Systems-based pharmacologic modelling to elucidate beta-lactam clinical pharmacodynamics and define optimal dosing regimens in severe pneumonia

基于系统的药理学模型阐明β-内酰胺临床药效学并确定重症肺炎的最佳给药方案

• 批准号: 10663370
• 负责人: Nathaniel James Rhodes
• 金额: $ 66.92万
• 依托单位: MIDWESTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-11 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10663370
• 关键词: Address; Affect; Alveolar; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Area Under Curve; Biological Assay; Bronchoalveolar Lavage Fluid; Cause of Death; Cefepime; Cessation of life; Classification; Clinical; Clinical Research; Clinical Treatment; Critical Illness; Data; Development; Diagnostic; Disease; Dose; Drug Exposure; Drug Kinetics; Epithelium; Failure; Funding; Goals; Health; Health Care Costs; Infection; Infrastructure; Intensive Care Units; Klebsiella aerogenes; Klebsiella oxytoca; Klebsiella pneumoniae; Knowledge; Liquid substance; Measures; Mechanical ventilation; Meropenem; Mission; Modeling; Nosocomial Infections; Nosocomial pneumonia; Organ; Outcome; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacodynamics; Phenotype; Piperacillin; Plasma; Pneumonia; Pseudomonas aeruginosa; Public Health; Regimen; Research; Research Infrastructure; Resistance; Risk; Risk Factors; Sampling; Site; Specimen; Statistical Models; System; Systems Biology; Time; Treatment Failure; United States National Institutes of Health; Universities; Validation; antibiotic resistant infections; beta-Lactams; biobank; clinical efficacy; clinical risk; combat; design; drug resistant pathogen; effective therapy; experience; falls; genome sequencing; high risk population; hospital analysis; improved; individual patient; mortality; mortality risk; participant enrollment; pathogen; pharmacodynamic model; pharmacokinetics and pharmacodynamics; pharmacologic; pneumonia treatment; prospective; response; sample collection; simulation; success; tool; treatment optimization; treatment strategy; whole genome

Project Abstract Hospital-acquired pneumonia (HAP) caused by antibiotic-resistant pathogens such as Klebsiella aerogenes, Klebsiella pneumoniae, and Pseudomonas aeruginosa are responsible treatment failure and mortality rates up to 50% and 30%, respectively. Beta-lactam antibiotics are a mainstay for the treatment of HAP, but antibiotic resistance continues to erode their clinical efficacy. Importantly, the efficacy of beta- lactams depends on achieving adequate pharmacokinetic-pharmacodynamic (PK/PD) exposures; however, many patients with HAP experience inadequate PK/PD because of changes in PK caused by critically illness. Precision Dosing strategies can overcome PK variability caused by critical illness, but Precision Dosing requires robust PK models and clinically validated PK/PD targets; such models and targets are currently lacking for patients with HAP. Without robust PK models and optimal PK/PD targets for antibiotic dosing, HAP patients will continue to experience high rates of treatment failure and death. Our proposal will adapt and extend the existing research infrastructure of the Successful Clinical Response In Pneumonia Therapy (SCRIPT) Systems Biology Center to robustly address these gaps in knowledge. Our long-term goal is to develop Precision Dosing strategies that overcome PK variability caused by severe illness. The project objective is to utilize the infrastructure, samples, and data collected in SCRIPT to develop PK models in critically ill patients with HAP—thereby facilitating the development and validation of HAP-specific PK/PD models. Our central hypothesis is: (1) inadequate beta-lactam PK is common with standard “one-size-fits all” HAP dosing regimens; which (2) increases the risk of re- infection; that in turn (3) increases the likelihood of clinical treatment failure in HAP. In Aim 1, we will develop and evaluate PK models for use in Precision Dosing of beta-lactams for HAP. In Aim 2, we will evaluate the impact of alveolar beta-lactam PK/PD on outcomes in HAP including (a) treatment success and (b) pathogen reinfection. In Aim 3, we will identify patients who experience alveolar-plasma PK profile discordance—making plasma a poor surrogate for alveolar concentrations and placing these patients at risk of underdosing for pneumonia treatment—and determine clinical risk factors for such discordance. Our study will provide clinically validated tools which will facilitate the actualization of Precision Dosing for patients with HAP. Our study will have a positive clinical impact by providing optimal PK sampling times, generalizable PK models, HAP-specific PK/PD targets, and validated risk factors for alveolar- plasma PK discordance, all of which can be applied at the bedside for patients with HAP. This research is significant because it will provide the tools required to achieve Precision Dosing in HAP, which will advance the NIH mission to protect and improve the health of patients affected by resistant pathogens.

项目摘要 由克雷伯菌等抗生素耐药病原体引起的医院获得性肺炎 (HAP) 产气杆菌、肺炎克雷伯菌和铜绿假单胞菌是治疗失败的原因 β-内酰胺类抗生素的死亡率分别高达 50% 和 30%。 HAP，但抗生素耐药性继续削弱其临床疗效，重要的是，β- 的疗效。 内酰胺类药物取决于是否达到足够的药代动力学-药效学 (PK/PD) 暴露量； 然而，许多 HAP 患者由于 PK 变化导致 PK/PD 不足。 危重疾病 精准给药策略可以克服危重疾病引起的 PK 变异，但是 精准给药需要稳健的 PK 模型和经过临床验证的 PK/PD 目标； 目前，HAP 患者缺乏稳健的 PK 模型和最佳 PK/PD 目标。 对于抗生素剂量，HAP 患者的治疗失败率和死亡率将继续较高。 我们的提案将调整和扩展成功临床的现有研究基础设施 肺炎治疗响应 (SCRIPT) 系统生物学中心将有力地解决这些差距 我们的长期目标是开发克服 PK 变异性的精准给药策略。 该项目的目标是利用收集到的基础设施、样本和数据。 在 SCRIPT 中开发危重 HAP 患者的 PK 模型，从而促进开发 HAP 特异性 PK/PD 模型的验证和验证 我们的中心假设是：(1) β-内酰胺 PK 不足。 与标准的“一刀切”HAP 给药方案很常见，这 (2) 增加了再次发生的风险； 感染；进而 (3) 增加 HAP 临床治疗失败的可能性。 在目标 2 中，我们将开发和评估用于 HAP β-内酰胺精确剂量的 PK 模型。 评估肺泡 β-内酰胺 PK/PD 对 HAP 结局的影响，包括 (a) 治疗成功 (b) 病原体再感染 在目标 3 中，我们将识别经历肺泡血浆 PK 特征的患者。 不一致——使血浆不能很好地替代肺泡浓度，并将这些患者置于 肺炎治疗剂量不足的风险，并确定这种不一致的临床风险因素。 我们的研究将提供经过临床验证的工具，这将有助于实现精确剂量 我们的研究将通过提供最佳 PK 采样产生积极的临床影响。 次、通用 PK 模型、HAP 特异性 PK/PD 目标以及经过验证的肺泡- 血浆 PK 不一致，所有这些都可以应用于 HAP 患者的床边。 意义重大，因为它将提供在 HAP 中实现精确剂量所需的工具，这将 推进 NIH 的使命，保护和改善受耐药病原体影响的患者的健康。