Paclitaxel drug is administered in the treatment of ovarian and breast cancer and also in Kaposi sarcoma. In spite of being nanomolar active, use of this drug is confined because of its low aqueous solubility, hence many prodrugs for increasing paclitaxel's solubility were formed, but the formation process was not rational. In the current study, quantitative structure property relationship (QSPR) models were formed for the solubility prediction of paclitaxel prodrugs. Structures of all molecules were optimized at the parameterization method 6 (PM6) and Austin Model 1 (AM1) levels, after which Dragon‐based 5250 descriptors and quasi‐mixture descriptors were calculated. Independent descriptors were selected in multiple steps, and QSPR models having 12 and 10 descriptors with R2 and Q2 values of 0.78 and 0.60 and 0.80 and 0.69 for AM1‐ and PM6‐optimized geometry datasets, respectively, were formed. Also, for substituent group dataset, QSPR models with 8 and 9 descriptors having R2 and Q2 values of 0.82 and 0.76 and 0.93 and 0.83 were determined for AM1‐ and PM6‐optimized geometry datasets, respectively. Quasi‐mixture descriptors, which were calculated for substituent group datasets, gave the QSPR model with R2 and Q2 values of 0.70 and 0.58 and 0.69 and 0.52 respectively for AM1‐ and PM6‐optimized geometries. After the models' development, the substituent group dataset was employed for the formation of docking and molecular dynamics simulation–based models for the metabolic study with CYP1A2 enzyme. It is anticipated that the proposed QSPR models will serve as a base for the designing of new paclitaxel prodrugs with improved aqueous solubility.

中文翻译：

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通过分子建模和模拟研究估计紫杉醇前药及其亚结构动力学特性的化学计量学方法用于溶解度预测

紫杉醇药物用于治疗卵巢癌和乳腺癌以及卡波西肉瘤。尽管具有纳摩尔活性，但由于其水溶性低，因此其使用受到限制，因此形成了许多用于增加紫杉醇溶解度的前药，但形成过程并不合理。在目前的研究中，定量结构特性关系 (QSPR) 模型被形成用于紫杉醇前药的溶解度预测。所有分子的结构都在参数化方法 6 (PM6) 和奥斯汀模型 1 (AM1) 水平上进行了优化，然后计算了基于 Dragon 的 5250 描述符和准混合物描述符。在多个步骤中选择独立描述符，QSPR 模型具有 12 和 10 个描述符，R2 和 Q2 值分别为 0.78 和 0.60 以及 0.80 和 0。分别为 AM1 和 PM6 优化的几何数据集形成了 69 个。此外，对于取代基组数据集，分别为 AM1 和 PM6 优化几何数据集确定了具有 8 个和 9 个描述符的 QSPR 模型，其 R2 和 Q2 值分别为 0.82 和 0.76 以及 0.93 和 0.83。针对取代基组数据集计算的准混合物描述符给出了 QSPR 模型，对于 AM1 和 PM6 优化的几何形状，R2 和 Q2 值分别为 0.70 和 0.58 以及 0.69 和 0.52。模型开发后，取代基组数据集用于形成基于对接和分子动力学模拟的模型，用于 CYP1A2 酶的代谢研究。预计所提出的 QSPR 模型将作为设计具有改进水溶性的新型紫杉醇前药的基础。