ICH M7 Classification and the involvement of (Q)SAR methods M7 Classificati… · ICH M7...
Transcript of ICH M7 Classification and the involvement of (Q)SAR methods M7 Classificati… · ICH M7...
Presenter: Susanne Glowienke, PCS, Novartis
44th ICGM, Basel, 2016
ICH M7 Classification and the involvement of (Q)SAR methods
Outline
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Introduction
ICH M7 hazard assessment
In silico systems and use of expert knowledge
• Case studies for ICH M7 classes 3,4 and 5
Compound specific risk assessment
• Case studies for ICH M7 class 1
Summary
Assessment strategy
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ICH M7 adapted process
Ames positiveImpurityIn silico
assessment
ICH Q3A
No mutagenicity alert
ICH M7
Mutagenicity alert
Purge factor
assessment
Insufficient purge
Ames test
ICH Q3A
Ames negative
ICH M7
Analytical testing
ICH M7
Scientific based
argumentation
Sufficient
purge
Ames test performed only for not purged compounds
ppm limit tests required for Ames positive/unpurged assumed mutagen
ICH-M7 implementation
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Purge factor tool (Lhasa consortium proposal)
Purge Safety Factor = predicted purge factor / required purge factor
Reference: A. Teasdale et.al, OPRD 2013-17(2), 22
> 10 x> 100 x≥ 1000 x
Potentially mutagenic (PMI)
or mutagenic impurity (MI)
Comparison of predicted purge factor*) vs. required
purge factor calculated from TTC or PDE requirements
Scientific justification• Apply purge approach
• Not necessarily
supported by analytical
data
Collect experimental data
on purge properties• E.g. solubility, reactivity etc.
to support scientific rationale
• Analytical data collection not
necessarily required
Measure purge factor• Spiking experiments
• Trace analysis as
required to support
scientific rationale
PSFPurge Safety Factor
Mutagenic impuritiesHazard assessment in ICH M7 guideline
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Hazard assessment involves:
• Initial analysis of actual and potential impurities by conducting database and literature searches for carcinogenicity and bacterial mutagenicity data
- Classification into Class 1, 2, or 5
• Assessment of Structure-Activity Relationships (SAR) that focuses on bacterial mutagenicity predictions
- Classification into Class 3, 4, or 5
Class Definition Proposed action for control
1 Known mutagenic carcinogen Compound specific limit
2 Known mutagen, unknown carcinogenic potential Control at or below acceptable limit (LTL / TTC)
3 Alerting structure unrelated to non-mutagen
No mutagenicity data
LTL/TTC or conduct bacterial mutagenicity assay
If mutagenic: class 2, if not mutagenic: class 5
4 Alerting structure related to non-mutagen
No mutagenicity data
Non-mutagenic impurity (ICH Q3A/B)
5 No structural alert; experimental lack of mutagenicity Non-mutagenic impurity (ICH Q3A/B)
Cohort of concern Individual control strategy
Cohort of concern
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Case by case approach
Compounds from structural classes of mutagens with extremely high carcinogenic potency
Aflatoxin like, N-nitroso-, and alkyl-azoxy structures
Acceptable intakes are (most probably) significantly lower compared to the standard TTC concept
For these compounds a case-by case approach needs to be developed to define acceptable intakes
Mutagenic impuritiesHazard assessment in ICH M7 guideline
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Hazard assessment involves:
• Computational toxicology assessment using (Q)SAR (Quantitative Structure Activity Relationship) methodologies
- Predict the outcome of bacterial mutagenicity assay
- Two (Q)SAR prediction methodologies that complement each other:
• Expert rule-based and statistical-based
• Absence of structural alerts is sufficient to conclude that impurity is of no mutagenic concern (no further testing recommended)
• Outcome of any computer system-based analysis can be reviewed with the use of expert knowledge
- provide additional supportive evidence on relevance of any positive, negative, conflicting or inconclusive prediction (rationale to support final conclusion
• Negative bacterial mutagenicity assay overrules structure-based concern
In silico systems
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Example for knowledge based system
Derek Nexus (Lhasa Ltd) – knowledge based
• Positive (structural alerts) and negative predictions possible
• Negative predicted compound is compared to Lhasa reference set of Ames test data, producing 3 possible outcomes
- (i) All features in molecule are found in accurately classified compounds from reference set: a negative prediction is displayed
- (ii) Features in molecule are found in non-alerting mutagens in reference set
• Prediction remains negative but misclassified features are displayed
• Relationship between misclassified features and mutagenic activity may be coincidental or contributory
- (iii) Features in molecule are not found in reference set
• Prediction remains negative and unclassified features are displayed
• Information on coverage
• (ii) and (iii) are subject to expert judgement
In silico systems used
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Derek Nexus – example for misclassified feature
No structural concern for mutagenicity detected
Oxazolidine moiety flagged as misclassified feature
• ICH M7 class 3, i.e. reference mutagen considered sufficiently similar to warrant Ames test? or
• Query other prediction tools/consider expert knowledge Class 4/5?
