Influence of Nitrite on Formation of Tobacco-Specific Nitrosamines in Electronic Cigarette Liquids and Aerosols.

Tobacco-specific nitrosamine (TSNA) formation occurred during aerosol generation from select commercial cig-a-like e-cigarette products. To understand the drivers behind the potential formation of TSNAs in electronic cigarette (e-cigarette) aerosols and e-liquids, model e-liquid systems were generated in the lab to demonstrate that nitrite can react with nicotine and minor alkaloids to form TSNAs in e-liquids. In the presence of nitrite and nicotine, TSNA levels in e-liquids increased over time and the process was accelerated by elevated temperature. Additionally, TSNAs formed during aerosol generation when nitrite was present in the corresponding e-liquids. The commercial e-cigarette products that showed higher levels and formation of TSNAs were observed to contain nitrite and minor alkaloid impurities in the corresponding e-liquids. This study provides valuable information about drivers for TSNA formation in e-liquids and e-cigarette aerosols that may be applied to the evaluation and quality assurance of e-cigarette products.

A comprehensive physiologically based pharmacokinetic (PBPK) model for nicotine in humans from using nicotine-containing products with different routes of exposure.

Physiologically based pharmacokinetic (PBPK) modeling can be a useful tool for characterizing nicotine pharmacokinetics (PK) from use of tobacco products. We expand a previously published PBPK model to simulate a nicotine PK profile, following single or multiple use of various tobacco products [cigarettes, smokeless tobacco, and electronic nicotine delivery systems, or a nicotine inhaler (NICOTROL)] The uptake route in the model was designed to allow for three uptake compartments: buccal cavity (BC), upper respiratory tract (URT) (conducting and transitional airways) and lower respiratory tract (alveolar region). Within each region, the model includes product-specific descriptions of the flux of nicotine into plasma, as well as the flux of nicotine from the BC and URT to the gastrointestinal tract. These descriptions are based on regional deposition and diffusion models of nicotine into plasma, which depends on the product type. Regional deposition flux combined with regional differences in physiological parameters (e.g., blood perfusion ratio and tissue thickness) play a key role in the product-specific PK profile of nicotine. The current model describes the slower flux of nicotine into plasma across the BC and URT, as well as the rapid flux known to occur in the alveolar region. Overall, the addition of the BC and respiratory tract compartments to the nicotine model provided simulation results that are comparable to the nicotine time-course plasma concentrations reported from clinical studies for the four product categories simulated.

Non-Targeted Analysis Using Gas Chromatography-Mass Spectrometry for Evaluation of Chemical Composition of E-Vapor Products.

The Premarket Tobacco Product Applications (PMTA) guidance issued by the Food and Drug Administration for electronic nicotine delivery systems (ENDSs) recommends that in addition to reporting harmful and potentially harmful constituents (HPHCs), manufacturers should evaluate these products for other chemicals that could form during use and over time. Although e-vapor product aerosols are considerably less complex than mainstream smoke from cigarettes and heated tobacco product (HTP) aerosols, there are challenges with performing a comprehensive chemical characterization. Some of these challenges include the complexity of the e-liquid chemical compositions, the variety of flavors used, and the aerosol collection efficiency of volatile and semi-volatile compounds generated from aerosols. In this study, a non-targeted analysis method was developed using gas chromatography-mass spectrometry (GC-MS) that allows evaluation of volatile and semi-volatile compounds in e-liquids and aerosols of e-vapor products. The method employed an automated data analysis workflow using Agilent MassHunter Unknowns Analysis software for mass spectral deconvolution, peak detection, and library searching and reporting. The automated process ensured data integrity and consistency of compound identification with >99% of known compounds being identified using an in-house custom mass spectral library. The custom library was created to aid in compound identifications and includes over 1,100 unique mass spectral entries, of which 600 have been confirmed from reference standard comparisons. The method validation included accuracy, precision, repeatability, limit of detection (LOD), and selectivity. The validation also demonstrated that this semi-quantitative method provides estimated concentrations with an accuracy ranging between 0.5- and 2.0-fold as compared to the actual values. The LOD threshold of 0.7 ppm was established based on instrument sensitivity and accuracy of the compounds identified. To demonstrate the application of this method, we share results from the comprehensive chemical profile of e-liquids and aerosols collected from a marketed e-vapor product. Applying the data processing workflow developed here, 46 compounds were detected in the e-liquid formulation and 55 compounds in the aerosol sample. More than 50% of compounds reported have been confirmed with reference standards. The profiling approach described in this publication is applicable to evaluating volatile and semi-volatile compounds in e-vapor products.

