Predicting liposome formulations by the integrated machine learning and molecular modeling approaches

作者：Run Han, Zhuyifan Ye, Yunsen Zhang, Yaxin Cheng, Ying Zheng, Defang Ouyang · 发表于：Asian Journal of Pharmaceutical Sciences · 年份：2023 · DOI：10.1016/j.ajps.2023.100811 · 被引用次数：45 · 研究领域：Nanoparticle-Based Drug Delivery、Lipid Membrane Structure and Behavior、Advanced Drug Delivery Systems

Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability. Due to the complex formulation components and preparation process, formulation screening mostly relies on trial-and-error process with low efficiency. Here liposome formulation prediction models have been built by machine learning (ML) approaches. The important parameters of liposomes, including size, polydispersity index (PDI), zeta potential and encapsulation, are predicted individually by optimal ML algorithm, while the formulation features are also ranked to provide important guidance for formulation design. The analysis of key parameter reveals that drug molecules with logS [-3, -6], molecular complexity [500, 1000] and XLogP3 (≥2) are priority for preparing liposome with higher encapsulation. In addition, naproxen (NAP) and palmatine HCl (PAL) represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability. The consistency between predicted and experimental value verifies the satisfied accuracy of ML models. As the drug properties are critical for liposome particles, the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations. The modeling structure reveals that NAP molecules could distribute into lipid layer, while most PAL molecules aggregate in the inner aqueous phase of liposome. The completely different ph...