Cont ntrolling lling p potent ntia ial g l geno notoxic xic - - PowerPoint PPT Presentation

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Michael.Burns@lhasalimited.org Dr Michael Burns Senior Scientist

Outline

• Background
• PMIs in synthesis
• Impurity carry-over workflow
• Purge calculations
• Mirabilis
• Origins
• Workflow for ICH M7
• Theoretical case study
• Ongoing Mirabilis developments

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Background

• The threat posed by (potential) mutagenic

impurities, (P)MIs, in drug substances arises; for example, from the use of reagents such as alkylating agents within the synthesis

• What makes them useful reagents in synthesis,

high reactivity, is often what makes them (P)MIs

• Virtually all syntheses will involve the use of

mutagenic or potentially mutagenic reagents or possess potential risk arising from a (P)MI formed in the process

• Any synthetic drug therefore may have a latent

(P)MI-related risk.

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Impurity Carry-over Workflow

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Reactive functionality In-silico toxicity prediction ICH M7 regulations Assess likelihood of impurity persisting Knowledge of physicochemical properties Testing unnecessary Test for impurity

Option 3 or 4 Not Purged Purged Option 3

API synthesis

Analytical challenge Time consuming  Expensive High utility Efficient syntheses

In-vitro toxicity

Mutagenic Teasdale et al’s scoring approach to purge prediction

Non-mutagenic

Mutagenic Option 1 or 2

Mutagenic?

Plan Implement

Purge Factor Calculation – Basic Principles

• The following key factors were defined in order to assess the potential

carry-over of a (P)MI:

• Reactivity, solubility, volatility and any additional physical process

designed to eliminate impurities e.g. chromatography.

• A score is assigned on the basis of the physicochemical properties of the

(P)MI relative to the process conditions

• These are then simply multiplied together to determine a ‘purge factor’

(for each stage).

• The overall purge factor is the product of the factors for individual stages.
• Predicted purge is then compared to required purge (this being based on

the safety limit and initial level introduced into the process).

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Original Purge Prediction Scoring System

• The original scoring system was built on basic principles – referred to as a ‘paper’

assessment because its not automated (manual calculation via spreadsheet)

• Reactivity shown to have largest effect
• Other factors especially solubility would also influence purging
• Scoring system originally designed to be conservative
• On validation this was experimentally observed
• It was decided that this should be retained rather than seeking absolute parity
• Urquhart et al recently demonstrated the approach for Atovaquone

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Physicochemical parameters Purge factor

Reactivity Highly reactive = 100 Moderately reactive = 10 Low reactivity/unreactive = 1 Solubility Freely soluble = 10 Moderately soluble = 3 Sparingly soluble = 1 Volatility Boiling point >20 °C below that of the reaction/ process solvent = 10 Boiling point within ±20 °C of that of the reaction/process solvent = 3 Boiling point >20 °C above that of the reaction/ process solvent = 1 Ionisability Ionisation potential of GTI significantly different from that of the desired product Physical processes: chromatography Chromatography: 10−100 based on extent of separation Physical processes: e.g. other scavenger resins Evaluated on an individual basis. Urquhart et al. Regul. Toxicol. Pharmacol., 2018, 99, 22-32

Paper Assessment – Case Study – AZD9056

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H N O O Cl H N O N Cl OH H N O N Cl OH 3-aminopropan-1-ol H2 /Pt H N O N Cl OH HCl in IPA Cl H N O N Cl Cl AZD9056 Aldehyde AZD9056 Imine AZD9056 Free Base Isopropyl chloride AZD 9056 Chloride AZD9056 HCl MeOH / water pure (by-product) (minor by-product) .HCl

Paper Assessment – Case Study – AZD9056

Comparison of the overall predictions with the experimental results shows that the predicted purge factor is in good correlation with each impurity

• Under-predicts the purge capacity of the process
• Clearly demonstrates risk of carry over to be low
• Predictions indicated where formation of an impurity needed to be regulated

through process control, rather than relying on the ability of the process to eliminate it.

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H N O O Cl H N O N Cl Cl Cl

Predicted: 10,000 Measured: 112,000 (Solubility, Reactivity) Predicted: 3 Measured: 10 (Solubility) Predicted: 10,000 Measured: 38,500 (Solubility, Volatility)

Is this simply about avoiding analytical testing?

