ACD/Labs, Inc, LT-08117 Vilnius, Lithuania.
J Pharm Sci. 2011 Jun;100(6):2147-60. doi: 10.1002/jps.22442. Epub 2011 Jan 26.
The extent of brain delivery expressed as steady-state brain/blood distribution ratio (log BB) is the most frequently used parameter for characterizing central nervous system exposure of drugs and drug candidates. The aim of the current study was to propose a physicochemical QSAR model for log BB prediction. Model development involved the following steps: (i) A data set consisting of 470 experimental log BB values determined in rodents was compiled and verified to ensure that selected data represented drug disposition governed by passive diffusion across blood-brain barrier. (ii) Available log BB values were corrected for unbound fraction in plasma to separate the influence of drug binding to brain and plasma constituents. (iii) The resulting ratios of total brain to unbound plasma concentrations reflecting brain tissue binding were described by a nonlinear ionization-specific model in terms of octanol/water log P and pK(a). The results of internal and external validation demonstrated good predictive power of the obtained model as both log BB and brain tissue binding strength were predicted with residual mean square error of 0.4 log units. The statistical parameters were similar among training and validation sets, indicating that the model is not likely to be overfitted.
脑内分布程度用稳态脑/血分配比(log BB)表示,是最常用于描述药物和候选药物中枢神经系统暴露的参数。本研究旨在提出一个用于 log BB 预测的物理化学 QSAR 模型。模型开发包括以下步骤:(i)编译并验证了一个由 470 个在啮齿动物中测定的实验 log BB 值组成的数据集,以确保所选数据代表了由被动扩散穿过血脑屏障控制的药物处置。(ii)对血浆中未结合分数进行校正,以分离药物与脑和血浆成分结合的影响。(iii)用非线性的、针对离解特性的模型,根据辛醇/水 log P 和 pK(a),描述总脑与未结合血浆浓度的比值,反映脑组织结合。内部和外部验证的结果表明,所得到的模型具有良好的预测能力,因为 log BB 和脑组织结合强度的预测残差均方误差为 0.4 个对数单位。统计参数在训练集和验证集之间相似,表明该模型不太可能过度拟合。