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.

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.

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.

Evaluating the relationship between biomarkers of potential harm and biomarkers of tobacco exposure among current, past, and nonsmokers: data from the National Health and Nutrition Examination Survey 2007-2012.

Potential long-term health effects from tobacco products can be estimated by measuring changes in biochemical indicators of disease mechanisms like inflammation. This study assesses the potential relationships between biomarkers of potential harm (BOPH) and biomarkers of cigarette smoke exposure (BOE) based on data from the NHANES (2007-2012, n = 17,293 respondents). Statistically significant relationships were observed between white blood cells (WBC) and high-density lipoprotein (HDL) and BOE; between WBC and high-sensitivity C-reactive protein and smoking status; and between WBC and HDL and smoking intensity. This analysis suggests that WBC and HDL are useful BOPH in studies assessing the health risks of cigarette smoking.

Predictors, indicators, and validated measures of dependence in menthol smokers.

This article presents a comprehensive review of the menthol cigarette dependence-related literature and results from an original analysis of the Total Exposure Study (TES), which included 1,100 menthol and 2,400 nonmenthol adult smokers. The substantial scientific evidence available related to age of first cigarette, age of regular use, single-item dependence indicators (smoking frequency, cigarettes per day, time to first cigarette, night waking to smoke), smoking duration, numerous validated and widely accepted measures of nicotine/cigarette dependence, and our analysis of the TES do not support that menthol smokers are more dependent than nonmenthol smokers or that menthol increases dependence.

Evaluation of spot urine as an alternative to 24h urine collection for determination of biomarkers of exposure to cigarette smoke in adult smokers.

OBJECTIVE: Exposure to cigarette smoke in adult smokers (SM) can be determined by measuring urinary excretion of selected smoke constituents or metabolites. Complete 24h urine collections are difficult to achieve in ambulatory clinical studies; therefore spot urine (SU) might be a useful alternative. The objective of this study was to evaluate the optimum time for SU collections, and to predict 24h urine biomarker excretion from SU collections. METHODS: SU samples were collected at three time points (early morning, post-lunch and evening) along with 24h collections in 37 healthy adult smokers. Nicotine and its five metabolites (nicotine equivalents, NE), metabolites of NNK (NNAL), pyrene (1-OHP), acrolein (HPMA), benzene (S-PMA) and butadiene (MHBMA) were measured in 24h and SU samples. Correlation and agreement between creatinine-adjusted SU and 24h urine collections were determined from the Pearson product-moment correlation, Bland-Altman and Lin's concordance correlation analyses. A random effect regression model was used to calculate the 24h biomarker excretion from SU collections. RESULTS: There were no significant differences (p>0.05) between the three SU collections for the selected biomarkers of exposure except for 3-HPMA, which showed a diurnal variation. Good correlation and statistical agreements were observed for creatinine-adjusted SU (all three time points) and 24h for most of the selected biomarkers. 24h biomarker excretion could be estimated for most of the biomarkers based on the regression model, with the early morning SU collections giving the best results for tobacco specific biomarkers NE (R(2)=0.66) and NNAL (R(2)=0.6). CONCLUSIONS: SU is a useful alternative to 24h urine collections for most of the selected biomarkers of exposure to cigarette smoke. The early morning SU appears to be the most feasible and practical option as an alternative to 24h collections.

Sources of and technical approaches for the abatement of tobacco specific nitrosamine formation in moist smokeless tobacco products.

The presence of TSNA has been suggested as a potentially important cancer risk factor for moist smokeless tobacco (MST) products. We describe studies of the impact of tobacco agronomic and production practices which influence TSNA formation. TSNA were measured at points in the MST production chain from the farm to the finished product at the end of shelf life. Analyses were conducted to define points at which TSNA may occur, the factors related to the magnitude of occurrence, and actions which may be taken to mitigate such occurrence. Weather conditions during the curing season can have a dramatic impact on TSNA levels in tobacco, with wet seasons markedly increasing TSNA levels in cured tobacco. TSNA levels in MST do not increase beyond levels in cured tobacco when production practices limit the presence of nitrate reducing bacteria. Therefore, TSNA in such products are a function of the agronomic practices and conditions under which tobacco is produced at the farm level. Regional and annual variation in TSNA levels results from the stochastic nature of agronomic factors related to TSNA formation during tobacco growing and curing.

