Population pharmacokinetic model

群体药代动力学模型
  • 文章类型: Journal Article
    原理:基于生理的药代动力学(PBPK)和群体药代动力学(PK)建模方法在非放射性药物开发和研究中被广泛接受,虽然这些方法在放射性药物开发中还没有主要作用。在这次审查中,我们进行了文献检索,以明确不同的研究目的和以前使用PBPK和群体PK模拟放射性药物的问题.方法:使用PubMed和Embase数据库进行文献检索。广泛的搜索词包括放射性药物,示踪剂,放射性,基于生理的药代动力学模型,PBPK,群体药代动力学模型和非线性混合效应模型。结果:根据对人口PK模型和PBPK模型的文献检索,本综述包括8篇文章和20篇文章,分别。包括的群体PK分析显示具有开发群体预测模型和描述个体变异性来源的附加值。PBPK模型的主要目的似乎与优化治疗(计划)有关,或者更具体地说:找到肽量和放射性的最佳组合,通过减少测量次数来优化治疗计划,为了个性化治疗,了解治疗前和治疗扫描之间的差异或了解患者之间的差异。其他主要研究主题是关于放射性药物比较,根据其肽特性选择配体,并更好地了解药物-药物相互作用。结论:在放射性药物研究中使用PK建模方法仍然很少,但可以扩展以更好地了解PK和放射性药物的全身分布。放射性药物的PK建模在附近的未来具有巨大的潜力,并且可以为放射性药物的不断发展的研究做出贡献。
    Rationale: Physiologically based pharmacokinetic (PBPK) and population pharmacokinetic (PK) modelling approaches are widely accepted in non-radiopharmaceutical drug development and research, while there is no major role for these approaches in radiopharmaceutical development yet. In this review, a literature search was performed to specify different research purposes and questions that have previously been answered using both PBPK and population PK modelling for radiopharmaceuticals. Methods: The literature search was performed using the databases PubMed and Embase. Wide search terms included radiopharmaceutical, tracer, radioactivity, physiologically based pharmacokinetic model, PBPK, population pharmacokinetic model and nonlinear mixed-effects model. Results: Eight articles and twenty articles were included for this review based on this literature search for population PK modelling and PBPK modelling, respectively. Included population PK analyses showed to have an added value to develop predictive models for a population and to describe individual variability sources. Main purposes of PBPK models appeared related to optimizing treatment (planning), or more specifically: to find the optimal combination of peptide amount and radioactivity, to optimize treatment planning by reducing the number of measurements, to individualize treatment, to get insights in differences between pre-therapeutic and therapeutic scans or to understand inter-patient differences. Other main research subjects were regarding radiopharmaceutical comparisons, selecting ligands based on their peptide characteristics and gaining a better understanding of drug-drug interactions. Conclusions: The use of PK modelling approaches in radiopharmaceutical research remains scarce, but can be expanded to obtain a better understanding of PK and whole-body distribution of radiopharmaceuticals in general. PK modelling of radiopharmaceuticals has great potential for the nearby future and could contribute to the evolving research of radiopharmaceuticals.
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  • 文章类型: Journal Article
    现在建议对许多抗真菌药物进行精确给药。唾液采样被认为是用于治疗药物监测(TDM)的血浆采样的非侵入性替代方法。然而,目前尚无临床验证的唾液模型.本研究的目的首先是,进行系统评价以评估支持基于唾液的唑类TDM的证据,棘白菌素,两性霉素B,和氟胞嘧啶.第二个目标是为符合条件的药物开发唾液群体药代动力学(PK)模型,基于证据。截至2019年7月,在PubMed®和Embase®上搜索了数据库,14项研究纳入了氟康唑的系统评价,伏立康唑,伊曲康唑,和酮康唑.没有发现伊沙武康唑的研究,泊沙康唑,氟胞嘧啶,两性霉素B,卡波芬金,米卡芬净,或者Anidulafungin.氟康唑和伏立康唑表现出良好的唾液渗透,氟康唑的平均S/P比为1.21(±0.31),伏立康唑为0.56(±0.18)。两者具有很强的相关性(r=0.89-0.98)。根据TDM的证据和现有数据,采用非线性混合效应模型(NONMEM7.4)对伏立康唑进行群体PK分析。来自11名患者的137种伏立康唑血浆和唾液浓度(10名成人,1名儿童)是从纳入研究的作者那里获得的。伏立康唑药代动力学最好用一级吸收的一室PK模型来描述,通过4.56L/h(36.9%CV)的间隙参数化,60.7L的分布体积,吸收率常数0.858(固定),生物利用度为0.849。伏立康唑从血浆到唾液的分布动力学与血浆动力学相同,但是分布程度较低,由0.5的比例因子(4%CV)建模。比例误差模型最好地解释了残差变异性。视觉和基于模拟的模型诊断证实了唾液模型的良好预测性能。开发的唾液模型提供了一个有希望的框架,以促进伏立康唑的基于唾液的精确给药。
    Precision dosing for many antifungal drugs is now recommended. Saliva sampling is considered as a non-invasive alternative to plasma sampling for therapeutic drug monitoring (TDM). However, there are currently no clinically validated saliva models available. The aim of this study is firstly, to conduct a systematic review to evaluate the evidence supporting saliva-based TDM for azoles, echinocandins, amphotericin B, and flucytosine. The second aim is to develop a saliva population pharmacokinetic (PK) model for eligible drugs, based on the evidence. Databases were searched up to July 2019 on PubMed® and Embase®, and 14 studies were included in the systematic review for fluconazole, voriconazole, itraconazole, and ketoconazole. No studies were identified for isavuconazole, posaconazole, flucytosine, amphotericin B, caspofungin, micafungin, or anidulafungin. Fluconazole and voriconazole demonstrated a good saliva penetration with an average S/P ratio of 1.21 (± 0.31) for fluconazole and 0.56 (± 0.18) for voriconazole, both with strong correlation (r = 0.89-0.98). Based on the evidence for TDM and available data, population PK analysis was performed on voriconazole using Nonlinear Mixed Effects Modeling (NONMEM 7.4). 137 voriconazole plasma and saliva concentrations from 11 patients (10 adults, 1 child) were obtained from the authors of the included study. Voriconazole pharmacokinetics was best described by one-compartment PK model with first-order absorption, parameterized by clearance of 4.56 L/h (36.9% CV), volume of distribution of 60.7 L, absorption rate constant of 0.858 (fixed), and bioavailability of 0.849. Kinetics of the voriconazole distribution from plasma to saliva was identical to the plasma kinetics, but the extent of distribution was lower, modeled by a scale factor of 0.5 (4% CV). A proportional error model best accounted for the residual variability. The visual and simulation-based model diagnostics confirmed a good predictive performance of the saliva model. The developed saliva model provides a promising framework to facilitate saliva-based precision dosing of voriconazole.
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