Estimating the Population Health Impact of Recently Introduced Modified Risk Tobacco Products: A Comparison of Different Approaches.

INTRODUCTION: Various approaches have been used to estimate the population health impact of introducing a Modified Risk Tobacco Product (MRTP). AIMS AND METHODS: We aimed to compare and contrast aspects of models considering effects on mortality that were known to experts attending a meeting on models in 2018. RESULTS: Thirteen models are described, some focussing on e-cigarettes, others more general. Most models are cohort-based, comparing results with or without MRTP introduction. They typically start with a population with known smoking habits and then use transition probabilities either to update smoking habits in the "null scenario" or joint smoking and MRTP habits in an "alternative scenario". The models vary in the tobacco groups and transition probabilities considered. Based on aspects of the tobacco history developed, the models compare mortality risks, and sometimes life-years lost and health costs, between scenarios. Estimating effects on population health depends on frequency of use of the MRTP and smoking, and the extent to which the products expose users to harmful constituents. Strengths and weaknesses of the approaches are summarized. CONCLUSIONS: Despite methodological differences, most modellers have assumed the increase in risk of mortality from MRTP use, relative to that from cigarette smoking, to be very low and have concluded that MRTP introduction is likely to have a beneficial impact. Further model development, supplemented by preliminary results from well-designed epidemiological studies, should enable more precise prediction of the anticipated effects of MRTP introduction. IMPLICATIONS: There is a need to estimate the population health impact of introducing modified risk nicotine-containing products for smokers unwilling or unable to quit. This paper reviews a variety of modeling methodologies proposed to do this, and discusses the implications of the different approaches. It should assist modelers in refining and improving their models, and help toward providing authorities with more reliable estimates.

The impact of cigarette and e-cigarette use history on transition patterns: a longitudinal analysis of the population assessment of tobacco and health (PATH) study, 2013-2015.

BACKGROUND: Population models have been developed to evaluate the impact of new tobacco products on the overall population. Reliable input parameters such as longitudinal tobacco use transitions are needed to quantify the net population health impact including the number of premature deaths prevented, additional life years, and changes in cigarette smoking prevalence. METHODS: This secondary analysis assessed transition patterns from PATH wave 1 (2013-14) to wave 2 (2014-15) among adult exclusive cigarette smokers, exclusive e-cigarette users, and dual users. Transition probabilities were calculated by taking into account factors including cigarette smoking and e-cigarette use histories and experimental or established use behaviors. Multinomial logistic regression models were constructed to further evaluate factors associated with transition patterns. RESULTS: Differential transition probabilities emerged among study subgroups when taking into account cigarette smoking and e-cigarette use histories and experimental or established use behaviors. For example, overall 45% of exclusive e-cigarette users in wave 1 continued using e-cigarettes exclusively in wave 2. However, we observed approximately 11 to 14% of wave 1 exclusive experimental e-cigarette users continued to use e-cigarette exclusively in wave 2, compared to about 62% of exclusive established e-cigarette users. The history of cigarette smoking and e-cigarette use is another important factor associated with transition patterns. Among experimental e-cigarette users, 7.5% of individuals without a history of cigarette smoking transitioned to exclusive cigarette smoking, compared to 30% of individuals with a history of cigarette smoking. Additionally, 1.3% of exclusive cigarette smokers in wave 1 transitioned to exclusive e-cigarette use, with the highest transition probability (3.7%) observed in the established cigarette smoker with a history of e-cigarette use subgroup. CONCLUSIONS: Product use histories and current use behaviors are important factors influencing transitions between product use states. Given that experimental users' transition behaviors may be more variable and more influenced by tobacco use history, long-term predictions made by population models could be improved by the use of transition probabilities from established users. As transition patterns might be changing over time, long-term transition patterns can be examined through analysis of future waves of PATH data.

