A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations

种药物的通用多室中枢神经系统分布模型结构可预测人类大脑靶位点浓度

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作者:Yumi Yamamoto, Pyry A Välitalo, Dirk-Jan van den Berg, Robin Hartman, Willem van den Brink, Yin Cheong Wong, Dymphy R Huntjens, Johannes H Proost, An Vermeulen, Walter Krauwinkel, Suruchi Bakshi, Vincent Aranzana-Climent, Sandrine Marchand, Claire Dahyot-Fizelier, William Couet, Meindert Danhof, Joh

Conclusions

A multi-compartmental brain pharmacokinetic model was developed and its structure could adequately describe data across nine different drugs. The model could be successfully translated to predict human brain concentrations.

Methods

A mathematical model consisting of several physiological brain compartments in the rat was developed using rich concentration-time profiles from nine structurally diverse drugs in plasma, brain extracellular fluid, and two cerebrospinal fluid compartments. The effect of active drug transporters was also accounted for. Subsequently, the model was translated to predict human concentration-time profiles for acetaminophen and morphine, by scaling or replacing system- and drug-specific parameters in the model.

Purpose

Predicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human brain disposition.

Results

A common model structure was identified that adequately described the rat pharmacokinetic profiles for each of the nine drugs across brain compartments, with good precision of structural model parameters (relative standard error <37.5%). The model predicted the human concentration-time profiles in different brain compartments well (symmetric mean absolute percentage error <90%). Conclusions: A multi-compartmental brain pharmacokinetic model was developed and its structure could adequately describe data across nine different drugs. The model could be successfully translated to predict human brain concentrations.

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