基于血药浓度监测的结核病合并糖尿病患者抗结核病精准治疗策略及应用研究

As in many other countries, accompanied by the increasing trend of diabetes mellitus (DM), tuberculosis (TB) remains to be at high burden in China and cause for severe public health concern. The anti-tuberculosis treatment is more complicated by the co-morbidity with DM and even the subsequent influences in blood drug concentration and structural/functional change of the in-vivo organ and tissue during the long-term treatment course. But the previous studies have limitations in the evidence and understanding of the mechanism behind the unsatisfactory treatment outcome due to the defect in study design and/or shortage of appropriate methods especially in DM-TB co-epidemic hotspot areas. The purpose of this study is to better understand the consequence of pathogen, host, drug and their interaction in relation to the treatment outcome of anti-TB treatment as well as to optimize and apply the therapeutic drug monitoring (TDM) based strategies to better guide the use of anti-microbial drugs for the anti-TB treatment among the pulmonary TB patients with DM. In the proposed study: Firstly, we will optimize and evaluate the established method and technology suitable for TDM in terms of the methodologies of blood drug concentration detection and minimal inhibitory concentration detection as well as the sampling strategies; Secondly, we will establish a treatment cohort of TB patients with DM and illustrate the population pharmacokinetics (PK) by using the established methods as well as modeling it to evaluate all the relevant influences; Thirdly, by relating the population PK data to the treatment outcome, we will identify all the indicators and their threshold associated with the negative treatment outcome as well as formulate the precise medication scenario. Last but most importantly, with the extensive participation of the key policy-makers and researchers, we will develop series of effective strategies centered on TDM as well as apply and evaluate its clinical value by the implementation study. Through a multidisciplinary research and multiple efforts from the relevant stakeholder and scientists, the proposed program will highlight to apply and translate the research to policy, for the goal of improving anti-TB treatment with a looming co-epidemic of DM and TB, and identify the gaps in knowledge base that needed to be addressed by further research.

我国是结核病和糖尿病双重高负担国家。糖尿病除了增加结核病易感性外，还影响结核病治疗预后。究其原因，是由于合并糖尿病患者抗结核病标准化治疗效果存在个体差异，并缺少基于过程监测的精准治疗策略。本研究旨在通过多中心前瞻性队列研究设计，掌握合并糖尿病患者的抗结核病治疗群体药代动力学/药效学规律特征及影响因素，同时与治疗预后做关联分析，形成精准治疗策略并进行临床应用价值评价。本项目将首先对申请人团队所研发的适宜技术及采样方法进行优化和系统评价；并在前瞻性治疗队列中，应用这些技术，系统描述合并糖尿病患者抗结核病治疗群体药代动力学规律特征并分析影响因素；进而与治疗预后进行关联，并结合病原体、宿主和用药情况等特征形成精准用药方案；不仅如此，通过参与式行动研究方法，分析影响诊治效果的主要环节和因素，形成精准治疗策略，并通过实施性研究评价其临床应用价值，从而形成能适应我国国情的精准治疗策略和实施方案。

我国是结核病和糖尿病双重高负担国家。糖尿病除了增加结核病易感性外，还影响结核病治疗预后。究其原因，是由于合并糖尿病患者抗结核病标准化治疗效果存在个体差异，并缺少基于过程监测的精准治疗策略。本研究旨在通过多中心前瞻性队列研究设计，掌握合并糖尿病患者的抗结核病治疗群体药代动力学/药效学规律特征及影响因素，同时与治疗预后做关联分析，形成精准治疗策略并进行临床应用价值评价。.方法学验证结果显示检测药物在各自校准曲线浓度范围内均具有良好的线性，确定系数均在0.993以上；具有良好的批内和批间准确度和精密度，所有浓度水平下的变异系数均在11.4%以内。在前瞻性治疗队列中，对58位合并糖尿病患者进行富集采血和群体药代动力学建模。各治疗药物在清除率、中央室分布容积等参数上存在显著个体间差异。除体重外，糖化血红蛋白（HbA1c）明显影响氯法齐明、莫西沙星、贝达喹啉、利奈唑胺的表观分布容积和清除率。另外，服药后0、2和6小时采样能兼具简单、便捷和预测准确性（R2=1.0）。研究期间共纳入结核患者464人，其中糖尿病共患人群58人，并整合另项队列73人进行分析。利奈唑胺、莫西沙星、贝达喹啉、氯法齐明的AUC/MIC及环丝氨酸T%>MIC与6月末痰转阴和转阴时间密切相关。机器学习算法识别了各临床目标值分别为：利奈唑胺AUC0-24h/MIC (130.35); 莫西沙星AUC0-24h/MIC(71.59)，贝达喹啉AUC0-24h/MIC (230.89),环丝氨酸T%>MIC(32.69)，氯法齐明AUC0-24h/MIC(98.16)。在所选择的示范地区，以治疗药物监测略作为干预组，以标准化治疗作为对照组。治疗药物监测干预组对象6月末痰转阴时间明显早于标准治疗组 。在血糖控制良好人群中，治疗药物监测干预能明显改善6月末痰转阴；但在血糖控制不佳人群，两组未见统计学意义。.本项目发表9篇研究论文，并做口头报告4次。研究发现对于治疗药物监测应用于特定结核病精准治疗的价值进行了前瞻性论证；首次确定了莫西沙星和利奈唑胺的临床目标值；确定了大部分药物的药代/药效学目标值与治疗效果相关，为进一步开展随机对照的临床实验提供了重要的临床目标值，并以此探索了基于药物暴露和敏感性的循证治疗思路，也为合并糖尿病患者的抗结核病治疗等复杂传染病精准化治疗策略的开发和评价提供了重要范例。

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