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Sheffield Conference 2010

Enhancing matched molecular pair analysis during lead optimisation

The University of Sheffield

GlaxoSmithKline

George Papadatos

15/07/2010 Sheffield Conference 2010 2

Outline

n Lead Optimisation

n Matched molecular pairs (MMPs) analysisq Matched pairs and transformationsq Context-sensitive molecular transformations

q Impact of the most frequent transformations

q Context descriptors

q Context-sensitive examples

n Conclusion


Lead optimisation (LO)

Activity at target X Acceptable ADME

Selectivity over target YDrug Candidate

chem

istr

y sp

ace

n Combinatorial chemistry n Multi-objective optimisation

What molecule(s) to synthesise next???


Example of a success LO story

HNN

N

HNN

N

N HNN

N

N

HN

CH3

O

HNN

N

N

HN O

HNN

N

N

HN O

CH3 HNN

N

N

HN O

CH3

N

NCH3

lead structure

Imatinib

Capdeville et al. (2002). Nature Reviews Drug Discovery, 1 (7), 493-502.


Matched molecular pair approach

n MMP data mining

n Which are the most frequent transformations in LO?

n What is the impact of a transformation on a property?q Identify trends on the effect of a transformation J

q Interpretable, inverse-QSAR approach Jq How can I make my compound more soluble? J

q Based on transformations that already exist K


Molecular transformations and context

SO

O

OCH3

SO

O

OCH3

F

FF

SO

O

OCH3

*

Context

transformation local environment

attachment point

single-point change

H >> CF3

Gleeson et al. (2009). Bioorganic & Medicinal Chemistry, 17 (16), 5906-5919.

Hajduk and Sauer (2008). Journal of Medicinal Chemistry, 51 (3), 553-564.

Leach et al. (2006). Journal of Medicinal Chemistry, 49 (23), 6672-6682.

Molecular transformation analysis

Matched pair Context ΔPTransformation

H >> CF3

H >> CF3

+1.50

-0.90

SO

O

OCH3

SO

O

OCH3

F

FF

SO

O

OCH3

*

N

N

CH3

O

N

N

CH3

O F

F

F

N

N

CH3

O

*

15/07/2010 7Sheffield Conference 2010

P = 5.1 P = 6.6

P = 5.7 P = 4.8


Our MMP study

n Large files of compounds tested in reliable, consistent assaysn No predefined list of transformations

q Unsupervised MMP generation

n Robust statistics based on large no. of examplesn Context-sensitive MMP analysis

q No assumptions for global effect of a transformationq Is the effect the same for local regions of chemical space?q Investigation for several descriptors which represent either the whole

context or the local environment where the change took placeq Identify discrepancies between global and local ∆P distributions

Papadatos et al. Manuscript submitted.


In detail…

1. Begin with a large dataset with property values P

2. Find matched pairsq Use an efficient MMP algorithm

3. Extract contexts and corresponding transform

4. Group transformations; calculate and bin ΔPs in 3 binsq Good – Bad – No Change

5. Analyse trends in the effect of each transformation

6. Generate context descriptorsq Describe either the whole molecule or the local environment

7. Study the influence of certain contexts on the ΔP distribution of each transformation


Fragment indexing-based MMP generation

* *>>

1) Enumerate all acyclic single cuts:

2) Index all the fragments:

3) The transformation then is:

Hussain and Rea (2010). Journal of Chemical Information and Modeling, 50 (3), 339–348.


Datasets and MMPs

Data set

# hERG Solubility Lipophilicity

Compounds 76K 94K 180K

Distinct MMPs 1,431,107 1,375,382 4,441,033

Distinct transforms 1,035,181 927,903 3,169,989

Frequent transforms (>300 examples each)

33 58 175

Distinct MMPs in frequent transforms

37,746 56,326 171,040


The most frequent transformations

hERG transformations0

1000

2000

3000

4000

5000

6000

7000

8000fr

eque

ncy


Impact of transformations – hERG

8326 4243 2849 2484 1441 1120 1106 1094 1071 1017 986 927 855 852 775

H>>

CH3

H>>

F

H>>

Cl

H>>

OCH

3

F>>C

l

F>>O

CH3

CH3>

>Cl

H>>

CF3

CH3>

>F

CH3>

>OCH

3

H>>

Et

Cl>>

OCH

3

CH3>

>CF3

H>>

Ph

H>>

OH

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Bad (ΔhERG ≥ 0.3) No change (-0.3 ≤ ΔhERG < 0.3) Good (ΔhERG < -0.3)


