Mutagenic Impurities: Precompetitive/Competitive Collaborative and Data Sharing Initiatives

Abstract

Pre-competitive and competitive collaborations have flourished during the development of the ICH M7 guidelines (mutagenic impurities). The majority of these interactions are 'honest broker' type arrangements, where an autonomous, independent third party is tasked with coordinating the activities of the consortium. Sometimes, more than one 'honest broker' can be involved in the activity. Lhasa has been at the centre of many of these collaborative initiatives; i.e., Derek Nexus Pharmaceutical Intermediates, Excipients, Zeneth, Mirabilis. The Product Quality Research Institute (PQRI) coordinated activities allowing a mechanistic understanding of the reaction mechanism for the formation of sulfonate esters, which after the Viracept withdrawal had been at the forefront of regulatory thinking. Other 'not for profit' organisations that have played a role in the advancement of the greater understanding of mutagenic impurities include the various trade organizations, i.e. the Pharmaceutical Research and Manufacturers of America (PhRMA) and the European Federation of Pharmaceutical Industry Associations (EFPIA).The other collaborative interactivity is the JDI (Just Do It). This is primarily focussed on analytical collaborative efforts, where individuals have collaborated on areas of mutual interest. To a lesser extent this has also been seen in the area of pharmaceutical intermediates, where individual companies have collaboratively pooled data on areas of common interest, e.g. acyl/sulfonyl halide false positives. The majority of the most significant JDI collaborations involve sharing feedback on the use and applicability of a second structure activity relationship system (SAR), analytical methodologies and analytical strategies.One of the key outcomes of these collaborations are peer reviewed scientific publications whereby other interested parties (including regulators) can be made aware of the findings from the consortia. Going forward, the area of mutagenic impurities enshrined under ICH M7, will continue to be a fruitful area for precompetitive collaborations of all types building on the success of the previous initiatives described.

Control of Mutagenic Impurities: Survey of Pharmaceutical Company Practices and a Proposed Framework for Industry Alignment

“…The synthesis of drug substances requires the use of reactive chemicals, reagents, catalysts, and other processing aids such that there is always the possibility for impurities to be present because of the act of chemical synthesis or subsequent degradation of the drug substance. − A robust manufacturing process has process and/or analytical controls for these potential impurities to ensure an active ingredient of high quality and purity for the treatment of patients. Based upon the highly reactive nature of many reagents, it is possible that some of the chemicals used or impurities produced may be DNA-reactive (mutagenic), and the risk posed by these mutagenic impurities must be controlled to significantly lower limits than other impurities to limit the potential risk of cancer to patients .…”

Management of pharmaceutical ICH M7 (Q)SAR predictions – The impact of model updates

“…Compounds were extracted from the PubChem database ( NIH-NLM, 2019 ) by matching them to structurally similar compounds in the Lhasa Vitic Intermediates database. The Vitic Intermediates database project is a data sharing initiative organized by Lhasa Limited in which proprietary mutagenicity data for drug intermediates, impurities and synthetic reagents are shared within a consortium of pharmaceutical companies for the benefit of all members ( Elder et al, 2015 ; Vitic, 2020 ). The 2017.1.0 Vitic Intermediates database contains 1341 proprietary impurity structures and the chemical space “populated” by the Vitic Intermediate dataset is probably the most relevant available for this evaluation.…”

Assessment and Reproducibility of Quantitative Structure–Activity Relationship Models by the Nonexpert

“…Data sharing initiatives may assist in building suitable data sets for modeling where data are lacking. 56 Developments in computational methods are being used to generate models for more complex endpoints. (2) The OECD principles provide a consistent approach for assessing models; however, this does not necessarily equate with reproducibility.…”

“…Lack of models may be a reflection of lack of data on which to the build models, or inherent complexity of the endpoint. Data sharing initiatives may assist in building suitable data sets for modeling where data are lacking . Developments in computational methods are being used to generate models for more complex endpoints.…”