# Project 2: Using user behavior data collected in the clinical lab to test hypotheses about advanced-generation ECIGs and generate population-level predictions regarding potential regulatory action

> **NIH NIH U54** · VIRGINIA COMMONWEALTH UNIVERSITY · 2021 · $606,063

## Abstract

Project Summary. The Center for the Study of Tobacco Products (CSTP) has developed a model for
evaluating novel tobacco products using, as exemplars, electronic cigarettes (ECIGs) that heat a liquid that
often contains nicotine, forming an aerosol that users inhale. Now, CSTP leverages its methodological and
ECIG expertise to pivot from product evaluation to an integrative theme of impact analysis. Specifically, the
CSTP proposes methods with which FDA can generate predictions regarding a potential regulation’s effects,
and then whether or not the predicted effects occur in the population can be tested. The CSTP’s model
assesses how potential regulation might influence product toxicity (Project 1), user behavior (Project 2), and
product addiction/abuse liability (Project 3). In this context, Project 2 will generate new data regarding the
effects of advanced-generation ECIGs on user behavior and will contribute, along with Projects 1 and 3, to
population-level predictions: Project 4 will test those population-level predictions.
FDA regulations are intended to promote health, but also may have harmful unintended consequences. For
example, limiting liquid nicotine to <20 mg/ml, as in the European Union (EU), could drive use of high power
ECIGs that deliver more nicotine and other toxicants. Unintended consequences may also occur from other
actions, like constraining ECIG nicotine flux (amount of emitted nicotine/unit time), or reducing flavor
availability. The consequences of these and other potential regulatory actions may be predicted using results
from clinical lab studies in which variables relevant to regulation are manipulated systematically and subjective
effects, puff topography (e.g., puff duration, volume), liquid consumption, and nicotine delivery are measured.
Results can inform testable population-level predictions regarding device/liquid preferences, amount of liquid
consumed, dual ECIG/tobacco cigarette use, and dependence. Thus, Project 2 specific aims are to use
established clinical lab methods to examine, in independent studies each involving 68 exclusive ECIG users
and 68 non-ECIG using smokers, the extent to which subjective effects, puff topography, liquid consumption,
and nicotine delivery are influenced by three potential regulatory actions: (1) limits on nicotine, (2) constraints
on nicotine flux, and (3) reduction in flavor availability. Project 2 is informed by the Contextual Knowledge Core
that ensures that independent variables reflect real-world conditions. Results will provide new data regarding
the role of nicotine concentration, flux, and flavor availability on ECIG user behavior, while informing
population-level predictions. Project 4 examines these predictions at the population level. Thus, this project is
part of a center with an integrative theme of impact analysis that draws from the team’s clinical lab expertise to
provide FDA tools that can be used to guide regulation development so that, by the time a regulati...

## Key facts

- **NIH application ID:** 10245301
- **Project number:** 5U54DA036105-09
- **Recipient organization:** VIRGINIA COMMONWEALTH UNIVERSITY
- **Principal Investigator:** Alison Breland
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $606,063
- **Award type:** 5
- **Project period:** 2013-09-30 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10245301

## Citation

> US National Institutes of Health, RePORTER application 10245301, Project 2: Using user behavior data collected in the clinical lab to test hypotheses about advanced-generation ECIGs and generate population-level predictions regarding potential regulatory action (5U54DA036105-09). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10245301. Licensed CC0.

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