In silico systems used
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Example for a statistical system
Sarah Nexus (Lhasa Ltd) – statistics-based
• Structures submitted for processing are fragmented
• Fragments are reviewed for activity versus inactivity by arranging 'interesting' fragments into a network of hypotheses (or nodes)
• Relevant hypotheses are used to inform an overall prediction of toxicity
• Predicts activity or inactivity in the Ames test and provides information on coverage of query compound
Overall prediction hypothesis for fragments is positive
in Ames test
In silico systems used
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Derek and Sarah Nexus
Combined ICH M7 prediction
Conflicting prediction: Assign to ICH M7 class 3 or 5?
• Negative Derek prediction:
because molecule is not an
arylboronic acid
• Positive Sarah prediction:
because borolane moiety is part of
aryl boronic acids present in
reference set
Result is ICH M7 class 5 because Ames test was negative
Use of expert knowledge
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Considerations for an overall assessment and expert review
Expert review
• Assess negative predictions
• Refute positive predictions
• Address inconclusive prediction results
• Deal with out of domain results
Use of expert knowledge
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Concluding a combined Derek and Sarah Nexus prediction
Equivocal Sarah prediction:
• Similar molecules in training set are not mutagenic
• Derek prediction does not show structural alert
• Conclusion: ICH M7 class 5 Confirmed by negative Ames test
Amino-tetrazole
Refuted negative prediction from two methodologies
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O-(2-Hydroxyethyl)hydroxylamine
Negative prediction for bacterial mutagenicity using both expert rule-based and statistical-based models
However, conflicting evidence for mutagenicity of different hydroxylamine salts in public domain
Conclusion: Potential mutagenic response on the basis of the hydroxylamine moiety should be further evaluated
• Assigned to ICH M7 class 3
Submitted for Ames assay testing where mutations in strain TA1535 in the absence of S9 were induced
Use of literature data
Refuting a positive prediction based on an ICH M7 class 4 analysis (Amberg et al., 2016)
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Series of similar impurities predicted to be positive in statistical-based model
Common feature responsible forpositive prediction highlighted in red
Ames data generated for one structure (= known negative) and applied to other impurities where R, R1, R2 or R3 varied but without additional alerting functionality
Impurities are considered analogs of the known negative compound sharing same highlighted positive structural features
• Assigned to ICH M7 class 4
No further Ames tests conducted
Assessing an out-of-domain prediction using public analogs (Amberg et al., 2016)
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Aminoacetonitrile
Out-of-domain for statistical-based models and predicted to be negative by the expert rule-based model
Structurally similar compounds shown to be not mutagenic except 3-chloropropionitrile which contains additional alerting structure (alkyl chloride)not shared with aminoacetonitrile
Out of domain result over-ruledand conclusion not mutagenic
• ICH M7 class 5
Compound-specific risk assessmentMutagenic carcinogens (ICH M7 class 1)
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• Compound-specific risk assessments to derive acceptable
intakes (AI) should be applied instead of TTC-based
acceptable intakes where sufficient carcinogenicity data
