Abstract
Currently dominating psychological theories of drug self-administration explain some experimentally observed facts, however, at the same time they lead to logical contradictions to other accepted facts. A quantitative pharmacological theory of self-administration behavior states that cocaine-induced lever pressing behavior occurs only when cocaine concentrations are within a certain range, termed the compulsion zone. These concentrations can be calculated using standard pharmacokinetic mathematical models. The lower and upper limits of this range of concentrations are the priming and satiety thresholds, respectively. This pharmacological theory explains all phases of the self-administration session but is particularly useful at explaining the intervals between drug injections during maintained self-administration in terms of a mathematical model that contains only three parameters: the drug unit dose, the satiety threshold and the drug elimination rate constant. The satiety threshold represents the equiactive agonist concentration. Because competitive antagonists increase equiactive agonist concentrations and the agonist concentration ratio increases linearly with antagonist concentration, the satiety threshold model allows the measurement of the in vivo potency of dopamine receptor antagonists using the classical mathematical method of Schild. In addition, applying pharmacokinetic models to the time course of the magnitude of the antagonist-induced increase in the satiety threshold allows the pharmacokinetics of antagonists to be calculated. Pharmacokinetic/pharmacodynamic models explain self-administration behavior and make this paradigm a rapid and high-content bioassay system useful for screening agonists and antagonists that interact with important neurotransmitter systems in the brain.
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Tsibulsky, V.L., Norman, A.B. (2012). Simple Deterministic Mathematical Model of Maintained Drug Self-administration Behavior and Its Pharmacological Applications. In: Gutkin, B., Ahmed, S. (eds) Computational Neuroscience of Drug Addiction. Springer Series in Computational Neuroscience, vol 10. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0751-5_1
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