Predicting the Temperature Evolution during Nanomilling of Drug Suspensions via a Semi-Theoretical Lumped-Parameter Model

Gulenay Guner; Dogacan Yilmaz; Helen F Yao; Donald J Clancy; Ecevit Bilgili

doi:10.3390/pharmaceutics14122840

Predicting the Temperature Evolution during Nanomilling of Drug Suspensions via a Semi-Theoretical Lumped-Parameter Model

Pharmaceutics. 2022 Dec 18;14(12):2840. doi: 10.3390/pharmaceutics14122840.

Authors

Gulenay Guner¹, Dogacan Yilmaz², Helen F Yao³, Donald J Clancy³, Ecevit Bilgili¹

Affiliations

¹ Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
² Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, NJ 07114, USA.
³ GlaxoSmithKline, Drug Product Development, Collegeville, PA 19426, USA.

Abstract

Although temperature can significantly affect the stability and degradation of drug nanosuspensions, temperature evolution during the production of drug nanoparticles via wet stirred media milling, also known as nanomilling, has not been studied extensively. This study aims to establish both descriptive and predictive capabilities of a semi-theoretical lumped parameter model (LPM) for temperature evolution. In the experiments, the mill was operated at various stirrer speeds, bead loadings, and bead sizes, while the temperature evolution at the mill outlet was recorded. The LPM was formulated and fitted to the experimental temperature profiles in the training runs, and its parameters, i.e., the apparent heat generation rate Q_gen and the apparent overall heat transfer coefficient times surface area UA, were estimated. For the test runs, these parameters were predicted as a function of the process parameters via a power law (PL) model and machine learning (ML) model. The LPM augmented with the PL and ML models was used to predict the temperature evolution in the test runs. The LPM predictions were also compared with those of an enthalpy balance model (EBM) developed recently. The LPM had a fitting capability with a root-mean-squared error (RMSE) lower than 0.9 °C, and a prediction capability, when augmented with the PL and ML models, with an RMSE lower than 4.1 and 2.1 °C, respectively. Overall, the LPM augmented with the PL model had both good descriptive and predictive capability, whereas the one with the ML model had a comparable predictive capability. Despite being simple, with two parameters and obviating the need for sophisticated numerical techniques for its solution, the semi-theoretical LPM generally predicts the temperature evolution similarly or slightly better than the EBM. Hence, this study has provided a validated, simple model for pharmaceutical engineers to simulate the temperature evolution during the nanomilling process, which will help to set proper process controls for thermally labile drugs.

Keywords: cooling; drug nanoparticles; heat generation; lumped parameter model; nanomilling; process modeling; wet stirred media mill.

Grants and funding

G2718B0/GlaxoSmithKline