Germani Massimiliano, Crivori Patrizia, Rocchetti Maurizio, Burton Philip S, Wilson Alan G E, Smith Mark E, Poggesi Italo
Prediction & Modelling, Nerviano Medical Sciences S.r.l., Milan, Italy.
Eur J Pharm Sci. 2007 Jul;31(3-4):190-201. doi: 10.1016/j.ejps.2007.03.008. Epub 2007 Mar 24.
The objective of this study was to evaluate a physiologically based pharmacokinetic (PBPK) approach for predicting the plasma concentration-time curves expected after intravenous administration of candidate drugs to rodents. The predictions were based on a small number of properties that were either calculated based on the structure of the candidate drug (octanol:water partition coefficient, ionization constant(s)) or obtained from the typical high-throughput screens implemented in the early drug discovery phases (fraction unbound in plasma and hepatic intrinsic clearance). The model was tested comparing the predicted and the observed pharmacokinetics of 45 molecules. This dataset included six known drugs and 39 drug candidates from different discovery programs, so that the performance of the model could be evaluated in a real discovery case scenario. The plasma concentration-time curves were predicted with good accuracy, the pharmacokinetic parameters being on average two- to three-fold of actual values. Multivariate analysis was used for identifying the candidate properties which were likely associated to biased predictions. The application of this approach was found useful for the prioritization of the in vivo pharmacokinetics screens and the design of the first-time-in-animal studies.
本研究的目的是评估一种基于生理学的药代动力学(PBPK)方法,用于预测候选药物静脉注射给啮齿动物后预期的血浆浓度-时间曲线。这些预测基于少量的性质,这些性质要么是根据候选药物的结构计算得出的(辛醇:水分配系数、电离常数),要么是从药物发现早期阶段实施的典型高通量筛选中获得的(血浆中未结合分数和肝脏内在清除率)。通过比较45种分子的预测药代动力学和观察到的药代动力学对该模型进行了测试。该数据集包括6种已知药物和来自不同发现项目的39种候选药物,以便在实际发现案例场景中评估模型的性能。血浆浓度-时间曲线预测准确率良好,药代动力学参数平均为实际值的两到三倍。使用多变量分析来识别可能与有偏差预测相关的候选性质。发现这种方法的应用有助于体内药代动力学筛选的优先级排序和首次动物研究的设计。