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Predicting the Purging of Impurities within an API Synthetic Pathway

pdf fileCovey-Crump E;

Streamlining Drug Discovery and Development: Leveraging Data Analysis and Modelling for Design

Dr. Liz Covey-Crump, Lhasa Limited

ICH M7 guidance on mutagenic impurities (MIs) supports the control of MIs based on a sufficient understanding of the active pharmaceutical ingredient (API) synthesis process such that the fate of an MI can be assessed. In cases where it can be predicted that an MI is purged through the synthesis pathway the requirement for analytical testing to prove the absence of this MI in the API is subsequently reduced.

A concept was brought forward by Dr. Andrew Teasdale (AZ) in which semi-quantitative “purge factors” are calculated for each stage in the process, based on physicochemical properties of the MI such as reactivity, solubility and volatility. This generates an overall predicted purge value which can be used to assess whether the MI will be below the TTC/PDE in the API.  The methodology has been designed to be conservative in the estimation of purge to prevent over-prediction. This approach is being used by several organisations within the pharmaceutical industry to support regulatory submissions at different stages of development. Our goal is to expand its use and standardise the approach through a consortium in order to establish a framework/software tool which predicts “purge factors” and provides sufficient evidence to support regulatory submissions. The key aims are to 1) standardise how calculations are performed throughout industry, 2) collate existing data and promote cross-industry data sharing to facilitate supported and accurate decision making, and 3) provide an automated in silico system which predicts purge factors based on experimental data and expert knowledge.

A successful international collaboration has been established, with a number of pharmaceutical companies (currently 11) guiding the development of the software and models for the prediction of physicochemical properties. This work has the potential to save both time and money in regards to analytical testing and also to ensure effort is focused correctly on those impurities that present a substantive risk.

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