Using Chemical Categories to Inform Quantitative Risk ......Dopamine D1 Receptor 27 453 59.54 99.05 98.61 0.492 25.19 0.13 0.41 0.198 Dopamine D2 Receptor 80 2589 77.44 96.66 95.41

Using Chemical Categories to Inform

Quantitative Risk Assessment

Society of Toxicology Annual Meeting 2018

Timothy E H Allen

Jonathan M Goodman, Steve Gutsell, Paul J Russell

Structural Alerts and Chemical Categories

In Silico Modelling for MIEs

Bowes, J., et al. (2012) Drug Discov., 11; 909.

https://www.ebi.ac.uk/chembl/

http://accelrys.com/products/collaborative-science/biovia-pipeline-pilot/

As they sit on the boundary between chemistry and biology, MIEs are a good target

for in silico modelling.

For receptor binding MIEs, the ability of a chemical to interact with a biological target

must be related to its chemistry.

What is it about these molecules that allow them to make these interactions?

To investigate this, receptor binding data was extracted from the open-source database

ChEMBL, and structural alerts were constructed using custom scripting in Pipeline Pilot.

These structural alerts can be thought of as defining a small area of chemical space

containing chemicals with very similar structure. These chemicals can be used for

read-across and quantitative activity estimation.

Target MIEs

Bowes, J., et al. (2012) Drug Discov., 11; 909.

GPCRs GPCRs (cont.) Enzymes Ion Channels

Adenosine A2a Receptor Histamine H1 Receptor Acetylcholinesterase Potassium Voltage Gated Channel KQT 1

Histamine H2 Receptor

Alpha-1a Adrenergic Receptor Cyclooxygenase 1 Serotonin 3A Receptor

Alpha-2a Adrenergic Receptor Muscarinic Acetylcholine Receptor M1 Cyclooxygenase 2

Beta-1 Adrenergic Receptor Muscarinic Acetylcholine Receptor M2 Nuclear Receptors

Beta-2 Adrenergic Receptor Muscarinic Acetylcholine Receptor M3 Dihydrofolate Reductase

Androgen Receptor

Cannabinoid CB1 Receptor Delta Opioid Receptor Histone Deacetylase 1

Cannabinoid CB2 Receptor Kappa Opioid Receptor Glucocorticoid Receptor

Mu Opioid Receptor Monoamine Oxidase A

Cholecystokinin Receptor A Transporters

Serotonin 1A Receptor Phosphodiesterase 4D

Dopamine D1 Receptor Serotonin 1B Receptor Dopamine Transporter

Dopamine D2 Receptor Serotonin 2A Receptor Thymidylate Synthase

Serotonin 2B Receptor Norepinephrine Transporter

Tyrosine-Protein Kinase

Vasopressin V1A Receptor Serotonin Transporter

Adenosine A2A Receptor Structural Alerts

Allen, T.E.H. et al. (2016) Chem. Res. Toxicol., 29; 1611.

319/779 Positives in Test Set

229 False Positives in Test Set

Adenosine A2A Receptor Structural Alerts


87 Chemicals total in ChEMBL

NO False Positives


229 False Positives in Test Set

Model Predictivity

Vs Previous Models

Target Alerts Binders SE SP Q MCC ΔSE ΔSP ΔQ ΔMCC

GPCRs

Adenosine A2A Receptor 115 2960 90.62 99.61 98.99 0.920 28.27 4.02 5.71 0.390

Alpha-1a Adrenergic Receptor 42 705 66.67 99.60 99.05 0.697 37.44 8.99 9.47 0.611

Alpha-2a Adrenergic Receptor 24 380 38.18 99.90 99.32 0.547 -0.91 1.93 1.90 0.312

Beta Adrenergic Receptors 76 1145 84.64 99.94 99.45 0.907

Beta-1 Adrenergic Receptor 52 694 71.96 99.23 98.79 0.655 -24.34 2.74 2.30 0.118

Beta-2 Adrenergic Receptor 57 770 77.39 99.25 98.82 0.718 -16.09 2.49 2.12 0.142

Cannabinoid CB1 Receptor 128 3738 88.76 93.76 93.31 0.688 71.71 -2.50 4.20 0.512

Cannabinoid CB2 Receptor 121 3405 86.70 93.94 93.34 0.668 65.36 -5.24 0.64 0.306

Cholecystokinin Receptor A 16 255 65.38 99.91 99.68 0.738 -32.05 8.20 7.93 0.482

Dopamine D1 Receptor 27 453 59.54 99.05 98.61 0.492 25.19 0.13 0.41 0.198

Dopamine D2 Receptor 80 2589 77.44 96.66 95.41 0.668 22.43 7.64 8.61 0.353

Model Predictivity


GPCRs (cont.)

