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Controlling potential genotoxic impurities encountered during API synthesis

Utilising expert knowledge of chemical properties to manage risk

[email protected]

Dr Michael BurnsSenior Scientist

mailto:[email protected]

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 4Not

Purged

PurgedOption 3

API synthesis

Analytical challengeTime consuming Expensive

High utility Efficient syntheses

In-vitro toxicity

Mutagenic

Teasdale et al’sscoring approachto 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 factorReactivity Highly reactive = 100

Moderately reactive = 10Low reactivity/unreactive = 1

Solubility Freely soluble = 10Moderately soluble = 3Sparingly 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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HN

O

O

Cl

HN

O

N

Cl

OH

HN

O

N

Cl

OH

3-aminopropan-1-ol

H2 /Pt

HN

O

N

Cl

OH

HCl in IPACl

HN

O

N

Cl

Cl

AZD9056 AldehydeAZD9056 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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HN

O

O

Cl

HN

O

N

Cl

Cl

Cl

Predicted: 10,000Measured: 112,000(Solubility, Reactivity)

Predicted: 3Measured: 10(Solubility)

Predicted: 10,000Measured: 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

RS

N

HN

R'

N

RS

N

N-

R'

O

-S

N

HN

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

of 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 stageof 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 of treatment

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Br

NH2Cl

O Cl

+

Br

HN

O

Cl

Br

HN

O

N

N

HN

N

NH

HN

O

NN

NN

N

K2CO3, DMF

NH2

NN

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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ICH M7 regulations



Testing unnecessary

Option 3 or 4

Purged

API synthesis

In-vitro toxicityTeasdale et al’sscoring approachto purge prediction

Mutagenic

Mutagenic?

Plan

Implement

Cl

O Cl

Br

NH2

Subject to evidence package

Impurity Carry-over Conclusion

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ICH M7 regulations



Testing unnecessary

Test for impurity

Option 3 or 4Not

Purged

API synthesis

In-vitro toxicity

Mutagenic

Teasdale et al’sscoring approachto purge prediction

Non-mutagenic

Mutagenic

Option 1 or 2

Mutagenic?

Plan

ImplementBr

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: Pazopanibhydrochloride 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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