Statistical data treatment for residence time distribution studies in pharmaceutical manufacturing

Pooja Bhalode; Sonia M Razavi; Huayu Tian; Andres Roman-Ospino; James Scicolone; Gerardo Callegari; Atul Dubey; Abdollah Koolivand; Scott Krull; Thomas O'Connor; Fernando J Muzzio; Marianthi G Ierapetritou

doi:10.1016/j.ijpharm.2024.124133

Statistical data treatment for residence time distribution studies in pharmaceutical manufacturing

Int J Pharm. 2024 Apr 19:657:124133. doi: 10.1016/j.ijpharm.2024.124133. Online ahead of print.

Affiliations

¹ Center of Plastics Innovation, University of Delaware, DE, USA.
² Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA.
³ Department of Chemical and Biomolecular Engineering, University of Delaware, DE, USA.
⁴ Pharmaceutical Continuous Manufacturing (PCM), United States Pharmacopeia, 12601 Twinbrook Parkway, Rockville, MD, USA.
⁵ Office of Pharmaceutical Quality, Center for Drug Evaluation Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Springs, MD 20993, USA.
⁶ Department of Chemical and Biomolecular Engineering, University of Delaware, DE, USA. Electronic address: mgi@udel.edu.

PMID: 38642620
DOI: 10.1016/j.ijpharm.2024.124133

Abstract

Residence time distribution (RTD) method has been widely used in the pharmaceutical manufacturing for understanding powder dynamics within unit operations and continuous integrated manufacturing lines. The dynamics thus captured is then used to develop predictive models for unit operations and important RTD-based applications ensuring product quality assurance. Despite thorough efforts in tracer selection, data acquisition, and calibration model development to obtain tracer concentration profiles for RTD studies, there can exist significant noise in these profiles. This noise can make it challenging to identify the underlying signal and get a representative RTD of the system under study. Such concerns have previously indicated the importance of noise handling for RTD measurements in literature. However, the literature does not provide sufficient information on noise handling or data treatment strategies for RTD studies. To this end, we investigate the impact of varying levels of noise using different tracers on measurement of RTD profile and its applications. We quantify the impact of different denoising methods (time and frequency averaging methods). Through this investigation, we see that Savitsky Golay filtering turns out to a good method for denoising RTD profiles despite varying noise levels. The investigation is performed such that the key features of the RTD profile (which are important for RTD based applications) are preserved. Subsequently, we also investigate the impact of denoising on RTD-based applications such as out-of-specification (OOS) analysis and RTD modeling. The results show that the degree of noise levels considered in this work do not significantly impact the RTD-based applications.

Keywords: Data pre-treatment; Denoising; Pharmaceutical manufacturing; RTD modeling; Residence time distribution.