AOPs to support in silico predictions: the link from chemistry to adverse effects
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1 AOPs to support in silico predictions: the link from chemistry to adverse effects Dr Timothy E H Allen University of Cambridge BTS Annual Congress th April 2017
2 Chemistry
3 Chemistry and the AOP
4 Chemistry and the AOP
5 Chemistry and the AOP MIE Prediction
6 Chemistry and the AOP MIE Prediction Source Exposure ADME
7 Chemistry and the AOP MIE Prediction In Chemico assays Source Exposure ADME
8 Chemistry and the AOP MIE Prediction Target In Chemico assays In Vitro assays Source Exposure ADME
9 Chemistry and the AOP MIE Prediction Target In Chemico assays In Vitro assays Source Exposure ADME
10 Chemical Categories for Genotox Domain Michael addition Schiff base S N 1 S N 2 S N Ar Mechanistic alerts Polarised alkenes Quinone and quinone-type chemicals P450-mediated activation to quinone and quinone-type chemicals P450-mediated activation of heterocyclic ring systems Structural alerts α,β-unsaturated aldehydes α,β-unsaturated ketones α,β-unsaturated esters α,β-unsaturated amides Enoch, S.J., et al. (2011) ATLA, 39; 131.
11 Chemical Categories for Genotox Enoch, S.J., et al. (2011) ATLA, 39; 131.
12 Chemical Categories for Genotox Enoch, S.J., et al. (2011) ATLA, 39; 131.
13 Chemical Categories for Genotox Enoch, S.J., et al. (2011) ATLA, 39; 131.
14 Chemical Categories for Genotox Enoch, S.J., et al. (2011) ATLA, 39; 131.
15 Chemical Categories for Genotox Enoch, S.J., et al. (2011) ATLA, 39; 131.
16 Chemical Categories for Genotox Enoch, S.J., et al. (2011) ATLA, 39; 131.
17 Chemical Categories for Genotox Enoch, S.J., et al. (2011) ATLA, 39; 131.
18 Quantitative Mechanistic Modelling for Skin Sensitisation For Schiff base domain skin sensitisers pec3 = 1.12 Σ σ logp 0.62 Aptula, A.O., et al. (2006) Chem. Res. Toxicol, 19; Roberts, D.W., et al. (2007) Chem. Res. Toxicol., 20; 1019.
19 Quantitative Mechanistic Modelling for Skin Sensitisation For Schiff base domain skin sensitisers pec3 = 1.12 Σ σ logp 0.62 LLNA measure of skin sensitization potential Aptula, A.O., et al. (2006) Chem. Res. Toxicol, 19; Roberts, D.W., et al. (2007) Chem. Res. Toxicol., 20; 1019.
20 Quantitative Mechanistic Modelling for Skin Sensitisation For Schiff base domain skin sensitisers pec3 = 1.12 Σ σ logp 0.62 LLNA measure of skin sensitization potential Polar Substituent constant Aptula, A.O., et al. (2006) Chem. Res. Toxicol, 19; Roberts, D.W., et al. (2007) Chem. Res. Toxicol., 20; 1019.
21 Quantitative Mechanistic Modelling for Skin Sensitisation For Schiff base domain skin sensitisers pec3 = 1.12 Σ σ logp 0.62 LLNA measure of skin sensitization potential Partition coefficient Polar Substituent constant Aptula, A.O., et al. (2006) Chem. Res. Toxicol., 19; Roberts, D.W., et al. (2007) Chem. Res. Toxicol., 20; 1019.
22 2D SARs for Receptor Binding 2D structural alerts have been developed to link chemical properties to MIEs for well-known human pharmacological targets (Bowes 2012) using open source ChEMBL receptor-binding data. Bowes, J., et al. (2012) Drug Discov., 11; Allen, T.E.H. et al. (2016) Chem. Res. Toxicol., 29; 1611.
23 AA2a Receptor Structural Alerts 319/779 Positives in Test Set 229 False Positives in Test Set Allen, T.E.H. et al. (2016) Chem. Res. Toxicol., 29; 1611.
24 AA2a Receptor Structural Alerts 319/779 Positives in Test Set 229 False Positives in Test Set 31/779 Positives in Test Set 87 Chemicals total in ChEMBL NO False Positives
25 Chemical Categories for Activity The well defined chemical categories provides by the V2.0 ChEMBL structural alerts provide good candidates for higher-level QSAR applications. To determine when this is useful it is helpful to understand the activity range, mean and standard deviation for each chemical category. Read across may useful in some situations for specific chemicals rather than a higher-level QSAR. Comparative Molecular Field Analysis provides a potential tool for the construction of 3D QSAR models based on the data and fragments already identified, where appropriate. This requires the alignment of molecules in the training set and calculation of steric and electronic fields around them. Tosco, P., Balle, T. (2011) J Mol. Model., 25; 777.
