In-silico study of ToxCast GPCR assays by quantitative structure-activity relationships (QSARs)...

Office of Research and Development

In-silico study of ToxCast GPCR assays by quantitative structure-activity relationships (QSARs) modeling

Kamel MansouriORISE postdoctoral fellow National Center for Computational Toxicology, U.S. EPA

The views expressed in this presentation are those of the author and do not

necessarily reflect the views or policies of the U.S. EPA

ACS 2014

11 August 2014, San Francisco

Office of Research and DevelopmentNational Center for Computational Toxicology

Outline

• ToxCast

–High-throughput Screening Data Generation

–Available data with lots of gaps to fill

–Time, cost, ethics

• GPCR assays

– Importance

–Classification models

–Regression models

–Predictions

• Future Plans

–Model other assays and extend the prediction list

2


Problem Statement

3

Too many chemicals to test with standard

animal-based methods

–Cost, time, animal welfare

Alternative


ToxCast / Tox21 Overall Strategy

• Identify targets or pathways linked to toxicity

• Develop high throughput assays for these targets or pathways

• Develop predictive systems models

– in vitro → in vivo

–in vitro → in silico

• Use predictive models (qualitative):

–Prioritize chemicals for targeted testing

• High Throughput Risk Assessments (quantitative)

• High Throughput Exposure Predictions

4

Office of Research and DevelopmentNational Center for Computational Toxicology 5

Testing under ToxCast and Tox21

Chemicals, Data and Release Timelines

Set Chemicals Assays Endpoints Completion Available

ToxCast Phase I 293 ~600 ~700 2011 Now

ToxCast Phase II 767 ~600 ~700 03/2013 Now

ToxCast E1K 800 ~50 ~120 03/2013 Now

Tox21 ~9000 ~80 ~150 Ongoing Ongoing

ToxCast Phase III ~900 ~300 ~300 Just starting 2014-2015

Chemicals

Assays

~600

~9000


QSAR for ToxCast targets: GPCRs

• G-Protein coupled receptors

• Common drug targets

• Chemicals are often promiscuous across GPCRs, leading to

off-target side effects

• ToxCast includes a large number of ~1000 diverse chemicals

6


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

GPCR_gOpiateK 4 76

GPCR_rOpiate_NonSelectiveNa 2 47

GPCR_rOpiate_NonSelective 1 67

GPCR_p5HT2C 7 68

GPCR_bDR_NonSelective 3 26

GPCR_r5HT_NonSelective 2 36

GPCR_rAdra1A 3 37

GPCR_h5HT2A 4 38

IC_rNaCh_site2 13 91

IC_rCaBTZCHL 3 62

GPCR_gH2 5 59

GPCR_rAdra1_NonSelective 4 40

GPCR_rmAdra2B 5 66

GPCR_hAdra2A 3 50

GPCR_rAdra2_NonSelective 3 54

GPCR_rAdra1B 6 43

GPCR_hH1 7 43

TR_gDAT 4 98

TR_hDAT 7 120

GPCR_hNK2 6 60

IC_rCaDHPRCh_L 1 50

GPCR_h5HT6 6 57

TR_hNET 12 137

GPCR_g5HT4 1 72

GPCR_hM2 8 58

GPCR_gMPeripheral_NonSelective 3 48

GPCR_hAdrb2 4 32

GPCR_hM4 5 66

GPCR_hM1 2 49

GPCR_hM3 6 57

GPCR_hM5 6 60

GPCR_hOpiate_D1 1 10

GPCR_hAdrb1 4 50

GPCR_h5HT5A 2 70

GPCR_hOpiate_mu 5 99

GPCR_hDRD4.4 6 54

GPCR_hDRD2s 10 66

GPCR_hDRD1 9 90

GPCR_hAdra2C 8 66

GPCR_h5HT7 13 101

1 0.6 0.6 0.3 0.4 0.4 0.5 0.4 0.5 0.5 0.6 0.4 0.4 0.4 0.4 0.5 0.3 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.5 0.5 0.3 0.5 0.4 0.4 0.4 0.2 0.3 0.3 0.4 0.3 0.3 0.4 0.3 0.4