• 3 Step reaction
• Starting material contains an aromatic N-oxide
• Alcohol converted to alkyl halide
• Coupled to a thiol
• Oxidation step
• Only final step isolated
• Impurity is un-reactive/highly soluble/non-

volatile

• No purge predicted in steps 1 and 2
• Spiking experiment 3000ppm
• Only reduced to 2000ppm

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N R OH N R Cl N R S N H N R' N R S N N

• R'

O

• S

N H N R' K+ Stage 1 Stage 2 Stage 3 K+ N+ NO2 O

• Purge calculations showed that control at starting material is required.

Mirabilis

• Mirabilis established to take basic principles of paper-

based predictions and augment them

• Key concepts:
• Use of an in silico template allows for greater consistency in terms
• f how predictions are structured and reported (reproducibility)
• Predictions by Mirabilis are informed from a knowledge base, the

basis of these is clearly visible (objectivity)

• Knowledge management & pre-competitive knowledge sharing

(supports further development)

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Origins of Mirabilis Knowledge Base

• Common alerting impurity types and

popular chemical transformations identified by the Mirabilis consortium

• 15 Impurity types
• 58 Transformation types

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• Each impurity type is analysed against every transformation to

assess potential purge, generating a reactivity matrix

• A consortium collaboration exercise resulted in an ‘expert

elicitation’ for each reactivity purge factor

• Lhasa scientists subsequently augment the ‘cells’ with scientific

comments including; references, examples and supporting data

Cell

Mirabilis workflow

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Synthetic scheme Identify impurity

Identify transformation

Identify purge relationship Return relevant purge and supporting info Add all unit operations (e.g. Work-up, extraction, filtration) Add purges and justification for each unit operation Combine purges to achieve total purge per stage Combine all stage purges to achieve total predicted purge Review reactivity purge assignment

Repeat for each stage

• f the synthesis

Calculate purge required to reach acceptable intake level

Define API daily dose

Define initial impurity level

Calculate purge ratio to establish control option 4 suitability and regulatory requirements

Establish API treatment length Identify acceptable intake from ICH M7 (e.g. >1 – 10 years = 10 μg/day)

Start

Theoretical Case Study - Imatinib

• Imatinib is an anti-cancer drug with a maximum daily dose of 800mg for

up to 3 years

• Hopkin et al published a 3-stage synthesis to Imatinib
• Additional steps include basic work up/extraction (stages 1 and 3), precipitation and wash (stage 2),

and column chromatography (stage 3)

• Six structures in the synthesis need to be analysed for potential ICM M7

control*

• ICH M7 control limit of 10 μg for any PMIs based on dose and duration
• f treatment

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Br NH2 Cl O Cl + Br HN O Cl Br HN O N N H N N NH HN O N N N N N K2CO3, DMF NH2 N N N Pd2dba3, XantPhos, NaOtBu, dioxane,

tBuOH

Et3N, DCM

Imatinib

Stage 1 Stage 2 Stage 3

Hopkin et al. Org. Biomol. Chem., 2013, 11, 1822-1839. http://www.glivec.com/dosing/ * ICH M7 does not actually apply to anti–cancer drugs

Which structures are PMIs?

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Purge Assessment in Mirabilis

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Cl O Cl Br NH2 Br HN O Cl

Regulatory Requirements

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Cl O Cl Br NH2 Br HN O Cl

PR = 1.25 x 106 PR = 12.5 PR = < 1

Barber et al, Regul. Toxicol. Pharmacol., 2017, 90, 22-28

Test for impurity

Impurity Carry-over Conclusion

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Reactive functionality In-silico toxicity prediction ICH M7 regulations Assess likelihood of impurity persisting Knowledge of physicochemical properties Testing unnecessary

Option 3 or 4 Purged

API synthesis In-vitro toxicity

Teasdale et al’s scoring approach to purge prediction Mutagenic

Mutagenic?

Plan Implement

Cl O Cl Br NH2

Subject to evidence package

Impurity Carry-over Conclusion

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Reactive functionality In-silico toxicity prediction ICH M7 regulations Assess likelihood of impurity persisting Knowledge of physicochemical properties Testing unnecessary Test for impurity

Option 3 or 4 Not Purged

API synthesis In-vitro toxicity

Mutagenic Teasdale et al’s scoring approach to purge prediction

Non-mutagenic

Mutagenic Option 1 or 2

Mutagenic?

