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Therapeutic Drug Monitoring of Biopharmaceuticals May Benefit From Pharmacokinetic and Pharmacokinetic-Pharmacodynamic Modeling

机译:生物动力学的治疗药物监测可能受益于药代动力学和药代动力学 - 药效所模拟

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摘要

Biopharmaceuticals, especially monoclonal antibodies, have been increasingly used to treat several chronic inflammatory diseases. Due to the complexity of their pharmacokinetics and concentration-effect relationship, therapeutic drug monitoring (TDM) has been used to optimize their dosing regimen. Up to date, several decisional algorithms have been developed to provide tools for monoclonal antibodies' therapeutic drug monitoring. However, these algorithms are unable to determine the individual optimal dosing scheme. The aim of this article is to deal with population pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PK-PD) modeling. Allowing the quantification of the variability of the dose-concentration-response relationship, population pharmacokinetic-pharmacodynamic modeling may be a valuable tool to determine the optimal dosing scheme. Based on population modeling, Bayesian estimators may be developed to optimize dosing schemes for each patient using limited sampling strategies. These estimators may allow accurate dosing adjustment for each patient individually.
机译:生物制药,尤其是单克隆抗体,越来越多地用于治疗几种慢性炎症疾病。由于其药代动力学和浓度效应关系的复杂性,治疗药物监测(TDM)已被用于优化其给药方案。迄今为止,已经开发了几种抗议算法,为单克隆抗体的治疗药物监测提供了工具。然而,这些算法无法确定各个最佳定量给料方案。本文的目的是应对人口药代动力学(PK)和药代动力学药物动力学(PK-PD)建模。允许定量剂量浓度 - 反应关系的可变性,人口药代动力学 - 药物动力学建模可以是确定最佳给药方案的有价值的工具。基于人口建模,可以开发贝叶斯估计器,以利用有限的采样策略优化每位患者的给药方案。这些估算器可以单独为每位患者准确给药调节。

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