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