Plan Implement

Br HN O Cl

Additional Mirabilis Developments in Progress

• Reactivity
• Increasing coverage of transformations and impurities
• Reaction mining to aid reactivity purge assignment
• Machine learning from a database of patented reactions
• Provide the user with supporting examples when purge is assigned
• Aid expert assessment with data in absence of transformation recognition
• Aid Lhasa scientist assessments in identifying conditions (and relationships) resulting in purge.
• Solubility – Identifying possible areas of solubility purging
• Predict solubility of structures in a given solvent
• Differences between impurities and products may infer a possible purge related

to liquid-liquid extraction

• Prime the user to consider whether a solubility purge may be a viable argument.
• Volatility – automatic look-up of common boiling points
• Comparison of known impurity boiling points against process temperatures
• Purge could be automatically assigned (subject to user confirmation).

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Questions

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References

• Teasdale, A.; Fenner, S.; Ray, A.; Ford, A.; Philips, A. A tool for the semiquantitative assessment of potentially genotoxic

impurity (PGI) carryover into API using physicochemical parameters and process conditions. Org. Process Res. Dev. 2010, 14, 943-945. DOI: 10.1021/op100071n

• Teasdale, A.; Elder, D.P. Risk assessment of genotoxic impurities in new chemical entities: Strategies to demonstrate control.
• Org. Process Res. Dev. 2013, 17, 221-230. DOI: 10.1021/op300268u
• Elder, D.P.; Okafo, G.; McGuire, M. Assessment of predictivity of semiquantitative risk assessment tool: Pazopanib

hydrochloride genotoxic impurities. Org. Process Res. Dev. 2013, 17, 1036-1041. DOI: 10.1021/op400139z

• McLaughlin, M.; Dermenjian, R.K.; Jin, Y.; Klapars, A.; Reddy, M.V.; Williams, M.J. Evaluation and control of mutagenic

impurities in a development compound: purge factor estimates vs measured amounts. Org. Process Res. Dev. 2015, 19, 1531-

• 1535. DOI: 10.1021/acs.oprd.5b00263
• Lapanja, N.; Zupančič, B.; Časar, R.T.; Orkič, D.; Uštar, M.; Satler, A.; Jurca, S.; Doljak, B. A generic industry approach to

demonstrate efficient purification of potential mutagenic impurities in the synthesis of drug substances. Org. Process Res. Dev. 2015, 19, 1524-1530. DOI: 10.1021/acs.oprd.5b00061

• Barber, C; Antonucci, V; Baumann, J-C.; Brown, R.; Covey-Crump, E.; Elder, D.; Elliott, E.; Fennell, J.W.; Gallou, F.; Ide, N.D.;

Jordine, G.; Kallemeyn, J.M.; Lauwers, D.; Looker, A.R.; Lovelle, L.E.; McLaughlin, Molzahn, R.; Ott, M.; Schils, D.; Schulte Oestrich, R.; Stevenson, N.; Talavera, R.; Teasdale, A.; Urquhart, M.W.; Varie, D.L.; Welch, D. A consortium-driven framework to guide the implementation of ICH M7 Option 4 control strategies. Regul. Toxicol. Pharmacol. 2017, 90, 22-28. DOI: 10.1016/j.yrtph.2017.08.008

• Betori, R.C.; Kallemeyn, J.M; Welch, D.S. A kinetics-based approach for the assignment of reactivity purge factors. Org.

Process Res. Dev. 2015, 19, 1517-1521. DOI: 10.1021/acs.oprd.5b00257

• Hopkin, M.D.; Baxendale, I. R.; Ley, S. V. An expeditious synthesis of imatinib and analogues utilising flow chemistry methods.
• Org. Biomol. Chem. 2013, 11, 1822-1839. DOI: 10.1039/C2OB27002A
• Urquhart, M.W.J.; Bardsley, B; Edwards, A.J.; Giddings, A; Griva, E; Harvey, J; Hermitage, S; King, F; Leach, S; Lesurf, C;

McKinlay, C; Oxley, P; Pham, T.N.; Simpson, A; Smith, E; Stevenson, N; Wade, C; White, A; Wooster, N. Managing emerging mutagenicity risks: Late stage mutagenic impurity control within the atovaquone second generation synthesis. Regul. Toxicol.

• Pharmacol. 2018, 99, 22-32. DOI: 10.1016/j.yrtph.2018.08.004

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