Jeffry Schroeter , Bahman Asgharian , Owen Price , Aaron Parks , Darren Oldson , Jonathan Fallica , Gladys Erives , Cissy Li , Olga Rass , Arit Harvanko , Kamau Peters , Susan Chemerynski
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引用次数: 0
Abstract
Electronic nicotine delivery systems (ENDS) heat a liquid solution typically containing propylene glycol, vegetable glycerin, water, nicotine, and flavor chemicals to deliver an aerosol to the user. ENDS aerosols are complex, multi-constituent mixtures of droplets and vapors. Lung dosimetry predictions require mechanistic models that account for the physico-chemical properties of the constituents and thermodynamic processes of the aerosol as it travels through the respiratory tract and deposits in lung airways. In this study, a model formulated to predict ENDS aerosol deposition in the oral cavity and lung airways was linked with a physiologically-based pharmacokinetic (PBPK) model to predict nicotine pharmacokinetics (PK) as a function of product characteristics and puff topography. Predicted plasma nicotine PK compared favorably with available experimental data and captured the rapid increase in plasma levels followed by a clearance phase after ENDS use. E-liquid nicotine concentration and puff duration substantially increased nicotine lung deposition and plasma nicotine levels. Increasing the puff duration from 1 to 5 s while assuming a constant aerosol flow rate resulted in an ∼5-fold increase in nicotine lung deposition (45.0 µg to 243.7 µg) and increased maximum plasma nicotine concentrations from 4.7 ng/mL to 25.0 ng/mL; increasing the e-liquid nicotine concentration from 1 % to 5 % yielded increases in nicotine lung deposition (41.0 µg to 204.5 µg) and maximum plasma nicotine concentration (4.2 ng/mL to 21.1 ng/mL). Model predictions demonstrate the sensitivity of ENDS aerosol lung deposition and plasma nicotine profiles to user behavior and allow for quantification of constituent deposition and nicotine absorption after ENDS use.
期刊介绍:
Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs