title: Measuring attractiveness for abuse of prescription opioids.
abstract: OBJECTIVE
  Prescription opioids are the second most misused/abused drug in the United States behind only marijuana. Recreational prescription opioid users appear to prefer some products over others; however, the extent to which attributes of any particular formulation account for such preferences has yet to be determined. The Opioid Attractiveness Technology Scaling was developed to identify the particular features of a prescription opioid that are relevant to its attractiveness for recreational use, and to use these features to model attractiveness for recreational use of particular prescription opioid formulations.
  
  
  METHODS
  Four hundred and ninety-one self-reported recreational prescription opioid users identified 43 product features as being relevant to determining whether a product is "attractive" or "unattractive" for recreational use. Average ratings were used to determine appropriate weights to be applied to the features. A factor analysis yielded 10, highly differentiated factors. Five hundred and sixty-four prescription opioid abusers were then asked to rate the extent to which the 43 features identified in Study 1 were relevant to specific prescription opioid products they had used.
  
  
  RESULTS
  Respondents provided an overall preference rating of these products and a model was created. A random intercept model yielded a significant pseudo R(2) of 0.14 (chi-square = 310.02, degrees of freedom [df] = 10, P < 0.001). The model fit least well, albeit significantly, for abusers who preferred to swallow the drug (pseudo R(2) = 0.06; chi-square = 55.52, df = 10, P < 0.001) and best for those who preferred to inject (pseudo R(2) = 0.37; chi-square = 199.34, df = 10, P < 0.001).
  
  
  CONCLUSIONS
  The relevance of the model is discussed along with possible modifications that might allow prediction of "attractiveness" of "abuse deterrent" formulations that have not yet been marketed.
authors.affiliation: Inflexxion, Inc., Newton, Massachusetts, USA. sfbutler@inflexxion.com
authors.forename: Stephen F
authors.initials: SF
authors.lastname: Butler
authors.forename: Kathrine C
authors.initials: KC
authors.lastname: Fernandez
authors.forename: Alan
authors.initials: A
authors.lastname: Chang
authors.forename: Christine
authors.initials: C
authors.lastname: Benoit
authors.forename: Leslie C
authors.initials: LC
authors.lastname: Morey
authors.forename: Ryan
authors.initials: R
authors.lastname: Black
authors.forename: Nathaniel
authors.initials: N
authors.lastname: Katz
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000701:Analgesics, Opioid; D002626:Chemistry, Pharmaceutical; D002648:Child; D003627:Data Interpretation, Statistical; D003710:Demography; D016527:Drug Costs; D005260:Female; D006306:Health Surveys; D006801:Humans; D020407:Internet; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological; D009293:Opioid-Related Disorders / Q000191:economics / Q000523:psychology*; D010146:Pain / Q000150:complications; D055553:Prescription Drugs; D055815:Young Adult
chemical_list: D000701:Analgesics, Opioid; D055553:Prescription Drugs