In this paper, we remind readers of several ICH guideline documents such as ICH Q3A, Q3B, Q3C, Q3D, Q6A, Q6B, M7, and ICH S9 which are related to the drug substance and drug product impurity limit setting. In particular, ICH Q6A clearly states that "specifications should focus on those characteristics found to be useful in ensuring the safety and efficacy of the drug substance and drug product"; however, recent negotiations between health authority and applicants (company) related to proposed marketing applications show that on a global level, batch experience, even when limited, plays an overwhelming role in developing impurity acceptance criteria rather than clinical relevance. The drawback of such practice and the great need to establish patient centric specifications (PCS) are highlighted. Secondly, this paper proposes approaches on how to establish patient centric criteria for drug substance and drug product impurity limits based on the principles outlined in ICH guideline documents and scientific literature. Three case studies are presented to illustrate the challenges in establishing PCS and the divergence of regulatory acceptance to such specifications. We propose some approaches that can be considered for specification setting based on clinical relevance in the drug development, registration and post-approval phases of a product life-cycle. Lastly, we give thoughts on the future perspective of this movement and offer recommendations to foster discussions between regulatory agencies and pharmaceutical industry on getting medicinal products that are safe and effective to the patient sooner to meet unmet medical needs without supply interruption concerns.
The production of vaccines is a complex biological process, with long cycle times and high variation in raw materials, growth rates, and test methods. Hundreds of variables are monitored for every batch of vaccine produced; however, the relationships between product quality and process variables are difficult to quantify. We describe how mining historical process data using random forests and partial least squares techniques enabled us to identify the drivers of variability in bulk vaccine yield and to implement new controls which significantly reduced the variation.
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