背景:根据内脏利什曼病(VL)的当前治疗方案,在一定比例的患者中可以看到寄生虫的复发。了解寄生虫动力学对于提高治疗效果和预测VL病例的患者复发至关重要。这项研究旨在表征血液中循环利什曼原虫寄生虫的动力学,在不同的抗利什曼治疗期间和之后,并找到疾病临床复发的预测因子。
方法:来自三项临床试验的数据,其中东非VL患者接受了各种抗利什曼治疗方案,结合在这项研究中。前,采用实时定量PCR(qPCR)对全血中的利什曼原虫动体DNA进行定量,during,治疗后长达六个月。使用非线性混合效应建模开发了集成的群体药代动力学-药效学模型。
结果:寄生虫增殖最好用指数生长模型来描述,体内寄生虫倍增时间为7.8天(RSE12%)。非西硝唑杀死寄生虫,脂质体两性霉素B,葡萄糖酸钠,通过将药物浓度与寄生虫消除率直接相关的线性模型最好地描述了米替福辛。治疗后,寄生虫的生长被认为受到宿主免疫系统的抑制,由治疗后的时间驱动的Emax模型描述。无法确定免疫反应的发作和幅度的高变异性的预测因子。治疗开始后第28天和第56天的基于模型的血液寄生虫负荷的个体预测是疾病临床复发的预测。
结论:这种半机械药代动力学-药效学模型充分捕获了治疗期间和治疗后的血液寄生虫动力学,并揭示了在治疗开始后第28天和第56天的高血寄生虫负荷是VL复发的早期指征,这可能是在临床试验环境中评估治疗方案的治疗效果的有用生物标志物。
BACKGROUND: With the current treatment options for visceral leishmaniasis (VL), recrudescence of the parasite is seen in a proportion of patients. Understanding parasite dynamics is crucial to improving treatment efficacy and predicting patient relapse in cases of VL. This study aimed to characterize the kinetics of circulating Leishmania parasites in the blood, during and after different antileishmanial therapies, and to find predictors for clinical relapse of disease.
METHODS: Data from three clinical trials, in which Eastern African VL patients received various antileishmanial regimens, were combined in this study. Leishmania kinetoplast DNA was quantified in whole blood with real-time quantitative PCR (qPCR) before, during, and up to six months after treatment. An integrated population pharmacokinetic-pharmacodynamic model was developed using non-linear mixed effects modelling.
RESULTS: Parasite proliferation was best described by an exponential growth model, with an in vivo parasite doubling time of 7.8 days (RSE 12%). Parasite killing by fexinidazole, liposomal amphotericin B, sodium stibogluconate, and miltefosine was best described by linear models directly relating drug concentrations to the parasite elimination rate. After treatment, parasite growth was assumed to be suppressed by the host immune system, described by an Emax model driven by the time after treatment. No predictors for the high variability in onset and magnitude of the immune response could be identified. Model-based individual predictions of blood parasite load on Day 28 and Day 56 after start of treatment were predictive for clinical relapse of disease.
CONCLUSIONS: This semi-mechanistic pharmacokinetic-pharmacodynamic model adequately captured the blood parasite dynamics during and after treatment, and revealed that high blood parasite loads on Day 28 and Day 56 after start of treatment are an early indication for VL relapse, which could be a useful biomarker to assess treatment efficacy of a treatment regimen in a clinical trial setting.