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Predicting the Purging of Impurities
within an API Synthetic Pathway
Dr Elizabeth Covey-Crump, Lhasa Limited
Content
• Introduction
• Development of an in silico system - Mirabilis
Industry Consortium
Standardisation
Regulators and Workflow
Knowledge base development
2
INTRODUCTION
3
Background
• The threat posed by mutagenic
impurities (MIs) in drug substances
generally arises from the use of
electrophilic reagents (alkylating
agents) within the synthesis.
Used to build the molecular structure.
e.g. carbon-carbon and carbon-nitrogen bond
forming reactions.
• Any synthetic drug therefore has a
latent MI-related risk.
Application of mutagenic reagents.
Impurities formed during the synthesis.
4
Scheme from Teasdale et al, Org. Process Res. Dev. (2013), 17, 221-230
Regulation
• In the development of active pharmaceutical ingredients
(APIs), proof that impurities are present below the
threshold of toxicological concern may be required
Analytical procedures can be used to demonstrate a
sufficiently low level
Can involve high costs and often resource-intensive
5
Control Approaches
Pierson et al, OPRD, 2009, 13, 285-291
7
ICH M7
8http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Multidisciplinary/M7/M7_Step_4.pdf
Option Details
1
• Test impurity in API and show below threshold level
• Periodic testing may be possible if can show sufficient
consistency else routinely measure
2 • Test in precursor (or reagent..) to show below threshold level
3
• Test in precursor (or reagent..) with acceptance ABOVE threshold
level when also supported by evidence that final impurity levels
are below threshold following subsequent process steps
4• Demonstrate understanding of process and consequent purge
sufficiently to not require any analytical testing
Physicochemical Parameters Purge Factor
Reactivity Highly Reactive = 100
Moderately reactive = 10
Low Reactivity / un-reactive = 1
Solubility Freely Soluble = 10
Moderately soluble = 3
Sparingly Soluble = 1
Volatility Boiling point >200C below that of the reaction/ process solvent = 10
Boiling point +/− 100C that of the reaction/ process solvent = 3
Boiling point >200C above that of the reaction/ process solvent = 1
Ionisability Ionisation potential of GI significantly different to that of the desired product
Physical Processes – chromatography Chromatography – GI elutes prior to desired product = 100
Chromatography – GI elutes after desired product = 10
Others evaluated on an individual basis
Teasdale et al, OPRD, 2013, 17, 221-230
• Semi-quantitative assessment
• Uses knowledge of process
• Inherently conservative
Control Option 4 – Practical Use of Purge Tool
10
CO2Bu
F
B N
N
OH
OH
N
HCl.HN
SO2Me
N
N
CO2Bu
F
N
N
F
OH N
N
F
TsO
N
BocN
SO2Me
N N
N
F
N
SO2Me
Ester
Coupled Ester Alcohol
Tosylate
Boronic Acid
DIBAL
THF
TosCl
Toluene solution
Toluene
SulphonamideBocSulphonmide
HCl, IPA
stage 1
stage 2 stage 3
stage 4
stage A Toluene
K2CO3
Mesylate salt
MSA / EtOHMolecular Weight =428.53Exact Mass =428.17
THF
SulfonamideBOC-Sulfonamide
Control Option 4 – Practical Use of Purge Tool
11
Identity / Structure of
MI of Concern Stage Details
Reactivity
(H=100, M=10,
L=1)
Solubility
(F=10, M=3,
L=1)
Volatility
(H=10, M=3,
L=1) Ionisability
Physical
Processes Rationale
Total multiple
per stage
Boronic acid Stage 1 100 10 1 1 1
IPC <1%
remaining,
highly soluble in
THF 1000
Stage 2 1 10 1 1 1 Soluble in THF 10
Stage 3 1
1 - non-
isolated 1 1 1 1
Stage 4 1 3 1 1 1 3
Final API 1 10 1 1 1 10
Tosyl chloride Stage 3 100
1 –non
isolated 1 1 1 High reactivity 100
Stage 4 100 10 1 1 1 High reactivity 1000
Final API 10 10 1 1 1 100
Tosylate Stage 4 100 10 1 1 1 1000
Final API 10 10 1 1 1 10
Isopropyl Chloride Stage A 1 10 10 1 1 B pt <Solvent 100
Stage 4 1 10 10 1 1 100
Final API 1 10 10 1 1 100
Overall calculated purge factor
Boronic acid 300000
Tosyl chloride 10000000
Tosylate 100000
IPC 1000000
DEVELOPMENT OF AN IN
SILICO SYSTEM - MIRABILIS
12
Mirabilis
• Goals:
Provide a software application which:
Standardises this approach for Industry and Regulators
Provides supporting data & expert knowledge
Provides a standardised report
Information and knowledge storage
• Why?
