In silico strategies to assess potentially mutagenic impurities under ICH M7

Transcription

1 In silico strategies to assess potentially mutagenic impurities under ICH M7 ST 2017 Chris Barber (DoS) Sabrina Snyder (US Account Manager) ik Marchetti (Product Manager)

2 In silico strategies to assess potentially mutagenic impurities under ICH M7 Adopted worldwide ICH M7 Supports the use of in silico models in decision-making Enables fast, safe decision-making

3 An integrated solution to ICH M7..developed through collaboration ICH M7 Impurity identification Mutagen? Purged? o Yes o Yes Treat as non-mutagenic impurity Control to limits in API o further action

4 An integrated solution to ICH M7..developed through collaboration The ability to assess an impurity as non-mutagenic or not present in the final API can offer significant efficiencies without compromising safety. In silico tools can provide a robust and cost-efficient solution provided they are fit-for-purpose Guidance Document on the Validation of (Quantitative) Structureactivity Relationship Models. ECD, 2007 Distinguishing between expert and statistical systems for application under ICH M7 Barber et. al. Reg. Tox. and Pharm. 2017, 84, 124 Establishing Good Computer Modelling Practice (GCMP) in the Prediction of Chemical Toxicity Judson et. al. Mol. Inf. 2015, 34, 276

5 An integrated solution to ICH M7..developed through collaboration ICH M7 Impurity identification Mutagen? Purged? o Yes o Yes Treat as non-mutagenic impurity Control to limits in API o further action

6 An integrated solution to ICH M7..developed through collaboration ICH M7 Impurity identification Mutagen? Purged? Database search In silico Test Measure In silico Expert system Statistical system Expert Assessment Expert Assessment Classification Control Report

7 Identifying potential mutagenic impurities Impurity identification bserved impurities Expected impurities ICH M7 Synthesis-derived Degradation-derived Starting material Reagent Catalyst / ligand Intermediates By-products Decomposition Reaction with - Packaging - Excipients

8 Identifying impurities from degradation ICH M7 ICH 3QB observed during manufacture or stability studies potential degradation pathways including from interaction with excipients and/or packaging relevant stress conditions Light Heat Humidity Acid/base hydrolysis xidation

9 Identifying impurities from degradation ICH M7 ICH 3QB umber of transformations Photochemical (37) relevant stress conditions Light Heat Humidity Acid/base hydrolysis xidation Expert review Rearrangement (31) Elimination (49) Addition (77) Hydrolysis (80) xidation(126) Zeneth includes predictions of reaction of the API under different conditions in the presence of excipients, solvents and degradants

10 Undertaking a database search Database searching Public data Proprietary data Exact match Mutagen Carcinogen Mutagen Carcinogen? Class 1 Class 2 Substructure / similarity search on-mutagen Class 5 Further supporting information for expert review M7 (Muller) classification

11 Undertaking a database search Vitic exus an authoritative source of data In-house data Public data Sources include: FDA TP ISSSTY Kirkland Hansen, Bursi MPDB Literature o. of records o. of compounds In vitro genetox 164,001 10,246 In vivo genetox 10,595 2,723 verall-call genetox 17,322 8,932 Carcinogenicity 16,419 3,865 Pre-competitive data Consortia of Lhasa members Aromatic amines 3, Intermediates 16,971 1,088 Excipients 2, Vitic contains raw, summary, and Lhasa overall call data + literature references Currently 400K records, 20K compounds across a wide range of tox endpoints

12 Lhasa Carcinogenicity Database Searchable repository of 6529 long-term carcinogenicity studies covering 1529 chemicals Builds upon work by Lois Swirsky Gold et al. Recalculates TD 50 values following the non-lifetable statistical method using a new R script Will be published this year Data will be expanded upon in the future

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15 Derek exus - an expert in silico prediction system Key endpoints of relevance for M7

16 Sarah exus a statistical model for mutagenicity Supplied with model built with Lhasa-curated public data ptimised to learn mutagenicity Fragmentation designed for reactivity-driven endpoints Self-organising Hierarchical etwork to maximise information gain Decision-tree to reduce the chance of coincidence Explicit applicability domain Confidence score is provided for each prediction Predictions are transparent and therefore interpretable

17 M7 Classification ltidisciplinary-guidelines.html M7 classification helps define how to control impurities Known non-mutagen Q Experimental Data? Known mutagenic carcinogen Known mutagen Class 1 Class 2 Class 5 Predicted non-mutagen - o experimental data Expert review of 2 in silico predictions + Predicted mutagen Class 3 Class 4 Predicted non-mutagen by discounting positive alert(s) because it is shared by a known, relevant non-mutagen

