DECS GCS Computational Chemistry, AstraZeneca R&D Mölndal, SE-43183 Mölndal, Sweden.
J Mol Graph Model. 2011 Aug;29(8):985-95. doi: 10.1016/j.jmgm.2011.04.004. Epub 2011 Apr 27.
Distribution over the blood-brain barrier (BBB) is an important parameter to consider for compounds that will be synthesized in a drug discovery project. Drugs that aim at targets in the central nervous system (CNS) must pass the BBB. In contrast, drugs that act peripherally are often optimised to minimize the risk of CNS side effects by restricting their potential to reach the brain. Historically, most prediction methods have focused on the total compound distribution between the blood plasma and the brain. However, recently it has been proposed that the unbound brain-to-plasma concentration ratio (K(p,uu,brain)) is more relevant. In the current study, quantitative K(p,uu,brain) prediction models have been built on a set of 173 in-house compounds by using various machine learning algorithms. The best model was shown to be reasonably predictive for the test set of 73 compounds (R(2)=0.58). When used for qualitative prediction the model shows an accuracy of 0.85 (Kappa=0.68). An additional external test set containing 111 marketed CNS active drugs was also classified with the model and 89% of these drugs were correctly predicted as having high brain exposure.
在药物发现项目中合成的化合物，其通过血脑屏障（BBB）的分布是一个需要考虑的重要参数。旨在作用于中枢神经系统（CNS）靶点的药物必须通过 BBB。相比之下，作用于外周的药物通常通过限制其到达大脑的潜力，优化以最小化 CNS 副作用的风险。从历史上看，大多数预测方法都集中在血浆和大脑之间的总化合物分布上。然而，最近有人提出，未结合的脑-血浆浓度比（K(p,uu,brain)）更相关。在当前的研究中，通过使用各种机器学习算法，在一组 173 种内部化合物上建立了定量 K(p,uu,brain)预测模型。结果表明，该最佳模型对 73 种化合物的测试集具有较好的预测能力（R(2)=0.58）。当用于定性预测时，该模型的准确率为 0.85（Kappa=0.68）。该模型还对包含 111 种上市 CNS 活性药物的额外外部测试集进行了分类，其中 89%的药物被正确预测为具有高脑暴露。