26 AA2aR Alert P(Ki) Values
27 AA2aR Alert P(Ki) Values
28 HDAC1 Alerts 10, 15, 88 Results RMSE 0.60
29 Molecular Fields Steric Space Steric Hindrance H-Bond Donor H-Bond Acceptor
30 MIE Atlas Targets Target Target Target GPCRs GPCRs (cont.) Ion Channels Adenosine A2a Receptor Serotonin 1A Receptor Glutamate (NMDA) Receptor Serotonin 1B Receptor Alpha-1a Adrenergic Receptor Serotonin 2A Receptor Potassium Voltage Gated Channel KQT-like Member 1 Alpha-2a Adrenergic Receptor Serotonin 2B Receptor Beta-1 Adrenergic Receptor Serotonin 3A Receptor Beta-2 Adrenergic Receptor Vasopressin V1A Receptor Sodium Channel V Subunit Alpha Cannabinoid CB1 Receptor Cannabinoid CB2 Receptor Enzymes Voltage Gated K Channel Subunit Kv7.1 Cholecystokinin Receptor A Acetylcholinesterase Nuclear Receptors Dopamine D1 Receptor Cyclooxygenase 1 Dopamine D2 Receptor Cyclooxygenase 2 Androgen Receptor Endothelin Receptor A Dihydrofolate Reductase Glucocorticoid Receptor Histamine H1 Receptor GAR Transformylase Histamine H2 Receptor Transporters Histone Deacetylase 1 Muscarinic Acetylcholine Receptor M1 Dopamine Transporter Muscarinic Acetylcholine Receptor M2 Monoamine Oxidase A Muscarinic Acetylcholine Receptor M3 Norepinephrine Transporter Phosphodiesterase 3A Delta Opioid Receptor Phosphodiesterase 4D Serotonin Transporter Kappa Opioid Receptor Mu Opioid Receptor Thymidylate Synthase Tyrosine-Protein Kinase Bowes, J., et al. (2012) Drug Discov., 11; 909.
31 AOP-Wiki Beta-2 adrenergic agonist induction of mesovarian leiomyomas Histamine (H2) receptor antagonism leading to reduced survival Acetylcholinesterase inhibition leading to acute mortality Cycloxygenase 1 inhibition leading to renal failure and mortality Cycloxygenase inhibition leading to reproductive failure/dysfunction (6 pathways) Glutamate receptor binding leading to neuronal cell death ERG inhibition leading to reduced survival Sodium channel inhibition leading to reduced survival Sodium channel inhibition leading to increased predation Sodium channel inhibition leading to congenital malformations Sodium channel inhibition leading to population decline Sodium channel modulation leading to acute mortality Androgen receptor agonism leading to reproductive dysfunction Androgen receptor antagonism leading to adverse effects in male foetus Decreased androgen receptor activity leading to Leydig cell tumors Androgen receptor activation leading to hepatocellular tumors and carcinomas Glucocorticoid receptor activation leading to increased disease susceptibility Glucocorticoid receptor mediated adult Leydig cell dysfunction leading to decreased male fertility Serotonin transporter inhibition leading to decreased shelter seeking and increased predation Serotonin transporter inhibition leading to population decline Serotonin transporter inhibition leading to population increase Serotonin transporter inhibition leading to decreased reproductive success and population decline Serotonin transporter inhibition leading to increased reproductive success and population increase
32 AOP-Wiki Beta-2 adrenergic agonist induction of mesovarian leiomyomas Histamine (H2) receptor antagonism leading to reduced survival Acetylcholinesterase inhibition leading to acute mortality Cycloxygenase 1 inhibition leading to renal failure and mortality Cycloxygenase inhibition leading to reproductive failure/dysfunction (6 pathways) Glutamate receptor binding leading to neuronal cell death ERG inhibition leading to reduced survival Sodium channel inhibition leading to reduced survival Sodium channel inhibition leading to increased predation Sodium channel inhibition leading to congenital malformations Sodium channel inhibition leading to population decline Sodium channel modulation leading to acute mortality Androgen receptor agonism leading to reproductive dysfunction Androgen receptor antagonism leading to adverse effects in male foetus Decreased androgen receptor activity leading to Leydig cell tumors Androgen receptor activation leading to hepatocellular tumors and carcinomas Glucocorticoid receptor activation leading to increased disease susceptibility Glucocorticoid receptor mediated adult Leydig cell dysfunction leading to decreased male fertility Serotonin transporter inhibition leading to decreased shelter seeking and increased predation Serotonin transporter inhibition leading to population decline Serotonin transporter inhibition leading to population increase Serotonin transporter inhibition leading to decreased reproductive success and population decline Serotonin transporter inhibition leading to increased reproductive success and population increase
33 Putative Mu Opioid Receptor AOP [1] The activation of the three (μ, δ, κ) opioid receptors leads to Gi/o protein activation [2] The activated Gi/o protein activates the GIRK (G protein-activated inwardly rectifying potassium) channel [3] Activation of GIRK channels induces hyperpolarization of the neurons via efflux of potassium ions and ultimately reduces neural excitability and heart rate [4] There appears to be two mechanisms by which the transmission of pain sensations are depressed; hyperpolarization of interneurons within the dorsal cord and depressing the release of the neurotransmitters associated with pain transmission Ikeda, K., et al. (2002) Neurosci. Res., 44; 121. Lipp, J. (1991) Neuropharmacol., 14; 131.