Factors affecting exposure to nicotine and carbon monoxide in adult cigarette smokers.

Exposure to cigarette smoke among smokers is highly variable. This variability has been attributed to differences in smoking behavior as measured by smoking topography, as well as other behavioral and subjective aspects of smoking. The objective of this study was to determine the factors affecting smoke exposure as estimated by biomarkers of exposure to nicotine and carbon monoxide (CO). In a multi-center cross-sectional study of 3585 adult smokers and 1077 adult nonsmokers, exposure to nicotine and CO was estimated by 24h urinary excretion of nicotine and five of its metabolites and by blood carboxyhemoglobin, respectively. Number of cigarettes smoked per day (CPD) was determined from cigarette butts returned. Puffing parameters were determined through a CreSS® micro device and a 182-item adult smoker questionnaire (ASQ) was administered. The relationship between exposure and demographic factors, smoking machine measured tar yield and CPD was examined in a statistical model (Model A). Topography parameters were added to this model (Model B) which was further expanded (Model C) by adding selected questions from the ASQ identified by a data reduction process. In all the models, CPD was the most important and highest ranking factor determining daily exposure. Other statistically significant factors were number of years smoked, questions related to morning smoking, topography and tar yield categories. In conclusion, the models investigated in this analysis, explain about 30-40% of variability in exposure to nicotine and CO.

Relationship between biomarkers of cigarette smoke exposure and biomarkers of inflammation, oxidative stress, and platelet activation in adult cigarette smokers.

BACKGROUND: Cigarette smoking is a risk factor for several diseases, including cardiovascular disease, chronic obstructive pulmonary disease, and lung cancer, but the role of specific smoke constituents in these diseases has not been clearly established. METHODS: The relationships between biomarkers of potential harm (BOPH), associated with inflammation [white blood cell (WBC), high sensitivity C-reactive protein (hs-CRP), fibrinogen, and von Willebrand factor (vWF)], oxidative stress [8-epi-prostaglandin F(2α) (8-epiPGF(2α))] and platelet activation [11-dehydro-thromboxin B(2) (11-dehTxB(2))], and machine-measured tar yields (grouped into four categories), biomarkers of exposure (BOE) to cigarette smoke: nicotine and its five metabolites (nicotine equivalents), 4-methylnitrosamino-1-(3-pyridyl)-1-butanol (total NNAL), carboxyhemoglobin, 1-hydroxypyrene, 3-hydroxypropylmercapturic acid, and monohydroxybutenyl-mercapturic acid, were investigated in 3,585 adult smokers and 1,077 nonsmokers. RESULTS: Overall, adult smokers had higher levels of BOPHs than nonsmokers. Body mass index (BMI), smoking duration, tar category, and some of the BOEs were significant factors in the multiple regression models. Based on the F value, BMI was the highest ranking factor in the models for WBC, hs-CRP, fibrinogen, and 8-epiPGF(2α), respectively, and gender and smoking duration for 11-dehTxB(2) and vWF, respectively. CONCLUSIONS: Although several demographic factors and some BOEs were statistically significant in the model, the R(2) values indicate that only up to 22% of the variability can be explained by these factors, reflecting the complexity and multifactorial nature of the disease mechanisms. IMPACT: The relationships between the BOEs and BOPHs observed in this study may help with the identification of appropriate biomarkers and improve the design of clinical studies in smokers.

The relationship between nicotine dependence scores and biomarkers of exposure in adult cigarette smokers.

