Comparison of in-vivo and in-silico methods used for prediction of tissue: plasma partition coefficients in rat
Comparison of in-vivo and in-silico methods used for prediction of tissue: plasma partition coefficients in rat
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DOI：
10.1111/j.2042-7158.2011.01429.x
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发表时间：
2012-03-01
影响因子：
3.3
通讯作者：
Aarons, Leon
中科院分区：
文献类型：
--
作者：
Graham, Helen;Walker, Mike;Aarons, Leon
目的使用文献中的方法预测大鼠组织：血浆分配系数（Kps）和分布容积值。确定哪种模型提供最准确的预测，以增加在基于生理学的药代动力学modeling.Methods中使用预测的药代动力学参数的置信度，对于11种大鼠组织中的81种化合物的数据集，6种模型用于预测Kps，4种用于预测V-ss，并将预测值与实验得出的值进行了比较。关键发现Rodgers等人的模型预测的Kp是最准确的，其中77%在实验值的三倍内。Poulin & Theil模型是最准确的预测的V-SS，87%的预测在three-folds.Conclusions本研究表明，在硅片模型可在文献中可用于准确地预测KP和V-SS在大鼠。Rodgers等人的模型已被证明可提供最准确的Kp预测，在所有药物类别和组织中具有一致的准确性。当不使用体内数据作为输入时，它也是最准确的V-ss预测值。然而，运输系统和其他机制，尚未完全理解，需要纳入这些类型的模型在未来进一步提高其适用性。
Objectives To use methods from the literature to predict rat tissue:plasma partition coefficients (Kps) and volume of distribution values. Determine which model provides the most accurate predictions to increase confidence in the use of predicted pharmacokinetic parameters in physiologically based pharmacokinetic modelling.Methods Six models were used to predict Kps and four to predict V-ss for a dataset of 81 compounds in 11 rat tissues, and the predictions were compared with experimentally derived values.Key findings Kp predictionsmade by the Rodgers et al. model were the most accurate, with 77% within threefold of experimental values. The Poulin & Theil model was the most accurate for the prediction of V-ss, with 87% of predictions within threefold.Conclusions This study has shown that in-silico models available in the literature can be used to accurately predict Kp and V-ss in rat. The Rodgers et al. model has been shown to provide the most accurate Kp predictions, with consistent accuracy across all drug classes and tissues. It was also the most accurate V-ss predictor when no in-vivo data were used as input. However, transporter systems and other mechanisms that are not yet fully understood need to be incorporated into these types of models in the future to further increase their applicability.