Saves time & money
Helps spot issues early and solve problems before they
arise
Facilitates submission of information to regulators
The Consortium
• For this project, Lhasa Limited are working closely with
the pharmaceutical industry
Currently thirteen companies form the consortium which
sponsor and guide development of the tool and the science
behind it (AZ, GSK, Pfizer, Eli Lilly, Vertex, Merck & Co, Roche,
Novartis, Bayer Healthcare, Abbvie, Sanofi, UCB, Janssen)
Working with them to
Standardise how purge factors are calculated
Identify gaps in knowledge
Provide data where possible
Build predictive models
Test/use the software versions
Engage with regulators
14
Using Purge Arguments in a Regulatory Setting
• Concept already part of ICH M7 guidance
• Lhasa engaging with regulators frequently on progress
• Consortium members are successfully using purge
calculations within regulatory submissions
• Consortium working together to come up with unified
approach of use of the approach within submissions and
how much supporting information is required
15
ICH M7 Option 4
Collect experimental data on purge properties
(solubility, reactivity, etc.) to support scientific
rationale.
Include predicted purge factors in submission for
developmental API route(s). Additionally, include
supporting experimental data on purge properties
in submission for commercial API route.
ICH M7 Option 1,2,3
Analytical testing and/or specification(s)
required at SMs, Intermediates, or API,
including trace analyses (as required).
Impurity requires management as PMI or MI
Potential M7 Option 4
Measure purge factors, including
trace analyses as required, to
support scientific rationale.
Include predicted and measured
purge factors in submission.
Typically, more detailed datasets are
expected for commercial vs.
developmental API routes
If measured purge factor
is insufficient, then ICH M7
Option 4 is not justified
ICH M7 Option 4
Data collection not
required
Include predicted
purge factors in
submission
Proposed PMI / MI Purge Factor Decision Tree
(Roland Brown, Vinny Antonucci, Mike Urquhart)
> 1000x > 100x
Establish PMI / MI strategy based upon comparison of Predicted purge factor
(Mirabilis) vs Required purge factor calculated from TTC or PDE requirements
> 10x
PROGRESS IN MAKING
PREDICTIONS FOR
PHYSICOCHEMICAL
PARAMETERS IN MIRABILIS
17
Making Predictions
• Reactivity
Reactivity knowledge base
Reaction mining
Kinetics modelling
• Solubility
• Volatility
18
Reaction Knowledge Base - Origins
• AZ used a “reactivity grid” to help reactivity purge factors
internally
• Based on expert knowledge of the reactivity of common
classes of mutagenic impurities under common reaction
conditions
19
Reaction Knowledge Base - Origins
• First version of the tool include this knowledge to aid in
decision making
Predictions needed to be supported in a short time period
• Used “expert elicitation” to define the purge values
The consortium was given the reactivity grid and asked to
give their expert opinion the proposed reactivity purge
factors
Lhasa collated the results and modified the grid accordingly
If five or more members agreed on a reactivity purge factor then
a consensus call was made
For those without consensus, a conservative call was made
• Provides starting point for knowledge development
20
21
Purge Factor = 1(10, 100)
• Why the purge factor has been assigned• Summary of data from literature, experiments etc
• Impurity reaction with individual components
• Impurity reaction in real scenario• Mechanisms?• Products?
• Scope and effects (eg temp, solvent, structural)
Literature references
Literature examples and supplementary
info
Experimental examples and supplementary
info
Reaction mining database summary and supplementary
info
Links to other models?
Further Development
Experimental Work and Kinetics Modelling
• Where there are gaps in knowledge
Experimental work being undertaken by the consortium
Protocol has been developed to measure the reaction
kinetics of a representative impurity in a variety of reaction
conditions and with various reactants/reagents
Assumption that impurity is present at low levels
Classes being looked at:
Arylboronic acids
Alkyl bromides
Aromatic amines
Hydrazines
23
• Those found to be non-reactive can be assigned a
reactivity purge factor of 1
• Those which are reactive can be examined further to
determine reactivity purge factor of 10 or 100
Factors taken into consideration include
Temperature
Time
Solvent
Steric and electronic effects (structure-reactivity relationships)
Competition between different reactive centres
24
Example – Phenyl boronic acid
25
Reaction Type Reagent Solvent Reactive?