BACKGROUND: Tobacco dependence is a multidimensional phenomenon. The Fagerström Test for Nicotine Dependence (FTND) is a widely administered six-item questionnaire used as a measure of nicotine dependence. It has been suggested that this test may not represent the entire spectrum of factors related to dependence. Also the relationship of this test with biomarkers of exposure to cigarette smoke has not been extensively studied. METHODS: Data from a multi-center, cross-sectional, ambulatory study of US adult smokers (the Total Exposure Study, TES) was analyzed. The FTND score and a number of additional questions related to smoking behavior, from an adult smoker questionnaire (ASQ) completed by 3585 adult smokers in the TES were analyzed. The 24-h urine nicotine equivalents, serum cotinine and blood carboxyhemoglobin were measured as biomarkers of exposure (BOE) to nicotine and carbon monoxide. Cigarette butts returned were collected during the 24-h urine collection period. RESULTS: The FTND showed moderate correlations with BOE, while selected questions from ASQ although statistically significant, had weaker correlations. FTND scores showed substantially weaker correlations without the question about cigarettes smoked per day (CPD). CPD and time to first cigarette (TTFC) had the most impact on BOE. CONCLUSION: Additional questions from ASQ did not appear to contribute towards refining the FTND test. The correlation of the FTND scores with nicotine and carbon monoxide seems to be primarily driven by CPD. CPD and TTFC were the most important factors correlating with exposure.

Short term effects of reduced exposure to cigarette smoke on white blood cells, platelets and red blood cells in adult cigarette smokers.

UNLABELLED: Previous studies indicate that cigarette smokers have a 5-30% higher white blood cell counts (WBC) compared to non-smokers and higher red blood cell counts. METHODS: This study was to pool hematology data from three similar studies and analyze the data for effects on WBC, its subpopulations, platelets, red blood cell count (RBC) and hematocrit in adult cigarette smokers three days after using an electrically heated cigarette smoking system (EHCSS) as a potential reduced exposure product (PREP) or no-smoking compared to smoking a conventional cigarette. RESULTS: Lower exposure to cigarette smoke in adult, long term smokers, by using an EHCSS or stopping smoking, leads to statistically significant decreases of up to 9% in WBC, neutrophils, lymphocytes, platelets, RBC and hematocrit within three days. Switching from CC-smoking to EHCSS-smoking or no-smoking resulted in lower WBC and vice versa within 3 days. CONCLUSION: This clinical model may be used as a screening tool to find new technologies that could provide insights on changes in inflammation resulting from the change in cigarette smoke.

The effect of menthol containing cigarettes on adult smokers' exposure to nicotine and carbon monoxide.

UNLABELLED: There is limited information comparing biomarkers of exposure (BOE) to cigarette smoke in menthol (MS) and non-menthol cigarette smokers (NMS). OBJECTIVE: To compare BOE to nicotine and carbon monoxide in MS and NMS. METHODS: Cross-sectional, observational, ambulatory, multi-centre study in 3341 adult cigarette smokers. Nicotine equivalents (NE) in 24h urine, NE/cigarette, COHb and serum cotinine were measured. Statistical analyses included analysis of variance and Wilcoxon test. RESULTS: Analyses of variance revealed no statistically significant effects of mentholated cigarettes on NE/24h, COHb, serum cotinine and NE/cigarette. On average MS smoked 15.0 and NMS 16.8 cigarettes/day. The unadjusted mean differences were as follows: MS had lower NE/24h (5.4%) and COHb (3.2%), higher serum cotinine (3.0%) and NE/cigarette (5.7%) than NMS. African-Americans MS smoked 40% fewer cigarettes, showed lower NE/24h (24%) and COHb (10%) and higher NE/cig (29%) and serum cotinine (8%) levels than their White counterparts. CONCLUSIONS: Smoking mentholated cigarettes does not increase daily exposure to smoke constituents as measured by NE and COHb. These findings are consistent with the majority of epidemiological studies indicating no difference in smoking related risks between MS and NMS.