Mechanisms of Altered Hepatic Transport: Impact on Drug Therapy

肝胆转运蛋白在药物处置、安全性和有效性方面的重要性是众所周知的 认可.确定参与这些过程的关键肝脏转运蛋白，并了解 影响其功能对于成功的药物开发和最佳药物治疗是至关重要的。总目标 该研究计划的目的是解决肝胆药物转运的主要知识缺口，并开发新的 评估和预测药物相互作用（DI）改变转运蛋白功能的影响的策略， 变异和疾病。这些信息是精准医学科学的基础，将有助于 预防药物性肝损伤（DILI）。我的实验室率先使用了人工培养的 肝细胞是目前广泛用于研究肝胆药物转运和肝毒性的一种强有力的体外工具， 我们是第一个评估非酒精性脂肪性肝炎的功能影响的人， 肝基底外侧外排转运蛋白表达增加与药物/代谢物分布相关， 人，并利用人肝动脉造影数据来评估肝DI。我们的合作团队是 开发可用于早期药物开发的计算工具，以识别具有以下特征的化合物： DILI的责任，我们制定了一个战略，结合生理参数和实验数据， 定量系统药理学（QSP）模型来评价DILI机制。这些高度创新的 这些方法可以改善对肝转运蛋白介导的DI和DILI易感性的预测， 药物治疗在本申请中，我们建议继续翻译基本的分子和细胞 通过解决以下关键问题来研究临床应用的机制： 转运体对胆汁酸（BA）的体内平衡和阴离子药物的处置至关重要？基底外侧的作用 转运体（例如，OSTα/β）在BA和阴离子药物/代谢物的肝脏和全身分布中的作用将被 阐明。这些信息将使我们能够更准确地预测BA介导的DILI，这是一种主要的药物治疗方法。 开发安全性问题和已批准药物退出市场的原因。肝脏怎么样 基底外侧BA和阴离子药物外排转运蛋白调节？关于BA之间相互作用的信息很少 暴露，肝转运动力学，以及这些蛋白质在人体内的调节。我们将填补这些关键的 知识差距，目前影响了肝转运蛋白功能、DI和BA的准确预测- 介导的DILI。什么样的体外、计算机模拟和体内工具可以帮助更有效、更准确地预测 转运蛋白功能改变对BA和阴离子药物处置的临床影响？常规使用的体外试验 预测转运蛋白介导的DI无法识别复杂的相互作用机制。发展 新的体外工具、体内探针和计算机模型将提供一种非常有效的方法来识别 肝转运蛋白介导的DI、BA介导的DILI易感因素，并提高预测准确性， 优化药物治疗，避免不良反应。

The importance of hepatobiliary transport proteins in medication disposition, safety and efficacy is well recognized. Identifying key hepatic transporters involved in these processes and understanding the factors that impact their function is critical to successful drug development and optimal pharmacotherapy. The overall goal of this research program is to address major knowledge gaps in hepatobiliary drug transport and develop novel strategies to assess and predict the impact of transporter function altered by drug interactions (DIs), genetic variation, and disease. This information is fundamental to the science of precision medicine, and will aid in preventing DIs and drug-induced liver injury (DILI). My laboratory pioneered the use of sandwich-cultured hepatocytes, a powerful in vitro tool now widely used to study hepatobiliary drug transport and hepatic transporter-mediated DIs. We were the first to assess the functional impact of non-alcoholic steatohepatitis- associated increases in hepatic basolateral efflux transporter expression on drug/metabolite disposition in humans, and to utilize human liver scintigraphy data to evaluate a hepatic DI. Our collaborative team is developing computational tools that can be used a priori in early drug development to identify compounds with DILI liability, and we formulated a strategy integrating physiological parameters and experimental data with a quantitative systems pharmacology (QSP) model to evaluate DILI mechanisms. These highly innovative approaches can improve predictions of hepatic transporter-mediated DIs and DILI liability, leading to safer medications. In the current application, we propose to continue translating fundamental molecular and cellular mechanisms to clinical applications by addressing the following key questions: Which hepatic basolateral efflux transporters are critical to bile acid (BA) homeostasis and anionic drug disposition? The role of basolateral transporters (e.g., OSTα/β) in the hepatic and systemic disposition of BAs and anionic drugs/metabolites will be elucidated. This information will enable us to more accurately predict BA-mediated DILI, a major drug development safety issue and reason that approved drugs are withdrawn from the market. How are hepatic basolateral BA and anionic drug efflux transporters regulated? Information is scarce on the interplay of BA exposure, hepatic transporter dynamics, and the regulation of these proteins in humans. We will fill these critical knowledge gaps, which currently compromise accurate predictions of hepatic transporter function, DIs, and BA- mediated DILI. What in vitro, in silico, and in vivo tools could help predict, more efficiently and accurately, the clinical impact of altered transporter function on BA and anionic drug disposition? In vitro assays routinely used to predict transporter-mediated DIs are unable to identify complex interaction mechanisms. Development of novel in vitro tools, in vivo probes, and in silico models will provide a powerfully efficient approach to identify hepatic transporter-mediated DIs, BA-mediated DILI susceptibility factors, and improve prediction accuracy to optimize pharmacotherapy and avoid adverse effects.