0.6 1 0.8 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.5 0.5 0.5 0.3 0.5 0.4 0.4 0.5 0.4 0.3 0.4 0.6 0.4 0.4 0.4 0.4 0.5

0.6 0.8 1 0.4 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.6 0.5 0.4 0.4 0.4 0.5 0.4 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.5 0.5 0.4 0.3 0.4 0.6 0.4 0.5 0.5 0.4 0.5

0.3 0.4 0.4 1 0.5 0.6 0.5 0.6 0.5 0.5 0.5 0.4 0.5 0.4 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.5 0.4 0.5 0.4 0.5 0.4 0.5 0.4 0.4 0.5 0.3 0.4 0.5 0.4 0.5 0.6 0.5 0.5 0.5

0.4 0.5 0.6 0.5 1 0.7 0.7 0.7 0.5 0.6 0.6 0.5 0.5 0.5 0.5 0.6 0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.6 0.5 0.5 0.5 0.4 0.5 0.3 0.4 0.4 0.4 0.6 0.6 0.5 0.5 0.5

0.4 0.5 0.6 0.6 0.7 1 0.8 0.8 0.5 0.6 0.6 0.6 0.6 0.6 0.6 0.7 0.7 0.5 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.5 0.5 0.5 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.6 0.6 0.6 0.5 0.6

0.5 0.5 0.6 0.5 0.7 0.8 1 0.8 0.6 0.6 0.7 0.7 0.7 0.7 0.7 0.8 0.7 0.4 0.4 0.3 0.4 0.4 0.5 0.5 0.5 0.6 0.4 0.6 0.5 0.5 0.6 0.3 0.4 0.4 0.4 0.6 0.5 0.6 0.6 0.6

0.4 0.5 0.6 0.6 0.7 0.8 0.8 1 0.6 0.7 0.7 0.6 0.7 0.6 0.6 0.7 0.7 0.4 0.4 0.4 0.5 0.4 0.5 0.5 0.6 0.6 0.5 0.6 0.6 0.5 0.6 0.4 0.4 0.5 0.5 0.6 0.6 0.6 0.5 0.6

0.5 0.6 0.6 0.5 0.5 0.5 0.6 0.6 1 0.8 0.8 0.5 0.6 0.5 0.6 0.6 0.5 0.5 0.5 0.4 0.6 0.4 0.6 0.6 0.6 0.7 0.4 0.7 0.5 0.5 0.6 0.4 0.4 0.4 0.5 0.4 0.5 0.5 0.5 0.5

0.5 0.6 0.6 0.5 0.6 0.6 0.6 0.7 0.8 1 0.8 0.5 0.6 0.5 0.6 0.6 0.5 0.5 0.5 0.5 0.6 0.4 0.5 0.6 0.6 0.7 0.5 0.6 0.6 0.5 0.6 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.5 0.5

0.6 0.6 0.6 0.5 0.6 0.6 0.7 0.7 0.8 0.8 1 0.6 0.6 0.6 0.6 0.7 0.6 0.5 0.5 0.5 0.6 0.5 0.6 0.6 0.7 0.7 0.5 0.7 0.6 0.6 0.7 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.6 0.6

0.4 0.4 0.5 0.4 0.5 0.6 0.7 0.6 0.5 0.5 0.6 1 0.6 0.6 0.7 0.8 0.6 0.4 0.4 0.3 0.5 0.4 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.6 0.5 0.6 0.6 0.6

0.4 0.4 0.5 0.5 0.5 0.6 0.7 0.7 0.6 0.6 0.6 0.6 1 0.8 0.8 0.7 0.7 0.5 0.4 0.3 0.4 0.5 0.5 0.6 0.6 0.6 0.5 0.6 0.6 0.6 0.6 0.4 0.4 0.5 0.5 0.6 0.6 0.6 0.6 0.6

0.4 0.4 0.5 0.4 0.5 0.6 0.7 0.6 0.5 0.5 0.6 0.6 0.8 1 0.8 0.7 0.7 0.4 0.4 0.3 0.4 0.4 0.5 0.5 0.6 0.6 0.5 0.6 0.6 0.6 0.6 0.4 0.4 0.5 0.4 0.6 0.5 0.6 0.6 0.6

