alexa Transition to drug addiction: a negative reinforcement model based on an allostatic decrease in reward function.


Journal of Addiction Research & Therapy

Author(s): Ahmed SH, Koob GF

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Abstract RATIONALE: The transition from initial drug use to drug addiction has been proposed to result from an allostatic decrease in reward function driven by an overactivation of brain antireward processes. OBJECTIVES: How decreased reward function explains compulsive drug use is not entirely clear at present, and is still a subject for debate. METHODS: We present a quantitative model of cocaine self-administration that integrates pharmacokinetic, pharmacodynamic, and motivational factors to address this question. The model assumes that reward system responsivity is a homeostatically regulated process where the desired level of responsivity (called the reward set point) is initially different from the baseline level. The reduction or correction of this difference or error in reward function would drive cocaine self-administration. RESULTS: Theoretical data obtained by computer simulation fit the experimental data obtained in animals self-administering cocaine (i.e., the within-session pattern of self-injections, the shape and curvature of the dose-injection function, the nonlinear relationship between drug intake and regulated drug effects). Importantly, simulation of an allostatic decrease in reward system responsivity exacerbates the initial error that drives self-administration, thereby increasing both the intake of, and the motivation for, the drug. This allostatic change manifests as a vertical shift in the dose-injection function similar to that seen in animals with escalating cocaine self-administration. CONCLUSIONS: The present model provides a satisfactory explanation of escalated drug intake and suggests a novel negative reinforcement view of addiction based on an allostatic decrease in reward function. This article was published in Psychopharmacology (Berl) and referenced in Journal of Addiction Research & Therapy

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