Rapid characterization of physical properties for the pharmaceutical pellet cores based on NIR spectroscopy and ensemble learning

偏最小二乘回归 颗粒 弹丸 集成学习 交叉验证 物理性质 人工神经网络 生物系统 数学 机器学习 人工智能 材料科学 统计 计算机科学 复合材料 生物
作者
Sijun Wu,Chaoliang Jia,Wang Li,Ye Cheng,Zheng Li,Wenlong Li
出处
期刊:European Journal of Pharmaceutics and Biopharmaceutics [Elsevier BV]
卷期号:197: 114214-114214 被引量:2
标识
DOI:10.1016/j.ejpb.2024.114214
摘要

During the development of sustained-release pellets, the physical characteristics of the pellet cores can affect drug release in the preparation. The method based on near-infrared (NIR) spectroscopy and ensemble learning was proposed to swiftly assess the physical properties of the pellet cores. In the research, the potential of three algorithms, direct standardization (DS), partial least squares regression (PLSR) and generalized regression neural network (GRNN), was investigated and compared. The performance of the DS, PLSR and GRNN models were improved after applying bootstrap aggregating (Bagging) ensemble learning. And the Bagging-GRNN model showed the best predictive capacity. Except for inter-particle porosity, the mean absolute deviations of other 11 physical parameters were less than 1.0. Furthermore, the cosine coefficient values between the actual and predicted physical fingerprints was higher than 0.98 for 15 out of the 16 validation samples when using the Bagging-GRNN model. To reduce the model complexity, the 60 variables significantly correlated with angle of repose, particle size (D50) and roundness were utilized to develop the simplified Bagging-GRNN model. And the simplified model showed satisfactory predictive capacity. In summary, the developed ensemble modelling strategy based NIR spectra is a promising approach to rapidly characterize the physical properties of the pellet cores.
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