Medicine Design, Medicinal Chemistry, Pfizer Worldwide R&D, 610 Main Street, Cambridge, Massachusetts 02139, United States.
Medicine Design, Pharmacokinetics, Dynamics, & Metabolism, Pfizer Worldwide R&D, Eastern Point Road, Groton, Connecticut 06340, United States.
ACS Chem Neurosci. 2021 Mar 17;12(6):1007-1017. doi: 10.1021/acschemneuro.0c00807. Epub 2021 Mar 2.
One of the objectives within the medicinal chemistry discipline is to design tissue targeting molecules. The objective of tissue specificity can be either to gain drug access to the compartment of interest (, the CNS) for Neuroscience targets or to restrict drug access to the CNS for all other therapeutic areas. Both neuroscience and non-neuroscience therapeutic areas have struggled to quantitatively estimate brain penetration or the lack thereof with compounds that are substrates of efflux transport proteins such as P-glycoprotein (P-gp) and breast cancer resistant protein (BCRP) that are key components of the blood-brain barrier (BBB). It has been well established that drug candidates with high efflux ratios (ER) of these transporters have poor penetration into brain tissue. In the current work, we outline a parallel analysis to previously published models for the prediction of brain penetration that utilize an alternate MDR1-MDCK cell line as a better predictor of brain penetration and whether a correlation between , rodent data, non-human primate (NHP), and human brain penetration data could be established. Analysis of structural and physicochemical properties in conjunction with parameters and preclinical data has been highlighted in this manuscript as a continuation of the previously published work.
在药物化学学科中，一个目标是设计靶向组织的分子。组织特异性的目标可以是使药物进入感兴趣的部位（如 CNS），以用于神经科学靶点，或者限制药物进入 CNS，用于所有其他治疗领域。神经科学和非神经科学治疗领域都一直在努力定量估计化合物的脑穿透性，或者缺乏对脑穿透性的估计，这些化合物是外排转运蛋白（如 P-糖蛋白 (P-gp) 和乳腺癌耐药蛋白 (BCRP)）的底物，这些蛋白是血脑屏障 (BBB) 的关键组成部分。已经证实，具有这些转运蛋白高外排比 (ER) 的药物候选物对脑组织的穿透性较差。在当前的工作中，我们概述了一种与之前发表的预测脑穿透性模型的平行分析，该模型利用了另一种 MDR1-MDCK 细胞系作为更好的脑穿透性预测指标，以及是否可以建立 啮齿动物数据、非人类灵长类动物 (NHP) 和人脑穿透性数据之间的相关性。在本文中，结合 参数和临床前 数据，强调了对结构和物理化学性质的分析，这是对之前已发表工作的延续。