0.4 0.4 0.5 0.5 0.5 0.6 0.7 0.6 0.6 0.6 0.6 0.7 0.8 0.8 1 0.7 0.7 0.5 0.4 0.3 0.4 0.4 0.5 0.6 0.6 0.6 0.5 0.6 0.6 0.6 0.6 0.4 0.4 0.5 0.4 0.6 0.6 0.6 0.6 0.6

0.5 0.5 0.6 0.5 0.6 0.7 0.8 0.7 0.6 0.6 0.7 0.8 0.7 0.7 0.7 1 0.8 0.5 0.5 0.4 0.5 0.5 0.6 0.6 0.6 0.7 0.6 0.6 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.7 0.6 0.7 0.7 0.7

0.3 0.4 0.5 0.5 0.6 0.7 0.7 0.7 0.5 0.5 0.6 0.6 0.7 0.7 0.7 0.8 1 0.4 0.5 0.4 0.5 0.5 0.5 0.5 0.6 0.7 0.6 0.7 0.7 0.7 0.7 0.6 0.6 0.6 0.5 0.7 0.7 0.7 0.7 0.6

0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.5 0.5 0.5 0.4 0.5 0.4 0.5 0.5 0.4 1 0.7 0.3 0.4 0.4 0.5 0.5 0.4 0.4 0.4 0.5 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.5 0.5

0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.5 0.5 0.7 1 0.5 0.4 0.5 0.6 0.5 0.4 0.5 0.4 0.5 0.4 0.5 0.5 0.4 0.4 0.5 0.5 0.4 0.5 0.5 0.5 0.5

0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.5 0.5 0.3 0.3 0.3 0.3 0.4 0.4 0.3 0.5 1 0.5 0.5 0.5 0.4 0.4 0.5 0.5 0.5 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.4 0.5

0.3 0.5 0.5 0.4 0.4 0.4 0.4 0.5 0.6 0.6 0.6 0.5 0.4 0.4 0.4 0.5 0.5 0.4 0.4 0.5 1 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.4 0.4 0.6 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.5 0.5

0.3 0.4 0.4 0.5 0.4 0.5 0.4 0.4 0.4 0.4 0.5 0.4 0.5 0.4 0.4 0.5 0.5 0.4 0.5 0.5 0.5 1 0.5 0.6 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.6 0.5 0.6 0.7 0.6 0.6 0.6

0.4 0.4 0.5 0.4 0.4 0.4 0.5 0.5 0.6 0.5 0.6 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.6 0.5 0.5 0.5 1 0.6 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.5 0.6 0.5 0.6 0.5 0.6 0.6 0.6 0.6

0.4 0.5 0.5 0.5 0.4 0.4 0.5 0.5 0.6 0.6 0.6 0.5 0.6 0.5 0.6 0.6 0.5 0.5 0.5 0.4 0.5 0.6 0.6 1 0.5 0.6 0.5 0.5 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6

0.5 0.5 0.5 0.4 0.5 0.4 0.5 0.6 0.6 0.6 0.7 0.5 0.6 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.5 0.4 0.5 0.5 1 0.8 0.5 0.9 0.7 0.8 0.8 0.4 0.4 0.4 0.4 0.5 0.6 0.5 0.6 0.5

0.5 0.5 0.5 0.5 0.6 0.5 0.6 0.6 0.7 0.7 0.7 0.5 0.6 0.6 0.6 0.7 0.7 0.4 0.5 0.5 0.4 0.4 0.5 0.6 0.8 1 0.6 0.8 0.7 0.8 0.8 0.5 0.5 0.4 0.5 0.5 0.6 0.5 0.6 0.5

0.3 0.3 0.4 0.4 0.5 0.5 0.4 0.5 0.4 0.5 0.5 0.4 0.5 0.5 0.5 0.6 0.6 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.6 1 0.6 0.7 0.7 0.6 0.6 0.7 0.5 0.5 0.6 0.7 0.6 0.6 0.5

0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.7 0.6 0.7 0.5 0.6 0.6 0.6 0.6 0.7 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.9 0.8 0.6 1 0.8 0.9 0.8 0.5 0.6 0.5 0.5 0.5 0.6 0.6 0.6 0.6

