Saleem Muhammad Talha, Shoaib Muhammad Harris, Yousuf Rabia Ismail, Siddiqui Fahad
Department of Pharmaceutics, Faculty of Pharmacy and Pharmaceutical Sciences, University of Karachi, Karachi, 75270, Pakistan.
Department of Pharmaceutics & Bioavailability and Bioequivalence Research Facility, Faculty of Pharmacy and Pharmaceutical Sciences, University of Karachi, Karachi, 75270, Pakistan.
Sci Rep. 2025 Mar 7;15(1):7922. doi: 10.1038/s41598-025-91601-z.
The study is based on applying Artificial Neural Network (ANN) based machine learning and Response Surface Methodology (RSM) as simultaneous bivariate approaches in developing controlled-release rivaroxaban (RVX) osmotic tablets. The influence of different types of polyethylene oxide, osmotic agents, coating membrane thickness, and orifice diameter on RVX release profiles was investigated. After obtaining the trial formulation data sets from Central Composite Design (CCD), an ANN-based model was trained to get the optimized formulations. The Physiological-based Pharmacokinetic (PBPK) modeling of the predicted formulation was performed by GastroPlus™ to simulate in vivo plasma profiles under fasting and fed conditions. In vitro release tests showed zero-order RVX release for up to 12 h. Using graphical and numerical methods, the predicted formulation generated by the prediction profiler was cross-validated by the CCD-based optimized formulation. Analysis of Variance (ANOVA) findings revealed no significant difference between the predicted and optimized formulations and these formulations have a shelf life of 22.47 and 17.87 months, respectively. The PBPK modeling of RVX push-pull osmotic pump (PPOP) tablets suggested enhanced bioavailability in the fasted (up to 82%) and fed (up to 98.5%) state compared to immediate-release tablets. The results indicated that ANN can be effectively used for osmotic systems due to their complex nature and nonlinear interactions between dependent and independent variables.
本研究基于应用基于人工神经网络（ANN）的机器学习和响应面法（RSM）作为同步双变量方法来开发控释利伐沙班（RVX）渗透泵片。研究了不同类型的聚环氧乙烷、渗透剂、包衣膜厚度和孔径对RVX释放曲线的影响。从中心复合设计（CCD）获得试验制剂数据集后，训练基于ANN的模型以获得优化的制剂。通过GastroPlus™对预测制剂进行基于生理的药代动力学（PBPK）建模，以模拟禁食和进食条件下的体内血浆曲线。体外释放试验表明，RVX在长达12小时内呈零级释放。使用图形和数值方法，由预测剖析器生成的预测制剂通过基于CCD的优化制剂进行交叉验证。方差分析（ANOVA）结果显示，预测制剂和优化制剂之间没有显著差异，这些制剂的保质期分别为22.47个月和17.87个月。与速释片相比，RVX推拉渗透泵（PPOP）片的PBPK建模表明，在禁食（高达82%）和进食（高达98.5%）状态下生物利用度提高。结果表明，由于渗透系统的复杂性质以及因变量和自变量之间的非线性相互作用，ANN可有效地用于渗透系统。
