probabilities of ligands of varying complexity (i.e. number of features) matching a binding site of complexity 12

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2 3 4 5 6 7 8 9 10 11 12Complexity of Ligand (I.e. number of ligand features)

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Match any1 matches2 matches3 matches4 matches5 matches6 matches7 matches8 matches 9 matches10 matches11 matches

As the ligand/receptor match becomes more complex the probability of anygiven molecule matching falls to zero. i.e. there are many more ways of getting it wrong than right!

Example from last slide

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Ligand Complexity

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Probability of matching just one way

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Ligand Complexity

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Probability of measuring binding

Probability of matching just one way

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Ligand Complexity

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Probability of measuring binding

Probability of matching just one way

Probability of useful event (unique mode)

The effect of potency (binding site 12; ligand complexity </=12)

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

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Probability of useful event (unique mode)

Too simple.Low probability of measuring affinity even if there is a unique mode

Too complex.Low probability of finding lead even if it has high affinity

Optimal.But where is itfor any given system?

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!!

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2 3 4 5 6 7 8 9 10 11 12Ligand Complexity

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

Leads vs Drugs Data taken from W. Sneader’s book “Drug Prototypes and their exploitation”

Converted to Daylight Database and then profiled with ADEPT

480 drug case histories in the following plots

Sneader Lead Sneader Drug WDI

Leads are less complex than drugs!!

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MW of Sneader Drugs

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Change in MW on going from Lead to Drug for 470 drugs

Average MW increase = 42

ADEPT plots for WDI & a variety of GW libraries

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

WDI

WDI

WDIWDI

WDI

WDI

Library compounds are often far too complex to be found as leads !!

In terms of numbers

Astra Zeneca data similar using hand picked data from literature

AZ increases typically even larger

RSC/SCI Medchem conference Cambridge 2001. MW increase ca. 70-90 depending on starting definitions

Average property values for the Sneader lead set, average changeon going to Sneader drug set and percentage change.

Av #arom

arom

% AvClogP

ClogP

% AvCMR

CMR

%

1.3 0.2** 15 1.9 0.5** 26 7.6 1.0** 14.5

Av # HBA

HBA

% Av #HBD

HBD

% Av #heavy

heavy

%

2.2 .3** 14 .85 -.05+ (4) 19. 3.0** 16

AvMW

MW

% AvMV

MV

% Av #Rot B

Rot B

%

272 42.0** 15 289 38.0** 13 3.5 .9** 23

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 you 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 find more 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 properties

Need for it to be appropriate assay and ligands

– e.g the extreme Mulbits (Multiple Bits) approach

– Mulbits are molecules of MW < 150 and highly soluble.

– Screen at up to 1mM

An example indicating how far this can be taken

– from 5 years ago - Thrombin:

– Screen preselected (in silico) basic Mulbits in a Proflavin displacement assay specific

– known to be be specific for P1 pocket.

Catch 21

Thrombin Mulbit to “drug”

NNS

OO

O

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O

NN

NH2

H

H

Thrombin IC50 = 4µM (15 min pre-incubation; for assay conditions see reference 23)

NHN

NH2

2-Amino Imidazole (5mM), as thesulphate, showed 30% displacementof Proflavin (18µM) from Thrombin (10µM)

(cf Benzamidine (at 5mM) shows 70% displacement) undersimilar conditions

Absorbance at 466nM relative to that at 444nM was used as the measure of amount of proflavin displaced

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.

Enzyme target - bangs per bucks

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MW of inhibitor

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Interesting monomer

Most interesting lead

Plot of Log Enzyme activity vs MW for “Interesting monomer” containing inhibitors

MWM

nM

H2L problems ?

Lipinski Data zone

Lead Continuum

350 Mwt >500 Mwt <200

Drug-likeLeadlike

HTS screeningNon-HTS

Shapes (Vertex )Needles(Roche)MULBITS(GSK)Crystallead(Abbott)SARbyNMR(Abbott)

Slide adapted from Andy Davis @ AZ

In conclusion

Lipinski etc does not go far enough in directing us to leads.

We have provided a model which explains why. “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

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