This review article is intended to serve as an overview of the current status of the computational tools and approaches available for predicting respiratory-tract dosimetry of inhaled particulate matter. There are two groups of computational models available, depending on the intended use. The whole-lung models are designed to provide deposition prediction for the whole lung, from the oronasal cavities to the pulmonary region. The whole-lung models are generally semi-empirical and hence provide more reliable results but within the range of parameters used for empirical correlations. The local deposition or computational fluid dynamics (CFD)-based models, on the other hand, utilize comprehensive theoretical and computational approaches but are often limited to upper respiratory tracts. They are based on theoretical principles and are applicable to a wider range of parameters, but less accurate. One of the difficulties with modeling of aerosol deposition in human lung is related to the complexity of the airways geometry and the limited morphometric data available. Another difficulty corresponds to simulation of the realistic physiological conditions of lung environment. Furthermore, complex physical and chemical phenomena associated with dense and multicomponent aerosols complicate the modeling tasks. All of these issues are addressed in this review. The progress made in each area in the last three decades and the challenges ahead are discussed along with some suggestions for future direction. The following subjects are covered in this review: introduction, aerosol deposition mechanisms, elements of a computational model, respiratory-tract geometry models, whole-lung models, CFD based models, cigarette smoke deposition models, and conclusion.

There is an ongoing debate regarding the potential of secondhand exposure of non-users to various chemicals from use of e-vapor products (EVPs). Room air levels of 34 chemicals (nicotine, propylene glycol (PG), glycerol, 15 carbonyl chemicals, 12 volatile organic chemicals (VOCs), and four selected trace elements) were measured where EVPs and cigarettes were used by n = 37 healthy adult tobacco users in an exposure chamber. The products used were MarkTen^® 2.5% Classic (Group I), a Prototype GreenSmoke^® 2.4% (Group II), Ego-T^® Tank with subjects’ own e-liquids (Group III) and subjects’ own conventional cigarettes (Group IV). Products were used under controlled conditions and 4-h ad libitum use. Background (without subjects) and baseline levels (with subjects) were measured. Cumulative 4-h. levels of nicotine, PG and glycerol measured were several-fold below the time-weighted average limits used in workplace exposure evaluation. Most the other chemicals (>75%) were at or below the limit of quantification during EVP use. Significant levels of chemicals (17 out of 34) were observed in Group IV. Overall, our results indicate that under the study conditions with the products tested, cumulative room air levels of the selected chemicals measured over 4-h were relatively small and were several-fold below the current occupational regulatory and consensus limits.

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.

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.

To ensure quality, consistency, and supply of cigarette products, a manufacturer may change materials, which can affect its product portfolio. Rather than testing each product individually to determine the effect of a change, designed experiments can be conducted using a subset of products, and statistical modeling can be performed to determine the harmful and potentially harmful constituent (HPHC) yields for the remaining products. To demonstrate this, we selected 30 representative cigarette products covering a wide range of tobacco blends, ingredients, and design parameters from a manufacturer's portfolio. Sets of cigarette products used papers produced with one type of manufacturing technology (control products) and two additional cigarette papers (changed products). The physical characteristics of the changed products' papers were similar to the control products but were manufactured using alternative methods, which could lead to differences in their chemical composition. The experiment was controlled to minimize variations among products, manufacturing, and testing. Linear regression was used to model the relationship between HPHC yields of the tested products. Twelve randomly selected products were used for validation by comparing predicted to measured yields. Model predictions were robust; differences between measured and predicted values were within standard repeatability limits, demonstrating the feasibility of this approach. https://doi.org/10.21423/jrs-v07hannel