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Developing human laboratory models of smoking lapse behavior for medication screening
Author(s) -
McKee Sherry A.
Publication year - 2009
Publication title -
addiction biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.445
H-Index - 78
eISSN - 1369-1600
pISSN - 1355-6215
DOI - 10.1111/j.1369-1600.2008.00135.x
Subject(s) - abstinence , smoking cessation , context (archaeology) , nicotine , medicine , food and drug administration , cigarette smoking , clinical psychology , psychology , psychiatry , environmental health , pathology , paleontology , biology
Use of human laboratory analogues of smoking behavior can provide an efficient, cost‐effective mechanistic evaluation of a medication signal on smoking behavior, with the result of facilitating translational work in medications development. Although a number of human laboratory models exist to investigate various aspects of smoking behavior and nicotine dependence phenomena, none have yet modeled smoking lapse behavior. The first instance of smoking during a quit attempt (i.e. smoking lapse) is highly predictive of relapse and represents an important target for medications development. Focusing on an abstinence outcome is critical for medication screening as the US Food and Drug Administration approval for cessation medications is contingent on demonstrating effects on smoking abstinence. This paper outlines a three‐stage process for the development of a smoking lapse model for the purpose of medication screening. The smoking lapse paradigm models two critical features of lapse behavior: the ability to resist the first cigarette and subsequent ad libitum smoking. Within the context of the model, smokers are first exposed to known precipitants of smoking relapse (e.g. nicotine deprivation, alcohol, stress), and then presented their preferred brand of cigarettes. Their ability to resist smoking is then modeled and once smokers ‘give in’ and decide to smoke, they participate in a tobacco self‐administration session. Ongoing and completed work developing and validating these models for the purpose of medication screening is discussed.

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