Evidence map›Paper›PMID 39897394›Full record

ArticleToxicology reports2025

Feasibility assessment of a pharmaco-kinetic behavior based yield model of salivary cotinine among tobacco users.

E C Hensel, R J Robinson

Abstract read
In one paragraph

Article in Toxicology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

2 authors.

E C HenselDepartment of Mechanical Engineering, Rochester Institute of Technology, Rochester, NY, United States.
R J RobinsonDepartment of Mechanical Engineering, Rochester Institute of Technology, Rochester, NY, United States.

Funding

Developing Evidence To Inform Regulatory Policy On Nicotine Content In E-LiquidsR01DA057687 · NIDA · ROCHESTER INSTITUTE OF TECHNOLOGY · PI Risa Jean Robinson · 2024 to 2026
$2.2M
Compensatory Behavior of E-cig Users in response to Reduced Nicotine E-liquidsR21DA050852 · NIDA · ROCHESTER INSTITUTE OF TECHNOLOGY · PI ROBINSON, RISA JEAN · 2020 to 2021
$403k
Understanding the Association Between Electronic Cigarette Aerosol Emissions, Tobacco Product Characteristics and User Topography and Consumption BehaviorR21ES029984 · NIEHS · ROCHESTER INSTITUTE OF TECHNOLOGY · PI HENSEL, EDWARD CHARLES · 2019 to 2020
$403k
NIDA NIH HHS R01 DA057687NIDA NIH HHS R21 DA050852NIEHS NIH HHS R21 ES029984
6 · The paper itself

Abstract

Methods: A secondary analysis was conducted using puff topography and salivary cotinine biomarker data from a prior two arm, two period cross-over study conducted in the natural environment over 17 days which enrolled 55 Juul electronic cigarette users. The study expands upon a previously validated behavior-based yield (BBY) which quantified aerosol emissions from a Juul ecig as a function of user behavior. A Pharmacokinetic Behavior-Based Yield (PkBBY) model is introduced which models the uptake of nicotine into the body and its subsequent metabolic decay into cotinine. A subset of the available participant data was used to train the PkBBY model and identify three parameters: a gain reflecting the conversion of nicotine into salivary cotinine concentration, and the half-lives of nicotine and salivary cotinine in the body. A separate subset of the available data was used for assessing performance of the PkBBY model against salivary cotinine biomarkers of exposure. Results: Model training demonstrated the PkBBY model was able to predict the bedtime salivary cotinine of participants within + /- 220 ng/mL 95 % confidence interval on the regression, based on their observed puff topography. The training algorithm estimated the conversion from nicotine ingested into the concentration of salivary cotinine as 28.8 [(ng/mL cotinine)/(mg nicotine ingested)], and the half-lives of nicotine and cotinine to be 4.4 and 49 [hours], respectively. The one-to-one intraclass correlation coefficient of the model applied to the assessment data set was 0.6, indicating moderate agreement between the predictions and the observed biomarkers, Limitations of the model associated with the data available for secondary analysis are discussed. Conclusions: The PkBBY model was internally validated and shows promise as a tool for establishing a causal relationship between puffing behavior and an established biomarker of nicotine exposure. Further work is needed to develop personalized PkBBY model parameters to account for variations in participant metabolism factors.

Indexed as

Electronic cigaretteNicotinePersonalized health carePharmacokineticsSalivary cotinineSalivary diagnosticsTobacco regulatory science

Identifiers

PMID39897394
PMCPMC11787490

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.