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预测Δ-9-四氢大麻酚引起的精神活性和认知效应:一种用于量化兴奋感和警觉性降低的PBPK-PD方法。

Predicting Δ-9-Tetrahydrocannabinol-Induced Psychoactive and Cognitive Effects: A PBPK-PD Approach to Quantifying Feeling High and Reduced Alertness.

作者信息

Qian Lixuan, Zhou Zhu

机构信息

Department of Chemistry, York College, City University of New York, Jamaica, New York 11451, United States.

出版信息

ACS Chem Neurosci. 2025 Aug 6;16(15):3059-3069. doi: 10.1021/acschemneuro.5c00417. Epub 2025 Jul 22.

Abstract

The increasing use of cannabis for medicinal and recreational purposes highlights the need to understand its psychoactive effects. Δ-9-tetrahydrocannabinol (THC), the primary psychoactive cannabinoid, is responsible for feeling high and reduced alertness after cannabis use. This study aimed to develop and verify physiologically based pharmacokinetic-pharmacodynamic (PBPK-PD) models to quantify the effects of THC and its active metabolite, 11-hydroxy-THC, on feeling high and reduction in alertness in healthy adults. The models were developed using Simcyp, based on our previously verified THC PBPK model. A direct response model with a maximum effect () function driven by the brain concentrations and an effect compartment was used to describe visual analogue scale (VAS) scores for feeling high after intravenous, oral, and inhaled THC administration. An indirect response model with an function driven by the brain concentrations was used to describe the reduction in VAS alertness scores after inhaled THC. Our models accurately captured the dose-response relationships for THC doses ranging from 2 to 86 mg for feeling high, and 2 to 69.4 mg for alertness reduction. The verified PBPK-PD model provides a robust tool for predicting the psychoactive and cognitive effects of THC, enabling improved assessment of cannabis-induced responses across diverse populations.

摘要

大麻在医疗和娱乐用途上的使用日益增加,这凸显了了解其精神活性作用的必要性。Δ-9-四氢大麻酚(THC)是主要的精神活性大麻素,会导致使用大麻后产生兴奋感并降低警觉性。本研究旨在开发并验证基于生理学的药代动力学-药效学(PBPK-PD)模型,以量化THC及其活性代谢物11-羟基-THC对健康成年人兴奋感和警觉性降低的影响。这些模型是基于我们之前验证过的THC PBPK模型,使用Simcyp开发的。一个由脑内浓度和效应室驱动的具有最大效应()函数的直接反应模型,用于描述静脉注射、口服和吸入THC后兴奋感的视觉模拟量表(VAS)评分。一个由脑内浓度驱动的具有函数的间接反应模型,用于描述吸入THC后VAS警觉性评分的降低。我们的模型准确地捕捉了2至86毫克THC剂量产生兴奋感以及2至69.4毫克THC剂量降低警觉性的剂量反应关系。经过验证的PBPK-PD模型为预测THC的精神活性和认知作用提供了一个强大的工具,有助于更好地评估不同人群中大麻引起的反应。

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