ACS Annual MeetingSan Francisco 2010

1. Molecular Similarity Characterization of
ADME Landscapes
ACS Annual Meeting
San 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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2. Outline
 Introduction
 Methods
 Results & discussions
 Use cases
 Conclusions
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3. 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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4. Do similar compounds have similar ADME properties?
Similar ADME Varies based on
Similarity 0.92 Similarity 0.85
Properties? descriptors used
O
OH
OH
OH
0.9 0.8 0.7
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5. Do different ADME endpoints have different landscapes?
# neighbors with same class
Probe Compound
Ratiosimilarity
# total neighbors
High Risk Compound
HLM
Low Risk Compound
4
Ratio0.9 0.8
0.9 0.8 0.7 5
8
Ratio0.8 0.67
12
RRCK
4
Ratio0.9 0.8
0.9 0.8 0.7 5
9
Ratio0.8 0.75
12
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6. Hypothesis: Visualizing Chemical Landscape
Identical Compounds
Ratio ~1.0
1.0
Ratio
Endpoint1
0.5 Endpoint2
Endpoint3
Endpoint4
Ratio=f(endpoint, similarity)
Ratio ~ High (low) 0.2 0.5 0.8 1
risk compounds/total
Similarity cutoff
compounds
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7. Datasets, Assays and Bins
Endpoint Description Result unit Low Risk High
Risk
-6
RRCK passive permeability in RRCK cell line 10 cm/sec >10 <=10
HLM metabolic stability using human liver microsomes µL/min/mg <20 >=20
-6
MDR Pgp influenced permeability and 10 cm/sec >10 <=10
efflux in MDCK-MDR1 cells
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 <3 >=3
Partition Coefficient
• 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
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8. Characterize Chemical Landscape: Proposed Workflow*
Full matrix FCFP6 Similarity • Structure similarity
(cmpd*endpoint) Tanimoto matrix • Fingerprint (4)
Select all high/low • MDL public keys
risk compounds in an • Atom pairs
Endpoint
• FCFP6
Select one similarity • ECFC4
cutoff • Coefficient (2)
• Tanimoto
Select one compound
Iterate all high • Cosine
Iterate all
Cutoffs Calculate the ratio of
risk compounds • Risk categorization (2)
(total 14) each compound • High risk
Ratiosimilarity
# neighbors _ with _ same _ class
total _# neighbors
• Low risk
• Endpoints (9)
Average the ratio of • Complexity: 4*2*2*9=144
all the compounds
Plot: Similarity cutoff
& ratio
Workflow for Plotting landscape of an endpoint using FCFP6 and tanimoto as similarity measurement
8 *Molecular Similarity Characterization of ADME Landscapes; Chen et al., JCIM, Submitted, 2010
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9. What are we evaluating?
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 …
 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)
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10. Results: 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
•
10 Ratio stays constant after a given certain similarity threshold (i.e. 0.4 in the case of CYP2C9 )
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11. Results: Compare Different Fingerprints*
(a) RRCK high risk (b) RRCK low risk
• Ratio is different among fingerprints, the order is always FCFP6> Atom-
pairs >ECFC4>MDL
*Molecular
11 Similarity Characterization of ADME Landscapes; Chen et al., JCIM, Submitted, 2010
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12. Results: Compare different similarity coefficients
(a) RRCK Low Risk (b) RRCK High Risk
• Ratio is different among similarity coefficients, the order is always
12 tanimoto>Cosine Pfizer Confidential

13. Use Case: Which one is better to optimize?
MDR:LOW
MDR: HIGH
N RRCK:HIGH
RRCK: LOW
O …
…
N N
N
N N S
Probability of Success?
MDR: LOW? MDR:LOW?
RRCK: LOW? RRCK:LOW?
O … N …
N
N
N
N N S
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14. Use Case: Data Driven Compound Prioritization?
l h
Ei (ratio) (1 E j (ratio))
i j
ADMET score
l h
CYP2D6
CYP2C9
CYP1A2
CYP3A4
Aq. Sol.
cLogP
RRCK
MDR
HLM
Compds # High SCORE
Risk
Compound1 - - + - - - - - - 1 0.688
Compound2 + - - - - - - - - 1 0.694
Compound3 - - - - - - - + + 2 0.623
Compound4 - - - + + - + - - 3 0.627
+ and - represent high risk and low risk endpoint, respectively
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15. 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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16. Results: Ranking matrix
CYP2C9
CYP2D6
CYP1A2
CYP3A4
Aq. Sol.
# high Score at Score at
cLogP
RRCK
MDR
HLM
endpoints similarity similarity
0.5 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
• + and - represent high risk and low risk endpoint, respectively
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17. Conclusion
 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
1.0
Ratio
Difficult
0.5
Easy
0.2 0.5 0.8 1
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Similarity cutoff

18. Reference
 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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19. Acknowledgement
 David Wild (School of Informatics and Computing, Indiana University)
 Veerabahu Shanmugasundaram (AB RU)
 Robyn Ayscue
 Hua Gao
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20. Thanks
Questions and Comments

21. Results
RRCK, ECFC4, Tanimoto, High Risk RRCK, ECFC4, Tanimoto, Low Risk
Heatmap for ratios of all compounds at 14 similarity cutoffs
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22. Discussion & 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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23. Backup—Normal distribution
700
620
600
554 548
523
500
456
397 396
400
331
308
300
263
212
198
200 185
161
148 150
121
104 101
100
42
0
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
Binned Ratio
RRCK, ECFC4, high, similarity 0.85 RRCK, ECFC4, high, similarity 0.65
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