The Brain Exposure Efficiency (BEE) Score

The blood-brain barrier (BBB), composed of microvascular tight junctions and glial cell sheathing, selectively controls drug permeation into the central nervous system (CNS) by either passive diffusion or active transport. Computational techniques capable of predicting molecular brain penetration are important to neurological drug design. A novel prediction algorithm, termed the Brain Exposure Efficiency Score (BEE), is presented. BEE addresses the need to incorporate the role of trans-BBB influx and efflux active transporters by considering key brain penetrance parameters, namely, steady state unbound brain to plasma ratio of drug (Kp,uu) and dose normalized unbound concentration of drug in brain (Cu,b). BEE was devised using quantitative structure–activity relationships (QSARs) and molecular modeling studies on known transporter proteins and their ligands. The developed algorithms are provided as a user-friendly open source calculator to assist in optimizing a brain penetrance strategy during the early phases of small molecule molecular therapeutic design.由微血管紧密连接与神经胶质细胞鞘构成的血脑屏障（blood-brain barrier, BBB），可通过被动扩散或主动转运的方式，选择性调控药物向中枢神经系统（central nervous system, CNS）的渗透。能够预测分子脑穿透性的计算技术，在神经科药物研发中具有重要价值。本研究提出了一种名为脑暴露效率评分（Brain Exposure Efficiency Score, BEE）的新型预测算法：该算法通过考量关键脑穿透参数——即药物的稳态游离脑/血浆浓度比（Kp,uu）与脑内药物的剂量标准化游离浓度（Cu,b），解决了需纳入跨血脑屏障内流与外流主动转运体作用的研究需求。脑暴露效率评分基于定量构效关系（quantitative structure–activity relationships, QSARs）以及针对已知转运蛋白及其配体的分子建模研究构建而成。本研究将所开发的算法封装为一款易用的开源计算器，旨在辅助小分子治疗药物研发早期阶段的脑穿透策略优化。