Development and sharing of ADME/Tox and Drug Discovery Machine learning models

Development and Sharing of ADME/Tox and Drug Discovery Machine Learning Models

Alex M. Clark1*, Krishna Dole2, Anna Coulon-Spector2, Andrew

McNutt2, George Grass3, Joel S. Freundlich4,5, Robert C. Reynolds6

and Sean Ekins2,7*

1 Molecular Materials Informatics, 1900 St. Jacques #302, Montreal H3J 2S1, Quebec, Canada2 Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA

3 G2 Research, Inc., PO Box 1242, Tahoe City, CA 961454Center for Emerging & Re-emerging Pathogens, Division of Infectious Diseases, Department of Medicine, Rutgers

University-New Jersey Medical School, Newark, New Jersey 07103, United States5Department of Pharmacology & Physiology, Rutgers University-New Jersey Medical School, Newark, New Jersey

07103, United States

6University of Alabama at Birmingham, College of Arts and Sciences, Department of Chemistry, 1530 3rd Avenue South,

Birmingham, AL 35294-1240, USA.

7 Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA

ADME/Tox models 15 yrs on: Then & Now• Datasets very small < 100 cpds• Heavy focus on P450• Models rarely used • Very limited number of

properties addressed• Few tools / agorithms used• Limited access to models

• Much bigger datasets > 1000s cpds >10,000

• Broader range of models• Models more widely used and

reported• More accessible models• Pharma making data available

70 hERG models (Villoutreix and

Taboroureau 2015) 19 protein binding models

(Lambrinidis et al 2015) 40 BBB models upto 2009

The Opportunity

•Get pharmas to use open source molecular descriptors and algorithms

•Benefit from initial work done by Pfizer/CDD

•Avoid repetition of open source tools vs commercial tools comparisons

•Change the mindset from real data to virtual data – confirm predictions

•ADME/Tox is precompetitive

•Expand the chemical space and predictivity of models

•Share models with collaborators – Companies could share data as models

Ekins and Williams, Lab On A Chip, 10: 13-22, 2010.

Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010

Model resources for ADME/Tox

CYP 1A2 2C9 2C19

Substrate (mM) phenacetin (10) diclofenac (10) omeprazole (0.5)

Inhibitor naphthoflavone sulfaphenazole tranylcypromine

Compounds IC50 (mM) IC50 (mM) IC50 (mM)

JSF-2019 2.25 3.55 10.8

Retinal dehydrogenase 1

ADME SARfari predicts importance of CYP1A2, CYP2C9, CYP2C19

The Naïve Bayes model was built with 142345 compounds (training and validation) and features 135 learned classes.

Testing by Dr. Joel Freundlich

The big idea (2009)

Challenge..There is limited access to ADME/Tox

data and models needed for R&D

How could a company share data but keep the

structures proprietary?

Sharing models means both parties use costly

software

What about open source tools?

Pfizer had never considered this - So we proposed a

study and Rishi Gupta generated models

Pfizer Open models and descriptors


• What can be developed with very large training and test sets?

• HLM training 50,000 testing 25,000 molecules

• training 194,000 and testing 39,000

• MDCK training 25,000 testing 25,000

• MDR training 25,000 testing 18,400

• Open molecular descriptors / models vs commercial descriptors

• Examples – Metabolic Stability


HLM Model with CDK and

SMARTS Keys:

HLM Model with MOE2D and

SMARTS Keys

# Descriptors: 578 Descriptors

# Training Set compounds:

193,650

Cross Validation Results: 38,730

compounds

Training R2: 0.79

20% Test Set R2: 0.69

Blind Data Set (2310

compounds):

R2 = 0.53

RMSE = 0.367

Continuous Categorical:

κ = 0.40

Sensitivity = 0.16

Specificity = 0.99

PPV = 0.80

Time (sec/compound): 0.252

# Descriptors: 818 Descriptors

# Training Set compounds:

193,930

Cross Validation Results: 38,786

compounds

Training R2: 0.77

20% Test Set R2: 0.69

Blind Data Set (2310

compounds):

R2 = 0.53

RMSE = 0.367

Continuous Categorical:

κ = 0.42

Sensitivity = 0.24

Specificity = 0.987

PPV = 0.823

Time (sec/compound): 0.303

PCA of training (red) and test (blue)

compounds

Overlap in Chemistry space

• Examples – P-gp


Open source descriptors CDK and C5.0 algorithm

~60,000 molecules with P-gp efflux data from Pfizer

MDR <2.5 (low risk) (N = 14,175) MDR > 2.5 (high risk) (N = 10,820)

Test set MDR <2.5 (N = 10,441) > 2.5 (N = 7972)

Could facilitate model sharing?