exists
• For known mutagenic carcinogen, a compound-specific
acceptable intake can be calculated based on carcinogenic
potency and linear extrapolation as default approach
• Limits for frequently used chemicals are given in the ICH
M7 addendum (step 2b)
Compound-specific risk assessment (ICH M7 class1)Methods proposed in ICH M7 addendum
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Linear mode of action
• Calculation of acceptable intake (AI) (standard method)
- Selection of studies (consider quality), usually from CPDB
- Selection of tumor and site, route of administration, human relevance of
tumor
- AI = TD50 / 50’000 x 50 kg
Published regulatory limits
Non-linear thresholded mode of action
• Calculation of permissible daily exposure (PDE) if clear evidence for
thresholded mechanism of action
Acceptable limit based on exposure in environment or food
Compound-specific risk assessmentExample benzyl chloride (ICH M7 addendum)
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Benzyl chloride
Mutagenic and genotoxic in vitro but not in mammalian systems in vivo
IARC classified as Group 2A, probably carcinogenic to humans Clear
2-year rodent carcinogenicity studies available
• Site-of-contact tumors probably caused by irritation/inflammation not
considered relevant for humans at low amounts as impurity
• Thyroid C-cell tumors were used to derive AI (acceptable intake)
- TD50 = 40.6 mg/kg/day
• Lifetime AI =40.6 (mg/kg/day)/50,000 x 50 kg = 41 µg/day
Case study ICH M7 class 1p-Aminophenol (NVS)
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Studies to assess mutagenic/carcinogenic potential in literature:Assay Species Result
Ames test Salmonella strains TA98, TA100, TA97, TA1535, TA1537; +/- rat and hamster S9
Negative
Ames test Salmonella strains TA98, TA100, TA1535, TA1537 and E. coli; +/- rat S9
Positive (E. coli + S9)
HGPRT test Chinese hamster ovary (CHO) cells; +/- rat S9 Negative
Mouse lymphoma L5178Y (TK+/TK-) cell Positive (- S9)
Chrom ab Human lymphocytes Clastogenic (+/- S9)
Mammalian Erythrocyte Micronucleus Test
Mouse (in vivo) Statistically significant increase in the frequency of micronucleated polychromatic erythrocytes
Mouse splenocyte micronucleus test
Mouse (in vivo) Positive results have been found with a maximum after 14 days of treatment
Micronucleus test by oral route (gavage) in rats for 13 weeks
Rat (in vivo; daily administration at doses of 12 and 30 mg/kg for 13 weeks
Negative (no increase in number of micronucleated PCEs)
Mammalian Bone Marrow Chromosomal Aberration Test
Rat (in vivo, single doses, MTD reached) Equivocal
Carcinogenicity studyoral
Rat, 50 males/females, 2, 5, 12 and 30 mg/kg Negative
Carcinogenicity study dermal
Mouse, 50 males/females, 0.04, 0.2 and 1.0% in hair dye formulation
Negative
Case study ICH M7 class 1p-Aminophenol – other considerations
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• EFSA (European food agency)
- Potential for induction of point mutations in mammalian cells in vitro could not be
excluded, albeit results were observed at cytotoxic concentrations
• SCCP (Committee for cosmetics)
- Genotoxic in vitro and in vivo; induces clastogenic effects that occur at high doses
in the presence of toxic effects
• Biological significance of these effects and their relevance for exposed humans has to be
further discussed
- Threshold-related mechanism of action likely suggesting that no genotoxic risk for
humans under normal use conditions as ingredient in cosmetic products exist
• Metabolite of acetaminophen (Paracetamol)
• Paracetamol-induced genotoxicity seem to involve dose thresholds; from epidemiological
