Project 1: Using toxicity testing data to test hypotheses about advanced-generation ECIGs and generate population-level predictions regarding potential regulatory action

项目摘要。烟草制品研究中心（CSTP）开发了一个模型， 使用电子香烟（ECIG）作为范例评估新型烟草产品，所述电子香烟加热液体， 通常含有尼古丁，形成使用者吸入的气雾剂。现在，CSTP利用其方法和 ECIG专业知识从产品评估转向影响分析的综合主题。具体而言是 CSTP提出了FDA可以预测潜在法规影响的方法， 然后可以检验预测的效应是否发生在群体中。CSTP模型 评估潜在法规如何影响产品毒性（项目1）、用户行为（项目2），以及 产品成瘾/滥用责任（项目3）。在此背景下，项目1将生成关于 先进的一代ECIG排放的有毒物质，并有助于人口水平的假设 监管影响（项目2和3）：项目4将测试这些人口水平假设。 FDA法规旨在促进健康，但也可能产生意想不到的后果。例如，在一个示例中， 如欧盟（EU）那样，将ECIG液体限制为<20 mg/ml尼古丁，可以推动使用更高的功率 ECIG会雾化更多的液体/烟，导致用户吸入更多的尼古丁和其他有毒物质。非预期 其他作用也可能产生后果，如限制ECIG尼古丁通量（排放的尼古丁量）， 尼古丁/单位时间），或降低香料的可用性。这些和其他潜在监管措施的后果 可以使用气溶胶研究方法来预测行动，这些方法允许严格控制关键参数，例如 由于装置功率、液体成分和抽吸形貌（例如，抽吸量/持续时间）。观察指标 包括产生气雾剂的量、预测的尼古丁递送、实际尼古丁产量 气溶胶毒物特征结果可以为关于器械/液体的可检验人群水平假设提供信息 偏好和双重ECIG/烟草香烟使用。因此，项目1的具体目标是使用已建立的气溶胶 研究方法，以研究ECIG排放如何受到三种潜在监管行动的影响：（1） 限制尼古丁，（2）限制尼古丁流量，和（3）减少香料的可用性。项目1由 背景知识核心，将完善目标2设备/液体的选择，并提供目标3 DIY方法。 项目1提供了关于尼古丁浓度、通量和液体可用性对ECIG的作用的新数据 毒性，同时告知关于潜在管制行动后果的预测。项目4 在人口水平上检验这些预测。因此，项目1是影响力综合主题的一部分 分析，并利用该小组的气溶胶研究专业知识，提供FDA的工具，可用于指导 制定法规，以便在法规生效时，预测人口水平的方法 现象已经测试了它，完善了它，并产生了数据，表明它的健康促进作用是 最大化和最小化意外后果。

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 1 will generate new data regarding the toxicants emitted by advanced-generation ECIGs and contribute to hypotheses about population-level regulatory impact (with Projects 2 and 3): Project 4 will test those population-level hypotheses. FDA regulations are designed to promote health, but may also have unintended consequences. For example, limiting ECIG liquids to <20 mg/ml nicotine, as in the European Union (EU), can drive use of higher power ECIGs that aerosolize more liquid/puff, leading users to inhale more nicotine and other toxicants. Unintended consequences may also occur from other action, 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 aerosol research methods that allow rigorous control of key parameters, such as device power, liquid constituents, and puff topography (e.g., puff volume/duration). Outcome measures include amount of aerosol produced, mathematically-predicted nicotine delivery, actual nicotine yield, and aerosol toxicant profile. Results can inform testable population-level hypotheses regarding device/liquid preferences and dual ECIG/tobacco cigarette use. Thus, Project 1 specific aims are to use established aerosol research methods to examine how ECIG emissions are influenced by three potential regulatory actions: (1) limits on nicotine, (2) constraints on nicotine flux, and (3) reduction in flavor availability. Project 1 is informed by the Contextual Knowledge Core that will refine Aim 2 device/liquid choices and provide Aim 3 DIY methods. Project 1 provides new data regarding the role of nicotine concentration, flux, and liquid availability on ECIG toxicity, while informing predictions regarding the consequences of potential regulatory action. Project 4 examines these predictions at the population level. Thus, Project 1 is part of an integrative theme of impact analysis and draws on the team’s aerosol research expertise to provide FDA tools that can be used to guide regulation development so that, by the time a regulation goes into effect, methods predictive of population-level phenomena have tested it, refined it, and generated data that show that its health-promoting effects are maximized and unintended consequences are minimized.