18 How Lhasa ICH M7 classification can help.sdf file API impurities User can add additional data Searches for carc and mut data from Lhasa and custom database

19 ICH M7 class generated and report produced Each impurity is classified according to whether there is Ames or Carcinogenicity information in addition to the Derek Prediction Users can also input experimental results for mutagenicity or carcinogencity which updates the ICH M7 Class

20 Mirabilis supporting expert assessment of purge Concept is part of the M7 guidelines Risk of potential mutagenic impurity Ames or 2 in silico models.. Present a purge argument for absence Risk mitigated -ve +ve Treat as a nonmutagen Control & monitor in final API Mutagenic Impurities: Precompetitive/Competitive Collaborative and Data Sharing Initiatives. Elder. rg. Process R & D, 2015, 19, 1486 Risk assessment of genotoxic impurities in new chemical entities: Strategies to demonstrate control. Teasdale (2013).. rg. Process R & D, 2013, 17, 221

21 An integrated solution to ICH M7..developed through collaboration ICH M7 Impurity identification Mutagen? Purged? Carc-DB Database search In silico Test Measure In silico Expert system Statistical system Expert Assessment Expert Assessment Classification Control Report

22 A case study Pimavanserin Selective 5HT2a inverse agonist on-dopaminergic anti-psychotic for Parkinson sufferers Approved by the FDA in 2016 (Acadia Pharmaceuticals) Br HH2 Cl Cl K2C3, KI, DMF i. H2, Raney-i ii. Acetic acid iii. ah C F W 2016/141003A1 Pimavanserin free base hemi-tartrate L-tartaric acid F

23 4-hydroxybenzaldehyde Processed through Derek exus and Sarah exus Both report a negative prediction (in Sarah s training set) 5 strains reported in Vitic (ref 1996)

24 A case study Pimavanserin Selective 5HT2a inverse agonist on-dopaminergic anti-psychotic for Parkinson sufferers Approved by the FDA in 2016 (Acadia Pharmaceuticals) Br HH2 Cl Cl K2C3, KI, DMF i. H2, Raney-i ii. Acetic acid iii. ah C F Pimavanserin free base hemi-tartrate L-tartaric acid F

25 1-Bromo-2-methylpropane (iso-butyl Bromide) Search in Vitic exact match

26 1-Bromo-2-methylpropane (iso-butyl Bromide) Search in Vitic exact match Reference quite old (1977) pre-dates ECD guidelines o pre-incubation Concentration was not reported Was tested in 5-strains +/- S9..but not using e. coli or TA102 [not that relevant for alkyl halides] Was tested in a desiccator (volatile compound) Read-across? Close analogues tested in the same paper were active n-bu-br Br s-bu-br Br Br t-bu-br Br neopentyl-br * Activity cliff would retest or consider as mutagenic!

27 A case study Pimavanserin Selective 5HT2a inverse agonist on-dopaminergic anti-psychotic for Parkinson sufferers Approved by the FDA in 2016 (Acadia Pharmaceuticals) Br HH2 Cl Cl K2C3, KI, DMF i. H2, Raney-i ii. Acetic acid iii. ah C F Pimavanserin free base hemi-tartrate L-tartaric acid F

28 Phosgene Processed through Derek exus and Sarah exus

29 Phosgene Test result in Vitic Tested negative in TA98/100 but unlikely to survive conditions Expect it to be Ames negative but treat as mutagenic

30 A case study Pimavanserin Selective 5HT2a inverse agonist on-dopaminergic anti-psychotic for Parkinson sufferers Approved by the FDA in 2016 (Acadia Pharmaceuticals) Br i. H2, Raney-i ii. Acetic acid iii. ah Cl Cl K2C3, KI, DMF C F Pimavanserin free base hemi-tartrate L-tartaric acid F

31 Benzyl isocyanate Processed through Derek exus and Sarah exus

32 Benzyl isocyanate Processed through Derek exus and Sarah exus Derek has a strong alert detailed expert analysis, lots of supporting publications

33 Benzyl isocyanate Processed through Derek exus and Sarah exus Derek has a strong alert detailed expert analysis, lots of supporting publications Sarah does not have a specific hypothesis for isocyanates

34 Benzyl isocyanate Processed through Derek exus and Sarah exus Derek has a strong alert detailed expert analysis, lots of supporting publications Sarah does not have a specific hypothesis for isocyanates But a range of analogues known majority are inactive Conservative expert review is positive

