title: A translational pharmacology approach to understanding the predictive value of abuse potential assessments.
abstract: Within the drug development industry the assessment of abuse potential for novel molecules involves the generation and review of data from multiple sources, ranging from in-vitro binding and functional assays through to in-vivo nonclinical models in mammals, as well as collection of information from studies in humans. This breadth of data aligns with current expectations from regulatory agencies in both the USA and Europe. To date, there have been a limited number of reviews on the predictive value of individual models within this sequence, but there has been no systematic review on how each of these models contributes to our overall understanding of abuse potential risk. To address this, we analyzed data from 100 small molecules to compare the predictive validity for drug scheduling status of a number of models that typically contribute to the abuse potential assessment package. These models range from the assessment of in-vitro binding and functional profiles at receptors or transporters typically associated with abuse through in-vivo models including locomotor activity, drug discrimination, and self-administration in rodents. Data from subjective report assessments in humans following acute dosing of compounds were also included. The predictive value of each model was then evaluated relative to the scheduling status of each drug in the USA. In recognition of the fact that drug scheduling can be influenced by factors other than the pharmacology of the drug, we also evaluated the predictive value of each assay for the outcome of the human subjective effects assessment. This approach provides an objective and statistical assessment of the predictive value of many of the models typically applied within the pharmaceutical industry to evaluate abuse potential risk. In addition, the impact of combining information from multiple models was examined. This analysis adds to our understanding of the predictive value of each model, allows us to critically evaluate the benefits and limitations of each model, and provides a method for identifying opportunities for improving our assessment and prediction of abuse liability risk in the future.
authors.affiliation: Global Safety Pharmacology bDrug Safety R&D Statistics, Drug Safety Research and Development, Pfizer Research and Development, Groton, CT 06340, USA.
authors.forename: David B
authors.initials: DB
authors.lastname: Horton
authors.forename: David M
authors.initials: DM
authors.lastname: Potter
authors.forename: Andy N
authors.initials: AN
authors.lastname: Mead
mesh_terms: D000818:Animals; D000697:Central Nervous System Stimulants / Q000009:adverse effects*; D016208:Databases, Factual / Q000706:statistics & numerical data; D004345:Drug Industry; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D009043:Motor Activity / Q000187:drug effects; D011237:Predictive Value of Tests; D012054:Reinforcement, Psychology; D012646:Self Administration; D019966:Substance-Related Disorders / Q000175:diagnosis* / Q000209:etiology*; D057170:Translational Research, Biomedical
chemical_list: D000697:Central Nervous System Stimulants