Formation of Diacetyl and Other α-Dicarbonyl Compounds during the Generation of E-Vapor Product Aerosols.

Exposure to diacetyl (DA) has been linked to the respiratory condition bronchiolitis obliterans. Previous research has demonstrated that DA and other α-dicarbonyl compounds can be detected in both the e-liquids and aerosols of e-vapor products (EVPs). While some EVP manufacturers may add these compounds as flavor ingredients, the primary objective of this work was to determine the potential for the formation of α-dicarbonyl compounds during the generation of aerosols from EVPs where no DA or other α-dicarbonyl compounds are added to the e-liquid. A novel ultraperformance liquid chromatography-mass spectrometry-based analytical method for the determination of DA, acetyl propionyl, glyoxal, and methylglyoxal was developed and validated. Next, eight commercially available cig-a-like-type EVPs were evaluated for α-dicarbonyl formation. Increased levels of α-dicarbonyls were observed in the aerosols of all evaluated EVPs compared to their respective e-liquids. Mechanistic studies were conducted using a model microwave reaction system to identify key reaction precursors for DA generated from propylene glycol (PG) and carbon-13-labeled glycerin (GLY). These studies, along with the corresponding retrosynthetic analysis, resulted in the proposed formation pathway where hydroxyacetone is generated from PG and/or GLY. Hydroxyacetone then participates in an aldol condensation with formaldehyde where formaldehyde can also be generated from PG and/or GLY; the resultant product then dehydrates to form DA. This proposed pathway was further investigated through in situ synthetic organic experiments within the model microwave reaction system. This work establishes that DA is formed in the aerosol generation process of the EVPs tested though at levels below toxicological concern.

Comparison of experimentally measured and computational fluid dynamic predicted deposition and deposition uniformity of monodisperse solid particles in the Vitrocell® AMES 48 air-liquid-interface in-vitro exposure system.

Accurately determining the delivered dose is critical to understanding biological response due to cell exposure to chemical constituents in aerosols. Deposition efficiency and uniformity of deposition was measured experimentally using monodisperse solid fluorescent particles with mass median aerodynamic diameters (MMAD) of 0.51, 1.1, 2.2 and 3.3 µm in the Vitrocell® AMES 48 air-liquid-interface (ALI) in vitro exposure system. Experimental results were compared with computational fluid dynamic, (CFD; using both Lagrangian and Eulerian approaches) predicted deposition efficiency and uniformity for a single row (N = 6) of petri dishes in the Vitrocell® AMES 48 system. The average experimentally measured deposition efficiency ranged from 0.007% to 0.43% for 0.51-3.3 µm MMAD particles, respectively. There was good agreement between average experimentally measured and the CFD predicted particle deposition efficiency, regardless of approach. Experimentally measured and CFD predicted average uniformity of deposition was greater than 45% of the mean for all particle diameters. During this work a new design was introduced by the manufacturer and evaluated using Lagragian CFD. Lagragian CFD predictions showed better uniformity of deposition, but reduced deposition efficiency with the new design. Deposition efficiency and variability in particle deposition across petri dishes for solid particles should be considered when designing exposure regimens using the Vitrocell® AMES 48 ALI in vitro exposure system.

Method development and validation of dissolution testing for nicotine release from smokeless tobacco products using flow-through cell apparatus and UPLC-PDA.

