Student Research Committee, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.
Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Xenobiotica. 2022 Apr;52(4):346-352. doi: 10.1080/00498254.2022.2076632. Epub 2022 May 30.
Renal clearance is one of the main pathways for a drug to be cleared from plasma. The aim of this study is to develop models to find out the relationship between the type of renal clearance, and structural parameters.Literature data were used to categorise the drugs into those that undergo tubular secretion and those that undergo reabsorption. Different structural descriptors (VolSurf descriptors, Abraham solvation parameters, data warrior descriptors, logarithm of distribution coefficient at pH = 7.4 (logD)) were applied to develop a mechanistic model for estimating renal clearance class whether its secretion or reabsorption.The results of this study show that logD and the number of hydrogen bond donors, as well as available uncharged species (AUS), are the most effective descriptors to establish mechanistic models for predicting renal clearance class. The classification models were established with a level of accuracy of more than 75%.Developed models of this study can be helpful to predict renal clearance class for new drug candidates with an acceptable error. Hydrophilicity and hydrogen bond formation ability of drugs are among the main descriptors.
肾清除率是药物从血浆中清除的主要途径之一。本研究旨在建立模型,以找出肾清除率的类型与结构参数之间的关系。
利用文献数据将药物分为经肾小管分泌和经重吸收的药物。应用不同的结构描述符(VolSurf 描述符、Abraham 溶剂化参数、DataWarrior 描述符、pH 值为 7.4 时的分配系数对数(logD))来建立一种机制模型,以估计肾清除率类别,无论是分泌还是重吸收。
研究结果表明,logD 和氢键供体的数量以及可用的非荷电种(AUS)是建立用于预测肾清除率类别的机制模型的最有效描述符。分类模型的准确性超过 75%。
本研究建立的模型可用于预测新候选药物的肾清除率类别,误差可接受。药物的亲水性和氢键形成能力是主要描述符之一。