![]() ![]() The motivation for drug-taking can also be assessed using multisymptomatic training paradigms and behavioral economics. SA models are constantly evolving, with the emphasis being placed on the importance of the duration of drug experience (i.e., session length) and the temporal pattern of drug delivery as a means to better model the transition to addiction. More commonly used are drug self-administration (SA) models, whereby drug delivery is contingent upon the animal’s motivation to take the drug. In models such as behavioral sensitization and conditioned place preference (CPP), an animal is non-contingently administered a drug, allowing drug delivery independent from the motivation to take the drug to be assessed. We then focus on how these models are used to assess abuse potential by pharmaceutical companies. In this review article, we build on the existing literature (García Pardo et al., 2017 Lynch, 2018) and discuss the various models that exist for studying addiction-related behaviors in animals, including individual variation in addiction-related behaviors, and commonalities between drug addiction and certain behavioral addictions. Regardless, creating better preclinical models of drug addiction is essential for elucidating the neurobiological mechanisms that contribute to addiction-related behaviors, and creating better treatment options for those afflicted with addiction. Environmental circumstances, behavioral traits and genetic factors all interplay with one another and affect an individual’s susceptibility to acquiring and maintaining the use of an addictive substance, as well as relapse propensity (Everitt and Robbins, 2016). The main difficulty with modeling drug addiction using nonhuman animals is capturing an inherently complex behavioral pathology using relatively simple behavioral protocols (Spanagel, 2017 Müller, 2018). Finally, after reviewing the face validity of the aforementioned models, we consider the most common standardized tests used by pharmaceutical companies to assess the addictive potential of a drug during clinical trials. We further examine current models of behavioral addictions such as gambling, a disorder included in the DSM-5, and exercise, mentioned in the DSM-5 but not included yet due to insufficient peer-reviewed evidence. Separation of more vulnerable animals according to these criteria, along with other innate predispositions including goal- or sign-tracking, sensation-seeking behavior or impulsivity, has established individual genetic susceptibilities to developing drug addiction and relapse vulnerability. In an attempt to better model the individual vulnerability to drug abuse that characterizes human addiction, the field has also established preclinical paradigms in which drug-induced behaviors are ranked by various criteria of drug use in the presence of negative consequences. ![]() We further describe paradigms where drug-taking is challenged by alternative rewards, such as appetitive foods or social interaction. For the latter, we elaborate on the different ways of mimicking craving and relapse, which include using acute stress, drug administration or exposure to cues and contexts previously paired with drug self-administration. This includes non-contingent models in which animals are passively exposed to rewarding substances, as well as widely used contingent models such as drug self-administration and relapse. We will review the most common preclinical models of addictive behavior and discuss the advantages and disadvantages of each. ![]() This understanding will arise first from animal models of addiction where experimentation at the level of circuits and molecular biology is possible. Despite decades of research, the options available to treat addiction are often ineffective because our rudimentary understanding of drug-induced pathology in brain circuits and synaptic physiology inhibits the rational design of successful therapies. Drug addiction is a neuropsychiatric disorder with grave personal consequences that has an extraordinary global economic impact. ![]()
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