Clancy Donald J, Guner Gulenay, Chattoraj Sayantan, Yao Helen, Faith M Connor, Salahshoor Zahra, Martin Kailey N, Bilgili Ecevit
GlaxoSmithKline R&D, Collegeville, PA 19426, USA.
Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
Pharmaceutics. 2024 Mar 13;16(3):394. doi: 10.3390/pharmaceutics16030394.
This study aimed to develop a practical semi-mechanistic modeling framework to predict particle size evolution during wet bead milling of pharmaceutical nanosuspensions over a wide range of process conditions and milling scales. The model incorporates process parameters, formulation parameters, and equipment-specific parameters such as rotor speed, bead type, bead size, bead loading, active pharmaceutical ingredient (API) mass, temperature, API loading, maximum bead volume, blade diameter, distance between blade and wall, and an efficiency parameter. The characteristic particle size quantiles, i.e., , , and , were transformed to obtain a linear relationship with time, while the general functional form of the apparent breakage rate constant of this relationship was derived based on three models with different complexity levels. Model A, the most complex and general model, was derived directly from microhydrodynamics. Model B is a simpler model based on a power-law function of process parameters. Model C is the simplest model, which is the pre-calibrated version of Model B based on data collected from different mills across scales, formulations, and drug products. Being simple and computationally convenient, Model C is expected to reduce the amount of experimentation needed to develop and optimize the wet bead milling process and streamline scale-up and/or scale-out.
本研究旨在开发一个实用的半机理建模框架，以预测在广泛的工艺条件和研磨规模下，药物纳米混悬液湿珠磨过程中的粒径演变。该模型纳入了工艺参数、配方参数和特定设备参数，如转子速度、珠粒类型、珠粒尺寸、珠粒装载量、活性药物成分（API）质量、温度、API装载量、最大珠粒体积、叶片直径、叶片与壁之间的距离以及一个效率参数。对特征粒径分位数，即 、 和 进行变换，以获得与时间的线性关系，同时基于三个不同复杂程度的模型推导该关系的表观破碎速率常数的一般函数形式。模型A是最复杂且最通用的模型，直接从微观流体动力学推导得出。模型B是基于工艺参数幂律函数的更简单模型。模型C是最简单的模型，它是基于从不同规模、配方和药品的磨机收集的数据对模型B进行预校准后的版本。由于模型C简单且计算方便，预计它将减少开发和优化湿珠磨工艺所需的实验量，并简化放大和/或扩大生产。
