Elucidation of an informed drug dosing scheme to minimize kidney injury

描述（由申请人提供）：批准的药物很少在“现实世界”患者中进行初步研究，以充分详细说明其毒性特征。因此，即使在获得食品和药物管理局批准后，也存在改进药物剂量方案的机会。在已知药物暴露-毒性关系和目标人群中药物暴露的可变性之后，数学建模技术的进步现在允许设计剂量方案，以最大限度地减少“现实世界”患者的毒性。万古霉素是一种原型药物，是治疗革兰氏阳性感染的基石，是急性肾损伤（AKI）的一种可预防的原因。由于超过50年的临床经验，许多了解万古霉素药代动力学（PK）暴露。然而，与AKI的药效学（PD）结果的关系仍然不明确。该项目的长期目标是整合有效的PK/PD模型（体外、动物和人类）和人类PK研究的数据，以构建临床药物给药策略，最大限度地减少抗生素暴露相关不良事件的可能性，同时最大限度地提高疗效。本申请的总体目标是采用万古霉素作为引起AKI的原型药物，以阐明PK/PD关系并确定最佳给药方案。本研究的中心假设是万古霉素暴露的强度和形状决定了AKI的发生和程度。我们的假设是根据观察得出的，即当代万古霉素给药方案在人类中发生AKI，最近的动物研究证实了人源万古霉素暴露时的因果关系。这项工作扩展了先前的临床研究、计算机研究和实验室工作，并采用了行之有效的技术来关注药物性AKI的预防。具体来说，使用动物毒性模型将允许仔细计划万古霉素暴露的排列，并绕过先前临床分析的缺点，即由于人的均匀给药方案而无法识别PK/PD终点。这项拟议研究的基本原理是，在设计最佳人类方案之前，必须明确界定药物暴露与毒性之间的联系。这个应用程序将解决两个特定的目标。在Aim #1中，万古霉素暴露导致急性肾损伤的情况将通过以下方法确定：1)在大鼠中采用严格控制的剂量范围和剂量分离研究；2)用新的生物标志物和传统的组织病理学测量AKI。在目标#2中，将使用蒙特卡罗模拟进行数学概率建模，其中包括：1)已知的危重病患者万古霉素暴露变异性；2)确定万古霉素诱导AKI的阈值；3)万古霉素疗效的靶点。我们期望所提出的工作将导致万古霉素剂量方案的结果，使AKI最小化，同时最大限度地提高“现实世界”患者的疗效。这一贡献预计将是重大的，因为优化药物治疗以避免可预防的不良事件是提高市场上现有药物安全性的第一步。

DESCRIPTION (provided by applicant): Approved drugs are rarely initially studied in "real-world" patients in a manner sufficient to adequately detail their toxicity profile. Thus, an opportunity exists to refine drug dosing schemes even after they are approved by the Food and Drug Administration. Advances in mathematical modeling techniques now allow design of dosing schemes that minimize toxicity in "real-world" patients after the drug exposure-toxicity relationship and the variability of drug exposure in the target population is known. Vancomycin is a prototype drug that is a cornerstone in the treatment of Gram positive infections and represents a preventable cause of Acute Kidney Injury (AKI). Owing to over 50 years of clinical experience, much is known about vancomycin pharmacokinetic (PK) exposure. However, the relationship with the pharmacodynamic (PD) outcome of AKI remains poorly defined. This project seeks as a long term goal to integrate data from validated PK/PD models (in vitro, animal, and human) and human PK studies to construct clinical drug dosing strategies that minimize the probability of antibiotic-exposure related adverse events while maximizing efficacy. The overall objective of this application is to employ vancomycin as a prototype drug that causes AKI to elucidate the PK/PD relationship and identify optimal dosing schemes. The central hypothesis of this research is that the intensity and shape of the vancomycin exposure profile accounts for the onset and the extent of AKI. Our hypothesis has been formulated from observations that AKI occurs with contemporary vancomycin dosing schemes in humans Recent animal studies confirm causality when humanized vancomycin exposures are used. This work expands upon previous clinical studies, in silico studies, and laboratory efforts, and employs well validated techniques to focus on the prevention of drug-induced AKI. Specifically, use of an animal toxicity model will allow for carefully planned permutations of vancomycin exposures and bypass the shortcomings of prior clinical analyses where PK/PD endpoints have not been discerned because of homogenous human dosing schemes. The rationale that underlies the proposed research is that the drug exposure-toxicity link must be clearly defined before optimal human regimens can be designed. This application will address two specific aims. In Aim #1, the vancomycin exposure profile that causes acute kidney injury will be determined by 1) employing carefully controlled dose-range and dose-fractionation studies in rats and 2) measuring AKI with novel biomarkers and traditional histopathology. In Aim #2, mathematical probability modeling will be conducted with Monte Carlo Simulations that incorporate 1) known vancomycin exposure variability in critical care patients and 2) identified thresholds for vancomycin induced AKI and 3) targets for vancomycin efficacy. We expect that the proposed work will lead to the outcome of vancomycin dosing schemes that minimize AKI while maximizing efficacy for "real-world" patients. This contribution is expected to be significan since optimizing drug therapies to avoid preventable adverse events is the first step to improving the safety of drugs already available in the market.