34 Chemical Category to Adverse Outcome
35 Glucocorticoid Receptor AOPs Credit: Allysan P. Scatterday, US EPA
36 Glucocorticoid Receptor AOPs Carlie LaLone Susan Laws and Gary Klinefelter Urmila Kodavanti Credit: Allysan P. Scatterday, US EPA
37 Chemistry and the AOP MIE Prediction Target In Chemico assays In Vitro assays Source Exposure ADME
38 Measuring MIEs in chemico Schultz, T.W., et al. (2007) Chem. Res. Toxicol., 20; 1359.
39 Measuring MIEs in chemico Schultz, T.W., et al. (2007) Chem. Res. Toxicol., 20; 1359.
40 Measuring MIEs in chemico RC Non-reactive Schultz, T.W., et al. (2007) Chem. Res. Toxicol., 20; 1359.
41 Measuring MIEs in chemico RC Non-reactive Schultz, T.W., et al. (2007) Chem. Res. Toxicol., 20; 1359.
42 Measuring MIEs in chemico RC Not a Michael acceptor Non-reactive Schultz, T.W., et al. (2007) Chem. Res. Toxicol., 20; 1359.
43 Chemistry and the AOP MIE Prediction Target In Chemico assays In Vitro assays Source Exposure ADME
44 Guiding in vitro 2D structural alerts have been developed for ToxCast assays to link chemical properties to MIEs in an analogous fashion to those made using ChEMBL receptorbinding data. While these models do not perform as well as the ChEMBL models, can they be used to guide in vitro testing? Do certain chemical characteristics align with specific in vitro ToxCast assays?
45 SE (%) Guiding AR in vitro Sensitivity % for Toxcast Assays 100 AR Binders AR Alert 6 AR Alert 7 AR Alert 9 AR Alert ToxCast AR Assay
46 Chemistry and the AOP MIE Prediction Target In Chemico assays In Vitro assays Source Exposure ADME
47 Exposure Chemistry can help our understanding of AOPs, can it also aid in better understanding of exposure? Credit: Stephen W. Edwards, US EPA
48 Aggregate Exposure Pathways The aggregate exposure pathway (AEP) concept is an analogous framework for organising exposure data to the AOP Credit: Stephen W. Edwards, US EPA Teeguarden, J.G., et al. (2016) Environ. Sci. Technol., 50; 4579.
49 Aggregate Exposure Pathways The aggregate exposure pathway (AEP) concept is an analogous framework for organising exposure data to the AOP Key Exposure State Credit: Stephen W. Edwards, US EPA Teeguarden, J.G., et al. (2016) Environ. Sci. Technol., 50; 4579.
50 Aggregate Exposure Pathways The aggregate exposure pathway (AEP) concept is an analogous framework for organising exposure data to the AOP Source Key Exposure State Credit: Stephen W. Edwards, US EPA Teeguarden, J.G., et al. (2016) Environ. Sci. Technol., 50; 4579.
51 Aggregate Exposure Pathways The aggregate exposure pathway (AEP) concept is an analogous framework for organising exposure data to the AOP Source Target Site Exposure Key Exposure State Credit: Stephen W. Edwards, US EPA Teeguarden, J.G., et al. (2016) Environ. Sci. Technol., 50; 4579.
52 Aggregate Exposure Pathways The aggregate exposure pathway (AEP) concept is an analogous framework for organising exposure data to the AOP Source Target Site Exposure Key Exposure State Key Transitional Relationship Credit: Stephen W. Edwards, US EPA Teeguarden, J.G., et al. (2016) Environ. Sci. Technol., 50; 4579.
53 Impact of ph and Temperature Credit: Stephen W. Edwards, US EPA, Jeremy A. Leonard, US EPA
54 AEP into AOP Credit: Stephen W. Edwards, US EPA Teeguarden, J.G., et al. (2016) Environ. Sci. Technol., 50; 4579.
55 Modelling the AOP MacKay, C., et al. (2013) ALTEX, 30;13.
56 Modelling the AOP MacKay, C., et al. (2013) ALTEX, 30;13.
57 Modelling the AOP MacKay, C., et al. (2013) ALTEX, 30;13.
58 Modelling the AOP MacKay, C., et al. (2013) ALTEX, 30;13.
59 Modelling the AOP MacKay, C., et al. (2013) ALTEX, 30;13.
60 Modelling the AOP MacKay, C., et al. (2013) ALTEX, 30;13.
61 Chemistry and the AOP MIE Prediction Target In Chemico assays In Vitro assays Source Exposure ADME
62 Acknowledgements Professor Jonathan Goodman Unilever Dr Paul Russell, Dr Steve Gutsell & colleagues at SEAC, Unilever Dr Russell Thomas, Dr Stephen Edwards & colleagues at USEPA, RTP The Centre for Molecular Informatics St. John s College
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