Mutagenic Impurity Risk Assessment Purge Tool Supporting ICH M7 Control Strategy

Transcription

1 Mutagenic Impurity Risk Assessment Purge Tool Supporting ICH M7 Control Strategy Ella Smoraczewska Lhasa Limited Sales Executive

2 Presentation utline Background & Case Study Regulatory Perspective MI Purge Tool Introduction MI Purge Tool - Reaction Knowledge Base MI Purge Tool Reaction Database MI Purge Tool Requirements Current Status and ext Steps

3 Presentation utline Background & Case Study Regulatory Perspective MI Purge Tool - Introduction MI Purge Tool - Reaction Knowledge Base MI Purge Tool Reaction Database MI Purge Tool Requirements Current Status and ext Steps

4 Background For synthesis it is usually necessary to use very reactive compounds (e.g. electrophilic reagents used in alkylations). These are often inherently mutagenic and mutagenic impurities (MIs) can also be generated during a synthesis route. Due to their reactive nature however, of many of these agents are unlikely to be present at the end of the process. Previously, Regulatory Authorities were reluctant to accept risk assessment based simply on chemical based arguments. A significant amount of effort has therefore been expended in many cases to prove a negative. The challenge is therefore to develop an approach that allows the likelihood of potential carryover of an MI to be assessed without exhaustive analytical testing.

5 Empirical Model Pierson et al examined this on the basis of the number of manufacturing stages away from the final product: Although useful, this is empirical and may only partially eliminate regulatory concerns.

6 Background Dr Andrew Teasdale has developed an idea which allows AstraZeneca to assess, at each stage of a synthesis process, the approximate likelihood that a mutagenic impurity (MI) will remain after that stage has been completed. Before the tool is used, a multi-disciplinary group works together to assess what the likely impurities are going to be and whether or not they are MIs.

7 Background The following key factors were defined in order to assess the potential carry-over of a MI: reactivity, solubility (in the solvent system used for crystallisation), volatility, ionisability, any additional physical process designed to eliminate impurities e.g. chromatography

8 Background The score assigned to each factor is on the basis of the physicochemical properties of the MI relative to the process conditions These are then multiplied together to determine a purge factor (for each stage) The overall purge factor is a multiple of the factors for individual stages. nce the purge factor has been calculated, decisions can be made to either change the manufacture process or synthesis route in order to limit/remove the impurity; or to delay/reduce analytical testing if there is enough evidence the impurity has been purged from the process.

9 Some Key Parameters in the Purge Factor

10 Publications describing approaches Strategies for the Evaluation of Genotoxic Impurity Risk Chapter 9 Genotoxic Impurities, Wiley, 2010 Andrew Teasdale (AZ), Dave Elder (GSK), Simon Fenner (AZ) Risk Assessment of Genotoxic Impurities in ew Chemical Entities : Strategies To Demonstrate Control rg. Process Res. Dev. 2013, 17, p Andrew Teasdale (AZ), David Elder (GSK), Sou-Jen Chang (Abbvie), Sophie Wang (Amgen), Richard Thompson (Boehringer), ancy Benz (Abbvie), and Ignacio H. Sanchez Flores (Takeda) The Assessment of Impurities for Genotoxic Potential and Subsequent Control in Drug Substance and Drug Product J. Pharm. Sci. vol. 102 issue 5, May 2013, p Dow, Linda K.; Hansen, Marvin M.; Pack, Brian W.; Page, Todd J.; Baertschi, Steven W.

11 Case Study The following case study illustrates both the outcome of the predictive purge factor and the real measured values Developmental compound: AZD9056 The synthetic scheme for the process concerned is presented in Figure 1. The GTIs concerned are the AZD9056 Aldehyde, AZD9056 Chloride and isopropyl chloride. rg. Process Res. Dev. 2013, 17, p

12 Pure Case Study - AZD9056 H H 3-aminopropan-1-ol H AZD9056 Aldehyde H 2 /Pt AZD9056 Imine H H AZD9506 Free Base Isopropyl chloride (by-product) H in IPA H H.H H AZD 9056 Chloride AZD9056 H (minor by-product) MeH / water

13 Case Study - AZD9056 H H 3-aminopropan-1-ol H AZD9056 Aldehyde AZD9056 Imine Stage Reactivity Solubility Volatility verall Purge Factor Reaction bserved Purge Factor Crude Isolated (Filtered) Purify (recrystallise) verall 10, ,000 Under- predicts the purge capacity of the process by an order of 10.

14 Pure Case Study - AZD9056 H H 3-aminopropan-1-ol H AZD9056 Aldehyde H 2 /Pt AZD9056 Imine H H AZD9506 Free Base Isopropyl chloride (by-product) H in IPA H H.H H AZD 9056 Chloride AZD9056 H (minor by-product) MeH / water

15 Case Study - AZD9056.H H H H H in IPA H Stage Reactivity Solubility Volatility verall Purge Factor Reaction Crude salt Isolated (Filtered) Purify (recrystallise) AZD9506 Free Base Isopropyl chloride (by-product) AZD9056 H verall 10,000 38,500 Under- predicts the purge capacity of the process by 4-fold. ot part of reaction byproduct. Present at high levels bserved Purge Factor (~5%) within the H / IPA reagent

16 Presentation utline Background & Case Study Regulatory Perspective MI Purge Tool Introduction MI Purge Tool - Reaction Knowledge Base MI Purge Tool Reaction Database MI Purge Tool Requirements Current Status and ext Steps

