Tailored Pharmacokinetic model to predict drug trapping in long-term anesthesia,Journal of Advanced Research

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Tailored Pharmacokinetic model to predict drug trapping in long-term anesthesia
Journal of Advanced Research ( IF 11.4 ) Pub Date : 2021-05-21 , DOI: 10.1016/j.jare.2021.04.004
Dana Copot ₁ , Clara Ionescu _{1,

2}

Affiliation

Introduction

In long-term induced general anesthesia cases such as those uniquely defined by the ongoing Covid-19 pandemic context, the clearance of hypnotic and analgesic drugs from the body follows anomalous diffusion with afferent drug trapping and escape rates in heterogeneous tissues. Evidence exists that drug molecules have a preference to accumulate in slow acting compartments such as muscle and fat mass volumes. Currently used patient dependent pharmacokinetic models do not take into account anomalous diffusion resulted from heterogeneous drug distribution in the body with time varying clearance rates.

Objectives

This paper proposes a mathematical framework for drug trapping estimation in PK models for estimating optimal drug infusion rates to maintain long-term anesthesia in Covid-19 patients. We also propose a protocol for measuring and calibrating PK models, along with a methodology to minimize blood sample collection.

Methods

We propose a framework enabling calibration of the models during the follow up of Covid-19 patients undergoing anesthesia during their treatment and recovery period in ICU. The proposed model can be easily updated with incoming information from clinical protocols on blood plasma drug concentration profiles. Already available pharmacokinetic and pharmacodynamic models can be then calibrated based on blood plasma concentration measurements.

Results

The proposed calibration methodology allow to minimize risk for potential over-dosing as clearance rates are updated based on direct measurements from the patient.

Conclusions

The proposed methodology will reduce the adverse effects related to over-dosing, which allow further increase of the success rate during the recovery period.

中文翻译：

定制的药代动力学模型来预测长期麻醉中的药物捕获

介绍

在长期诱导的全身麻醉病例中，例如那些由正在进行的 Covid-19 大流行背景唯一定义的病例，催眠药和镇痛药从体内的清除遵循异常扩散，传入药物在异质组织中捕获和逃逸率。有证据表明，药物分子倾向于在缓慢作用的隔室中积聚，例如肌肉和脂肪体积。目前使用的依赖于患者的药代动力学模型没有考虑由于体内药物分布不均而随时间变化清除率引起的异常扩散。