Department of Pharmacy, School of Chemical Sciences and Pharmacy, Central University of Rajasthan, Bandarsindri, Ajmer, Rajasthan -305817, India.
Department of Pharmaceutical Sciences, Mohanlal Sukhadia University, Udaipur, Rajasthan 313001, India.
Int J Biol Macromol. 2024 Nov;279(Pt 1):135123. doi: 10.1016/j.ijbiomac.2024.135123. Epub 2024 Aug 27.
This study aims to develop sorafenib-loaded self-assembled nanoparticles (SFB-SANPs) using the combined approach of artificial neural network and design of experiments (ANN-DoE) and to compare it with other machine learning (ML) models. The central composite design (CCD) and ML algorithms were used to screen the effects of concentrations of both the polymers (polyethyleneimine and fucoidan) on the outcome responses, i.e., particle size and entrapment efficiency with defined constraints. The prediction from different ML models (bootstrap forest, K-nearest neighbors, artificial neural network, generalized regression-lasso and support vector machines) were compared with ANN-DoE model. The ANN-DoE model showed better accuracy and predictability and outperformed all the other models. This depicted that the concept of using ANN and DoE combination approach provided the best, uncomplicated and cost-effective way to optimized the nanoformulations. The optimized formulation generated from the ANN-DoE combined model was further evaluated for characterization and anticancer activity. The optimized SFB-SANPs were prepared using the polyelectrolyte complexation method with Polyethyleneimine (PEI) as a cationic polymer and fucoidan (FCD) as an anionic. The SFB-SANPs were nanometric in size (280.4 ± 0.089 nm) and slightly anionic in nature (zeta potential = -6.03 ± 0.92 mV) with an encapsulation efficiency of 95.56 ± 0.30 %. The drug release from SFB-SANPs was controlled and sustained in the cancer microenvironment (pH 5.0). The SFB-SANPs were compatible with red blood cells (RBCs), and the % hemolysis was found to be <5.0 %. The anticancer activity of the SFB-SANPs exhibited an IC at 2.017 ± 0.516 μM against MDMB-231 cells, showing a significantly high inhibitory effect on breast cancer cell lines. Therefore, the nanocarriers developed using various ML tools inherit a huge promise in anticancer drug delivery.
本研究旨在采用人工神经网络（ANN）与实验设计（DoE）相结合的方法，开发索拉非尼自组装纳米粒（SFB-SANPs），并将其与其他机器学习（ML）模型进行比较。采用中心复合设计（CCD）和 ML 算法筛选两种聚合物（聚乙烯亚胺和褐藻糖胶）浓度对粒径和包封效率等结果的影响，并在规定的约束条件下进行筛选。比较了不同 ML 模型（自举森林、K-最近邻、人工神经网络、广义回归-套索和支持向量机）的预测值与 ANN-DoE 模型。ANN-DoE 模型显示出更好的准确性和预测能力，优于所有其他模型。这表明，采用 ANN 和 DoE 组合方法的概念为优化纳米制剂提供了最佳、简单和具有成本效益的方法。从 ANN-DoE 组合模型中得到的优化配方进一步进行了表征和抗癌活性评价。采用聚电解质络合法制备 SFB-SANPs，以聚乙烯亚胺（PEI）为阳离子聚合物，褐藻糖胶（FCD）为阴离子聚合物。SFB-SANPs 粒径为纳米级（280.4 ± 0.089 nm），带轻微负电荷（Zeta 电位=-6.03 ± 0.92 mV），包封效率为 95.56 ± 0.30%。SFB-SANPs 在癌症微环境（pH 5.0）中具有控制和持续的药物释放。SFB-SANPs 与红细胞（RBCs）相容，溶血率<5.0%。SFB-SANPs 的抗癌活性在 2.017 ± 0.516 μM 时对 MDMB-231 细胞表现出显著的抑制作用，对乳腺癌细胞系具有显著的抑制作用。因此，采用各种 ML 工具开发的纳米载体在抗癌药物传递方面具有巨大的潜力。
