Quantitative Analysis of the QMS for Pharmaceutical Manufacturing

Abstract

Abstract Purpose To propose a statistical methodology for quantitative analysis of the quality management system (QMS) of pharmaceutical manufacturing. Methods (1) Based on the manufacturing data from two established active pharmaceutical ingredient (API) manufacturers in China from 2010 to 2019, the linear regression with Pearson correlation coefficient is used to find the correlations between the proposed QMS operation indicators and performance indicators. (2) A stepwise multiple linear regression is used to identify the independent operation indicators with the biggest impact on a given performance indicator. (3) The Akaike Information Criterion is used to predict the performance indicators based on the operation indicators. Results (1) Correlation: the right-first-time rate correlates strongly with various changes and deviations; the customer complaints correlate with changes, deviations, and CAPAs; the deficiency rate of foreign inspections correlates with deviations and CAPAs; and the CAPA on-time completion rate correlates with changes, deviations, and the ratio of employees in quality. (2) Impact: the right-first-time rate and the customer complaints are mostly impacted by the total deviations; the deficiency rate of foreign inspections is mostly impacted by deviations in equipment and instrument, and deviations due to human error; the CAPA on-time completion rate is mainly impacted by deviations in facility and utilities. (3) Predictability: the right-first-time rate, the customer complaints, the deficiency rate of foreign inspections, and the CAPA on-time completion rate can all be predicted based on the existing data with statistical significance. Conclusions Deviations emerge as a key leading indicator for the performance of QMS. The proposed statistical methodology provides a basis for the data-driven quality management and regulation, whose visibility and predictability are likely to progress as the data accumulates.