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Generics Perspective:
Success Strategies for Genotoxic Impurity
Identification, Assessment and Control
Raphael Nudelman, Ph.D.
Head of Chemical &
Computational Toxicology
Genotoxic Impurities
London, June 2015
§ Understand the basis of the strategy used for GTI
identification, assessment and control
§ Outline key pitfalls and how they are overcome
§ Examine how we use in silico tools and (Q)SAR as part
of the assessment strategy
§ Identify how ICH M7 impacts GTI assessment strategies
Scope
Who we are
�3
Global R&D
Discovery and Product Development
Non Clinical Development
Non Clinical Safety
Raphael Nudelman
Chemical & Computational Toxicology
Generics
R&D
Specialty
R&DAPI units EHS
Who we are
� For DS
� Examine entire RoS (manual + computational) to
identify PGIs
• sm, reagents, solvents, intermediates, byproducts, products
� Assess actual + reasonably expected PGIs
� Calculate purge factors
� Control recommendations
A + B
D
E + F
G
C
Strategy
� For DP
� Identify potentially reactive degradation products
(real + potential)
� Control recommendations
Strategy
StrategyControl recommendations
� Compound specific limit (Class 1)
� TTC (Class 2 + 3)
� ICH Q3 (Class 4 + 5)
� LTL adjusted TTC
� Purgeability (no control)
� Justifiable higher exposure
• Metabolites
• Food-related
• Pharmacopoeial levels
• Reference listed drug (RLD)
• Chemical class mitigation / read-across
� Monofunctional alkyl chlorides (halides?)
� α,β-Unsaturated aldehydes (ketones, esters, amides?)
� Alkyl sulfonates?
• Advanced cancer treatment
• Tox data (in vivo mutagenicity/carcinogenicity)
StrategyControl recommendations
� When levels can’t be justified we recommend:
� Ames test
� Alternative in vivo mutagenicity studies
StrategyOther recommendations
Pitfalls
� Identifying potentially reactive structures
� Initial analysis based on structurally alerting
functional groups from Müller (2006)
Müller et al., Reg Tox Pharm 2006, 44, 198-211
Pitfalls
� Identifying potentially reactive structures
� Initial analysis based on structurally alerting
functional groups from Müller (2006)
� Subsequent in silico predictions are much more
regioselective, thus precluding PGIs identified
manually, and conversely often identify many more
PGIs
Pitfalls
� Identifying potentially reactive structures
� Initial analysis based on structurally alerting
functional groups from Müller (2006)
� Subsequent in silico predictions are much more
regioselective, thus precluding PGIs identified
manually, and conversely often identify many more
PGIs
� Solution: involve ChemTox as early as possible
Using in silico tools
� Prior to M7
� Use one in silico tool
� Result: potential for false negatives
� Post M7
� Use 2 complementary in silico tools
� Statistical based tool has too many false positives
� Conflicting predictions
“6. HAZARD ASSESSMENT ELEMENTS
===.
===.
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.”
Using in silico tools
Using in silico tools
Case studies requiring expert analysis
CompoundDerek
alert
Sarah
alertConsensus Remarks
Control
recommendation
Adipic acid
Inactive Negative
(100%
confidence)
Negative Negative Ames
test (Toxnet)
ICH Q3A
qualification
threshold
Plausible
(potential
alkylating
agent)
Positive
(alkyl
halide)
Positive Monofunctional
alkyl halide
(note 5 in M7)
TTCx10
15 µg/day
or run Ames test
Using in silico tools
Compound
Derek alert
for
mutagenicity
Sarah alertConsensus
PredictionRemarks
Control
recommendation
Benzyl chloride
Plausible
(potential
alkylating
agent)
Positive
(100%
confidence)
Positive
Positive Ames test
(Toxnet).
Mutagenic
carcinogen (Class
1) with harmonic
mean TD50
of 61.5
mg/kg/day in the
Carcinogenic
Potency Database
(CPDB)
Compound
specific threshold
of 61.5
µg/person/day is
calculated by
linear
extrapolation from
the TD50
Benzyl bromide
Plausible
(potential
alkylating
agent)
Positive
(100%
confidence)
PositiveNegative in 2-year
carcinogenicity
study
ICH Q3A
qualification
threshold
Using in silico tools
CompoundDerek alert for
mutagenicity
Sarah
alert
Consensus
PredictionRemarks
Control
recommendation
Inactive Positive Equivocal -TTC
or run Ames test
Phosphorus
oxychloride
InactiveOutside
domainEquivocal
Purge knowledge
may be used to
avoid analytical
testing
TTC
if not purged out
Using in silico tools
CompoundDerek alert for
mutagenicity
Sarah
alert
Consensus
PredictionRemarks
Control
recommendation
Diisopropyl
azodicarboxylate
(DIAD)
Inactive Outside
domainNegative
Sarah Nexus could not
associate an
appropriate training set
to this compound and
thus considered it “out
of domain”. Further
expert evaluation of
this compound showed
no structural alerts.
ICH Q3A
qualification
threshold
1,3-
Difluorobenzene
Inactive Positive EquivocalThis compound
is a class 3 impurity
TTC
or run Ames test
Using in silico tools
F
F
Compound
Derek alert
for
mutagenicity
Sarah
alert
Consensus
PredictionRemarks
Control
recommendation
4,6-Dichloro-2-
methylpyrimidine
Inactive Positive Negative
The positive Sarah alert
can be dismissed because
the training set
compounds contain alkyl
halides moieties which
are known mutagens, and
are not present in this
compound.
ICH Q3A
qualification
threshold
1-indanoneInactive Positive Negative
The positive Sarah alert
can be dismissed because
the training set
compounds contain
PAHs which are known
mutagens, and are not
present in this
compound.
ICH Q3A
qualification
threshold
Using in silico tools
CompoundDerek alert for
mutagenicity
Sarah
alert
Consensus
PredictionRemarks
Control
recommendation
THP-protected
intermediate
Inactive in bacterium;
Plausible in mammal
(alkyl aldehyde
precursor)
Negative Negative
The plausible
alert for
mutagenicity in
mammalian cells
is out of the scope
of the ICH M7
guideline
ICH Q3A
qualification
threshold
Using in silico tools
Compound
Derek alert
for
mutagenicity
Sarah
alert
Consensus
PredictionRemarks
Control
recommendation
Inactive Positive Negative
1. The examples in the
training set contain
alerting moieties that
are not present here.
2. α,β -Unsaturated
ketones are rarely
mutagenic*
ICH Q3A
qualification
threshold
Plausible
(α,β -
unsaturated
aldehyde)
Positive Positive -TTC
or run Ames test
Using in silico tools
*Snodin & McCrossen, Regul. Toxicol. Pharmacol., 2013, 67, 299-316
§ Strategy used for GTI identification, assessment
and control
§ Key pitfalls and how they are overcome
§ How we use in silico tools as part of the
assessment strategy
§ How ICH M7 impacts GTI assessment strategies
Summary