Preclinical abuse potential testing using behavioral economics and drug self-administration demand-curve analysis: A strategy to improve resolution of drug stratification for regulatory control.

New chemical entities (NCEs) that act on the central nervous system are synthesized in pharmaceutical drug development programs for investigational purposes or by clandestine laboratories for illicit purposes. One component in the regulatory evaluation process of any NCE is abuse potential assessment, which involves both preclinical and human laboratory experiments to compare a NCE with an established and validated positive control that has known abuse potential in humans. Although multiple procedures are available in both preclinical and human laboratories, this review has focused on preclinical drug self-administration procedures because these results are heavily weighted in the regulatory evaluation process. Preclinical drug self-administration procedures have demonstrated high predictive validity for human abuse potential, and procedures that use simple fixed-ratio schedules of reinforcement, as recommended by the current U.S. Food and Drug Administration guidance, have good sensitivity for detecting drug reinforcement and abuse potential. However, these procedures have displayed poor resolution for ranking NCEs along an abuse-potential continuum that may inform controlled substance schedule placement. This review discusses the utility of behavioral economic/demand-curve experimental designs as a method to improve the resolution for differentiating relative reinforcing strength across drugs and ranking abuse potential for considerations of regulatory control. Theoretical benefits of demand-curve analysis are considered, and experimental parameters that optimize those benefits are reviewed. The influence of chronic drug exposure and history on demand-curve metrics of drug reinforcing strength and abuse potential is also reviewed. Future directions are highlighted toward empirically determining the utility of behavioral economic approaches for preclinical abuse potential assessment. SIGNIFICANCE STATEMENT: Simple fixed-ratio schedules of reinforcement have good sensitivity for detecting drug reinforcement and abuse potential but have poor resolution for ranking chemical entities along an abuse-potential continuum. Behavioral economic/demand-curve approaches maybe a method to increase resolution for abuse potential assessment. Accordingly, a preclinical abuse potential algorithm incorporating both simple fixed-ratio schedules and behavioral economic/demand-curve methods is described. Finally, the manuscript describes how individual subject traits and state variables, including chronic drug exposure and history, impact behavioral economic metrics of drug abuse potential.