FDA/CDER Current Practices for (Q)SAR Analysis under ICH M7
Naomi L. Kruhlak, PhD
Lead, Chemical Informatics Program
Division of Applied Regulatory Science
Office of Clinical Pharmacology
Office of Translational Sciences
FDA Center for Drug Evaluation and Research
Lhasa Limited vICGM
April 16, 2014
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Disclaimer
 The findings and conclusions in this presentation have not been formally disseminated by the FDA and should not be construed to represent any agency determination or policy.
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Overview
 Introduction to (Q)SAR in context of Chemical Informatics at CDER
 ICH M7 Guideline
• Definitions
• Methodologies
 (Q)SAR assessment at FDA/CDER
• Software
• Process
• Expert analysis steps
• Reporting
 Concluding remarks
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Chemical Informatics Program
 An applied regulatory research group that:
• Creates toxicological and clinical effect databases • Develops rules for quantifying in vitro, animal and human endpoint data
• Evaluates data mining and (Q)SAR software
• Develops toxicological and clinical effect prediction models through collaborations with software companies
 A consultation service that:
• Provides (Q)SAR evaluations for drugs, metabolites, contaminants, degradants, etc. to FDA/CDER safety reviewers
• Performs structure‐similarity searching for read‐across purposes
• Provides expert interpretation of (Q)SAR data submitted to FDA/CDER
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(Q)SAR Modeling: What is it?
 Identifies correlations between chemical structural features and biological activity
 Uses the results of actual laboratory testing
• General assumption: Similar molecules exhibit similar physicochemical and biological properties
 Make prediction of a compound’s biological activity based on its chemical structure
• rapidly
• consistently
QSAR – quantitative – statistically derived model
SAR – qualitative – expert rule‐based model
(Q)SAR
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When is (Q)SAR Modeling Used in Drug Development?
Discovery
Nonclinical
Clinical
Ph1
Post‐Approval
Ph2
Ph3
(Q)SAR
Pharma
Regulatory
ICH M7
Knowledge Building
First example of (Q)SAR predictions as the sole basis of a regulatory decision for drug safety 6
The ICH M7 (Step 2) Guideline
ASSESSMENT AND CONTROL OF DNA REACTIVE (MUTAGENIC) IMPURITIES IN PHARMACEUTICALS TO LIMIT POTENTIAL CARCINOGENIC RISK
Section 6:
“A computational toxicology assessment should be performed using (Q)SAR methodologies that predict the outcome of a bacterial mutagenicity assay. Two (Q)SAR prediction methodologies that complement each other should be applied. One methodology should be expert rule‐based and the second methodology should be statistical‐based. (Q)SAR models utilizing these prediction methodologies should follow the validation principles set forth by the Organisation for Economic Co‐operation and Development (OECD).”
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The ICH M7 (Step 2) Guideline
“Definition of (Q)SAR and SAR:
In the context of this guideline, refers to the relationship between the molecular (sub) structure of a compound and its mutagenic activity using (Quantitative) Structure‐Activity Relationships derived from experimental data.”
“Structural alert:
In the context of this guideline, a chemical grouping or molecular (sub) structure which is associated with mutagenicity.”

Structural alert
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(Q)SAR Software Used by FDA/CDER
 Predictions Using Statistical Correlations
• CASE Ultra
MultiCASE, Inc.
• Model Applier
Leadscope, Inc.
• Sarah Nexus
Lhasa Limited
 Predictions Based on Human Expert Rules
• Derek Nexus
Lhasa Limited
• Model Applier
Leadscope, Inc.