Histamine H1 Receptor 32 672 48.73 99.84 99.15 0.625 20.25 3.38 3.61 0.475

Histamine H2 Receptor 7 191 39.58 99.99 99.74 0.612 -39.58 17.47 17.23 0.509

Muscarinic Acetylcholine Receptor M1 58 887 67.74 98.56 97.90 0.575 23.79 16.43 16.59 0.478



Delta Opioid Receptor 98 2550 89.78 94.27 94.08 0.589 37.13 0.48 2.08 0.243

Kappa Opioid Receptor 97 2347 88.69 94.73 94.44 0.614 37.34 0.79 2.55 0.263

Mu Opioid Receptor 102 2793 92.41 93.94 93.85 0.639 38.85 -0.55 1.69 0.235

Serotonin 1A Receptor 71 1777 70.63 96.92 95.78 0.579 8.53 0.40 0.76 0.077

Serotonin 1B Receptor 27 396 58.77 99.51 99.11 0.559 -27.19 6.69 6.35 0.272


Serotonin 2B Receptor 50 750 67.18 97.58 97.07 0.452 48.21 3.17 3.92 0.378

Vasopressin V1A Receptor 47 651 70.00 99.97 99.51 0.825 -3.89 17.68 17.34 0.646

Model Predictivity


Enzymes

Acetylcholinesterase 78 1355 75.96 99.58 98.84 0.800 9.29 15.82 15.61 0.570

Cyclooxygenase 1 23 379 40.95 99.61 99.08 0.443 -1.90 12.25 12.12 0.358

Cyclooxygenase 2 60 964 71.22 99.52 98.84 0.741 0.36 11.01 10.76 0.470

Dihydrofolate Reductase 22 404 58.00 99.97 99.60 0.735 -7.00 8.59 8.46 0.554

Histone Deacetylase 1 72 1202 80.63 99.63 99.11 0.828 4.38 25.11 24.54 0.641

Monoamine Oxidase A 24 533 41.72 99.92 99.17 0.601 -9.27 24.72 24.28 0.533

Phosphodiesterase 4D 25 385 55.08 99.97 99.51 0.718 -11.02 6.71 6.53 0.490

Thymidylate Synthase 6 239 5.56 100.00 99.56 0.235 -25.93 1.85 1.72 0.091

Tyrosine-Protein Kinase 40 568 53.02 100.00 99.40 0.726 0.67 8.60 8.50 0.555

Model Predictivity


Ion Channels

Potassium Voltage Gated Channel KQT 1 18 295 30.00 100.00 99.52 0.546 -45.00 52.44 51.77 0.509


Nuclear Receptors

Androgen Receptor 63 1598 75.07 99.91 99.19 0.846 6.82 1.90 2.05 0.273

Glucocorticoid Receptor 86 2201 87.61 99.96 99.48 0.929 70.80 1.51 4.20 0.725

Transporters

Dopamine Transporter 31 1908 42.53 99.04 96.93 0.504 4.83 11.04 10.81 0.359

Norepinephrine Transporter 47 2616 61.60 97.81 95.82 0.596 -15.20 60.74 56.57 0.530

Serotonin Transporter 46 3480 49.46 97.20 93.80 0.500 -27.57 59.51 53.32 0.422

Average (All Targets) 66.30 98.58 97.80 0.666 +9.81 +10.70 +11.03 +0.384

Adenosine A2A Receptor Alert P(Ki) Values



P(Ki) = 8.42 +/- 0.68Range = 6.99 – 9.00

n = 14

CoMFA and Quantitative Predictions

Quantitative Activity Predictions

Tosco, P., Balle, T. (2011) J Mol. Model., 25; 777.

2D structure activities provide an incomplete picture of receptor binding interactions, as

molecules and targets are 3D environments.

Can a Three Dimensional approach provide improved molecular activity

estimates and more insight into these MIEs?

Comparative Molecular Field Analysis (CoMFA) provides a potential tool for the

construction of 3D QSAR models based on the data and fragments already identified.

2D structural alerts provide chemical categories for the use of these higher level

calculations.

This requires the alignment of molecules in the training set and calculation of steric and

electronic fields around them. These fields are then analysed using PLS regression

analysis to give a quantitative predictive model.

3DQSAR Chemical Categories

GR Alert Results Categories

RMSE ≈ 0.56

5

6

7

8

9

10

11

5 6 7 8 9 10 11

Experim

enta

l A

ctivity (

pIC

50)

Predicted Activity (pIC50)

GR Alerts 2, 23, 75, 86, 106 Test Set Predictions

CORR

Alert 2

Alert 23

Alert 75

Alert 86

Alert 106

LOO Analysis

Alert SDEP

2 0.3564

23 0.8043

75 0.6139

86 0.9856

106 0.9880

5

6

7

8

9

10

11

5 6 7 8 9 10 11

Experim

enta

l A

ctivity (

pIC

50)

Predicted Activity (pIC50)

