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(Q)SAR Evaluation of Drug Impurities from the US FDA Scientific Perspective
Naomi L. Kruhlak, Ph.D.Scientific Lead, Computational Toxicology Consultation Service
Division of Applied Regulatory ScienceOffice of Clinical PharmacologyOffice of Translational SciencesFDA’s Center for Drug Evaluation and Research
2019 Pharmaceutical Industry and Regulators Symposium, Brasilia, Brazil
May 29, 2019
2
Disclaimer
The findings and conclusions in this presentation reflect the views of the authors and should not be construed to represent FDA’s views or policies.
The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the Department of Health and Human Services.
www.fda.gov
3
Outline
▪ Chemical informatics at FDA/CDER
▪ ICH M7(R1) Guideline
• Application of expert knowledge
• Structural analog searching
• Applicability domain
▪ FDA/CDER Computational Toxicology Consultation Service
• Chemical registration
• (Q)SAR prediction workflow
▪ Evaluation of Applicant (Q)SAR data
• Software acceptability
• Out-of-domain results
• Reporting
www.fda.gov
Chemical Informatics at FDA/CDER
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Chemical Informatics at FDA/CDER
▪ Applied regulatory research
• Create chemical structure-linked toxicological and clinical effect databases
• Develop and enhance toxicological and clinical effect prediction models through external collaborations
• Develop best practices for the application of computational models
▪ Computational Toxicology Consultation Service
• Provide (Q)SAR evaluations for drugs, metabolites, impurities, degradants, etc. to FDA/CDER safety and quality reviewers
• Perform structure-based searching for read-across purposes
• Provide expert interpretation of (Q)SAR data submitted to FDA/CDER
• Maintains a repository of chemical structures evaluated internally or used for CDER cheminformatics projects (currently ~30K)
www.fda.gov
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In-House
(Q)SAR Consult
Database
Clinical Endpoints:• Liver toxicity
• Cardiotoxicity
• Renal toxicity
Non-Clinical Endpoints:• Genetic toxicity
• Rodent carcinogenicity
• Reproductive/developmental
toxicity
• Phospholipidosis
Reference Datasets:• Validation
• Read-across
• Public
• In-house
Training Sets:
Non-Clinical Toxicity
Clinical AEs
Benchmarking
Consultations
Pharmacological Endpoints:• Opioid receptor binding
• Blood-brain barrier permeability
Regulatory Research AreasChemical
Registration
System
Chemical Structures
Web-Based Searchable Databases
Data Sets
In Silico Models
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▪ (Q)SAR modeling• Global models
• Binary endpoints
• Different modeling algorithms – combined predictions
• Apply expert knowledge to (Q)SAR analyses
▪ Commercial software used under RCAs • SAR and QSAR modeling and prediction platforms
• Data visualization tools
• Commercial databases
• Access to collaborative networks and consortia
(Q)SAR Modeling at FDA/CDER
www.fda.gov
8
(Q)SAR Software Used by FDA/CDER
▪ Statistical-Based Models
• CASE Ultra MultiCASE, Inc.
• Model Applier - Statistical Models Leadscope, Inc.
• Sarah Nexus Lhasa Limited
▪ Expert Rule-Based Models
• Derek Nexus Lhasa Limited
• Model Applier - Expert Alerts Leadscope, Inc.
• CASE Ultra - Expert Alerts MultiCASE, Inc.
All software above are used by FDA/CDER under Research Collaboration Agreements (RCAs)www.fda.gov
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▪ Different methodologies yield different predictions
• Predictions are complementary
• Yields higher sensitivity and negative predictivity
• Second statistical system improves coverage
▪ Predictions are chemically meaningful and transparent
• Structural alerts and associated training set structures can be identified to explain why a prediction was made
• Application of expert knowledge is facilitated
▪ Software and models are publicly available
• Our results are reproducible by pharmaceutical applicants and others
(Q)SAR Software Selection Criteria
www.fda.gov
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ICH M7(R1) Guideline
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ASSESSMENT AND CONTROL OF DNA REACTIVE (MUTAGENIC) IMPURITIES IN PHARMACEUTICALS TO LIMIT POTENTIAL CARCINOGENIC RISK
▪ Goal of hazard assessment is to assign impurity class
www.fda.gov
(Q)SAR
experimental data
experimental data or (Q)SAR
The ICH M7(R1) Guideline
Mutagenic
Non-mutagenic
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Section 6:
“A computational toxicology assessment should be performed using (Q)SAR methodologies that predict the outcome of a bacterial mutagenicity assay(Ref. 6). 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 general validation principles set forth by the Organisation for Economic Co-operation and Development (OECD).
The absence of structural alerts from two complementary (Q)SAR methodologies (expert rule-based and statistical) is sufficient to conclude that the impurity is of no mutagenic concern, and no further testing is recommended (Class 5 in Table 1).”
