Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California 94143-0912, USA.
Adv Drug Deliv Rev. 2012 Jan;64(1):95-109. doi: 10.1016/j.addr.2011.12.008. Epub 2011 Dec 21.
In modeling blood-brain barrier (BBB) passage, in silico models have yielded ~80% prediction accuracy, and are currently used in early drug discovery. Being derived from molecular structural information only, these models do not take into account the biological factors responsible for the in vivo outcome. Passive permeability and P-glycoprotein (Pgp, ABCB1) efflux have been successfully recognized to impact xenobiotic extrusion from the brain, as Pgp is known to play a role in limiting the BBB penetration of oral drugs in humans. However, these two properties alone fail to explain the BBB penetration for a significant number of marketed central nervous system (CNS) agents. The Biopharmaceutics Drug Disposition Classification System (BDDCS) has proved useful in predicting drug disposition in the human body, particularly in the liver and intestine. Here we discuss the value of using BDDCS to improve BBB predictions of oral drugs. BDDCS class membership was integrated with in vitro Pgp efflux and in silico permeability data to create a simple 3-step classification tree that accurately predicted CNS disposition for more than 90% of 153 drugs in our data set. About 98% of BDDCS class 1 drugs were found to markedly distribute throughout the brain; this includes a number of BDDCS class 1 drugs shown to be Pgp substrates. This new perspective provides a further interpretation of how Pgp influences the sedative effects of H1-histamine receptor antagonists.
在模拟血脑屏障 (BBB) 通透性的过程中，计算模型的预测准确率约为 80%，目前被用于早期药物发现。这些模型仅基于分子结构信息，并未考虑到导致体内结果的生物学因素。已成功认识到被动通透性和 P 糖蛋白 (Pgp，ABCB1) 外排可影响外源性物质从大脑中的排出，因为 Pgp 已知在限制口服药物通过血脑屏障进入人体方面发挥作用。然而，这两个特性本身并不能解释大量上市的中枢神经系统 (CNS) 药物的 BBB 穿透性。生物药剂学药物处置分类系统 (BDDCS) 已被证明可用于预测人体中的药物处置，特别是在肝脏和肠道中。在这里，我们讨论了使用 BDDCS 改善口服药物对 BBB 预测的价值。将 BDDCS 分类与体外 Pgp 外排和计算渗透性数据相结合，创建了一个简单的 3 步分类树，该分类树准确预测了我们数据集内 153 种药物中的 90%以上的 CNS 分布情况。发现 BDDCS 类别 1 的约 98%的药物明显分布于整个大脑；其中包括一些被证明是 Pgp 底物的 BDDCS 类别 1 药物。这一新视角进一步解释了 Pgp 如何影响 H1-组胺受体拮抗剂的镇静作用。
