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This study focuses on multivariate experimental design and statistical analysis to optimize the process of Olaparib 1. Quality by design (QbD) methodology was adopted for optimization of the Olaparib process consisting of three reaction steps: (1) amidation, (2) deprotection, and (3) acylation. Every chemical conversion was studied in isolation, employing risk assessment to identify key material attributes and key process parameters that may have the potential to impact the reaction. Thereafter, the screening design of experiment (DoE) was employed to scrutinize the factors that significantly impacted yield. Moving forward, the scrutinized factors which were found to impact the responses, the set of critical material attributes (CMAs) and critical process parameters (CPPs), were considered for optimization by applying I-Optimal design to define design space arriving at a robust setting wherein the predefined targets were supposedly optimal. To our delight, we got 95, 91, and 75% yield with more than 99% purity in amidation, deprotection, and acetylation, respectively, which enabled us to systematically identify design space to meet the desired quality target of the product consistently. More importantly, to distinguish the CMAs and CPPs, these elements ought to be monitored to have control of the quality parameter throughout the active pharmaceutical ingredient (API) value chain until commercial manufacturing followed by marketing. Eventually, we have developed a greener process in comparison to precedented one for Olaparib 1.
Manda et al. (Mon,) studied this question.