0.4 0.4 0.5 0.4 0.5 0.4 0.5 0.6 0.5 0.6 0.6 0.5 0.6 0.6 0.6 0.6 0.7 0.4 0.4 0.4 0.4 0.5 0.6 0.6 0.7 0.7 0.7 0.8 1 0.8 0.8 0.5 0.6 0.5 0.5 0.6 0.6 0.6 0.6 0.6

0.4 0.4 0.5 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.5 0.6 0.6 0.6 0.6 0.7 0.4 0.5 0.4 0.4 0.5 0.6 0.6 0.8 0.8 0.7 0.9 0.8 1 0.8 0.6 0.6 0.6 0.5 0.6 0.6 0.6 0.6 0.6

0.4 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.5 0.6 0.6 0.6 0.6 0.7 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.8 0.8 0.6 0.8 0.8 0.8 1 0.6 0.6 0.6 0.6 0.6 0.7 0.7 0.8 0.7

0.2 0.4 0.4 0.3 0.3 0.4 0.3 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.5 0.6 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.6 0.5 0.5 0.6 0.6 1 0.7 0.6 0.6 0.6 0.6 0.6 0.7 0.6

0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.5 0.6 0.4 0.4 0.5 0.5 0.5 0.6 0.5 0.4 0.5 0.7 0.6 0.6 0.6 0.6 0.7 1 0.7 0.6 0.7 0.7 0.7 0.7 0.6

0.3 0.4 0.4 0.5 0.4 0.4 0.4 0.5 0.4 0.4 0.5 0.4 0.5 0.5 0.5 0.5 0.6 0.4 0.5 0.5 0.5 0.6 0.5 0.5 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.7 1 0.6 0.7 0.7 0.7 0.8 0.7

0.4 0.6 0.6 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.4 0.5 0.4 0.4 0.5 0.5 0.4 0.5 0.5 0.5 0.5 0.6 0.5 0.4 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 1 0.5 0.6 0.7 0.6 0.7

0.3 0.4 0.4 0.5 0.6 0.6 0.6 0.6 0.4 0.5 0.6 0.6 0.6 0.6 0.6 0.7 0.7 0.4 0.4 0.4 0.5 0.6 0.5 0.5 0.5 0.5 0.6 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.5 1 0.8 0.7 0.7 0.7

0.3 0.4 0.5 0.6 0.6 0.6 0.5 0.6 0.5 0.5 0.6 0.5 0.6 0.5 0.6 0.6 0.7 0.4 0.5 0.5 0.6 0.7 0.6 0.6 0.6 0.6 0.7 0.6 0.6 0.6 0.7 0.6 0.7 0.7 0.6 0.8 1 0.7 0.7 0.7

0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.5 0.6 0.6 0.6 0.6 0.6 0.6 0.7 0.7 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.6 0.7 0.7 0.7 0.7 0.7 1 0.8 0.8

0.3 0.4 0.4 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.6 0.7 0.7 0.5 0.5 0.4 0.5 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.8 0.7 0.7 0.8 0.6 0.7 0.7 0.8 1 0.7

0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.5 0.5 0.6 0.6 0.6 0.6 0.6 0.7 0.6 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.7 1

