Recent drug recalls (e.g., valsartan and ranitidine) linked to
the discovery of nitrosamine impurities have led to increased regulatory
scrutiny in the manufacturing process of marketed medicines, notably
for determining any sources of risk for nitrosamine (or related N-nitroso compound) formation within the manufacturing process.
This review seeks to aid the risk assessment process through identifying
known conditions and reactants through which N-nitroso
compounds can be formed.
In this paper we describe Zeneth, a new expert computational system for the prediction of forced degradation pathways of organic compounds. Intermolecular reactions such as dimerization, reactions between the query compound and its degradants, as well as interactions with excipients can be predicted. The program employs a knowledge base of patterns and reasoning rules to suggest the most likely transformations under various environmental conditions relevant to the pharmaceutical industry. Building the knowledge base is facilitated by data sharing between the users.
The ICH M7 guidance provides a series of flexible control options for the control of (potentially) mutagenic impurities (PMIs) that fully align with key risk-based principles. This includes option 4, which leverages existing process knowledge and/or data to justify control of PMIs without the need for routine analytical release testing during manufacturing. One such technique highlighted uses systematic, semiquantitative calculations to define the degree of "purge" of PMIs within a synthetic route to an active pharmaceutical ingredient (API) based on physicochemical properties of the impurities in question, and the manufacturing process being undertaken. This paper introduces a consortium-led initiative, Mirabilis, which aims to build on the semiquantitative purge approach, and harmonize industry best practices by enabling the calculations to be conducted in a standardized, consistent, and reproducible manner. The development of an expert-derived knowledge base for the prediction of reactivity by enhancing expert opinion using evidence derived from the published literature and experimental data is also discussed. Furthermore, this paper describes the application of Mirabilis software for the processes involved in the synthesis of verubecestat, naloxegol oxalate, and camicinal.
Zeneth is a new software application capable of predicting degradation products derived from small molecule active pharmaceutical ingredients. This study was aimed at understanding the current status of Zeneth's predictive capabilities and assessing gaps in predictivity. Using data from 27 small molecule drug substances from five pharmaceutical companies, the evolution of Zeneth predictions through knowledge base development since 2009 was evaluated. The experimentally observed degradation products from forced degradation, accelerated, and long-term stability studies were compared to Zeneth predictions. Steady progress in predictive performance was observed as the knowledge bases grew and were refined. Over the course of the development covered within this evaluation, the ability of Zeneth to predict experimentally observed degradants increased from 31% to 54%. In particular, gaps in predictivity were noted in the areas of epimerizations, N-dealkylation of N-alkylheteroaromatic compounds, photochemical decarboxylations, and electrocyclic reactions. The results of this study show that knowledge base development efforts have increased the ability of Zeneth to predict relevant degradation products and aid pharmaceutical research. This study has also provided valuable information to help guide further improvements to Zeneth and its knowledge base.
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