How to Apply (Q)SAR Under ICH M7
www.fda.gov
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OECD Validation Principles
▪ To facilitate the consideration of a (Q)SAR model for regulatory purposes, it should be associated with the following information:
1) a defined endpoint
2) an unambiguous algorithm
3) a defined domain of applicability
4) appropriate measures of goodness-of–fit, robustness and predictivity
5) a mechanistic interpretation, if possible
OECD (2007) Guidance document on the validation of (Quantitative) structure activity-relationship [(Q)SAR] models.www.fda.gov
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Expert Knowledge
Model output “… can be reviewed with the use of expert knowledge in order to provide additional supportive evidence on relevance of any positive, negative, conflicting or inconclusive prediction and provide a rationale to support the final conclusion.”
For example:
▪ Identify and interpret alerting portion of the molecule
▪ Consider mechanism of reactivity, where possible
▪ Assess training set structures used to derive an alert and mitigating features
▪ Consider data from structurally similar compound (analog)
Expert knowledge is applied to all (Q)SAR analyses conducted in-house by FDA/CDERwww.fda.gov
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(Q)SAR Model
NegativeMutagenicity
Prediction
Structural Alert
Mitigating Features
Application of Expert Knowledge
▪ Identify and interpret alerting portion of the molecule
▪ Consider mechanism of reactivity, where possible
▪ Assess training set structures used to derive alerts and mitigating features [review model output]
▪ Consider data from structurally similar compounds (analogs) not used by the model [search supplemental databases]
www.fda.gov
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QueryMolecule
0.90Negative
0.77Negative
0.51Negative
0.49Positive
Identifying Structural Analogs
www.fda.gov
Global Similarity Searching Similarity Index
Analogs with relevant alert environment
0.47Negative
No shared alert
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Sub-Structure Searching for Analogs
▪ Returns multiple hits containing a particular sub-structure, e.g. primary aromatic amine
▪ Can refine the query to identify the most relevant analogs
• For bacterial mutagenicity, local similarity is important.
• The most globally similar analog may not be the most relevant.
282 hits
7 hits
856 hits
Negative Negative Negative Positive
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▪ Applicability Domain: Region of chemical space within which a model makes predictions with a given reliability
▪ Chemical space defined by structural attributes/properties of training set molecules
Applicability Domain Measurement
www.fda.gov
Descriptor 1
Training
Test
PhysicochemicalDescriptors
MolecularFragments
E.g.,
GlobalSimilarity
Test chemical fragments must be “known” to model
67% 53%
49% 48%
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▪ Overall, different models have different coverage (applicability domain measurement)
• Even models using the same general method (e.g., fragment-based statistical models) can differ in coverage
• Can be used to our advantage to obtain a valid prediction
• However, when multiple models yield OODs, then extra attention needed
Out-of-Domain (OOD) Definitions
www.fda.gov
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▪ For 3 models in combination (n = 519 chemicals):• Predictions for 50% of chemicals in agreement• Predictions for 13% of chemicals changed based on expert knowledge
+ = positive − = negativeEqv = equivocalOOD = out-of-domain
Impact of Expert Knowledge
www.fda.gov
▪ Particularly useful for resolving ambiguous (Q)SAR outcomes, such as equivocal predictions or out-of-domain results
Chem. No. Chemical Name
Bacterial Mutagenicity OverallSoftware
Prediction
OverallExpert
PredictionModel
1Model
2Model
3
1 Chemical 1 - - - - -
2 Chemical 2 - - Eqv Eqv +
3 Chemical 3 + - OOD + +
4 Chemical 4 - OOD - - -
5 Chemical 5 + + - + +
6 Chemical 6 - OOD OOD OOD OOD
7 Chemical 7 Eqv Eqv - Eqv -
FDA/CDER Computational Toxicology Consultation Service (CTCS)
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▪ Have we seen this compound before?▪ Are experimental data available?▪ Have we previously performed a (Q)SAR analysis for this
compound?▪ Are there data for related compounds?
NoYes
Common ICH M7 Review Questions
Chemical registration enables us to answer these questions
www.fda.gov
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Send data to requestor
Is chemical
in DB?
No
Yes
Standardize structure; create molfile; add to
DB
Adequate expt’ldata?
No
Yes
Prior CDER (Q)SAR
analysis?
No
Yes
Send report to requestor
Applicant (Q)SAR
data?
Yes
Acceptable?
Yes
Send report to requestor
No
No Run (Q)SAR analysis
Send report to requestor
CDER Chemical Dictionary▪ 30K structures▪ Experimental data▪ Prior (Q)SAR consult reports
CTCS Chemical Registration Process
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Evaluation of Applicant (Q)SAR Data
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(Q)SAR Software Acceptability
▪ Under the ICH M7 guideline, Applicants may submit (Q)SAR analyses performed using models that are fit-for-purpose• Commercially available• Freely available• Constructed in-house
▪ CDER has prior knowledge of several commercial and freely available (Q)SAR software
▪ For software that CDER has no prior knowledge of, supporting documentation demonstrating that a model is fit-for-purpose is recommended (e.g., QMRF)• Predict bacterial (Ames) mutagenicity• 2 models: expert rule-based and statistical-based• Consistent with OECD Validation Principles
www.fda.gov
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▪ Typically, only problematic (Q)SAR submissions are sent to us for evaluation
• Well-documented submissions are handled by review divisions
• If a reviewer is concerned about the quality of a submission, or it uses an unfamiliar software, it is sent to us.
