Mathematical modeling for optimal control of BK virus infection in kidney transplant recipients

肾移植受者 BK 病毒感染最佳控制的数学模型

• 批准号: 10741703
• 负责人: JANICE MARIE MCCARTHY
• 金额: $ 20.26万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-06 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10741703
• 关键词: Address; Adult; Affect; Algorithms; Antiviral Agents; BK Virus; Calibration; Characteristics; Childhood; Clinical; Clinical Data; Clinical Management; Clinical Treatment; Clinical Trials; Collaborations; Communicable Diseases; Complex; Consensus; Coupled; Creatinine; Cytotoxic T-Lymphocytes; Data; Data Analyses; Data Science; Data Set; Data Sources; Development; Differential Equation; Dose; Effectiveness; Ensure; Equation; Funding; Goals; Graft Rejection; Human; Immune; Immune response; Immunologics; Immunology; Immunosuppression; Immunosuppressive Agents; Incidence; Infection; Intervention; Kidney; Kidney Cell Infection; Kidney Diseases; Kidney Transplantation; Knowledge; Laboratories; Lead; Learning; Literature; Machine Learning; Mathematics; Measurement; Measures; Mediating; Modeling; Modernization; Monitor; Morbidity - disease rate; National Institute of Allergy and Infectious Disease; Opportunistic Infections; Organ; Organ Transplantation; Outcome; Output; Patient Care; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Physicians; Proliferating; Publishing; Regimen; Research; Risk; Sample Size; Schedule; Software Tools; Solid; Specific qualifier value; Statistical Methods; System; Testing; Therapeutic immunosuppression; Time; Translating; Transplant Recipients; Transplantation; Transplantation and Immune System; United States National Institutes of Health; Validation; Viral; Viral Load result; Viremia; Virus; Virus Diseases; Withdrawal; Work; allograft rejection; clinical practice; control theory; cost; design; dosage; dynamic system; experience; flexibility; graft function; immunoregulation; improved; individual patient; innovation; kidney cell; mathematical model; model development; neural network; novel; optimal control theory; personalized management; personalized medicine; post-transplant; predictive modeling; renal damage; software development; standard of care; tool; transplant centers

BK virus (BKV) infection and nephropathy is a major cause of organ loss following kidney transplantation. There are no effective antivirals for BKV, and standard clinical practice is to reduce immunosuppression, which raises the risk of allograft rejection. There is no consensus on how to safely reduce immunosuppression, and treatment of BKV viremia varies by the attending physician and transplant center. To address this gap in our knowledge for how to optimally manage kidney transplant recipients with BKV viremia, we propose to use mathematical modeling and optimal control theory to develop software-guided management of immunosuppression personalize for individual patients. To translate these models and algorithms to clinical reactive, we need to calibrate and validate them on longitudinal data of kidney transplant recipients with BKV viremia. We propose to utilize NIAID-funded Clinical Trials in Organ Transplantation (CTOT) data sets available in ImmPort, together with pediatric and adult CTOT kidney transplant data not currently in ImmPort, to build the largest longitudinal BKV monitoring data set and use it to calibrate our models. We build this proposed work on our published mathematical model of BKV viremia, which accurately models BKV proliferation and infection of kidney cells, the elicitation of anti-viral and allo-specific cytotoxic T cells that damage kidney cells and reduce graft function, and the non-replenishment of damaged kidney cells resulting in rising creatinine levels. We propose to extend and refine the model to fit the longitudinal clinical data from CTOT kidney transplant recipients with BKV viremia more accurately. To better utilize the extensive patient data from the CTOT studies, we also propose an innovative approach to build a more accurate BKV model by learning the equations describing immune regulation directly from data. Learning will be performed using a neural network emulator, which can approximate arbitrarily complex mathematical functions. This machine learning approach has the advantage of being more flexible and using ancillary data sources that allow a greater degree of personalized model characterization. The CTOT data will be used to inform immune response modeling and to validate the initial model refinements as well as assist us in further model development. We then layer receding horizon control (RHC) algorithms, also known as model predictive control, on top of the mathematical models, to provide adaptive guidance on optimal immunosuppression doses customized to individual patients. The calibration of these models is critical, and a critical component of our proposal is the use advanced statistical methods to estimate model parameters from sparse longitudinal data and to perform sensitivity analysis. Finally, the proposal benefits immensely from the collaboration of three experienced clinicians who manage kidney transplant recipients, who will provide domain expertise, help with the interpretation of data, and guide model and software development to ensure clinical utility. The expected outcome is a useful tool to guide immunosuppressive management that could be tested for its effectiveness in improving patient outcomes and reducing the morbidity of immunosuppressive therapy.

BK病毒(BKV)感染和肾病是肾移植后器官丢失的主要原因。那里 对BKV没有有效的抗病毒药物，标准的临床实践是减少免疫抑制，这增加了 同种异体移植排斥的风险。对于如何安全地减少免疫抑制和治疗，还没有达成共识。 BKV病毒血症的发生率因主治医生和移植中心而异。为了弥补我们知识中的这一鸿沟 对于如何最佳地处理患有BKV病毒血症的肾移植患者，我们建议使用数学方法 建模和最优控制理论用于开发软件指导的免疫抑制管理 为个别患者量身定制。为了将这些模型和算法转化为临床反应，我们需要 对患有BKV病毒血症的肾移植受者的纵向数据进行校正和验证。我们建议 共同利用ImmPort上提供的NIAID资助的器官移植临床试验(CTOT)数据集 与儿童和成人CTOT肾移植数据目前不在ImmPort，以建立最大的纵向 BKV监测数据集，并使用它来校准我们的模型。我们将这项拟议的工作建立在我们已出版的 BKV病毒血症的数学模型，它准确地模拟了BKV的增殖和肾脏细胞的感染， 诱导抗病毒和同种异体特异性细胞毒T细胞，破坏肾脏细胞，降低移植肾功能； 受损的肾脏细胞不能得到补充，从而导致肌酐水平升高。我们建议延长和 改进模型以适应CTOT肾移植受者合并BKV病毒血症的纵向临床数据 准确地说。为了更好地利用来自CTOT研究的大量患者数据，我们还提出了一种创新的 通过直接学习免疫调节方程建立更准确的BKV模型的方法 从数据中。学习将使用神经网络仿真器进行，该仿真器可以任意逼近 复杂的数学函数。这种机器学习方法的优点是更灵活和 使用允许更大程度的个性化模型表征的辅助数据源。CTOT数据 将用于通知免疫反应建模和验证初始模型的改进以及辅助 美国在进一步的模型开发中。然后我们提出分层滚动时间域控制(RHC)算法，也称为模型 预测控制，在数学模型的基础上，提供最优的自适应指导 针对个别患者定制的免疫抑制剂量。这些模型的校准是至关重要的，并且 我们建议的关键部分是使用先进的统计方法来估计模型参数 稀疏纵向数据，并执行敏感性分析。最后，该提案极大地受益于 管理肾移植受者的三名经验丰富的临床医生的合作，他们将提供域 专业知识，帮助解释数据，指导模型和软件开发，以确保临床 实用程序。预期的结果是一个有用的工具来指导免疫抑制治疗，可以测试 它在改善患者预后和降低免疫抑制治疗的发病率方面的有效性。