QSPR models for the prediction of apparent volume of distribution
QSPR models for the prediction of apparent volume of distribution
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DOI：
10.1016/j.ijpharm.2006.03.043
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发表时间：
2006-08-17
影响因子：
5.8
通讯作者：
Nokhodchi, Ali
中科院分区：
文献类型：
--
作者：
Ghafourian, Taravat;Barzegar-Jalali, Mohammad;Nokhodchi, Ali
分布容积（V-d）的估计对于药物选择以及治疗中的维持和负荷剂量计算都至关重要。它还可用于药物生物半衰期的预测。本研究采用定量结构-药代动力学关系（QSPR）技术预测分布容积。从文献中整理了129种药物的Vd值。结构描述符包括通过专用软件计算的分配、量子力学、分子力学和连接性参数以及从ACD labs/log D数据库获得的pK（a）值。采用遗传算法和逐步回归分析进行变量选择和模型建立。使用留多法验证模型。QSPR分析导致了一些显着的模型，分别为酸性和碱性药物，并为所有的药物。验证研究表明，所选模型的预测平均误差倍数在1.79和2.17之间。虽然酸和碱的单独QSPR模型的预测误差低于所有药物的模型，外部验证研究表明，酸获得的方程的适用性有限。因此，推荐了只需要计算结构描述符的通用模型。QSPR模型能够预测属于不同化学类别的药物的分布体积，其预测误差与其他更复杂的预测方法（包括常用的种间定标）的预测误差相似。模型中的结构描述符可以根据已知的分布机制和药物的分子结构来解释。(c)2006 Elsevier B. V.保留所有权利。
An estimate of volume of distribution (V-d) is of paramount importance both in drug choice as well as maintenance and loading dose calculations in therapeutics. It can also be used in the prediction of drug biological half life. This study employs quantitative structure-pharmacokinetic relationship (QSPR) techniques for the prediction of volume of distribution. Values of Vd for 129 drugs were collated from the literature. Structural descriptors consisted of partitioning, quantum mechanical, molecular mechanical, and connectivity parameters calculated by specialized software and pK(a) values obtained from ACD labs/log D database. Genetic algorithm and stepwise regression analyses were used for variable selection and model development. Models were validated using a leave-many-out procedure. QSPR analyses resulted in a number of significant models for acidic and basic drugs separately, and for all the drugs. Validation studies showed that mean fold error of predictions for the selected models were between 1.79 and 2.17. Although separate QSPR models for acids and bases resulted in lower prediction errors than models for all the drugs, the external validation study showed a limited applicability for the equation obtained for acids. Therefore, the universal model that requires only calculated structural descriptors was recommended. The QSPR model is able to predict the volume of distribution of drugs belonging to different chemical classes with a prediction error similar to that of the other more complicated prediction methods including the commonly practiced interspecies scaling. The structural descriptors in the model can be interpreted based on the known mechanisms of distribution and the molecular structures of the drugs. (c) 2006 Elsevier B.V. All rights reserved.