Dottorato in NEUROSCIENZE DEL COMPORTAMENTO

Titolo della tesi: Translating human drug use patterns into rat models: exploring interindividual differences via refined drug self-administration procedures

Drug users instrumentalize the drug dosing-timing relationship (i.e. preferred drug dose in the preferred time) to produce their desired effects (i.e. euphoria, withdrawal avoidance, etc.). This is achieved by harnessing drug type, dosage, route, and frequency of drug taking. Yet, preclinical addiction research often employs self-administration and choice procedures based on discrete, as opposed to continuous dimension strategies, characterized by pre-selected experimenter-imposed unit-doses spaced by timeouts. This approach imposes constraints on the dose-time relationship voluntary harnessed by individuals with drug-addiction in real-world. This dissertation is devoted to the refinement of animal models of drug addiction. The considerations for the refinements stem from a detailed analysis of naturalistic patterns of drug taking in humans and are based on a strict pharmacokinetics and pharmacodynamics analysis of the drugs being investigated. The ambition is to guide preclinical researchers toward the self-administration procedure for neuropharmacological studies, tailored to the specific drug being investigated and is largely motivated by the limited advancements in available treatment options stemming from preclinical insights. This dissertation is devoted to the refinement of animal models of drug addiction. The considerations for the refinements stem from a detailed analysis of naturalistic patterns of drug-taking in humans and are based on a strict pharmacokinetics and pharmacodynamics analysis of the drugs being investigated. The ambition is to guide preclinical researchers toward the optimal self-administration procedure for neuropharmacological studies, tailored to the specific drug being investigated and is largely motivated by the limited advancements in available treatment options stemming from preclinical insights.