• Quality Issues: single methodology, read-across only, overall conclusions conflict with predictions with no explanation
▪ General rule is: Trust, but verify. Predictions are re-run only if there is a concern.
▪ Predictions with the most recent software version are preferred. Old predictions are acceptable unless there are known model changes that could impact conclusions.
▪ Expert analysis serves as a buffer to prediction changes with different software versions
Applicant (Q)SAR submissions
www.fda.gov
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Out-of-Domain Results
▪ Common problem for new drug impurities
▪ 18% of impurities in new drugs approved in 2016 and 2017 had an out-of-domain (Q)SAR result, based on an internal study
▪ An out-of-domain result is not a prediction and does not contribute to a Class 5 assignment
▪ Application of expert knowledge can be used to address these gaps but higher bar to acceptance
▪ FDA/CDER uses a 2nd statistical system to resolve most out-of-domains in internal analyses
▪ These are areas with the greatest need for improved databases and models
www.fda.gov
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Example – Out-of-Domain (OOD)
ModelBacterial
MutagenicityExpert Rule-Based NegativeStatistical-Based OOD*
Default Overall Prediction OODExperimental Data Negative
ModelBacterial
MutagenicityExpert Rule-Based NegativeStatistical-Based OOD*
Default Overall Prediction OODOverall Expert Prediction Negative
*Contains unknown fragment and/or has no nearest neighbors
Ames data from API is acceptable to qualify impurity since only difference is an additional non-reactive group
Late-stage Impurity
Parent Drug
Class 5*Contains same unknown fragment and/or has no nearest neighbors
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Relevant Information for Reporting
▪ Materials and methods• Name and version of software and (Q)SAR models used• Prediction classification criteria, such as the cutoff or threshold values to
define a positive/negative/equivocal result
▪ Results and Conclusions• Individual predictions, as well as the overall conclusion (impurity class)• Confirmation that the impurity is within the model’s domain of applicability• Description of any confirmatory application of expert knowledge, including
analogs (where appropriate)• Rationale for superseding any prediction
▪ Appendix• Raw (Q)SAR outputs• Ames data for structurally related compounds used to confirm or refute a
prediction
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Commonly Used Report Format
+ = positive; - = negative; Eqv = equivocal; OOD = out-of-domainwww.fda.gov
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0
100
200
300
400
500
600
700
800
900
1000
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
# o
f co
nsu
lts/
chem
ical
s
# (Q)SAR Consults # Chemicals Analyzed
CTCS (Q)SAR Consult Statistics
▪ 32% of consults include Applicant-submitted data
▪ 75% of consults for generic drugs
▪ Average of 17 chemicals evaluated per week
▪ Turnaround time of 10 business days
880 chemicals
297 consults
ICH M7 Finalized
(Q)SAR Consults, 2009-2018
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Concluding Remarks
▪ Application of expert knowledge is an important component of (Q)SAR assessment under ICH M7
• Model transparency and interpretability facilitate application of expert knowledge
• Effective structural analog searching is critical
• Expert review of predictions is standard practice at FDA/CDER
▪ Regulatory (Q)SAR submissions still vary significantly in quality.Areas for improvement:
• Use of appropriate models (expert rule-based and statistical-based) that are consistent with OECD validation principles— May need to provide supporting documentation
• Appropriate handling of out-of-domain results• Adequate documentation of assessments, particularly if model
predictions are overruled based on expert knowledgewww.fda.gov
33
Concluding Remarks
▪ Internal process improvements enable CTCS to handle a large volume of (Q)SAR consultation requests
• Dedicated team of (Q)SAR experts
• Close communication and collaboration with review staff to ensure needs are met
• Robust chemical registration system
• Integration of (Q)SAR consults into review management platform
▪ External collaboration and outreach ensure access to state-of-the-art models and databases• Conduit for interacting with pharmaceutical stakeholders to share
knowledge and experiences
• Participation in industry consortia advancing the science of (Q)SAR modeling
• Identifies opportunities for future projectswww.fda.gov
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Support:
• Critical Path Initiative• ORISE
• RCA partners
Acknowledgements
Govindaraj KumaranChris EllisLidiya StavitskayaBenon MugabeBecca RaczCurran LandryNeil HartmanMarlene KimLauren WoodardSuresh JayasekaraJian Yang[Andy Zych][Keith Burkhart]
www.fda.gov