GR Alerts 2, 23, 75, 86, 106 Test Set Predictions

CORR

Alert 2

Alert 23

Alert 75

Alert 86

Alert 106

LOO Analysis

Alert SDEP

2 0.2935

23 0.6323

75 0.4119

86 0.4625

106 0.5821

GR Alert Results 3DQSAR

RMSE ≈ 0.51

3DQSAR Predictivity

TEST SET RMSE LOO SDEP

Alert CATS MMFF94 QM G03 CATS MMFF94 QM G03

GR Alert 2 0.33 0.32 0.30 0.3564 0.3079 0.2935

GR Alert 23 0.68 0.56 0.47 0.8043 0.6393 0.6323

GR Alert 75 0.61 0.48 0.45 0.6139 0.5457 0.4119

GR Alert 86 0.47 0.72 0.69 0.9856 0.5511 0.4625

GR Alert 106 0.77 0.68 0.80 0.9880 0.6917 0.5821

HERG Alert 15 0.63 0.72 0.71 0.6486 0.5896 0.6232

HERG Alert 17 0.47 0.69 0.85 0.5919 0.6823 0.5159

HERG Alert 20 0.17 1.26 0.85 0.4393 0.5243 0.9353

HERG Alert 46 2.69 1.87 2.49 2.4256 1.6608 0.5557

HERG Alert 47 1.52 2.24 2.07 2.1657 2.0774 1.5545

MOR Alert 4 0.65 0.47 0.52 0.6413 0.4463 0.4648

MOR Alert 6 0.96 0.73 0.44 1.1348 0.4218 0.4544

MOR Alert 48 0.38 0.49 0.34 0.5791 0.3660 0.3402

MOR Alert 100 0.93 0.73 0.75 1.0221 0.5962 0.5766

MOR Alert 111 0.58 0.64 0.55 0.6540 0.3638 0.4082

3DQSAR Predictivity

TEST SET RMSE LOO SDEP

Alert CATS MMFF94 QM G03 CATS MMFF94 QM G03

COX 2 Alert 5 0.99 1.10 1.04 1.1172 1.1617 1.1481

COX 2 Alert 26 0.63 0.52 0.52 0.6839 0.5441 0.8432

COX 2 Alert 45 0.99 0.78 0.81 1.0767 0.6605 0.6380

COX 2 Alert 57 0.77 0.68 0.66 0.8134 0.6016 0.6305

COX2 Alert 93 0.41 0.70 0.41 0.3576 0.2766 0.3385

DT Alert 2 0.58 0.40 0.31 0.6341 0.5745 0.9951

DT Alert 9 0.37 0.45 0.29 0.5487 0.3884 0.4184

DT Alert 59 0.87 0.87 0.89 0.8867 0.6716 0.7031

DT Alert 75 0.66 0.93 0.90 0.7310 0.8118 0.9629

DT Alert 90 0.67 0.62 0.63 1.0760 0.4218 0.7094

9 7 9 3 11 11

4 8 13 3 12 10

12 10 3 19 2 4

Glucocorticoid Receptor Interactions

Bledsoe, R.K., et al. (2002) Cell, 110; 93.








Bledsoe, R.K., et al. (2002) Cell, 110; 93.

Transition State Modelling and the Ames Test

The Ames Mutagenicity Assay

The Ames test is an in vitro assay that determines if a compound is able to mutate DNA.

An S9 mix of rat liver enzymes are used to simulate metabolism of the parent

compound.

Therefore the Ames assay without the S9 mix can be considered as an in vitro assay

measuring the MIE for the direct covalent binding of a parent compound to DNA.

In this MIE a nucleophilic guanine base attacks an electrophilic parent compound.

Could this could be modelled using a transition state search and activation

energy calculation?

Structural alerts indicating α,β-unsaturated carbonyls as Michael acceptors that can

covalently modify DNA and Ames data from the OECD QSAR Toolbox were used to

test this hypothesis.

Enoch, S.J., et al. (2011) ATLA, 39; 131.

http://www.oecd.org/chemicalsafety/risk-assessment/theoecdqsartoolbox.htm

Modelling the Transition State


ΔG‡


DFT - Optimization: B3LYP, 6-31+G(d), iefpcm

‡

ΔG‡


DFT - Optimization: B3LYP, 6-31+G(d), iefpcm; SPE: M062X, def2tzvpp, iefpcm

‡

ΔG‡

25.9 kcal mol-1

Transition State Energies

DFT - Optimization: B3LYP, 6-31+G(d), iefpcm; SPE: M062X, def2tzvpp, iefpcm


?

4-Hydroxy-5-methyl-3-furanone

ΔG‡ = 25.8 kcal mol-1



Hiramoto, K., et al. (1996) Mut. Res., 359; 119.



“HMF found to be mutagenic probably due to

generation of active oxygen radicals”

Hiramoto, K., et al. (1996) Mut. Res., 359; 119.

3-(Dichloromethylene)-2,5-pyrrolidinedione




Haddon, W.F., et al. (1996) J. Agric. Food Chem., 44; 256.



Haddon, W.F., et al. (1996) J. Agric. Food Chem., 44; 256.



ΔG‡ (kcal mol-1)

21.2

24.4

16.1


Acknowledgements

• Professor Jonathan Goodman

• Unilever

• Dr Paul Russell, Dr Steve Gutsell & colleagues at SEAC, Unilever

• Dr Matthew Grayson

• The Centre for Molecular Informatics

• St. John’s College

Documents

Using Chemical Categories to Inform Quantitative Risk ......Dopamine D1 Receptor 27 453 59.54 99.05 98.61 0.492 25.19 0.13 0.41 0.198 Dopamine D2 Receptor 80 2589 77.44 96.66 95.41