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应用实验设计®方法驱动的人工智能和机器学习对反相高效液相色谱法进行系统优化,以同时分析溶液、人血浆、纳米胶囊和乳液中的两种药物(环孢素 A 和依托度酸)。

Application of Design of Experiments® Approach-Driven Artificial Intelligence and Machine Learning for Systematic Optimization of Reverse Phase High Performance Liquid Chromatography Method to Analyze Simultaneously Two Drugs (Cyclosporin A and Etodolac) in Solution, Human Plasma, Nanocapsules, and Emulsions.

机构信息

Departments of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Sila Katamur, Changsari, Kamrup, Assam, 781101, India.

Bioinformatics Group, Applied Biology Division, CSIR-Indian Institute of Chemical Technology, Tarnaka, Hyderabad, Telangana, 500007, India.

出版信息

AAPS PharmSciTech. 2021 May 13;22(4):155. doi: 10.1208/s12249-021-02026-6.

Abstract

The objectives of current investigation are (1) to find out wavelength of maximum absorbance (λ) for combined cyclosporin A and etodolac solution followed by selection of mobile phase suitable for the RP-HPLC method, (2) to define analytical target profile and critical analytical attributes (CAAs) for the analytical quality by design, (3) to screen critical method parameters with the help of full factorial design followed by optimization with face-centered central composite design (CCD) approach-driven artificial neural network (ANN)-linked with the Levenberg-Marquardt (LM) algorithm for finding the RP-HPLC conditions, (4) to perform validation of analytical procedures (trueness, linearity, precision, robustness, specificity and sensitivity) using combined drug solution, and (5) to determine drug entrapment efficiency value in dual drug-loaded nanocapsules/emulsions, percentage recovery value in human plasma spiked with two drugs and solution state stability analysis at different stress conditions for substantiating the double-stage systematically optimized RP-HPLC method conditions. Through isobestic point and scouting step, 205 nm and ACN:HO mixture (74:26) were selected respectively as the λ and mobile phase. The ANN topology (3:10:4) indicating the input, hidden and output layers were generated by taking the 20 trials produced from the face-centered CCD model. The ANN-linked LM model produced minimal differences between predicted and observed values of output parameters (or CAAs), low mean squared error and higher correlation coefficient values in comparison to the respective values produced by face-centered CCD model. The optimized RP-HPLC method could be applied to analyze two drugs concurrently in different formulations, human plasma and solution state stability checking.

摘要

本研究的目的在于

(1)确定环孢素 A 与依托度酸联合溶液的最大吸收波长(λ),并选择适用于反相高效液相色谱法(RP-HPLC)的流动相;(2)为分析设计确定分析目标概况和关键分析属性(CAA);(3)借助完全因子设计筛选关键方法参数,然后采用中心复合面设计(CCD)优化方法,并结合人工神经网络(ANN)和列文伯格-马夸尔特(LM)算法,确定 RP-HPLC 条件;(4)采用联合药物溶液对分析程序进行验证(准确度、线性、精密度、稳健性、特异性和灵敏度);(5)确定载双药纳米囊/乳液的药物包封效率值、人血浆中两种药物的加标回收率值以及不同应激条件下的溶液稳定性分析,以证实双阶段系统优化的 RP-HPLC 方法条件。通过等吸收点和试探步骤,分别选择 205nm 和 ACN:HO 混合物(74:26)作为 λ 和流动相。ANN 拓扑结构(3:10:4)表示输入、隐藏和输出层,是通过采用 20 次中心复合 CCD 模型产生的试验得到的。与各自的中心复合 CCD 模型产生的数值相比,ANN 链接的 LM 模型产生的输出参数(或 CAA)的预测值和观察值之间的差异更小,均方误差更小,相关系数更高。优化后的 RP-HPLC 方法可用于同时分析不同制剂、人血浆中的两种药物以及溶液稳定性检查。

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