PDF(Pubmed)
Abstract：
Drug discovery is a challenging process, with many compounds failing to progress due to unmet pharmacokinetic criteria. Lipophilicity is an important physicochemical parameter that affects various pharmacokinetic processes, including absorption, metabolism, and excretion. This study evaluated the lipophilic properties of a library of ipsapirone derivatives that were previously synthesized to affect dopamine and serotonin receptors. Lipophilicity indices were determined using computational and chromatographic approaches. In addition, the affinity to human serum albumin (HSA) and phospholipids was assessed using biomimetic chromatography protocols. Quantitative Structure-Retention Relationship (QSRR) methodologies were used to determine the impact of theoretical descriptors on experimentally determined properties. A multiple linear regression (MLR) model was calculated to identify the most important features, and genetic algorithms (GAs) were used to assist in the selection of features. The resultant models showed commendable predictive accuracy, minimal error, and good concordance correlation coefficient values of 0.876, 0.149, and 0.930 for the validation group, respectively.
摘要：
药物发现是一个具有挑战性的过程，由于未满足药代动力学标准，许多化合物未能进展。亲脂性是影响各种药代动力学过程的重要物理化学参数，包括吸收，新陈代谢，和排泄。这项研究评估了先前合成的影响多巴胺和5-羟色胺受体的ipsapirone衍生物文库的亲脂性。使用计算和色谱方法确定亲脂性指数。此外,使用仿生色谱方案评估了对人血清白蛋白(HSA)和磷脂的亲和力.使用定量结构-保留关系(QSRR)方法来确定理论描述符对实验确定的性质的影响。计算了多元线性回归(MLR)模型来识别最重要的特征，和遗传算法(GA)被用来帮助选择特征。所得到的模型显示出良好的预测准确性，最小误差，和良好的一致性相关系数值0.876，0.149和0.930的验证组，分别。
