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.

中文翻译：

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脑暴露效率（BEE）得分。

由微血管紧密连接和神经胶质细胞鞘组成的血脑屏障（BBB）通过被动扩散或主动转运选择性地控制药物渗透到中枢神经系统（CNS）中。能够预测分子脑渗透的计算技术对神经药物设计很重要。提出了一种新颖的预测算法，称为脑暴露效率得分（BEE）。BEE通过考虑关键的大脑渗透参数，即稳态未结合脑与血浆的血浆比率（Kp，uu）和剂量在脑中的药物标准化未结合浓度，来解决跨BBB流入和外排主动转运蛋白作用的需求（幼兽）。BEE是使用定量结构-活性关系（QSAR）和已知转运蛋白及其配体的分子建模研究设计的。所开发的算法是作为用户友好的开源计算器提供的，可在小分子分子治疗设计的早期阶段协助优化脑渗透策略。