• Toxtree
Joint Research Centre
All software above are used by FDA/CDER under Research Collaboration Agreements (RCAs)
Many other software and models are acceptable under ICH M7 guideline
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(Q)SAR Software Selection Criteria
 Software utilize different prediction methodologies
• Prediction algorithms
• Chemical structural descriptors
 Predictions are complementary
• What one software program misses another may pick up
• Discordant predictions are acceptable
 Predictions are chemically meaningful and transparent
• Structural alerts and associated training set structures can be identified to explain why a prediction was made
 Software and models are publicly available
• Our results are reproducible by pharmaceutical sponsors and others
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FDA / CDER Computational Toxicology Consultation Service
 Commonly used for
• Contaminants, degradants, metabolites
• Interpretation of equivocal toxicology study results
• Retrospective analysis of post‐market safety signals  Capabilities offered
• (Q)SAR analysis
• Structure similarity searching (“read across”)
 Online submission form
• FDA Intranet
 ICH M7
• Running or re‐running full analysis
• Filling data gaps
• Providing expert interpretation of Sponsor‐submitted data
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(Q)SAR Analysis Steps Under ICH M7 1) Check chemical structure is correct (e.g., crosscheck with molecular weight and molecular formula)
2) Check for empirical carcinogenicity and mutagenicity data in the literature
3) Generate predictions for impurity and API, if appropriate, using three software platforms
4) Archive software output: prediction reports, similar analogs, training set structures supporting alerts 5) Perform expert analysis of prediction
6) Review empirical data for structural analogs, where available
7) Report raw predictions and expert conclusions
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Examples of Expert Analysis Considerations
 Positive predictions
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Is the same alert identified in multiple models?
Does alert make biological/mechanistic sense?
Is the alert shared with the API?
How high is the prediction confidence? Overall? Per alert/feature?
Is the alert based on a sufficient number of training set examples?
How relevant are the training set structures from which the alert is derived?
How similar are training set structures? Substructurally? Globally?
 Negative predictions
• How high is the prediction confidence?
• Appropriate mitigating factors applied?
• Reactive moieties in test compound represented in training set?
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Difficult Outcomes  Equivocal predictions
• May result from negative and positive contributions to prediction
• May result from alert based on low number of training set examples
• May be overruled using expert analysis and additional structural analogs
 Out‐of‐Domain responses
• Present a bigger challenge
• Options:
‒ Run a third model
‒ Assess why the test compound is uncovered
‒ Supplement model deficiencies with supporting data (much higher bar to acceptance)
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Report Excerpt
O
HO
R
Model
Derek Nexus
Statistical Model 1
Statistical Model 2
Overall Software Prediction
Overall Expert Prediction
Salmonella Mutagenicity
NSA
Eqv
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“Impurity A is predicted to be negative overall for Salmonella mutagenicity. Statistical Model 1 predicted Impurity A to be equivocal, based in part on Substructure X, shown below. This fragment was derived from 58 training set compounds, of which 46 (79%) were positive in experimental testing; however, an examination of the positive training set structures containing this fragment showed that almost all contain the fragment as part of a fused ring system, either as an anthraquinone or naphthoquinone, providing a significantly different environment than in Impurity A. Consequently, the equivocal prediction for Impurity A is downgraded to negative. Additionally, three highly similar analogs to Impurity A containing Substructure X (shown below) were experimentally tested and shown to be negative for Salmonella mutagenicity.” Reporting
 For all software platforms
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Software and models used, including versions and dates Raw conclusions of the software
Narrative describing expert analysis of predictions
Justification if prediction is overturned
Additional supporting evidence, such as empirical data for structural analogs
Final expert conclusions
Model output files archived
 Additional considerations for in‐house or non‐widely‐used software
• Training set details
• Performance and coverage statistics (e.g., cross‐validation, external validation with benchmark set)
• Prediction transparency and interpretability, including structural alert 16
identification
Concluding Remarks
Prediction transparency and interpretability are key
• Identification of structural alerts is important
• Impacts choice of software and models
Raw predictions can be overruled through use of appropriate supporting evidence
• Assessment of data and/or decisions within the model
• Empirical data for structural analogs
• Overruling out‐of‐domain calls more challenging Role of FDA/CDER Computational Toxicology Consultation Service evolving under ICH M7:
Running entire analysis
Filling prediction and/or data gaps
Interpreting sponsor submissions
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Acknowledgements
Chemical Informatics Team Members:
Lidiya Stavitskaya
Barbara Minnier
Dongyu Guo
Mark Powley
Neil Hartman
Somdutta Saha
Contact Information: Naomi.Kruhlak@fda.hhs.gov
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