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Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model

机译:使用全面的基于生理的药代动力学模型预测多个CNS隔室中的药物浓度-时间曲线

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摘要

Drug development targeting the central nervous system (CNS) is challenging due to poor predictability of drug concentrations in various CNS compartments. We developed a generic physiologically based pharmacokinetic (PBPK) model for prediction of drug concentrations in physiologically relevant CNS compartments. System‐specific and drug‐specific model parameters were derived from literature and in silico predictions. The model was validated using detailed concentration‐time profiles from 10 drugs in rat plasma, brain extracellular fluid, 2 cerebrospinal fluid sites, and total brain tissue. These drugs, all small molecules, were selected to cover a wide range of physicochemical properties. The concentration‐time profiles for these drugs were adequately predicted across the CNS compartments (symmetric mean absolute percentage error for the model prediction was <91%). In conclusion, the developed PBPK model can be used to predict temporal concentration profiles of drugs in multiple relevant CNS compartments, which we consider valuable information for efficient CNS drug development.
机译:由于各种CNS隔室中药物浓度的可预测性较差,因此针对中枢神经系统(CNS)的药物开发具有挑战性。我们开发了一种基于生理的通用药代动力学(PBPK)模型,用于预测生理相关CNS隔室中的药物浓度。系统特定和药物特定的模型参数来自文献和计算机模拟预测。使用来自大鼠血浆,脑细胞外液,2个脑脊髓液部位和整个脑组织中的10种药物的详细浓度-时间曲线对模型进行了验证。这些都是小分子的药物经过选择,可以涵盖广泛的理化特性。这些药物的浓度-时间曲线可在中枢神经系统各个隔室中得到充分预测(模型预测的对称平均绝对百分比误差<91%)。总之,开发的PBPK模型可用于预测多个相关CNS隔室中药物的时间浓度分布,我们认为这些信息对于有效的CNS药物开发是有价值的信息。

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