Pfizer Confidential

Molecular Similarity Characterization of ADME Landscapes

ACS Annual MeetingSan Francisco 2010

Bin Chen‡, Rishi Gupta* and Eric Gifford†

‡ School of Informatics and Computing, Indiana University, Bloomington, IN 47408* Anti Bacterial Research Unit, Pfizer Global R&D, Groton, CT 06340

† Computational Sciences CoE, Pfizer Global R&D, Groton, CT 06340

Molecular Similarity Characterization of ADME Landscapes

ACS Annual MeetingSan Francisco 2010

Bin Chen‡, Rishi Gupta* and Eric Gifford†

‡ School of Informatics and Computing, Indiana University, Bloomington, IN 47408* Anti Bacterial Research Unit, Pfizer Global R&D, Groton, CT 06340

† Computational Sciences CoE, Pfizer Global R&D, Groton, CT 06340

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OutlineOutline

Introduction

Methods

Results & discussions

Use cases

Conclusions

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What has been done so far?What has been done so far?

A lot of excellent work in the Activity space using a variety of similarity methods and descriptors

Current work focuses primarily on ADME end points and Molecular properties while examining various descriptor types and similarity methods

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Do similar compounds have similar ADME properties?Do similar compounds have similar ADME properties?

0.9 0.8 0.7

OH

OH

OH

O

Similarity 0.92 Similarity 0.85Varies based on descriptors used

Similar ADME Properties?

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Do different ADME endpoints have different landscapes?Do different ADME endpoints have different landscapes?

0.9 0.8 0.7

0.9 0.8 0.7

HLM

RRCK

8.05

49.0 Ratio

67.012

88.0 Ratio

8.05

49.0 Ratio

75.012

98.0 Ratio

High Risk Compound

Low Risk Compound

Probe Compound neighborstotal

classsamewithneighborsRatiosimilarity #

#

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Hypothesis: Visualizing Chemical LandscapeHypothesis: Visualizing Chemical Landscape

0.5 0.80.2

0.5

1.0

Identical Compounds Ratio ~1.0

Similarity cutoff

Rat

io

1

Ratio=f(endpoint, similarity)

Ratio ~ High (low) risk compounds/total

compounds

Endpoint1

Endpoint3

Endpoint2

Endpoint4

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Datasets, Assays and BinsDatasets, Assays and Bins

• Full matrix consisting of 17787 compounds and 9 endpoints• Solubility and cLogP are predicted endpoints using in-house computational models

on datasets with more than 10K compounds ,the rest are experimental results

Endpoint Description Result unit Low Risk High Risk

RRCK passive permeability in RRCK cell line 10-6 cm/sec >10 <=10

HLM metabolic stability using human liver microsomes

µL/min/mg <20 >=20

MDR Pgp influenced permeability andefflux in MDCK-MDR1 cells

10-6 cm/sec >10 <=10

CYP1A2 CYP1A2 inhibition in a substrate cocktail assay % Inhibition <10 >=10

CYP3A4 CYP3A4 inhibition in a substrate cocktail assay % Inhibition <10 >=10

CYP2D6 CYP2D6inhibition in a substrate cocktail assay % Inhibition <10 >=10

CYP2C9 CYP2C9 inhibition in a substrate cocktail assay % Inhibition <10 >=10

*Solubility  ADMET Aqueous Solubility properties Solubility level >2 <=2

*cLogP logarithm partition coefficient Octanol-Water Partition Coefficient

<3 >=3

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Characterize Chemical Landscape: Proposed Workflow*Characterize Chemical Landscape: Proposed Workflow*

Full matrix (cmpd*endpoint)

Similarity matrix

Select all high/low risk compounds in an

Endpoint

Calculate the ratio of each compound

Average the ratio of all the compounds

Iterate all highrisk compounds

FCFP6Tanimoto

• Structure similarity• Fingerprint (4)

• MDL public keys• Atom pairs• FCFP6• ECFC4

• Coefficient (2)• Tanimoto• Cosine

• Risk categorization (2)• High risk• Low risk

• Endpoints (9)• Complexity: 4*2*2*9=144

Workflow for Plotting landscape of an endpoint using FCFP6 and tanimoto as similarity measurement

Select one similarity cutoff

Select one compound

Iterate all Cutoffs(total 14)

Plot: Similarity cutoff & ratio

neighborstotal

classsamewithneighborsRatiosimilarity _#

___#

*Molecular Similarity Characterization of ADME Landscapes; Chen et al., JCIM, Submitted, 2010

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What are we evaluating?What are we evaluating?