Dopaminergic, Serotonin, Adrenergic

Adrenergic

Muscarinic

Histamine

Histamine

GPCR assays – Cluster analysis

GPCR Aminergic assays with >=65% similarity

7


http://dx.doi.org/10.1016/j.str.2011.05.012

Muscarinic AChR

GPCR Family Tree

8

Biogenic Amine

Receptors

Dopaminergic

Histamine

Serotonin

Adrenergic

http://dx.doi.org/10.1016/j.str.2011.05.012


GPCR Targets for QSAR modeling

9

Assay Symbol Name

Hm1-5 CHRM1-5 cholinergic receptor, muscarinic 1-5

gMPeripheral_NonSelective M1 Muscarinic receptor peripheral

hAdrb2 ADRB2 adrenergic, beta-2-, receptor, surface

bDR_NonSelective DRD1 Dopamine receptor D1

h5HT2A HTR2A 5-hydroxytryptamine (serotonin) receptor 2A

rAdra1A,B Adra1a,b adrenergic, alpha-1A-B, receptor

rAdra1_NonSelective Adra1a adrenergic, alpha-1A-, receptor

hH1 HRH1 Histamine receptor H1

gH2 Hrh2 Guinea pig histamine receptor H2

rAdra2_NonSelective Adra2a adrenergic, alpha-2A-, receptor

hAdra2A ADRA2A adrenergic, alpha-2A-, receptor

rmAdra2B Adra2b adrenergic, alpha-2B-, receptor


10

INITIAL LIST OF CHEMICALSFile parsing & first check

Check for mixtures

Check for salts & counter ions

Normalization of structures

Manual inspection

Removal of duplicates

Treatment of tautomeric forms

CURATED DATASET

Check for inorganics

Curation of chemical structures for QSAR

KNIME workflow

(EPA/NCCT, DTU-food, UNC/MML)


11

Training & prediction sets:

• Training set (ToxCast):

• 1005 Chemicals with data 18 Endpoints

• Binary classes (qualitative models)

• AC50 (quantitative models)

• Prediction Set (Human Exposure Universe):

• 32464 Chemicals to be predicted

Molecular descriptors calculation:

• 1022 molecular descriptors 2D chemical structures.

• Software: Indigo, RDKit, CDK and MOE.

• Reduce collinearity,

• correlation threshold of 0.96 was applied Constant

& near constant removed

• Descriptors with missing values removed.

• The remaining set consisted of 470 descriptors.

QSAR-ready data


Preliminary structure-activity analysis

(Self-organizing maps)

12

Supervised-learning SOM map of all actives for

the 18 assays with the set of 470 descriptors.

Fitting

NER Ac

81.79 95.4

5-fold CV

NER AC

66.8 89.5

ROC curves of the SOM map in fitting

for active and non-active compounds.


13

A

B

Method PLSDA KNN SVM

Endpoint

(Positives)

Dsc LVs Fitting 5-fold CV Dsc k Fitting 5-fold CV Dsc C Fitting 5-fold CV

NER Ac NER Ac NER Ac NER Ac NER Ac NER Ac

hH1 (37) 26 5 91.9 89.4 91.5 88.6 27 1 81.1 96.2 81.2 96.3 30 5 87.8 99.1 68.5 96.9

hM1-5 (76) 15 3 84.1 82.9 85.7 83.6 19 4 76.7 93.8 78.8 94.3 12 10 94.7 99.2 76.6 94.7

All (115) 20 5 84.0 85.8 82.3 84.1 22 1 76.8 90.3 78.1 90.1 16 10 94.3 98.7 75.9 92.1

• Partial least squares -discriminamt analysis

• K nearest neighbors

• Support vector machines

Machine learning methods evaluated

Genetic algorithm for feature selection


Endpoint N activ

(/1005)

N Desc LVs Fitting 5-fold CV

NER Sn Sp NER Sn Sp

hH1 37 35 3 0.89 0.89 0.89 0.90 0.92 0.89

gH2 54 21 3 0.93 0.96 0.89 0.93 0.96 0.89

hM1 37 15 2 0.92 0.95 0.89 0.92 0.95 0.89

hM2 50 21 2 0.87 0.82 0.92 0.87 0.82 0.92

hM3 38 13 2 0.89 0.95 0.84 0.89 0.95 0.83

hM4 50 24 2 0.89 0.88 0.89 0.90 0.90 0.89

hM5 56 16 3 0.87 0.88 0.87 0.89 0.91 0.87

gMPeripheral_NS 39 20 3 0.93 0.95 0.90 0.93 0.95 0.90

rAdra1A 35 23 2 0.93 0.94 0.91 0.93 0.94 0.91

rAdra1B 38 30 3 0.91 0.92 0.90 0.89 0.89 0.89

rAdra1_NS 34 26 2 0.91 0.91 0.90 0.91 0.91 0.90

rAdra2_NS 41 30 2 0.89 0.90 0.87 0.89 0.90 0.88

hAdra2A 45 30 2 0.87 0.91 0.84 0.88 0.91 0.84

rmAdra2B 51 25 2 0.87 0.84 0.90 0.88 0.86 0.89

hAdrb2 26 31 2 0.90 0.92 0.87 0.90 0.92 0.87

r5HT1_NS 17 21 2 0.90 0.94 0.86 0.90 0.94 0.86

h5HT2A 25 24 2 0.95 1.00 0.90 0.95 1.00 0.90

bDR_NS 16 28 2 0.96 1.00 0.91 0.95 1.00 0.91

PLSDA models

for binary

classification.

