ich m7 - lhasa limited
TRANSCRIPT
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Agenda
• The ICH M7 Guidelines
• The Process
• Negative Predictions
• Managing out of domains
• Using Strain Profiles
• Some Examples
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The ICH M7 Guidelines
ICH
M7
Supports the use of in silico models
in decision-making
Adopted worldwide
Enables fast, safe decision-making
http://www.ich.org/products/guidelines/multidisciplinary/article/multidisciplinary-guidelines.html
1 Feb2018
14 March 2018
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The ICH M7 Guidelines
• Control measures can be determined by considering• mutagenic potential
http://www.ich.org/products/guidelines/multidisciplinary/article/multidisciplinary-guidelines.html
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The ICH M7 Guidelines
• Control measures can be determined by considering• mutagenic potential
• concentrations of impurities
http://www.ich.org/products/guidelines/multidisciplinary/article/multidisciplinary-guidelines.html
Option Control Process
1 [impurity] < specification in final drug product
2 [impurity] < specification in raw material, starting material or intermediate
3
[impurity] > specification in raw material, starting material or intermediate
+ fate and purge during subsequent steps ⇒ [impurity] in the final drug substance is
below the acceptable limit
4Sufficient knowledge of fate and purge to give confidence that final [impurity] <
acceptable limit without analytical testing
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Decision-making under ICH M7
Database
searching
ICH
M7
Expert in silico
prediction
Statistical in
silico prediction
Expert
assessment
Classification
Reporting
Purge mitigation
Control
Impurity
identification
Test
Storing
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Undertaking a Database Search
Public data
Pre-competitive
shared data
In-house dataClass 1
Mutagen
Carcinogen
Mutagen
Carcinogen ?
Class 2
Non-Mutagen
Class 5
Exact
match
Databases
Substructure /
similarity search Further supporting information for
expert review
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Undertaking a Database Search
Public data
Pre-competitive
data
Sources include:
• FDA
• NTP
• ISSSTY
• Kirkland
• Hansen, Bursi
• MPDB
• Literature
In vitro genetox
• 185,018 | 10,602
In vivo genetox
• 12,488 | 2,995
Overall-call genetox
• 35,092 | 10,932
Carcinogenicity
• 16,398 | 3,866
Aromatic amines
• 9,402 | 666
Intermediates
• 21,507 | 1,338
Excipients
• 2,985 | 1,071
Consortia of Lhasa
members
In-house data
Freely available on-line resourcehttps://www.lhasalimited.org/products/lhasa-carcinogenicity-
database.htm
Long term carc studies
• 6,529 | 1529
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Expert Review Using in silico Models
Database
searching
ICH
M7
Classification
Reporting
Purge mitigation
Control
Impurity
identification
Test
Storing
Expert in silico
prediction
Statistical in
silico prediction
Expert
assessment
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Expert Review Using in silico Models
• Guidance documentation is readily available
• Establishing best practise in the application of expert review of
mutagenicity under ICH M7.
• Barber… Regul. Toxicol. Pharmacol. 2015, 73, 367
• Principles and procedures for implementation of ICH M7
recommended (Q)SAR analyses.
• Amberg… Regul. Toxicol. Pharmacol. 2016, 77, 13
• Lhasa’s website contains publications, presentations, examples...
• https://www.lhasalimited.org/ich-m7.htm
• Regulator’s website
• https://www20.anvisa.gov.br/coifaeng/calculos.html
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Expert Review Using in silico Models
• “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”
• “If warranted, the outcome of any computer system-based
analysis 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”
http://www.ich.org/products/guidelines/multidisciplinary/article/multidisciplinary-guidelines.html
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• A model should meet the OECD principles• a defined endpoint;
• an unambiguous algorithm;
• a defined domain of applicability;
• appropriate measures of goodness-of-fit, robustness and predictivity;
• a mechanistic interpretation, if possible.”
• A model must also support expert review• Make predictions and have good accuracy for your chemical space
• Provide some meaningful measure of confidence
• Be regularly updated with new knowledge
• Ideally be well understood by regulatory authorities
• Be transparent and highlight areas of uncertainty
• Make predictions that you understand, support or can overturn
Expert Review – Model Selection
Distinguishing between expert and statistical systems for application under ICH M7.
