Evaluation of Calibration Burden for Monitoring of a Pharmaceutical Continuous Manufacturing Line using Near-Infrared Spectroscopy

Abstract

Process analytical technology (PAT) tools are an important part of process monitoring and control in pharmaceutical continuous manufacturing (CM) that help ensure product quality. However, there is hesitancy to adopt PAT due, in part, to the high start-up costs. A portion of the cost is the calibration burden associated with developing an appropriate multivariate data analysis (MVDA) method to extract the desired information from the spectral outputs of spectroscopic PAT tools. This has generated research interest in reduced calibration burden MVDA methods, such as iterative optimization technology (IOT) algorithms, as alternatives to conventional modeling approaches like partial least squares (PLS) regression.

The goal of the presented research is to compare the calibration burden of three different MVDA methods (direct IOT, indirect IOT, PLS regression) at two drug loading levels (low and high) of pharmaceutical powder blends in a CM line. The blends were binary mixtures consisting of an active pharmaceutical ingredient and a coprocessed excipient blend. The coprocessed excipient blend was leveraged to reduce formulation complexity and streamline process development, benefiting the application of IOT algorithm. Calibration burden was assessed in terms of time, material, and financial costs.

Utilizing a near-infrared spectroscopic PAT tool, it was found that MVDA methods that utilized IOT algorithms demonstrated a notably reduced calibration burden compared to the PLS models, while predicting blend potency with similar accuracy.

Highlights

NIR PAT application using IOT at low and high drug loadings.

PLS and IOT deployment for API content monitoring during continuous manufacturing.

Using coprocessed excipient as single pure component for IOT approaches.

Detailed comparison of calibration burden between IOT and PLS for NIR PAT.

Materials

The binary pharmaceutical powder mixture utilized in this study was composed of Ibuprofen 70 (IBU70) and Kollitab® DC 87 L(Kollitab®). IBU70 (BASF, USA), with an average particle size around 70 μm, was used as the model cohesive and agglomerated API. Kollitab® (BASF, Germany) is a lactose-based coprocessed excipient specifically developed for both batch and continuous direct compression (CDC) of solid oral dosage forms. It represents an all-in-one excipient, with all the required functionalities for formulating an immediate release tablet. Its composition includes lactose monohydrate as the filler, fine crospovidone as the disintegrant PVA-PEG copolymer as a peroxide-free binder, and sodium stearyl fumarate as hydrophilic lubricant. Three separate lots of Kollitab® (A, B.1, and B.2) were utilized throughout the study.

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Adam J. Rish, Cassidy Kurt, Joao Marcos Assis, Owen Rehrauer, Raúl S. Rangel-Gilc, Edward Taylora
Evaluation of Calibration Burden for Monitoring of a Pharmaceutical Continuous Manufacturing Line using Near-Infrared Spectroscopy
Sentronic US Corp., Saddle Road, Norwalk, CT 06851, USA
GEA Pharmaceutical Technology Center, Columbia, MD 21045, USA
BASF Corporation, Tarrytown, NY 10591, USA
Department of Chemical Engineering, University of Puerto Rico at Mayaguez, PR 00681, USA