Impact of transformations - Sol

10695 4273 4037 3104 1676 1566 1288 1363 1351 1158 1323 1359 1152 1216 1163 993 H

>>CH

3

H>>

F

H>>

Cl

H>>

OCH

3

F>>C

l

CH3>

>Cl

H>>

Ph

H>>

Et

H>>

CF3

CH3>

>Ph

CH3>

>OCH

3

Cl>>

OCH

3

CH3>

>CF3

CH3>

>F

F>>O

CH3

H>>

OH

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Bad (ΔSol < -0.3) No change (-0.3 < ΔSol ≤ 0.3) Good (ΔSol ≥ 0.3)


Impact of transformations – logD24405 9902 8267 6187 3799 3301 3093 2784 2703 2699 2618 2601 2539 2505 2442 2373 2026 2011 1690 1421 1406

H>>

CH3

H>>

F

H>>

Cl

H>>

OCH

3

F>>C

l

H>>

Et

CH3>

>Cl

H>>

CF3

CH3>

>OCH

3

Cl>>

OCH

3

H>>

Ph

CH3>

>F

H>>

OH

CH3>

>CF3

CH3>

>Ph

F>>O

CH3

CH3>

>i-P

r

Cl>>

CF3

H>>

C#N

Bn>>

Ph

H>i

-Pr

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Bad (ΔlogD ≥ 0.3) No change (-0.3 ≤ ΔlogD < 0.3) Good (ΔlogD < -0.3)

Context representation I


N

N

CH3

O

*

atom path length

in bonds

0 C1 C=C, C-N, C-C2 C=C-N, C-N-C, C-C=O3 C=C-N-C, C-C-N-C4 C=C-N-C=O… and so forth…

1 0 1 1 1 0 1 1 0 0 1 1

Daylight path-based fingerprintsPipeline Pilot clustering component

N

HNO

Murcko frameworks

‘C1CCC(C1)NC(=O)c1cccnc1’

‘*[Hf][Ni][V]’

N

N

CH3

O

*

Reduced graphs

Whole molecule approach


Context representation II

N

N

CH3

O

*

bond radius

0 1 2 3

C.3 C.3 C.3 N.am

C.3 C.3

*

N

N

CH3

O

*

‘0-C.3;1-*,1-C.3,1-C.3;2-C.3,2-C.3;3-N.am’

Circular atom environments

N

N

CH3

O

*

Localised reduced graphs

‘*[Hf]’

Local environment approach


N=2484

N=161 N=109N=2151

Global ΔhERG distribution

Local ΔhERG distributions – localised reduced graphs

p = 3.9E-8 p = 0.58 p = 1.3E-6

*[A]: aliphatic linker *[B]: featureless arom. ring *[C]: H-bond acceptor arom. ring

vs.

H>>OCH3 - hERG


N=303

Global ΔhERG distribution

Local ΔhERG distribution – Murcko frameworks

vs.

N=41

p = 1.8E-8

‘*C(=O)Nc1ccc(cc1)c2nnco2’

HN*

O

N N

O

Cyclohexyl>>Phenyl - hERG


Global ΔlogD distributionN=1027

p = 3.7E-64 p = 2.4E-11

‘0-C.3;1-*Du;1-O.3;2-C.ar;3-C.ar;3-C.ar’ ‘0-C.3;1-*Du;1-C.ar;2-C.ar;2-N.ar;3-C.ar;3-C.ar’

Local ΔlogD distributions – localised atom environmentsvs.

N=152 N=41

O

*

CHHC*" *CH CH

N

*

*"*

Isopropyl>>Phenyl – logD


Summary of results

Cases where p < 0.01

Descriptor hERG (33) Solubility (58) Lipophilicity (175)

Daylight NNs 187 367 1220

Murcko frameworks 159 243 1242

Reduced graphs 165 320 1329

Atom environments 229 274 1719

Localised RG node 32 91 439


On descriptors and generalisability

n Interpretable descriptorsq Hierarchical view: Whole molecule à local environment

n Daylight fingerprints and Murcko frameworksq Chemotype-specific

q Specificity vs. generalisability

n Atom environments, localised reduced graphsq Chemotype independent

q More generalisable

q Atom environments more specific than local reduced graphs


Summary

n MMP analysisq Provides data-driven, interpretable guidelines for LO

n Context-sensitive molecular transformationsq Enhancing the current state of practiceq Provide a methodology to incorporate contextual information

q Focussing on a local region of chemical spacen Common practice in lead optimisation

q Surveyed some of the useful descriptors

n Future plansq Make the findings more accessible to the medicinal chemists


Acknowledgements

n University of Sheffieldq Muhammad Alkarouri, Val Gillet, Visakan Kadirkamanathan, Peter

Willett

n GSKq Iain McLay, Chris Luscombe, Nicola Richmond, Giampa Bravi, Stephen

Pickett

q Tony Cooper, Simon Macdonald, John Pritchard

n Project Sponsors

n All you for listening

Chemoinformatics Research Group


ICCS 2008, Holland

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