Barber… Regul. Toxicol. Pharmacol. 2017, 84, 124
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Derek – an Expert System
1 Defined endpoint
2 Unambiguous algorithm
3 Defined applicability domain
4 Performance measure
5 mechanistic interpretation
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Derek – Positive Predictions
CFSANn=1535
100 98
80
49
0
25
50
75
100
Certain Probable Plausible Equivocal
Accuracy of predictions
Assessing confidence in predictions made by knowledge-based systems. Judson… Toxicol. Res., 2013, 2, 70
Distinguishing between expert and statistical systems for application under ICH M7. Barber…Regul. Toxicol. Pharmacol. 2017, 84, 124
• Highlights the alert and gives a level of confidence
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Derek – Negative Predictions
• “I have no concerns, there is nothing new for me here..
..I’ve seen these all features before in negative compounds”
• “There is nothing new for me here..
..but there is a feature that was in a compound I falsely predicted
as negative”
• “There is a feature I haven’t seen before
..you might want to check that out!”
If you asked a human expert the absence of a positive prediction..
More confidentAmount of
expert review
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Derek – Negative Predictions
?
Is it alerting?
Contains unknown
features?
Contains features in
known false negatives?
InactiveInactive with
misclassified features
Inactive with
unclassified features
N
N N YY
It’s difficult, but important, to make negative predictions. Williams… Reg. Tox. and Pharmacol. 2016, 76, 79
YPositive prediction
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Derek – Negative (unclassified feature)
• Highlights fragments in contexts not seen in public data
• May be present in confidential data
• Often driven by unusual ring systems
• Expert review recommended
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Derek – Negative (misclassified feature)
• Highlights fragments seen in false positive predictions
• This is still a negative prediction
• It can be triggered for many reasons
• Inconsistent data• Will flag if only seen once (and many negative tests are seen)
• An innocent spectator
• A missing alert
• Expert review recommended
• Search for additional supporting compounds containing fragment
• Review conflicting results• Higher hurdle to dismiss positive results but you can
• Non-standard assay, impure sample…
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Performance against 3 proprietary data sets – frequency (negative predicitivity)
Derek – Negative Predictions
?
Is it alerting?
Contains unknown
features?
Contains features in
known false negatives?
InactiveInactive with
misclassified features
Inactive with
unclassified features
N
N N YY
It’s difficult, but important, to make negative predictions. Williams… Reg. Tox. and Pharmacol. 2016, 76, 79
YPositive prediction
86-90%
(87-94%)
7-9%
(86-93%)
3-5%
(86-95%)
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Using Predictions from Expert Systems
• Positive and negative predictions are not sufficient
• OCED principles are not sufficient
• Expert review also requires:
• Levels of confidence
• Where to focus attention
• Supporting examples
• Peer-reviewed expert commentaries
• Supporting literature references
Positive
plausible
probable
certain
Equivocal
Negative
..no mis/unclassified
..with misclassified
..with unclassified
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Expert Review Using in silico Models
Likely to conclude positiveVery strong evidence would be needed to overturn both
predictions
UncertainLikely to conclude positive without strong evidence to
overturn a positive prediction
Likely to conclude positiveLack of a second prediction
suggests insufficient evidence to draw any other
conclusion
In silicoprediction 1
In silicoprediction 2
Positive
Positive
Positive
O.O.D. or equivocal
Positive
Negative
Negative
O.O.D. or equivocal
Negative
Negative
UncertainConservatively could assign as positive.
May conclude negative with strong evidence showing feature driving a ‘no prediction’ is
present in the same context in known negative examples (without deactivating features)
Likely to conclude negativeExpert review should support this conclusion – e.g. by assessing any
concerning features (misclassified, unclassified, potentially reactive...)
