Department of Pharmacokinetics, Dynamics, and Metabolism, Pfizer Global Research & Development, Groton Laboratories, Pfizer Inc., Groton, CT 06340, USA.
Pharmaceutical Candidate Optimization, Bristol-Myers Squibb, Princeton, NJ 08543, USA.
Adv Drug Deliv Rev. 2017 Jul 1;116:92-99. doi: 10.1016/j.addr.2017.05.014. Epub 2017 May 26.
Membrane transporters play a key role in the absorption, distribution, clearance, elimination, and transport of drugs. Understanding the drug properties and structure activity relationships (SAR) for affinity to membrane transporters is critical to optimize clearance and pharmacokinetics during drug design. To facilitate the early identification of clearance mechanism, a framework named the extended clearance classification system (ECCS) was recently introduced. Using in vitro and physicochemical properties that are readily available in early drug discovery, ECCS has been successfully applied to identify major clearance mechanism and to implicate the role of membrane transporters in determining pharmacokinetics. While the crystal structures for most of the drug transporters are currently not available, ligand-based modeling approaches that use information obtained from the structure and molecular properties of the ligands have been applied to associate the drug-related properties and transporter-mediated disposition. The approach allows prospective prediction of transporter both substrate and/or inhibitor affinity and build quantitative structure-activity relationship (QSAR) to enable early optimization of pharmacokinetics, tissue distribution and drug-drug interaction risk. Drug design applications can be further improved through uncovering transporter protein crystal structure and generation of quality data to refine and develop viable predictive models.
膜转运蛋白在药物的吸收、分布、清除、消除和转运中发挥着关键作用。了解药物对膜转运蛋白的亲和力的药物特性和构效关系（SAR）对于在药物设计过程中优化清除率和药代动力学至关重要。为了促进早期识别清除机制，最近引入了一个名为扩展清除分类系统（ECCS）的框架。ECCS 使用早期药物发现中易于获得的体外和物理化学性质，已成功用于识别主要的清除机制，并表明膜转运蛋白在决定药代动力学中的作用。虽然大多数药物转运蛋白的晶体结构目前尚不可用，但基于配体的建模方法利用从配体的结构和分子性质获得的信息来关联与药物相关的性质和转运体介导的处置。该方法允许对转运蛋白的底物和/或抑制剂亲和力进行前瞻性预测，并构建定量构效关系（QSAR），从而能够早期优化药代动力学、组织分布和药物相互作用风险。通过揭示转运蛋白的晶体结构和生成高质量的数据来改进和开发可行的预测模型，可以进一步提高药物设计应用。
