Co-prescription trends in a large cohort of subjects predict substantial drug-drug interactions.
Pharmaceutical prescribing and drug-drug interaction data underlie recommendations on drug combinations that should be avoided or closely monitored by prescribers. Because the number of patients taking multiple medications is increasing, a comprehensive view of prescribing patterns in patients is important to better assess real world pharmaceutical response and evaluate the potential for multi-drug interactions. We obtained self-reported prescription data from NHANES surveys between 1999 and 2010, and confirm the previously reported finding of increasing drug use in the elderly. We studied co-prescription drug trends by focusing on the 2009-2010 survey, which contains prescription data on 690 drugs used by 10,537 subjects. We found that medication profiles were unique for individuals aged 65 years or more, with ≥98 unique drug regimens encountered per 100 subjects taking 3 or more medications. When drugs were viewed by therapeutic class, it was found that the most commonly prescribed drugs were not the most commonly co-prescribed drugs for any of the 16 drug classes investigated. We cross-referenced these medication lists with drug interaction data from Drugs.com to evaluate the potential for drug interactions. The number of drug alerts rose proportionally with the number of co-prescribed medications, rising from 3.3 alerts for individuals prescribed 5 medications to 11.7 alerts for individuals prescribed 10 medications. We found 22% of elderly subjects taking both a substrate and inhibitor of a given cytochrome P450 enzyme, and 4% taking multiple inhibitors of the same enzyme simultaneously. By examining drug pairs prescribed in 0.1% of the population or more, we found low agreement between co-prescription rate and co-discussion in the literature. These data show that prescribing trends in treatment could drive a large extent of individual variability in drug response, and that current pairwise approaches to assessing drug-drug interactions may be inadequate for predicting real world outcomes.
Lilly Research Labs IT, Eli Lilly and Company, Indianapolis, Indiana, United States of America.
Jeffrey J
JJ
Sutherland
Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio, United States of America.
Thomas M
TM
Daly
Lilly Research Labs IT, Eli Lilly and Company, Indianapolis, Indiana, United States of America.
Xiong
X
Liu
Drug Disposition and Toxicology, Eli Lilly and Company, Indianapolis, Indiana, United States of America.
Keith
K
Goldstein
Global Patient Outcomes and Real World Evidence, Eli Lilly and Company, Indianapolis, Indiana, United States of America.
Joseph A
JA
Johnston
Sano Informed Prescribing, Brentwood, Tennessee, United States of America.
Timothy P
TP
Ryan
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D015331:Cohort Studies; D004347:Drug Interactions; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D006306:Health Surveys; D006801:Humans; D008875:Middle Aged; D009749:Nutrition Surveys; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data / Q000639:trends; D055815:Young Adult