• LVs:

Latent variables

• NER:

Balanced accuracy


15

Endpoint N activ Std. Dev N Desc LVs Fitting 5-fold CV

R2 RMSE Q2 RMSE

hH1 37 8.17 16 3 0.83 3.35 0.76 3.96

gH2 54 8.09 18 3 0.65 4.76 0.52 5.56

hM1 37 7.28 17 2 0.89 2.41 0.83 2.83

hM2 50 6.72 27 3 0.78 3.10 0.63 4.03

hM3 38 6.33 25 3 0.92 1.76 0.78 2.41

hM4 50 6.58 18 2 0.86 2.46 0.81 2.75

hM5 56 6.47 15 2 0.76 3.11 0.74 3.24

gMPeripheral_NS 39 6.51 11 2 0.77 3.09 0.62 3.94

rAdra1A 35 6.50 18 3 0.78 3.01 0.67 3.70

rAdra1B 38 8.34 29 2 0.85 3.18 0.71 3.72

rAdra1_NS 34 9.06 12 1 0.81 3.85 0.71 4.75

rAdra2_NS 41 7.80 34 2 0.80 3.41 0.63 4.21

hAdra2A 45 9.03 9 2 0.72 4.70 0.48 6.43

rmAdra2B 51 5.44 17 3 0.76 2.66 0.63 3.27

hAdrb2 26 7.87 23 3 0.87 2.83 0.77 3.73

r5HT1_NS 17 12.67 8 3 0.98 1.66 0.96 2.57

h5HT2A 25 7.58 14 2 0.88 2.52 0.74 3.31

bDR_NS 16 5.38 18 3 0.98 0.66 0.92 1.48

PLS models

for AC50

estimation

• LVs:

Latent variables

• Q2:

Validation accuracy


Clustered heatmap of the 18 assays (experimental and predicted values of AC50)

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Training set: 1005 chemicals

Predicted: 778 chemicals

Chem

icals

Assays

Data gaps of ToxCast for 18 GPCR assays filled


Examples of chemical structures

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Most potent in training set Most potent in prediction set


Summary and future directions

• High accuracy classification & regression models were built on quality data

• ToxCast data gaps filled with:

1. Qualitative predictions: active/ inactive

2. Quantitative predictions for active chemicals: estimation of AC50

• ToxCast is providing large data sets ready for building new QSAR models

–300 gene targets

–1000-8000 chemicals

• Forms the basis for predicting other inventories

– Validate with external test sets

– Apply all models on a list of 32,000 chemicals: Human Exposure Universe

– Use QSAR predictions to help regulators in the prioritization procedures 18


Acknowledgements

EPA NCCT

Rusty Thomas

Kevin Crofton

Keith Houck

Ann Richard

Richard Judson

Tom Knudsen

Matt Martin

Woody Setzer

John Wambaugh

Monica Linnenbrink

Jim Rabinowitz

Steve Little

Agnes Forgacs

Jill Franzosa

Chantel Nicolas

Bhavesh Ahir

Nisha Sipes

Lisa Truong

Max Leung

Kamel Mansouri

Eric Watt

Corey Strope

EPA NCCT

Nancy Baker

Jeff Edwards

Dayne Filer

Jayaram Kancherla

Parth Kothiya

Jimmy Phuong

Jessica Liu

Doris Smith

Jamey Vail

Hao Truong

Sean Watford

Indira Thillainadarajah

Christina Baghdikian

Contributors:

Nisha Sipes

Richard Judson

www.epa.gov/ncct/

US EPA National Center for Computational Toxicology

In-silico study of ToxCast GPCR assays by quantitative structure-activity relationships (QSARs)...

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