35 A case study Pimavanserin Selective 5HT2a inverse agonist on-dopaminergic anti-psychotic for Parkinson sufferers Approved by the FDA in 2016 (Acadia Pharmaceuticals) Br HH2 Cl Cl K2C3, KI, DMF i. H2, Raney-i ii. Acetic acid iii. ah C F Pimavanserin free base hemi-tartrate L-tartaric acid F

36 For larger sets of compounds, use batch mode

37 Can Mirabilis help assess the risk of the impurity surviving synthesis? Br HH2 Cl Cl K2C3, KI, DMF i. H2, Raney-i ii. Acetic acid iii. ah C F Pimavanserin free base hemi-tartrate L-tartaric acid F

38 Can Mirabilis help assess the risk of the impurity surviving synthesis?

39 1. Enter the route

40 2. Confirm the reaction classes

41 2. Confirm the reaction classes

42 3. Assign the impurities to be tracked

43 3. Assign the impurities to be tracked

44 4. Establish purge factors

45 4. Establish purge factors

46 Mirabilis knowledge base supports expert assessment Expert commentary including mechanistic explanation and analysis of the applicability and certainty of the purge Supporting Literature References Predicted Purge Factor Impact of changing factors including such as temperature, solvent, specific reagents, stereoelectronics for reactivity-based purge Measured data for relevant analogues A consortium-driven framework to guide the implementation of ICH M7 ption 4 control strategies <manuscript in preparation>

47 5. btain total purge across full route

48 6. Establish whether below threshold of concern.. M7 specifies acceptable limits of mutagenic ICH M7 impurities in final drug product Are you below this? MMMMMMMMMMMMMMMM oooo aaaaaaaaaaaaaa llllllllllll dddddddddddd sssssssssssssssss PPPPPPPPPP VVVVVVVVVV ffffffff MMMMMMMMMMMMMMMMMM = Expected level in final API A consortium-driven framework to guide the implementation of ICH M7 ption 4 control strategies. <manuscript in preparation>

49 ptions depend upon the predicted / measured levels ICH M7 ption 1 ption 2 Analysis show below limit in API Analysis shows below limit in precursor / intermediate ption 3 Analysis shows above limit in precursor / intermediate - but with knowledge of purge can be assured that level in the API is below the acceptable limit without the need for any additional testing ption 4 Prediction of purge with sufficient confidence to be below acceptable limit in API such that no analytical testing is recommended. [impurity not in specification]

50 Mirabilis is built on an approachacceptedby regulators Risk assessment of genotoxic impurities in new chemical entities: Strategies to demonstrate control. Teasdale.. rg. Process Res. Dev. 2013, 17, 221 A Tool for the Semi-quantitative Assessment of Potentially Genotoxic Impurity (PGI) Carryover into API Using Physicochemical Parameters and Process Conditions. Teasdale.. rg. Process Res. Dev. 2010, 14, 943 Evaluation and Control of Mutagenic Impurities in a Development Compound: Purge Factor Estimates vs Measured Amounts. Mclaughlin.. rg. Process Res. Dev. 2015, 19, 1531

51 Mirabilis was built through a pre-competitive collaboration Currently there is a consortium of 19 companies Designed to fit agreed best workflow Purge factor values Initial values from expert elicitation Literature review to support/evaluate Wet chemistry undertaken to address knowledge gaps Performance evaluation on-going Suggested safety margins and levels of supporting information proposed A consortium-driven framework to guide the implementation of ICH M7 ption 4 control strategies <manuscript in preparation> Engagement with regulators on-going

52 Conclusions on-mutagen (Vitic) accepted Class 5 non-mutagen Br on-standard (negative) Ames data discounted by expert Class 3 mutagen High purge (3x10 8 ) predicted by Mirabilis M7 ption 4 (not specified in submission) Cl Cl Derek contains unclassified : Sarah out of domain Treated as Class 3 mutagen High purge (1x10 11 ) predicted by Mirabilis M7 ption 4 (not specified in submission) C Derek positive : Sarah negative. Treated as Class 3 mutagen Low purge predicted by Mirabilis Evaluate ptions 1 or 2 (test in API or precursor)

53 An integrated solution to ICH M7..developed through collaboration ICH M7 Impurity identification Mutagen? Purged? Carc-DB Database search In silico Test Measure In silico Expert system Statistical system Expert Assessment Expert Assessment Classification Control Report

54 Questions