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Mirabilis
The Scientific Future
Lhasa Limited ICGM, Basel
13 October 2016Martin Ott
Mirabilis
A tool to estimate the purge (removal) of potentially genotoxic impurities in drugs
• To support the application of ICH M7 guidance
• Option 4
• “Understand process parameters and impact on residual
impurity levels (including fate and purge knowledge) with
sufficient confidence that the level of the impurity in the drug
substance will be below the acceptable limit such that no
analytical testing is recommended for this impurity”
• Develop a knowledge-based approach to provide support,
evidence and guidance for expert assessment
2
Mirabilis – objective
• Precompetitive consortium of 16 companies
• Sharing knowledge and data
• Desire for
• A standardised process with clear definitions of steps
• Application of an accepted scientific approach
• Conservative, robust and trustworthy predictions
• A transparent tool that
• Can be challenged by experts
• Provides evidence that can support good decisions
• Creates reports with direct access to more support if needed
3
Mirabilis – led by a consortium
• Continued scientific guidance and oversight by consortium
• Sharing examples of purge estimates
• Confirm that these are conservative and robust
• Part of the validation of the science
• Will form a publication - 1Q17
• Establishing agreement on
• When an estimated purge is considered sufficient
• Level of information needed to support decision
4
Primary goal for Mirabilis
• Trusted by regulators
• Purge predictions are:
• Conservative
• Consistent/standardised
• Transparent
5
Purge factor calculation
• The threat posed by potential
mutagenic impurities (PMI) arises
from reagents used in synthesis:
• electrophilic reagents (alkylating
agents)
• DNA interchelators
• pro-mutagenic e.g. metabolically
activated
• Any synthetic drug therefore has a
latent MI-related risk
Scheme from Teasdale et al, Org. Process Res. Dev. (2013), 17, 221-230 6
Purge factor calculation
• The original approach: test for the presence of a PMI in
the final API
Very simplistic approach and fails to account for:
• Inherent reactive nature of the PMI
• An understanding the fate of the PMI in the synthetic processes
applied
It is a paradox that the very reactivity that renders the
chemical a concern from a safety perspective can be
the same property that will generally ensure that it is
effectively removed in downstream processes.
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Scientific prioritisation
1. Reactivity purge predictions
• Development of “knowledge matrix”
• Reaction mining to supplement/support predictions
2. Solubility purge predictions
• Data-driven approach for common compounds
• Solubility prediction (research)
3. Other, e.g. volatility purge predictions8
Scientific prioritisation – Reactivity
• Knowledge gathering to fill matrix
• Prioritised – fill most crucial gaps first
• Coverage (2016 and beyond) agreed with members
• Reaction mining to supplement/support predictions
• In research phase – demonstrate concept first
• Pilot study in preparation to mine member lab data
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Knowledge matrix (reactivity)
• Matrix
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Knowledge matrix (reactivity)
• 57 reaction types - 28 prioritised ( 31)
• 15 impurity classes - 4 prioritised
• 124 / 855 (14.5%) “Predicted Reactivity Purge
Factor Knowledge Matrix” points (now called “Cells”)
have been prioritised for 2016
• Speed of development can be expected to increase
in 2017
12
Reactivity prediction & knowledge base
The matrix is implemented as an in silico reaction
knowledge base, which will:
Automatically recognise the structural class of an
impurity
Automatically recognise the transformation in a
synthetic step
Generate a predicted reactivity purge factor based on
empirical data
Present transparent and robust scientific evidence to
support the predicted reactivity purge factor assigned
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Knowledge base editor – reactivity matrix
Knowledge matrix (reactivity)
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Knowledge base editor – impurity class
Impurity class
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Knowledge base editor – transformation class
Transformation class
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Cell contents
Index
Purge factor
Executive summary
Dependencies
Comments
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Knowledge base editor – cell contents
Purge factor
Summary
Dependencies
Comments
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Operation – route editor
Enter route
19
Operation – route editor
Enter route
20
Operation – route editor
Enter route
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Operation – route editor
Enter route
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Impurity new impurity
• A reactivity-based purge usually generates a new compound
which may or may not be a impurity of concern
• This does not affect the effectiveness of that purge – the new
compound, if is a pMI, should itself be treated as a new
impurity
• It is the user’s decision whether this impurity should be tracked
• The new impurity should “inherit” any purge value from its
parent
• Mirabilis does not support this yet
• This will be part of a future treatment of concentrations
23
Impurity new impurity (same class)
24
Impurity new impurity (different class)
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Impurity belonging to multiple classes
• More than one impurity type may be recognised
More than one purge factor prediction – which one to choose?
• It was suggested to choose the more conservative value
“There is still genotoxic/mutagenic potential left”
• However, the compound will be purged by a factor of 100!
• We track the purging of compounds, not of functional groups and/or
genotoxic/mutagenic potential
• Any compounds newly generated by purging reactions should be
in turn considered for tracking, subject to the user’s judgment 26
Conclusion
• The purge tool concept provides a cost-effective, quick and
effective way of assessing the risk posed by a MI in a
regulatory risk assessment.
• The development of an in silico tool provides the basis for
a consistent and systematic cross-industry approach which
is based on expert knowledge.
• Use of these predictions and transparent expert knowledge
aligns directly with principles defined in ICH M7.
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Conclusion
• Development of the tool is progressing well
• Enthusiastic and committed consortium of 16 members
• Providing scientific guidance and oversight
• Knowledge will be expanded and refined
• Datamining techniques may assist in finding supporting
evidence for reactivity purge factors
• Further research into solubility is planned for the future
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TimTransformation
Engine
SimonUser
Documentation
MukeshKnowledge
Base
DianeUser Experience
Design
AlanTransformation
Engine
MartinLead
Scientist
NatashaKnowledge
Base MikeKnowledge
BaseSimonProductAnalyst
TomProductTesting
DanKnowledge
Editor
AhmedMirabilisSoftware
PaulLead
Developer
DavidKnowledge
Editor
SuziProject
Management
LizBusiness
Development
Mirabilis 2016 Project Team
SusanneKnowledge
Base
AntonioKnowledge
Base
LaraKnowledge
Base
TonyKnowledge
Base
SamKnowledge
Base
JingMirabilisSoftware
DavidReporting
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Questions?