Applying green chromatography methods is currently one of the challenges in liquid chromatography. Among different strategies, using cyclodextrin (CD) mobile phase modifiers was applied in this paper. CDs can form inclusion complexes with a wide variety of hydrophobic organic compounds and, consequently, affect their retention behavior. CD-containing mobile phases possess complicated complexation and adsorption equilibria so retention is dependent not only on chromatographic parameters and properties of the compound but also on properties of compound–CD complex. Docking study was used to calculate association constants of the selected antipsychotics (risperidone, olanzapine, and their impurities) and β–CD complexes and predict which part of the molecule structure will most likely incorporate into the β–CD cavity. Quantitative structure-retention relationship model (QSRR) for selected model substances was built employing artificial neural network (ANN) technique. Reliable QSRR model was obtained using molecular descriptors, complex association constants, and chromatographic factors. The multilayer perceptron network with 11-8-1 topology, trained with back propagation algorithm, showed the best performance. Root mean square error for training, validation, and test was 0.2954, 0.3633, and 0.4864, respectively. The correlation coefficient (R²) between experimentally obtained retention factor values [k(exp)] and values computed or predicted by ANN [k(ANN)] was 0.9962 for training, 0.9927 for validation, and 0.9829 for test, indicating good predictive ability of the model. The optimized network was used for development of green chromatography method for separation of risperidone and its related impurities, as well as olanzapine and its related impurities in a relatively short run time and with low consumption of organic modifier. The developed methods were validated in accordance with ICH Q2 (R1) quideline and all parameters fulfilled the defined criteria. The greenness of the proposed methods has also been demonstrated.

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目前，绿色色谱法的应用是液相色谱法的挑战之一。在不同的策略中，本文使用了环糊精（CD）流动相改性剂。CD可以与多种疏水性有机化合物形成包合物，并因此影响其保留行为。含CD的流动相具有复杂的络合和吸附平衡，因此保留不仅取决于色谱参数和化合物的性质，还取决于化合物-CD络合物的性质。对接研究用于计算所选抗精神病药（利培酮，奥氮平及其杂质）和β-CD络合物的缔合常数，并预测分子结构的哪一部分最有可能掺入β-CD腔中。利用人工神经网络（ANN）技术建立了所选模型物质的定量结构-保留关系模型（QSRR）。使用分子描述符，复杂缔合常数和色谱因子获得了可靠的QSRR模型。经过反向传播算法训练的具有11-8-1拓扑结构的多层感知器网络表现出最佳性能。训练，验证和测试的均方根误差分别为0.2954、0.3633和0.4864。相关系数（R 经过反向传播算法训练，显示出最佳性能。训练，验证和测试的均方根误差分别为0.2954、0.3633和0.4864。相关系数（R 经过反向传播算法训练，显示出最佳性能。训练，验证和测试的均方根误差分别为0.2954、0.3633和0.4864。相关系数（R²）在实验获得的保留因子值[k（exp）]与ANN [k（ANN）]计算或预测的值之间，训练的值为0.9962，验证的值为0.9927，测试的值为0.9829，表明该模型具有良好的预测能力。优化的网络用于开发绿色色谱法，用于在相对较短的时间内且有机改性剂的消耗量较低的情况下分离利培酮及其相关杂质以及奥氮平及其相关杂质。根据ICH Q2（R1）quideline对开发的方法进行了验证，所有参数均满足定义的标准。还证明了所提出方法的绿色性。

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