in silico prediction of genotoxicity
TRANSCRIPT
In Silico Prediction of Genotoxicity:
Current Applications & Future Perspectives
Senior Scientist
Robert Foster
Overview
• In silico systems for the prediction of genotoxicity
• Current applications: what do we use now?• Rule-based
• Statistical-based
• Focus on mutagenicity
• Future perspectives: how do we improve performance?• Building models for other genotoxicity endpoints
• Developing new technologies
• Conclusions
Origins of in silico genotoxicity prediction
Ashby & Tennant, Mutat Res, 1988, 204, 17-115
Dataset
In silico genotoxicity predictions systems
Metabolism
Mechanism Protocol
ExamplesActivity
Log P
Functional groups
Molecular weight
ExamplesActivity
Statistical-based
Computer algorithm generated (Q)SAR
model
Expert rule-based
Human expert implemented structural alerts
Derek & Sarah transparency
Chemicalreactivity
Similar chemicals
Reactivemetabolites
Protocol and limitations of Ames assay
Mechanismsof activity
Functionalgroups
Interpretation of strain data
Supporting data
How well do they predict genotoxicity?
• Good performance for Ames mutagenicity data• Performance will depend on chemical space
• Analysis of 5 different (Q)SAR systems• Expert rule-based and statistical-based
Performance metric
Average performance
Public Proprietary
Balanced accuracy 77% 66%
Sensitivity 74% 58%
Specificity 81% 73%
Coverage 95% 92%
Barber et al, Reg Toxicol Pharmacol, 2016, 76, 7-20
Improving predictivity of mutagenicity
• Increase chemical space coverage of models by adding data• Public &/or proprietary data sources
• Donation of proprietary data encourages collaboration to benefit scientific community• ~35% of Derek alerts for mutagenicity are constructed or refined using proprietary data
• Models chemical space unique to members
• Improves predictivity in chemical space most relevant to members
• Models generalised for mutual benefit
• Reduction of incorrect predictions & testing under ICH M7
Extending predictions beyond mutagenicity
Are non-mutagenicity predictions any good?
Derek validations for chromosome damage
Total TP FP TN FN
875 291 66 348 170
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BA Sens Spec PPV NPV
Validation of Derek againstFDA in vitro CA dataset
Hsu et al, Reg Toxicol Pharmacol, 2018, 99, 274-288
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BA Sens Spec PPV NPV
Validation of Derek against binding assay data from ChemBL
Total TP FP TN FN
85 3 10 40 32
• Derek performs strongly against in vitro CA dataset with DNA reactive chemicals• Derek performs less well against chemicals binding non-covalently e.g.
topoisomerase inhibitors
Creating a statistical-based model for CD
• Prepared training set with data primarily from Vitic
Assay Compounds Positive Negative Inconclusive Conflicted
in vitro CA 3268 1507 1503 98 160
in vitro MN 655 425 230 8 47
in vitro CD 3516 1683 1568 94 171
CD = conservative call using CA & MN 462 compounds contain CA & MN data
64.6% concordance (299/463 agree)
• Chromosome damage model prepared using Sarah
• Significantly increased sensitivity compared to Derek chromosome damage
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BA Sens Spec PPV NPV Cov
External validation of Derek & Sarah in vitro CD models
Nexus Data TP FP TN FN Eq OD
Derek CD 31 59 1696 542 0 0
Sarah CD 216 484 1095 314 0 219
Combined 207 482 1118 327 0 194
Can we access proprietary data?
Adverse Outcome Pathway (AOP) framework
Ankley et al. Environ Toxicol Chem, 2010, 29, 730-741
http://www.oecd.org/chemicalsafety/testing/adverse-outcome-pathways-molecular-screening-and-toxicogenomics.htm
What information is included in an AOP?
Query Compound
Adverse outcome
Molecular initiating event
Key event
Building a network of AOPs
• Knowledge of MIEs/KEs in alerts in Derek Nexus were rearranged• 85 KEs linking compound class to carcinogenicity were assigned to Derek alerts
• Multiple pathways were developed into a network
Lhasa AOPs relating to genotoxicity
• 18 AOPs associated with genotoxic events implemented in the network• Extracted from Derek alerts relating to genotoxicity endpoints
• 19 genotoxicity assays associated with the network• Ames test, in vitro chromosome aberration, in vivo micronucleus test, γH2AX biomarker assay
Frequency of
AOPs 18
MIEs 18*
KEs >50*
assays 19
In vitro assays 14
In vivo assays 5
Assay measurements 19
*some grouping of KEs
Integrating assay data & models
Ames test
In vivo transgenic
rodent assay
P53 increase
γH2AX increase
Carcinogenicity
Toxic
ant
Electrophilic reaction with
DNA
DNA point mutation
Inherited DNA
mutation
Topoisomerase II poisoning
Chromosome Aberration
Genetic instability
Model
Chromosome aberration
testModel
= molecular initiating event
= key event
= adverse outcome
= model
= assay
= bioassay marker
Kaptis
= molecular
= cellular
= organ
= assay & assay measurement
Conclusions
• Current applications: what do we use now?• Two major classes of in silico systems – expert rule-based & statistical-based• Majority of development focused towards improving prediction of mutagenicity
• Driven by regulatory acceptance under ICH M7
• Future perspectives: how do we improve performance?• Improve predictive performance for other genotoxicity endpoints
• Develop current models through access to greater amounts of data• Design novel (Q)SAR systems to model specific endpoints such as chromosome
damage• Develop new technologies such as adverse outcome pathways
• Can we make predictions for genotoxicity without data endpoint-specific assays?• Leverage evidence from multiple sources & combine with knowledge of biological
pathways & mechanisms of genotoxicity to create a genotoxicity risk assessment
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