Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, NJ, USA.
GlaxoSmithKline, Drug Product Development, GlaxoSmithKline, Collegeville, PA, 19426, USA.
Pharm Res. 2022 Sep;39(9):2065-2082. doi: 10.1007/s11095-022-03346-3. Epub 2022 Aug 2.
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.
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.
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.
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.
纳米混悬剂已被用于提高难溶性药物的生物利用度。本研究采用耦合实验-焓平衡方法，探索在湿搅拌介质磨机中制备纳米混悬剂时的温度演变。
在三种搅拌速度、珠载量和珠粒尺寸水平下进行研磨。记录随时间变化的温度，然后使用焓平衡模型通过拟合转化为热的功率分数 ξ 进行模拟。此外，通过幂律 (PL) 和机器学习 (ML) 方法预测了 5 个不同测试运行的初始和最终功率、ξ 和温度曲线。
在较高的搅拌速度和珠载量/尺寸下，热量生成更高，这可以用较高的功率消耗来解释。尽管焓平衡模型只有一个拟合参数 ξ ，但其对温度演变的拟合非常好，均方根误差 (RMSE) 为 0.40-2.34°C。PL 和 ML 方法在测试运行中提供了相当好的温度曲线预测，RMSE 分别为 0.93-4.17°C 和 1.00-2.17°C。
我们确定了研磨参数对产热-功率的影响，并展示了当与 PL-ML 方法耦合时焓平衡模型的模拟-预测能力。