Developing dissolution testing methods to measure the nicotine release profiles from smokeless tobacco products is valuable for product assessment and product-to-product comparisons. In this work, we developed a robust dissolution method to study the in vitro release of nicotine from smokeless tobacco products using the U.S. Pharmacopeia flow-through cell dissolution apparatus 4 (USP-4). We further developed and validated a sensitive Ultra Performance Liquid Chromatography coupled to Photodiode Array detector (UPLC-PDA) method for the accurate quantitation of the released nicotine into artificial saliva, which is our selected dissolution medium. We have successfully shown the applicability of the validated method by investigating the release profiles of nicotine from various commercial and CORESTA reference smokeless tobacco products [CRP 1.1 (Swedish-style snus pouch), CRP 2.1 (American-style loose moist snuff), CRP 4 (loose-leaf chewing tobacco) and CRP 4.1 (chopped loose-leaf chewing tobacco)]. Nicotine release profiles were analyzed by calculating the difference factor (f1) and similarity factor (f2) by adopting a methodology referenced in the Guidance for Industry from FDA's Center for Drug Evaluation and Research (CDER) and by fitting the release profile curves using a first order kinetic model. Nicotine release was found to be dependent on the form and cut of the smokeless tobacco products, with a slower release observed for snus and loose-leaf, compared to chopped and loose moist snuff smokeless tobacco. This dissolution methodology can be extended to measure and compare release of other constituents from smokeless tobacco products and has the potential for method standardization.

A Computational Model for Assessing the Population Health Impact of Introducing a Modified Risk Claim on an Existing Smokeless Tobacco Product.

Computational models are valuable tools for predicting the population effects prior to Food and Drug Administration (FDA) authorization of a modified risk claim on a tobacco product. We have developed and validated a population model using best modeling practices. Our model consists of a Markov compartmental model based on cohorts starting at a defined age and followed up to a specific age accounting for 29 tobacco-use states based on a cohort members transition pathway. The Markov model is coupled with statistical mortality models and excess relative risk ratio estimates to determine survival probabilities from use of smokeless tobacco. Our model estimates the difference in premature deaths prevented by comparing Base Case ("world-as-is") and Modified Case (the most likely outcome given that a modified risk claim is authorized) scenarios. Nationally representative transition probabilities were used for the Base Case. Probabilities of key transitions for the Modified Case were estimated based on a behavioral intentions study in users and nonusers. Our model predicts an estimated 93,000 premature deaths would be avoided over a 60-year period upon authorization of a modified risk claim. Our sensitivity analyses using various reasonable ranges of input parameters do not indicate any scenario under which the net benefit could be offset entirely.

A distributed computational model for estimating room air level of constituents due to aerosol emission from e-vapor product use.

Most indoor air quality models reported in the literature are well-mixed models. A well-mixed model estimates the room average concentration of constituents from sources. It does not provide information on (1) how far and how fast the emitted chemicals travel in the indoor space? And (2) how the concentration changes as a function of distance from the emission source? We developed a distributed model, using computational fluid dynamics and thermodynamics principles, which allows for aerosol dispersion in an indoor space and includes evaporation and condensation of constituents in a multi-compound aerosol mixture. The distributed model can estimate the spatial and temporal variations of the concentration of individual constituents present in the emitted aerosol in vapor and particulate phases separately. Results from the model were compared with the published experimental data and were found to be in good agreement. A sensitivity analysis was performed to evaluate the impact of various parameters that affect the air level of the emitted constituents within an indoor space, including rate of emission, the rate of air exchange, etc. The model can also be used to estimate the level of second hand exposure in a confined space where e-vapor products (EVPs) are used.

A Well-Mixed Computational Model for Estimating Room Air Levels of Selected Constituents from E-Vapor Product Use.

Concerns have been raised in the literature for the potential of secondhand exposure from e-vapor product (EVP) use. It would be difficult to experimentally determine the impact of various factors on secondhand exposure including, but not limited to, room characteristics (indoor space size, ventilation rate), device specifications (aerosol mass delivery, e-liquid composition), and use behavior (number of users and usage frequency). Therefore, a well-mixed computational model was developed to estimate the indoor levels of constituents from EVPs under a variety of conditions. The model is based on physical and thermodynamic interactions between aerosol, vapor, and air, similar to indoor air models referred to by the Environmental Protection Agency. The model results agree well with measured indoor air levels of nicotine from two sources: smoking machine-generated aerosol and aerosol exhaled from EVP use. Sensitivity analysis indicated that increasing air exchange rate reduces room air level of constituents, as more material is carried away. The effect of the amount of aerosol released into the space due to variability in exhalation was also evaluated. The model can estimate the room air level of constituents as a function of time, which may be used to assess the level of non-user exposure over time.