Gulenay Guner1, Sherif Elashri1, Mirsad Mehaj1, Natasha Seetharaman1, Helen F Yao2, Donald J Clancy2, Ecevit Bilgili3. 1. Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, NJ, USA. 2. GlaxoSmithKline, Drug Product Development, GlaxoSmithKline, Collegeville, PA, 19426, USA. 3. Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, NJ, USA. bilgece@njit.edu.
Abstract
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.
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.
Authors: Hywel D Williams; Natalie L Trevaskis; Susan A Charman; Ravi M Shanker; William N Charman; Colin W Pouton; Christopher J H Porter Journal: Pharmacol Rev Date: 2013-01 Impact factor: 25.468