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Mirabilis - A Semi-Automated Tool for Predicting and Managing the Purging of Mutagenic Impurities During Synthesis

pdf fileDrewe WC; Antonucci V;

The ICH M7 guidance on mutagenic impurities (MIs) offers greater flexibility in terms of the mechanisms available to demonstrate the control of impurities of mutagenic concern during active ingredient synthesis. Options available now range from performing analytical measurements, to demonstrating purging during processing, to setting specifications at appropriate control points in a process. Whichever option(s) are selected as the control strategy for a particular compound, the strategy must be based upon a sufficiently robust scientific understanding of the chemical reactivity and physicochemical properties of the MI to accurately predict its fate in the manufacturing processes to which it is exposed. The concept of applying semi-quantitative “purge factors” for chemical reactivity, solubility, and volatility to predict whether an MI will be below the acceptable safety limit defined within ICH M7 (e.g. Threshold for Toxicological Concern (TTC)) in the final drug substance is not a new concept. MI purge prediction has been used by multiple organisations within the pharmaceutical industry to support regulatory submissions for new product approvals, but has not been applied consistently across companies and phases of clinical development, and therefore has not gained consistent feedback from regulatory health authorities. The reasons for the present state are MI purge predictions are currently performed with a “paper-based” approach (e.g. manual entries in spread sheets), where scientists predict MI purging somewhat subjectively based upon their current process knowledge and experience, which are often incomplete during development of the manufacturing route. Such a process often leads to inconsistency and lack of transparency in MI purge factor predictions, especially in cases where insufficient scientific evidence is available to support the predictions made by an individual scientist.

The first part of the presentation highlights the progress being made in the Mirabilis Consortium towards establishing a scientifically-robust approach towards MI purge prediction based upon an agreed-upon knowledge database and supported by a semi-automated computer-based system to implement conventions. The second part of this presentation will highlight some examples of using the Mirabilis tool being developed to predict MI purge, plus will propose a regulatory decision tree which considers how these reliable MI purge predictions could be implemented in an overall MI control strategy and employed as an important tool in regulatory submissions.

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