Optimal quantification of residence time distribution profiles from a quality assurance perspective

• Published in: International journal of pharmaceutics 2023-03, Vol.634, p.122653-122653, Article 122653
• Main Authors: Bhalode, Pooja, Razavi, Sonia M., Roman-Ospino, Andrés, Scicolone, James, Callegari, Gerardo, Tian, Geng, Koolivand, Abdollah, Krull, Scott, Ierapetritou, Marianthi G., Muzzio, Fernando J.
• Format: Article
• Language: English
• Subjects: Chemometrics; Limit of Detection; Lot Quality Assurance Sampling; Technology, Pharmaceutical - methods
• ISSN: 0378-5173; 1873-3476
• DOI: 10.1016/j.ijpharm.2023.122653

[Display omitted] Residence time distribution (RTD) has been widely applied across various fields of chemical engineering, including pharmaceutical manufacturing, for applications such as material traceability, quality assurance, system health monitoring, and fault detection. Determination of a representative RTD, in principle, requires an accurate process analytical technology (PAT) procedure capturing the entire range of tracer concentrations from zero to maximum. Such a wide concentration range creates at least two problems: i) decreased accuracy of the model across the entire range of concentrations, relating to limit of quantification, and ii) ambiguity associated with the detection of the tracer for low concentration levels, relating to limit of detection (LOD). These problems affect not only the RTD profile itself, but also RTD-based applications, which can potentially lead to erroneous conclusions. This article seeks to minimize the impact of these problems by understanding the relative importance of different features of RTD on the detection of out-of-specification (OOS) products. In this work, the RTD obtained experimentally was truncated at different levels, to investigate the impact of the truncation of RTD on funnel plots for OOS detection. The main finding is that the tail of the RTD can be truncated with no loss of accuracy in the determination of exclusion intervals. This enables the manufacturing scientist to focus entirely on the peak region, maximizing the accuracy of chemometric models.