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Updated: Jul 4, 2026

Administration of Δ9-Tetrahydrocannabinol (THC) in Adolescent and Adult Mice
Published on: August 1, 2025
Mind the Gap: Unraveling the Pharmacokinetic Variability of Δ9-Tetrahydrocannabinol (THC)
Shuang Lu1, Xin Liu1, Linda Truong2,3
1Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmaceutical Sciences, Fudan University, Shanghai, China.
Abstract:
The global rise in the legalization and medical use of cannabis has underscored the need to understand the pharmacology of its major active compound, Δ9-tetrahydrocannabinol (THC). THC displays complex pharmacokinetic behaviors and marked inter-individual variability, which contribute to inconsistent therapeutic outcomes. Population pharmacokinetic (popPK) modeling has emerged as a key approach to characterizing this variability and guiding individualized dosing strategies. This study systematically reviewed existing popPK models for THC to identify major determinants of pharmacokinetic variability and support future research in medicinal cannabis. A comprehensive search of PubMed, Embase, Web of Science, and Scopus identified 12 popPK models from 11 studies. While both THC and cannabidiol (CBD) were initially screened, the modeling literature was found to be significantly imbalanced; consequently, this review focuses on the 9 identified THC models, with CBD data analyzed in the Supplementary Material. All reviewed THC models employed two- or three-compartment structures, with variability most pronounced in the absorption phase. Between-subject variability in clearance (17%-50%) and volume of distribution (10%-57%) was moderate, whereas bioavailability exhibited wide variability ranging from 22% to 91%. This absorption-related variability was strongly influenced by administration route, usage status, and formulation type. The establishment of a standardized popPK model repository for THC provides a valuable reference for future model development, offering insights into the pharmacokinetic determinants of cannabinoid exposure and supporting model-informed precision dosing in clinical cannabinoid therapy.
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