1 Reduction H2 Pd/C Dioxane No
2 NaBH4 MeOH, THF, DCM No
3 LiAlH4 THF No
4 DIBAL-H THF, DCM No
5 Oxidation H2O2 DCE, DCM, CH3CN Yes
6 Peracetic Acid DCM Yes*
7 Oxone CH3CN, H2O, H2O:CH3CN Yes**
8 TEMPO DCM Yes***
9 Acids Aq HCl CH3CN, THF No
10 Conc. H2SO4 H2O No
11 Aq H2SO4 H2O, Dioxane, CH3CN No
12 HBr/HOAc DCM No
13 Bases Aqueous NaHCO3 CH3CN No
14 10% NaOH CH3CN, Dioxane, H2O No
15 50% NaOH H2O Yes
16 DBU CH3CN, DCE No
17 Amide Bond Formation CDI (with benzoic acid) DCM No
18 EDAc/HOPO (with benzoic acid) DMF No
19 Benzoyl chloride THF No
20 Nucleophiles MeOH THF No
21 Benzyl amine THF No
22 Other Reagents SOCl2 DCE No
23 NCS DCE No
24 NCS/TEA DCE No
25 NBS DCE Yes****
26 Boc2O/TEA THF No
27 TMSCl/TEA THF No
28 Cross-Coupling RuPhos-Pd complex (25 mol%), K2CO3, THF/H2O ?
29 Pd2dba3 (12.5 mol%), PtBu3HBF4 (25 mol%), TEA, THF ?*Reaction was complete within 5 minutes at -78°C
**Reaction was complete within 5 minutes at 2.5°C
***Reaction was complete within 5 minutes at 2.8°C
****Reaction was complete within 5 minutes at 3.2°C
26
Betori et al, OPRD, 2015, 19, 1517-1523
Defining relevant knowledge
• Summary
Brief synopsis
Overall purge value
Range
• Additional information to support / provide confidence
Mechanistic rationale & expert assessment
Impact of key parameters
Specific substrate
Specific reagents
Impact of solvent
Time
No. of equivalents
Temperature
Potential competing / alternative reactions 27
Knowledge entry structure example
28
Arylboronic acid
Reduction of ester to alcohol
1 (no reaction)
Temp. (°C):
Time (min.):
Solvents:
Reagents:
• There is no evidence of esters reacting with arylboronic
acids.
• Very strong reducing agents (e.g. LiAlH4) may react with
arylboronic acids, but less strong ones will not.
• Even strong reducing agents will react preferentially with
the ester.
• Even when an excess of strong reducing agent is used
and the arylboronic acid is reduced, most work-up
conditions (e.g. aqueous) will result in regeneration of
the arylboronic acid.
PMI class
Reaction Type
Predicted reactivity
purge factor
High-level summary
of predicted reactivity
purge factor entry
Data ranges
- temperature
- time
- solvents used
- reagents used
−20-0 – No difference in purge.
30-90 – No difference in purge.
Methanol, DCM, Et2O, THF, Dioxane –
No difference in purge.
LiAlH4, LiBH4, NaBH4, BH3 –
Only LiAlH4, being very reactive, has
been observed to react with arylboronic
acid in the absence of the ester.
Knowledge entry structure example (ctd.)
Arylboronic acids are not expected to readily undergo reaction in the
reaction conditions described.
The reduction of esters to alcohols is a well-established reaction,
commonly used reducing agents include lithium borohydride, lithium
aluminium hydride and similar. Borane or sodium borohydride can
also be used but the reaction would be slow [Clayden et al].
There is no evidence of ester and arylboronic acid functional groups
reacting with each other. An example where a compound contained
ester and pinacol boronate ester functional groups in adjacent
positions on a phenyl ring, NaBH4 preferentially reduced the ester to
the alcohol which then cyclised to a boronic acid lactone [Zhang et
al, 2012]. However, the product is still an arylboronic acid derivative.
Kinetic data supplied by a Lhasa member shows that phenylboronic
acids do not react with the following reducing agents: H2+Pd/C,
NaBH4, LiAlH4 and DIBAL-H. The experiments were carried out in
methanol, DCM, THF and dioxane but this did not affect the results.
However, boronic acids and derivatives have been shown to react
with LiAlH4 in diethyl ether to give the corresponding boranes or
borohydride compounds [Biscoe, 2004; Graham, 2005]. Water
(aqueous work-up) will re-oxidise these to the boronic acids [Hall,
2012].
Detailed supporting
information considering:
- Specific substrate
- Specific reagents
- Impact of solvent
- Time
- No. of equivalents
- Temperature
- Potential competing /
alternative reactions
Solubility – current thoughts
• Very difficult to predict (smaller compounds potentially
easier)
• Easier to interpolate between similar compounds using
prediction methods
• Collect a targeted dataset of solubility measurements of
common mutagens in a range of solvents accessible
within Mirabilis
• Definitions of solubility in the context of Mirabilis to be
finalised
Conclusion
• The semi-quantitative approach described to estimate
residual impurity in APIs is becoming increasingly well
established
• International consortium (currently 13 pharmaceutical
companies + Lhasa) guiding the development of an in
silico tool
• Knowledgebase to provide predictions and supporting
information within Mirabilis is under development
31
Acknowledgements
• Dr Andrew Teasdale
• Dr Martin Ott
• Dr Susanne Stalford
• Mirabilis consortium
32