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

被引:8
|
作者
Guner, Gulenay [1 ]
Elashri, Sherif [1 ]
Mehaj, Mirsad [1 ]
Seetharaman, Natasha [1 ]
Yao, Helen F. [2 ]
Clancy, Donald J. [2 ]
Bilgili, Ecevit [1 ]
机构
[1] New Jersey Inst Technol, Otto H York Dept Chem & Mat Engn, Newark, NJ 07102 USA
[2] GlaxoSmithKline, Drug Prod Dev, Collegeville, PA 19426 USA
关键词
enthalpy balance; heat dissipation; poorly water-soluble drugs; process modeling; wet stirred media milling; THERMAL-CONDUCTIVITY; PROCESS PARAMETERS; BREAKAGE KINETICS; SCALE-UP; NANOSUSPENSION; FORMULATION; OPTIMIZATION; NANOPARTICLES; FENOFIBRATE; DISSOLUTION;
D O I
10.1007/s11095-022-03346-3
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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 xi. Moreover, initial and final power, xi, 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 xi, the enthalpy balance model fitted the temperature evolution well with root mean squared error (RMSE) of 0.40-2.34 degrees 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 degrees 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.
引用
收藏
页码:2065 / 2082
页数:18
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