O.O.D. = out of domain
Establishing best practise in the application of expert review of mutagenicity under ICH M7 Reg. Tox. and Pharmacol. 2015, 73, 367
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Expert Review
Harder
Easiest
Expert
Review
Low confidence
Mis- or unclassified
Poor coverage of important
fragments
Own knowledge /
proprietary data disagrees
Close relevant examples
agree
Good coverage of
fragments
Both predictions agree
Fits own knowledge /
proprietary data
Conflicting predictions
Equivocal or out of domain
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Expert Review – Conflicting Results
O.O.D. 2 0 3
+ 10 0 17
Equiv 8 0 6
- 38 2 12
- Equiv +
• Lhasa data sharing consortium group (n=777; 32% positive)
Frequency of Outcomes (%)
O.O.D. 29 - 62
+ 30 - 70
Equiv 23 - 51
- 8 15 34
- Equiv +
Probability of being positive (%)
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Managing out of domains
https://www.lhasalimited.org/publications/dealing-with-out-of-domain-qsar-predictions-for-ich-m7-a-
regulatory-and-industrial-perspective/4476 (Kruhlak , Sept 2017)
Slides reproduced with permission of the author
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Managing out of domains
https://www.lhasalimited.org/publications/dealing-with-out-of-domain-qsar-predictions-for-ich-m7-a-
regulatory-and-industrial-perspective/4476 (Kruhlak , Sept 2017)
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Managing out of domains
https://www.lhasalimited.org/publications/dealing-with-out-of-domain-qsar-predictions-for-ich-m7-a-
regulatory-and-industrial-perspective/4476 (Kruhlak , Sept 2017)
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Using Strain Profiles in Expert Review
• Ames test uses different strains with different sensitivities
• A negative result not tested in 5 strains may miss something
• Strain-specific models perform badly• Too many data gaps
• Too many different strains used
• You should know if a negative result for an analogue was
tested in the most appropriate strain• Particularly if this is a key piece of data for expert review
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• Hypothesis-level strain information• Which strain is most sensitive for chemicals in this class?
• How comprehensive is the data for supporting negative ex’s?
• Compound-level strain information• Which strains was this compound tested in?
• Is it negative because it is missing a key strain?
Using Strain Profiles in Expert Review
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Agenda
• The ICH M7 Guidelines
• The Process
• Negative Predictions
• Managing out of domains
• Using Strain Profiles
• Some Examples
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Example 1
• Conflicting predictions from the expert and statistical
systems
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Example 1
• Well supported alert• No reason to immediately dismiss a positive prediction
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Example 1
• Positive hypothesis for the epoxide was overwhelmed by others• Close examples are also positive
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Example 1
• Model’s closest compound was not tested in
5-strains or in the presence of S9
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Example 1
Mutagenic
Equivocal
Pro-mutagenic
arguments
Non-mutagenic
arguments
Non-mutagenic
Key hypothesis (epoxide) is positive
and this is supported by many
relevant examples that give good
coverage of the structure Key negative compound was
only tested in 2 strains w/out
metabolic activation
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Example 2
• One equivocal and one weakly positive prediction..• You could ‘conservatively’ treat as a positive prediction
but…
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Example 2
• Derek specifically notes that positive results are not
driven by the acid chloride but by the solvent• Literature references are included…
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Example 2
• Sarah makes a weakly positive prediction• Insufficient data for own hypothesis – not all examples relevant
• Links to the source data allows review of solvents
acetone DMSOTHF
DMSO N/A
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Example 2
Mutagenic
Equivocal
Pro-mutagenic
arguments
Non-mutagenic
arguments
Non-mutagenic
Half the relevant
examples are inactive
supporting DX
comments & publ’n
Alert tells us that
activity is driven by
reaction with solvent
and not by ROCl
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Example 3
• Conflicting predictions from the expert and statistical
systems
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Example 3
• Clear negative prediction
..with no mis- or unclassified features
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Example 3
Positive 1/6 times in TA100
Positive 1/1 in D3052
(1984)
1 positive reference
(no experimental details
or strain information..)
• Positive prediction overturned by expert• Other reasons for activity or weak positive evidence
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Example 3
Mutagenic
Equivocal
Pro-mutagenic
arguments
Non-mutagenic
arguments
Non-mutagenic
Positive examples have
more plausible reasons
for activity or have weak
evidence
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Example 4
• Conflicting predictions from the expert and statistical
systems
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Example 4
• Specifically includes sulphinates and includes references
• Describes mechanism
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Example 4
• No specific hypothesis for this functional group
• Insufficient relevant examples to accept the prediction
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Example 4
Mutagenic
Equivocal
Pro-mutagenic
arguments
Non-mutagenic
arguments
Non-mutagenic
Supporting
examples not
that relevantNo hypothesis
of alkyl
sulphinate
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Summary of expert review
• Good software will help you reach a conclusion• You need to be able to convince yourself!
Conflicted predictions
• use of strain information
Two weak positives were overturned
• expert system commentary + key ref’s
• links to original underlying data
Conflicted predictions
• ability to look for other causes of
activity in statistical system
• review of underlying data (of
remaining positives)
Conflicted predictions
• expert system commentary
• assessment of supporting
compounds (statistical system)
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Tools to support ICH M7 assessments
Database
searching
ICH
M7
Expert in silico
prediction
Statistical in
silico prediction
Expert
assessment
Classification
Reporting
Purge mitigation
Control
Impurity
identification
Test
Storing
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