Antibiotic Model-Informed Precision Dosing in Critical Illness

危重疾病中抗生素模型知情的精确剂量

• 批准号: 10673735
• 负责人: Sonya C Tang Girdwood
• 金额: $ 39.75万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10673735
• 关键词: Address; Adult; Affect; Anti-Bacterial Agents; Antibiotics; Clinical; Critical Illness; Dangerousness; Data; Disease; Dose; Drug Kinetics; Electronic Health Record; Ensure; Foundations; Frequencies; Individual; Intensive Care Units; Knowledge; Modeling; Morbidity - disease rate; Odds Ratio; Outcome; Patient Admission; Patients; Pharmaceutical Preparations; Pharmacodynamics; Physiological; Population; Public Health; Publishing; Regimen; Research; Resources; Risk; Therapeutic Intervention; Time; Toxic effect; bactericide; beta-Lactams; cost; improved; improved outcome; individual patient; innovation; model building; models and simulation; mortality; nephrotoxicity; neurotoxicity; patient population; personalized approach; precision medicine; programs; prospective; support tools; tool

PROJECT SUMMARY Pathophysiologic changes during critical illness can affect drug pharmacokinetics (PK, how the body affects the drug) and pharmacodynamics (PD, how the drug affects the body), but the current paradigm of drug dosing is a standard “one-size-fits-all” approach, rather than a personalized approach. Emerging data in adults demonstrate increased risk of morbidity and mortality with standard doses of antibiotics in critically ill patients due to lack of PD target attainment. β-lactam antibiotics are a prime example of drugs demonstrating high PK and PD variability in critically ill patients. Thus, critically ill patients are at risk of having low antibiotic exposures leading to ineffective bactericidal activity or high antibiotic exposures resulting in toxicity. By using real-time drug concentrations, individual patient and disease factors, and population PK models, model-informed precision dosing (MIPD) can ensure adequate antibiotic exposure while avoiding toxicity. My proposed research program will address three critical knowledge gaps that are necessary to fill prior to implementation of antibiotic MIPD. First, the appropriate patient populations who will most benefit from precision dosing remain unknown. Implementation of MIPD for every patient admitted to the intensive care unit may be resource-intensive and costly. However, simply increasing antibiotic doses or frequency of administration without data-driven management can be dangerous as it carries risks for antibiotic-associated toxicity, including nephrotoxicity and neurotoxicity. Therefore, it is critical to optimize the benefit-to-risk ratio of therapeutic interventions for individual patients, a fundamental concept of precision medicine. Second, there remains a knowledge gap on the association of precision dosing of antibiotics and clinical outcomes. Studies examining antibiotic exposure and outcomes in adults have had mixed results; some show improved outcomes with PD target attainment and some show no difference in outcomes with regards to target attainment. Third, many of the antibiotic population PK models needed for MIPD have not been prospectively validated in critically ill patients, so it is unknown which models should be used for precision dosing. To address these knowledge gaps, Project 1 will utilize innovative modeling and simulation to identify patient and disease factors associated with antibiotic under-exposure (risk of ineffective antibacterial activity) or over-exposure (risk of toxicity) and investigate mechanisms underlying toxicities. Project 2 will investigate the effect of precision dosing on clinical outcomes by evaluating the association between PD target attainment and clinical outcomes at the individual level. Project 3 will prospectively validate our models and previously published models to ensure accurate predictive ability in critically ill patients. With these prospectively validated models, we will lay the foundation to build MIPD decision support tools integrated with the electronic health record to generate individual patient PK and PD profiles for precision dosing in critically ill patients. These tools will be essential to implement antibiotic MIPD to guide clinicians in dosing regimen selection for critically ill patients.

项目摘要 危重病期间的病理生理变化可影响药物药代动力学（PK，身体如何 影响药物）和药效学（PD，药物如何影响身体），但目前的药物 剂量是标准的“一刀切”方法，而不是个性化方法。成人新兴数据 证明危重患者使用标准剂量抗生素的发病率和死亡率风险增加 由于缺乏PD目标实现。β-内酰胺抗生素是表现出高PK的药物的主要例子 和PD变异性。因此，重症患者存在抗生素暴露水平低的风险 导致无效的杀菌活性或导致毒性的高抗生素暴露。通过使用实时 药物浓度、个体患者和疾病因素以及群体PK模型，模型知情 精确给药（MIPD）可以确保足够的抗生素暴露，同时避免毒性。 我提议的研究计划将解决三个关键的知识差距，这些差距是在 实施抗生素MIPD。首先，最能受益于 精确剂量仍然未知。为每一位入住重症监护室的患者实施MIPD 可能是资源密集型和昂贵的。然而，仅仅增加抗生素剂量或频率， 没有数据驱动管理的管理可能是危险的，因为它会给与数据相关的 毒性，包括肾毒性和神经毒性。因此，关键是要优化受益风险比， 针对个体患者的治疗干预是精准医疗的基本概念。二是 关于抗生素精确剂量与临床结果之间的关系，仍然存在知识空白。研究 检查成人的抗生素暴露和结果有不同的结果;有些结果显示改善 与PD目标实现有关，有些人在目标实现方面的结果没有差异。第三、 MIPD所需的许多抗生素群体PK模型尚未在 危重患者，因此不知道应使用哪种模型进行精确给药。解决这些 知识差距，项目1将利用创新的建模和模拟来识别患者和疾病 与抗生素暴露不足（抗菌活性无效的风险）或过度暴露（风险）相关的因素 毒性），并研究潜在的毒性机制。项目2将研究精度的影响 通过评价PD目标实现与临床结局之间的相关性，确定给药对临床结局的影响 在个人层面上。项目3将前瞻性地验证我们的模型和以前发布的模型， 确保对重症患者的准确预测能力。有了这些前瞻性验证的模型，我们将奠定 建立与电子健康记录集成的MIPD决策支持工具的基础， 危重患者精确给药的个体患者PK和PD特征。这些工具将是必不可少的， 实施抗生素MIPD，指导临床医生为危重患者选择给药方案。