CDK +fragment descriptors MOE 2D +fragment descriptors

Kappa 0.65 0.67

sensitivity 0.86 0.86

specificity 0.78 0.8

PPV 0.84 0.84

MoDELS RESIDE IN PAPERS

NOT ACCESSIBLE…THIS IS

UNDESIRABLE

How do we share them?

How do we use Them?

Open Extended Connectivity Fingerprints

ECFP_6 FCFP_6• Collected,

deduplicated, hashed

• Sparse integers

• Invented for Pipeline Pilot: public method, proprietary details

• Often used with Bayesian models: many published papers

• Built a new implementation: open source, Java, CDK– stable: fingerprints don't change with each new toolkit release

– well defined: easy to document precise steps

– easy to port: already migrated to iOS (Objective-C) for TB Mobile app

• Provides core basis feature for CDD open source model serviceClark et al., J Cheminform 6:38 2014

Select dataset actives in vault for model

Build model

Select dataset and actives

BBB Model output

View models

View predictions and Applicability

Applicability = 1 then molecule is in the model training setSelect more models…

Exporting models from CDD

Clark et al., JCIM 55: 1231-1245 (2015)

Export model from CDD and open in mobile apps

Clark et al., JCIM 55: 1231-1245 (2015)

Machine Learning – Different tools• Models generated using : molecular

function class fingerprints of maximum

diameter 6 (FCFP_6), AlogP, molecular

weight, number of rotatable bonds,

number of rings, number of aromatic

rings, number of hydrogen bond

acceptors, number of hydrogen bond

donors, and molecular fractional polar

surface area.

• Models were validated using five-fold

cross validation (leave out 20% of the

database).

• Bayesian, Support Vector Machine and

Recursive Partitioning Forest and single

tree models built.

• RP Forest and RP Single Tree models

used the standard protocol in Discovery

Studio.

• 5-fold cross validation or leave out 50%

x 100 fold cross validation was used to

calculate the ROC for the models

generated

• *fingerprints only Ai et al., ADDR 86: 46-60, 2015

KCNQ1

Ames Bayesian model built with 6512 molecules (Hansen et al., 2009)

Features important for Ames actives. Features important for Ames inactives.

Ames Bayesian model built using CDD Models showing ROC for 3 fold cross validation. Note only FCFP_6 descriptors were used

FCFP6 fingerprint models in CDD

Clark et al., JCIM 55: 1231-1245 (2015)

ECFP6 fingerprint only models in MMDS

Clark et al., JCIM 55: 1231-1245 (2015)

Using AZ-ChEMBL data for CDD Models

• Human microsomal intrinsic clearance

• Rat hepatocyte intrinsic clearance

• Human protein binding• Octanol water (logD7.4)

• Solubility pH7.4

Results for Bayesian model cross validation. 5-fold and Leave one out (LOO) validation with Bayesian models generated with Discovery Studio and Open Models implemented in the mobile app MMDS. * = previously published

Ekins et al Drug Metab Dispos In Press 2015

Transporter models

Ekins et al Drug Metab Dispos In Press 2015

Transporter models

Summary and Next Steps

• Shown that open source models/ descriptors comparable to previously published models with commercial software

• Implemented Bayesian machine learning in CDD Vault• Can be used on private or public data• Can enable sharing of models in CDD Vault• Enabled export of models – can use models in 3rd part mobile apps or

other tools• Demonstrated various ADME/Tox models and transporters

• Additional work with Dr. Joel Freundlich and Dr Alex Perryman on microsomal stability models

• Provide more information on models and predictions• Visualize training set molecules vs test compounds• Use a model to predict compounds and then test them

Acknowledgements

• Antony Williams• Steven Wright • Barry Bunin and all colleagues at CDD

• Award Number 9R44TR000942-02 “Biocomputation across distributed private datasets to enhance drug discovery” from the NIH National Center for Advancing Translational Sciences.

• R41-AI108003-01 “Identification and validation of targets of phenotypic high throughput screening” from NIH National Institute of Allergy and Infectious Diseases

• Bill and Melinda Gates Foundation (Grant#49852 “Collaborative drug discovery for TB through a novel database of SAR data optimized to promote data archiving and sharing”).

Models can be accessed at

• http://molsync.com/bayesian1

• http://molsync.com/transporters

http://molsync.com/bayesian1

http://molsync.com/transporters