RESUMEN
Statistical quality and process controls (SQC and SPC) are used for monitoring, trending, and ultimately improving biopharmaceutical manufacturing processes and operations. The purpose of this paper is to highlight characteristic features of bioprocess data and their impact on typical SQC and SPC applications, specifically control charts for individual observations (I-chart) and estimation of process performance index (Ppk). Simulated data were used in an attempt to mimic bioprocess data by inducing inhomogeneity, nonstationarity, autocorrelation, and outliers. The first specific part highlights the roles of within and overall standard deviation (SD) estimates for 3σ limits and their impacts on frequently applied sensitizing rules for control charts, i.e. Nelson's rules 1-4. The second part deals with the often-asked question of how many observations are required for estimation of robust 3σ limits. In the third part, five popular approaches for treating censored data (results below or equal to limit of quantification, ≤LOQ) were compared and their impact on 3σ limits and Ppk estimates were assessed. The final section summarizes the typical issues faced by the practitioner in the application of SQC and SPC and provides remedies for setting up robust and efficient control charts for biopharmaceutical process monitoring. Overall, this study shows that process monitoring and subsequent assessment without taking into consideration this atypical nature of biopharmaceutical process can lead to increased false alarm rates, thus impacting the batch release or even possibility of rejecting good batches.
Asunto(s)
Productos Biológicos , Recolección de Datos , Control de CalidadRESUMEN
How to decide whether a statistically significant trend is of practical relevance? In the context of stability data of pharmaceuticals, this publication provides a way forward to use different measures of (method) variability to compare to the observed changes over time. A panel of analytical experts assessed whether statistically significant trends were of practical relevance or not. For different types of assessing variability, recommendations for decision criteria were derived that best matched these assessments, i.e., finding a suited balance between not detecting a relevant trend and between flagging a trend wrongly as relevant. For this purpose, more than 60 data sets from Biosimilar projects of Sandoz/Novartis were leveraged. Hence, this article provides a scientific way to assess whether a statistically significant trend is of practical relevance or not, and a case study is presented and discussed.
Asunto(s)
Biosimilares FarmacéuticosRESUMEN
Establishing comparability of the originator and its biosimilar at the structural and functional level, by analyzing so-called quality attributes, is an important step in biosimilar development. The statistical assessment of quality attributes is currently in the focus of attention because both the FDA and the EMA are working on regulatory documents for advising companies on the use of statistical approaches for strengthening their comparability claim. In this paper, we first discuss "comparable" and "not comparable" settings and propose a shift away from the usual comparison of the mean values: we argue that two products can be considered comparable if the range of the originator fully covers the range of the biosimilar. We then introduce a novel statistical testing procedure (the "tail-test") and compare the operating characteristics of the proposed approach with approaches currently used in practice. In contrast to the currently used approaches, we note that our proposed methodology is compatible with the proposed understanding of comparability and has, compared to other frequently applied range-based approaches, the advantage of being a formal statistical testing procedure which controls the patient's risk and has reasonable large-sample properties.