Optimizing GVHD Prevention with Systems Pharmacology Models

项目总结 定量系统药理学(QSP)是一个快速发展的领域，它整合了体外可用药物， 临床前和临床数据代表现有知识，以实现反向转换。反向 翻译使用实时人类临床数据来直接通知新发现、现有疗法和 疾病进展的属性。在这里，我们寻求成为第一个建立QSP模型来先验地预测 使用总体先验的患者间药代动力学和药效学(总体药动或 PopPK模型)和生理预测(使用基于生理的药代动力学或PBPK模型) 结合基于体外和临床前数据的药效学模型。我们将应用这种新型的混合动力车 PopPK-PBPK-PD QSP模型用于异基因造血细胞移植(HCT) 微妙的平衡，如将细胞从一个个体(供体)移植到另一个个体(宿主，HCT接受者)。引导式 根据我们的初步数据，我们的工作假设是QSP建模可以最大限度地减少个体间的差异 这些免疫抑制剂，同时也优化了新的移植物抗宿主病(GVHD)方案 移植环磷酰胺(PTCy)。目的1利用PopPK和PBPK确定PTCy的最佳剂量 模特们。我们的临床前数据显示，PTCy的剂量窗口很窄，中间剂量有 最低的GVHD比率。为了达到每个患者的最佳PTCy剂量，我们寻求开发一种验证PopPK- PBPK模型基于我们在定量4-羟基环磷酰胺(4HCY)方面的独特专业知识， CY细胞毒性代谢物的主要前体，并使用PopPK引导剂量个性化CY。这 我们将使用我们的回顾和前瞻性队列(n=150)来开发混合的PopPK-PBPK CY模型。这个 未来的队列将被国家癌症研究所(NCI)和希望之城(COH)录取。NCI的队列 将确定PTCy剂量和计划是否可以在不影响GVHD比率的情况下减少(75%)； Coh队列将使用传统的PTCy剂量。在目标2中，我们将描述药物动力学和 霉酚酸及其靶酶肌苷的药效学 单磷酸脱氢酶(IMPDH)。与CY一样，MPA具有实质性的药动学可变性，但 不同的代谢和转运途径，因此需要单独的药代动力学模型。我们寻求创造 PopPK-PBPK-PD模型用于确定最适的血浆暴露浓度和IMPDH活性。在《目标3》中，我们将 建立T细胞反应和急性GVHD的定量系统药理学(QSP)模型。我们的临床前研究 数据显示，PTCy预防急性移植物抗宿主病与降低CD4+CD25-Foxp3- 常规T细胞(Tcon)在+7d后优先扩增为CD4+CD25+Foxp3+ +21天的调节性T细胞(Tregs)。在完全集成的免疫反应模型(公司)的基础上，我们寻求 整合体外、临床前和临床数据，建立QSP模型。

PROJECT SUMMARY Quantitative systems pharmacology (QSP) is a rapidly expanding area that integrates available in vitro, preclinical, and clinical data representing existing knowledge to achieve a reverse translation. Reverse translation uses real-time human clinical data to directly inform new discoveries, of existing therapies and attributes of disease progression. Here, we seek to be the first to build a QSP model to, a priori, predict interpatient pharmacokinetics and pharmacodynamics using population priors (population pharmacokinetic or popPK modeling) and physiologic predictions (using physiologically based pharmacokinetic or PBPK modeling) combined with pharmacodynamic models based on in vitro and preclinical data. We will apply this novel hybrid popPK-PBPK-PD QSP model to allogeneic hematopoietic cell transplant (HCT) because its success requires a delicate balance as the grafting of cells from one individual (donor) to another (host, the HCT recipient). Guided by our preliminary data, our working hypothesis is that QSP modeling can minimize interindividual variability of these immunosuppressants, while also optimizing the novel graft versus host disease (GVHD) regimen of post- transplant cyclophosphamide (PTCy). Aim 1 seeks to identify the optimal PTCy dose using popPK and PBPK models. Our preclinical data shows that PTCy has a narrow dose window, with intermediate doses having the lowest GVHD rates. To achieve the optimal PTCy dose in each patient, we seek to develop a validate a popPK- PBPK model building upon our unique expertise in quantitating 4-hydroxycyclophosphamide (4HCY), the primary precursor to the cytotoxic metabolite of CY and personalizing CY using popPK-guided dosing. This hybrid popPK-PBPK CY model will be developed using our retrospective and prospective (n=150) cohort. The prospective cohort will be enrolled at National Cancer Institute (NCI) and City of Hope (COH). The NCI cohort will determine if the PTCy dose and schedule can be reduced (by 75%) without compromising GVHD rates; the COH cohort will use the traditional PTCy dosing. In Aim 2, we will characterize the pharmacokinetics and pharmacodynamics of mycophenolic acid (MPA), the active metabolite of MMF, with its target enzyme inosine monophosphate dehydrogenase (IMPDH). Like CY, MPA has substantive pharmacokinetic variability but different metabolic and transport pathways so separate pharmacokinetic models are needed. We seek to create a popPK-PBPK-PD model to identify the optimal plasma exposure of MPA and IMPDH activity. In Aim 3, we will create a quantitative systems pharmacology (QSP) model of T-cell response and acute GVHD. Our preclinical data show that acute GVHD prevention with PTCy is associated with reduction of CD4+CD25-Foxp3- conventional T-cell (Tcon) proliferation at day +7 followed by the preferential expansion of CD4+CD25+Foxp3+ regulatory T cells (Tregs) at day +21. Building upon fully-integrated immune response model (FIRM), we seek integrate in vitro, preclinical, and clinical data to build a QSP model.