17 Regulatory Perspective The Purge Tool approach is outlined in the ICH M7 Step 2 draft guideline. Understanding of process parameters and impact on residual impurity levels (including fate and purge knowledge) with sufficient confidence that the level of the impurity in the drug substance will be below the acceptable limit such that no analytical testing is needed for this impurity. P.11 Section 8.1 Control of Process Related Impurities, ption 4 M7_Step_2.pdf

18 ews from ICH M7 EWG Meeting saka, ovember 2013 Paper by Teasdale et al. (input from industry): Risk Assessment of Genotoxic Impurities in ew Chemical Entities : Strategies To Demonstrate Control rg. Process Res. Dev., 2013, 17 (2), pp 221 This paper is currently referenced in the ICH M7 Guideline: Assessment by purge factor

19 Regulatory Update AZ have for some period been using the purge tool as part of the risk assessment of MIs. Used as basis of argument of effective control This includes Phase 1 / 2 IDs and more recently DA. Used both as the sole basis for control (analogous to option 4 in ICH M7) and as part of weight of evidence argument with data. Transparency is important

20 Presentation utline Background & Case Study Regulatory Perspective MI Purge Tool - Introduction MI Purge Tool - Reaction Knowledge Base MI Purge Tool Reaction Database MI Purge Tool Requirements Current Status and ext Steps

21 Mutagenic Impurities Purge Tool Project The MI Purge tool project will create a software application which should: Automate this approach Predicted Likelihood of impurity remaining (i.e. purge factors) Scientific rationale for predicted purge factor + expert comments Provide supporting data & expert knowledge Provide a report (detailed or summary) Plan for information to support regulatory submissions

22 Business eed Saves time & money. Captures existing knowledge, benefits future projects and enables efficient organisation of relevant data. Helps spot issues early and solve problems before they arise. Provides a common platform and reporting method for scientists from different disciplines (e.g. CMC, Safety Assessment, Regulatory Toxicology). Facilitates submission of information to regulators - standardised approach and format. Cheaper and more secure maintenance than developing bespoke in-house solution.

23 MI Purge Tool Steering Group 1-3 representatives from each participating organisation Direct the development of the tool Provide scientific expertise & advice Show and Tell sessions Regular meetings (generally quarterly)

24 Presentation utline Background & Case Study Regulatory Perspective MI Purge Tool - Introduction MI Purge Tool - Reaction Knowledge Base MI Purge Tool Reaction Database MI Purge Tool Requirements Current Status and ext Steps

25 Reaction Knowledge Base Will calculate predicted reactivity purge factors (PRPFs) for unknown compounds based on existing knowledge. Aim is to identify structural class and transformation type based on user input and previously learnt knowledge. Focus has been on trying to refine the reaction knowledge base moving from an approach based completely on intuition to one more closely aligned to actual data.

26 Presentation utline Background & Case Study Regulatory Update MI Purge Tool - Reaction Knowledge Base MI Purge Tool Reaction Database MI Purge Tool Requirements Current Status and ext Steps

27 MI Purge Tool Reaction Database Data Sharing Consortia share in-house purge data. Synthesis reactions steps and observed purge data. Collected in software to support purge factor predictions adds weight of evidence to support submissions. Transparency is key!

28 verall Tool Design User enters starting material & reaction type -sulfonation to sulfonamide User enters reagent & reaction conditions Excess tosyl chloride inert solvent, ~0-25deg C User enters possible impurities e.g. aryl chloride User enters phys chem properties for impurities where this is known or ranges if not R2 H R1 S Reactivity; Solubility; Volatility; Ionisability; etc. Software assesses whether data for similar compounds is present in the database. Data is presented Report Generated (Summary or Detailed) verall purge factor calculated If necessary, user overrides with actual or theoretical data and provides a comment to record decision Hyperlinks to relevant literature provided Purge Factors Calculated

29 Presentation utline Background & Case Study Regulatory Update MI Purge Tool - Reaction Knowledge Base MI Purge Tool Reaction Database MI Purge Tool Requirements Current Status and ext Steps

30 Knowledge and Data Sharing ptions Examples of data and knowledge sharing: Data sharing Structures and all supporting information disclosed and stored within the database Knowledge sharing Data disclosed to Lhasa scientists knowledge represented without disclosing data to consortium (e.g. several Derek exus alerts) Knowledge sharing riginal data not shared with Lhasa or consortium (e.g. development of reaction grid)

31 Plan for Implementation YEAR 0 YEAR 1 YEAR 2 YEAR 3 Requirements Gathering; Members Workflow Release 1 Captures Workflow Allows users to enter Purge estimates Documents outcome (internal report) Captures estimates/structures Members harvest/test common reagents/ solvents against common conditions Database of purge factors for nonconfidential common reagents Release 2 Build on v1 Shows previous relevant entries Exposes database to include added nonconfidential member data Product Build on v2 Allows non-confidential data sharing between companies Starts automating Purge estimates

32 Presentation utline Background & Case Study Regulatory Update MI Purge Tool - Reaction Knowledge Base MI Purge Tool Reaction Database MI Purge Tool Requirements Current Status and ext Steps

33 Current Status Consortium members: Abbvie, AZ, GSK, Lilly, Pfizer, Roche, ovartis. 2+ more applying for funding in Q High Level Requirements collected Steering group meetings began 21 st ovember 2013 F2F meeting 17 th March 2014, RSC, Burlington House, London. 17 participants from 9 companies All Pharma

34 Timeline Prototype Testing/ Show & Tells Pre-Project Phase Funding Requirements Letter of Intent Quarterly Consortium Meetings First Release Jan 15 Quarterly Consortium Meetings Quarterly Consortium Meetings Product Release Jan 17 Product Phase Project Start Jan 14 Second Release Jan 16

35 Questions?