studies of acetaminophen no conclusive evidence for carcinogenicity in man
• p-Aminophenol «qualified» in acetaminophen studies
Case study ICH M7 class 1p-Aminophenol – taken together
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Derivation of PDE (permitted daily exposure) justified:
• Genotoxic potential in vitro and in vivo but evidence for thresholded
effect
• Available data from oral and dermal carcinogenicity studies suggests p-
aminophenol is not carcinogenic
PDE using carcinogenicity study
• PDE carcinogenicity, oral = 30 mg/kg x 50 kg / 5 x 10 x 1 x 10 x 1 = 3 mg/day(Adjustment factors: 5: extrapolation rats to humans, 10: variability between individuals, 1:
life-time study, 10: genotoxic effects, 1: 30 mg/kg is NOAEL for carcinogenicity)
PDE using repeat dose tox study: nephrotoxicity ≥ mid dose
• PDE repeat dose, oral = 10 mg/kg x 50 kg / 5 x 10 x 5 x 3 x 1 = 670 µg/day(Adjustment factors: 5: extrapolation rats to humans, 10: variability between individuals, 5:
3 month study, 3: dose-dependent tubular nephrosis, 1: NOAEL used)
Summary
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Hazard assessment of ICH M7 suggests assignment of
impurities into 5 classes together with appropriate
consequences:
Ames test, limitation, ICH Q3A/B impurity, PDE
For (Q)SAR assessment use of expert knowledge plays
key role
Compound specific risk assessments require careful
review of available data (including non-carcinogenicity
endpoints)
Useful publications
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In silico prediction under ICH M7 guideline
Establishing best practice in the application of expert review of mutagenicity under ICH M7Chris Barber, Alexander Amberg, Laura Custer, Krista L. Dobo, Susanne Glowienke, Jacky Van Gompel, Steve Gutsell, Jim Harvey, Masamitsu Honma, Michelle O. Kenyon, Naomi Kruhlak, Wolfgang Muster, Lidiya Stavitskaya, Andrew Teasdale, Jonathan Vessey, Joerg Wichard
Regulatory Toxicology and Pharmacology 73 (2015) 367-377
It's difficult, but important, to make negative predictionsRichard V. Williams, Alexander Amberg, Alessandro Brigo, Laurence Coquin, Amanda Giddings, Susanne Glowienke, Nigel Greene, Robert Jolly, Ray Kemper, Catherine O'Leary-Steele, Alexis Parenty, Hans-Peter Spirkl, Susanne A. Stalford, Sandy K. Weiner, Joerg Wichard
Regulatory Toxicology and Pharmacology 76 (2016) 79-86
Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtainedChris Barber, Alex Cayley, Thierry Hanser, Alex Harding, Crina Heghes, Jonathan D. Vessey, Stephane Werner, Sandy K. Weiner, Joerg Wichard, Amanda Giddings, Susanne Glowienke, Alexis Parenty, Alessandro Brigo, Hans-Peter Spirkl, Alexander Amberg, Ray Kemper, Nigel Greene
Regulatory Toxicology and Pharmacology 76 (2016) 7-20
Principles and procedures for implementation of ICH M7 recommended (Q)SAR analysesAlexander Amberg, Lisa Beilke, Joel Bercu, Dave Bower, Alessandro Brigo, Kevin P. Cross, Laura Custer, Krista Dobo, Eric Dowdy, Kevin A. Ford, Susanne Glowienke, Jacky van Gompel, James Harvey, Catrin Hasselgren, Masamitsu Honma, Robert Jolly, Raymond Kemper, Michelle Kenyon, Naomi Kruhlak, Penny Leavitt, Scott Miller, Wolfgang Muster, John Nicolette, Andreja Plaper, Mark Powley, Donald P. Quigley, M. Vijayaraj Reddy, Hans-Peter Spirkl, Lidiya Stavitskaya, Andrew Teasdale, Sandy Weiner, Dennie S. Welch, Angela White, Joerg Wichard, Glenn J. Myatt
Regulatory Toxicology and Pharmacology. Regulatory Toxicology and Pharmacology 77 (2016) 13-24
Thank you
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Acknowledgement
Colleagues from TRD (in particular Guido Jordine)
for purge factor flow chart