A Systems Pharmacology approach to predict the effects of pregnancy and infectious diseases on transporter-mediated drug disposition

总结 已知妊娠和炎症（由于感染性疾病）都会改变药物的药代动力学（PK） 通过改变转运蛋白和/或药物代谢酶（例如CYP）的表达和活性。 定量由妊娠和/或细胞因子（炎症期间升高）引起的药物PK变化是 对合理设计感染性疾病孕妇用药方案具有重要意义。而 妊娠和细胞因子对CYP清除药物PK的影响已得到充分描述，这些数据 都是运输者所缺少的然而，对每一种可能运输的毒品获得后者， 给孕妇（有或没有感染）施用在逻辑上是不可能的。因此，替代 可以概括药物、转运蛋白和促炎性传染病的方法是 迫切需要。这些方法应通过以下方式准确预测转运蛋白体内活性的改变： 妊娠和促炎细胞因子。在这个建议中，我们提出了一个系统药理学的方法 预测妊娠和/或促炎性感染性疾病对转运蛋白介导的 药物PK。我们的假设是，药物PK的变化幅度可能与妊娠和/或细胞因子有关。 通过使用探针药物的临床PK研究和体外实验数据预测，以及 基于生理学的药代动力学（PBPK）建模和模拟（M&S）。转运蛋白探针药物， 不像荧光探针药物，它们具有非选择性的局限性。为了克服这一局限性，我们 提出了一种双管齐下的方法，该方法利用原代人类细胞（肝细胞、肾上皮细胞）， 细胞和肠上皮细胞）和表达感兴趣的单个转运蛋白的转染细胞。 使用定量靶向蛋白质组学，人类细胞将使我们能够确定怀孕的影响 激素或细胞因子对这些细胞中转运蛋白表达的影响。转运蛋白转染的细胞研究 将使我们能够确定药物的内在转运清除率由一个单一的转运蛋白每pmol的 传送器。然后，将原代细胞中的体外内源性转运蛋白介导的清除率外推至 使用PBPK M&S进行体内研究。结合起来，这些数据将使我们能够预测转运蛋白介导的 在有感染和无感染的孕妇中使用药物。这些研究将解决我们的关键差距， 了解怀孕和/或促炎性传染病对转运蛋白的影响- 介导的药物处置。由于我们的方法可以应用于其他药物和其他炎症 疾病在整个怀孕期间，其意义远远超出药物和炎症性疾病 在这里调查。我们希望在NIH-FAPESP倡议下考虑此应用程序 （NOT-TW-16-001）。我们仅请求支持此处提出的体外研究以及PBPK M&S FAPESP将支持巴西感染性疾病孕妇的临床PK研究。 在巴西获得的临床PK数据将用于验证我们的PBPK模型预测。

SUMMARY Pregnancy and inflammation (due to infectious diseases) are each known to alter drug pharmacokinetics (PK) by changing the expression and activity of transporters and/or drug-metabolizing enzymes (e.g. CYPs). Quantifying changes in drug PK caused by pregnancy and/or cytokines (elevated during inflammation) is important for rational design of dosing regimens of drugs for pregnant women with infectious diseases. While changes in the PK of CYP-cleared drugs by pregnancy and cytokines have been well-delineated, such data are sorely missing for transporters. However, obtaining the latter for every possible transported drug administered to pregnant women (with or without infection) is logistically impossible. Therefore, alternative approaches that can generalize across drugs, transporters and pro-inflammatory infectious diseases are urgently needed. These approaches should accurately predict the alteration in in vivo activity of transporters by pregnancy and pro-inflammatory cytokines. In this proposal, we propose a systems pharmacology approach to predict the effects of pregnancy and/or pro-inflammatory infectious diseases on transporter-mediated drug PK. Our hypothesis is that the magnitude of change in drug PK by pregnancy and/or cytokines can be predicted through clinical PK studies using probe drugs and in vitro experimental data as well as Physiologically Based Pharmacokinetic (PBPK) modeling and simulation (M&S). Transporter probe drugs, unlike CYP probe drugs, have the limitations that they are not selective. To overcome this limitation, we propose a two-pronged approach which utilizes both primary human cells (hepatocytes, renal epithelial cells, and intestinal enterocytes) and transfected cells expressing individual transporters of interest. Using quantitative targeted proteomics, the human cells will allow us to determine the effects of pregnancy hormones or cytokines on the expression of transporters in these cells. The transporter-transfected cell studies will allow us to determine the intrinsic transport clearance of a drug by a single transporter per pmol of a transporter. Then, the in vitro intrinsic transporter-mediated clearances in primary cells will be extrapolated to in vivo using PBPK M&S. Combined, these data will allow us to predict transporter-mediated clearance of drugs in pregnant women with and without infection. These studies will address a critical gap in our understanding of the effects of pregnancy and/or pro-inflammatory infectious diseases on transporter- mediated drug disposition. Since our approach can be applied to other drugs and other inflammatory diseases throughout pregnancy, its significance goes well beyond the drugs and inflammatory diseases investigated here. We would like this application to be considered under the NIH-FAPESP initiative (NOT-TW-16-001). We are requesting support for only the in vitro studies proposed here as well as PBPK M&S. FAPESP will support the clinical PK studies in pregnant women with infectious diseases in Brazil. The clinical PK data obtained in Brazil will be used to verify our PBPK model predictions.