Calculate the ratio of all compounds, individually. Average the ratio of all the compounds at each similarity threshold,

ignoring the ratio is 0 (either no same class neighbor or no neighbor)

Compound ID Similarity 0.9 Similarity 0.8 Similarity 0.7 …

PF_1 0.9 0.9 0.7 …

PF_2 1 0.5 0.7 …

PF_3 0.95 0.8 0.7 …

… … … … …

PF_N 0.91 0.85 0.68 …

average 0.95 0.85 0.7 …

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Results: Compare Different EndpointsResults: Compare Different Endpoints

(a) ECFC4, Tanimoto, low risk (b) ECFC4, Tanimoto, high risk

• Rate of “fall” of a given curve defines how easy/difficult it would be to modify a compound and modify its property i.e. transform a compound from being high risk to low risk or vice versa

• Compounds in MDR are relatively difficult to come out of a High Risk Class compared to HLM at any given similarity cutoff

• Ratio stays constant after a given certain similarity threshold (i.e. 0.4 in the case of CYP2C9 )

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Results: Compare Different Fingerprints*Results: Compare Different Fingerprints*

(a) RRCK high risk(a) RRCK high risk (b) RRCK low risk

• Ratio is different among fingerprints, the order is always FCFP6> Atom-pairs >ECFC4>MDL

*Molecular Similarity Characterization of ADME Landscapes; Chen et al., JCIM, Submitted, 2010

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Results: Compare different similarity coefficientsResults: Compare different similarity coefficients

(a) RRCK Low Risk (b) RRCK High Risk

• Ratio is different among similarity coefficients, the order is always tanimoto>Cosine

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SN

N

N

N

MDR:LOWRRCK:HIGH…

Use Case: Which one is better to optimize?Use Case: Which one is better to optimize?

N

ON

MDR: HIGHRRCK: LOW…

SN

N

N

N

MDR:LOW?RRCK:LOW?…

MDR: LOW?RRCK: LOW?…

Probability of Success?

N

ON

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Use Case: Data Driven Compound Prioritization?Use Case: Data Driven Compound Prioritization?

Compds # High Risk

SCORE

Compound1 - - + - - - - - - 1 0.688

Compound2 + - - - - - - - - 1 0.694

Compound3 - - - - - - - + + 2 0.623

Compound4 - - - + + - + - - 3 0.627

hl

ratioEratioE

ADMET

h

jj

l

ii

score

))(1()(

+ and - represent high risk and low risk endpoint, respectively

HLM

RR

CK MD R

CY

P1A

2C

YP

3A4

CY

P2D

6C

YP

2C9

Aq.

Sol

.

cLog

P

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Potential Combinations Potential Combinations

• 4 descriptor types are used• 2 similarity metrics are used• 9 endpoints,• 512 combinations.• Overlap means some compounds with higher risk endpoints should go first than those

with lower e.g.: MDL+Tanimoto Coeff.

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• + and - represent high risk and low risk endpoint, respectively• totally, 9 endpoints and 512 combinations

# high endpoints

Score at similarity 0.5

Score at similarity 0.6

+ + + + + + + + + 9 0.326676 0.275558

- + + + + + + + + 8 0.372088 0.333456

+ - + + + + + + + 8 0.372646 0.332717

- - + + + + + + + 7 0.418058 0.390616

+ + - + + + + + + 8 0.374459 0.336353

... ... ... ... ... ... ... ... ... … … …

- - + + + + + + + 1 0.679591 0.714969

+ + - - - - - - - 2 0.635992 0.660706

- + - - - - - - - 1 0.681403 0.718605

+ - - - - - - - - 1 0.681962 0.717866

- - - - - - - - - 0 0.727373 0.775765

Results: Ranking matrixResults: Ranking matrixH

LM

RR

CK M

D R

CY

P1A

2C

YP

3A4

CY

P2D

6C

YP

2C9

Aq.

Sol

.

cLog

P

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ConclusionConclusion

Small structural changes result in change of class (High/Low Risk) within a given endpoint

Different endpoints behave differently from each other e.g. MDR may be difficult to modify than CYP2C9

Curves are relatively parallel to each other independent of descriptor and similarity metric

Derived scoring function out of the plots to prioritize compounds (for screening or series selection)

Ratios could be used for differentiating between “difficult” endpoints versus “easy” endpoints

0.5 0.80.2

0.5

1.0

Similarity cutoff

Rat

io

1

Difficult

Easy

0.5 0.80.2

0.5

1.0

Similarity cutoff

Rat

io

1

Difficult

Easy

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ReferenceReference Martin YC et al. Do Structurally Similar Molecules Have Similar Biological

Activity?. J. Med. Chem. 2002, 45, 4350-4358 Medina-Franco, JL; et al. Characterization of Activity Landscapes Using 2D and

3D Similarity Methods: Consensus activity Cliffs. J. Chem. Inf. Model. 2009, 49, 477-491

Segall MD, et al. Focus on Success: Using a Probabilistic Approach to Achieve an Optimal Balance of Compound Properties in Drug Discovery. Expert Opin. Drug Metab. Toxicol. 2006, 2, 325-37

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AcknowledgementAcknowledgement

David Wild (School of Informatics and Computing, Indiana University)

Veerabahu Shanmugasundaram (AB RU)

Robyn Ayscue Hua Gao

ThanksQuestions and Comments

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ResultsResults

Heatmap for ratios of all compounds at 14 similarity cutoffs

RRCK, ECFC4, Tanimoto, High Risk RRCK, ECFC4, Tanimoto, Low Risk

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Discussion & further workDiscussion & further work

Normal distribution Outliers analysis Ranking function validation Implementation

On virtue of full matrix and ADME predictive model, any given compound can be assigned a score for prioritization

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Backup—Normal distributionBackup—Normal distribution

Binned Ratio

0

100

200

300

400

500

600

700

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1

42

104 101 121

185

331

523

554 548

456

620

397 396

263

198

148

212

161 150

308

RRCK, ECFC4, high, similarity 0.85 RRCK, ECFC4, high, similarity 0.65