An Enthalpy-Balance Model for Timewise Evolution of Temperature during Wet Stirred Media Milling of Drug Suspensions.

Autor: Guner G; Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, NJ, USA., Elashri S; Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, NJ, USA., Mehaj M; Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, NJ, USA., Seetharaman N; Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, NJ, USA., Yao HF; GlaxoSmithKline, Drug Product Development, GlaxoSmithKline, Collegeville, PA, 19426, USA., Clancy DJ; GlaxoSmithKline, Drug Product Development, GlaxoSmithKline, Collegeville, PA, 19426, USA., Bilgili E; Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, NJ, USA. bilgece@njit.edu.
Jazyk: angličtina
Zdroj: Pharmaceutical research [Pharm Res] 2022 Sep; Vol. 39 (9), pp. 2065-2082. Date of Electronic Publication: 2022 Aug 02.
DOI: 10.1007/s11095-022-03346-3
Abstrakt: Purpose: Nanosuspensions have been used for enhancing the bioavailability of poorly soluble drugs. This study explores the temperature evolution during their preparation in a wet stirred media mill using a coupled experimental-enthalpy balance approach.
Methods: Milling was performed at three levels of stirrer speed, bead loading, and bead sizes. Temperatures were recorded over time, then simulated using an enthalpy balance model by fitting the fraction of power converted to heat ξ. Moreover, initial and final power, ξ, and temperature profiles at 5 different test runs were predicted by power-law (PL) and machine learning (ML) approaches.
Results: Heat generation was higher at the higher stirrer speed and bead loading/size, which was explained by the higher power consumption. Despite its simplicity with a single fitting parameter ξ, the enthalpy balance model fitted the temperature evolution well with root mean squared error (RMSE) of 0.40-2.34°C. PL and ML approaches provided decent predictions of the temperature profiles in the test runs, with RMSE of 0.93-4.17 and 1.00-2.17°C, respectively.
Conclusions: We established the impact of milling parameters on heat generation-power and demonstrated the simulation-prediction capability of an enthalpy balance model when coupled to the PL-ML approaches.
(© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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