Predicting the Purging of Impurities within an API Synthetic Pathway. Dr Elizabeth Covey-Crump, Lhasa Limited

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1 Predicting the Purging of Impurities within an API Synthetic Pathway Dr Elizabeth Covey-Crump, Lhasa Limited

2 Content Introduction Development of an in silico system - Mirabilis Industry Consortium Standardisation Regulators and Workflow Knowledge base development 2

3 INTRODUCTION 3

4 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,

5 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

6 Control Approaches Pierson et al, OPRD, 2009, 13,

7 7

8 ICH M7 Option 1 Details 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 ucts/guidelines/multidisciplinary/m7/m7_step_4.pdf 8

9 Semi-quantitative assessment Uses knowledge of process Inherently conservative 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 >20 0 C below that of the reaction/ process solvent = 10 Ionisability Boiling point +/ 10 0 C that of the reaction/ process solvent = 3 Boiling point >20 0 C above that of the reaction/ process solvent = 1 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,

Control Option 4 Practical Use of Purge Tool F F F Ester CO 2 Bu stage 1 THF F CO 2 Bu N N DIBAL TosCl N THF Toluene stage 2 HO N stage 3 TsO Toluene solution N N HO B OH N N Coupled Ester Alcohol

10 Control Option 4 Practical Use of Purge Tool F F F Ester CO 2 Bu stage 1 THF F CO 2 Bu N N DIBAL TosCl N THF Toluene stage 2 HO N stage 3 TsO Toluene solution N N HO B OH N N Coupled Ester Alcohol Tosylate Boronic Acid BocN N SO 2 Me HCl, IPA stage A HCl.HN N SO 2 Me stage 4 Toluene BOC-Sulfonamide BocSulphonmide Sulphonamide Sulfonamide K 2 CO 3 F N N N Molecular Weight = Exact Mass = N SO 2 Me MSA / EtOH Mesylate salt 10

11 Control Option 4 Practical Use of Purge Tool 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 Boronic acid Stage Physical Processes Total multiple Rationale per stage IPC <1% remaining, highly soluble in THF 1000 Tosyl chloride Stage Stage Soluble in THF 10 Stage nonisolated Stage Final API non isolated High reactivity 100 Stage High reactivity 1000 Final API Tosylate Stage Final API Isopropyl Chloride Stage A B pt <Solvent 100 Stage Final API Overall calculated purge factor Boronic acid Tosyl chloride Tosylate IPC

12 DEVELOPMENT OF AN IN SILICO SYSTEM - MIRABILIS 12

13 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

14 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

15 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

16 Proposed PMI / MI Purge Factor Decision Tree (Roland Brown, Vinny Antonucci, Mike Urquhart) Impurity requires management as PMI or MI Establish PMI / MI strategy based upon comparison of Predicted purge factor (Mirabilis) vs Required purge factor calculated from TTC or PDE requirements > 1000x > 100x > 10x ICH M7 Option 4 Data collection not required Include predicted purge factors in submission 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. 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 ICH M7 Option 1,2,3 Analytical testing and/or specification(s) required at SMs, Intermediates, or API, including trace analyses (as required). If measured purge factor is insufficient, then ICH M7 Option 4 is not justified

17 PROGRESS IN MAKING PREDICTIONS FOR PHYSICOCHEMICAL PARAMETERS IN MIRABILIS 17

18 Making Predictions Reactivity Reactivity knowledge base Reaction mining Kinetics modelling Solubility Volatility 18

19 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

20 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 21

Further Development Experimental examples and

supplementary info Literature references Purge

been assigned Summary of data from literature,

individual components Impurity reaction in real

22 Further Development Experimental examples and supplementary info Literature examples and supplementary info Literature references 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) Reaction mining database summary and supplementary info Links to other models?

23 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

24 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

25 Example Phenyl boronic acid 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 25

26 Betori et al, OPRD, 2015, 19,

27 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

28 Knowledge entry structure example PMI class Reaction Type Predicted reactivity purge factor High-level summary of predicted reactivity purge factor entry Arylboronic acid Reduction of ester to alcohol 1 (no reaction) There is no evidence of esters reacting with arylboronic acids. Very strong reducing agents (e.g. LiAlH 4 ) 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. Data ranges - temperature - time - solvents used - reagents used Temp. ( C): Time (min.): Solvents: Reagents: 20-0 No difference in purge No difference in purge. Methanol, DCM, Et 2 O, THF, Dioxane No difference in purge. LiAlH 4, LiBH 4, NaBH 4, BH 3 Only LiAlH 4, being very reactive, has been observed to react with arylboronic acid in the absence of the ester. 28

29 Knowledge entry structure example (ctd.) Detailed supporting information considering: - Specific substrate - Specific reagents - Impact of solvent - Time - No. of equivalents - Temperature - Potential competing / alternative reactions 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, NaBH 4 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: H 2 +Pd/C, NaBH 4, LiAlH 4 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].

30 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

31 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

32 Acknowledgements Dr Andrew Teasdale Dr Martin Ott Dr Susanne Stalford Mirabilis consortium 32

Progress on Building a Tool for Predicting the Purging of Mutagenic Impurities During Synthesis. A Teasdale

Progress on Building a Tool for Predicting the Purging of Mutagenic Impurities During Synthesis Teasdale Progress on Building a Tool for Predicting the Purging of Mutagenic Impurities During Synthesis