Background: Food and Drug Administration (FDA) regulations mandate that package inserts (PIs) include observed or predicted clinically significant drug-drug interactions (DDIs), as well as the results of pharmacokinetic studies that establish the absence of effect.
Objective: To quantify how frequently observed metabolic inhibition DDIs affecting US-marketed psychotropics are present in FDA-approved PIs and what influence the source of DDI information has on agreement between 3 DDI screening programs.
Methods: The scientific literature and PIs were reviewed to determine all drug pairs for which there was rigorous evidence of a metabolic inhibition interaction or noninteraction. The DDIs were tabulated noting the source of evidence and the strength of agreement over chance. Descriptive statistics were used to examine the influence of source of DDI information on agreement among 3 DDI screening tools. Logistic regression was used to assess the influence of drug class, indication, generic status, regulatory approval date, and magnitude of effect on agreement between the literature and PI as well as agreement among the DDI screening tools.
Results: Thirty percent (13/44) of the metabolic inhibition DDIs affecting newer psychotropics were not mentioned in PIs. Drug class, indication, regulatory approval date, generic status, or magnitude of effect did not appear to be associated with more complete DDI information in PIs. DDIs found exclusively in PIs were 3.25 times more likely to be agreed upon by all 3 DDI screening tools than were those found exclusively in the literature. Generic status was inversely associated with agreement among the DDI screening tools (odds ratio 0.11; 95% CI 0.01 to 0.89).
Conclusions: The presence in PIs of DDI information for newer psychotropics appears to have a strong influence on agreement among DDI screening tools. Users of DDI screening software should consult more than 1 source when considering interactions involving generic psychotropics.
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http://dx.doi.org/10.1345/aph.1R150 | DOI Listing |
PLoS One
January 2025
Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin, China.
Predicting Drug-Drug Interactions (DDIs) enables cost reduction and time savings in the drug discovery process, while effectively screening and optimizing drugs. The intensification of societal aging and the increase in life stress have led to a growing number of patients suffering from both heart disease and depression. These patients often need to use cardiovascular drugs and antidepressants for polypharmacy, but potential DDIs may compromise treatment effectiveness and patient safety.
View Article and Find Full Text PDFToxicol Rep
June 2025
Department of Environmental Management and Toxicology, Dennis Osadabey University, Asaba, Delta State, Nigeria.
This study was conducted to evaluate the health risks related to eating crabs and periwinkles from Southern Nigerian coastal areas that are contaminated by crude oil. Periwinkles and crabs from contaminated locations were tested for Polycyclic aromatic hydrocarbon (PAH) and heavy metal (HM) levels using US-EPA standard, and the health risks to humans of eating these seafood were assessed. 20 samples of periwinkles and crabs were collected from crude oil-polluted coastal areas.
View Article and Find Full Text PDFInterdiscip Sci
January 2025
College of Science, Dalian Jiaotong University, Dalian, 116028, China.
Accurate prediction of drug-drug interaction (DDI) is essential to improve clinical efficacy, avoid adverse effects of drug combination therapy, and enhance drug safety. Recently researchers have developed several computer-aided methods for DDI prediction. However, these methods lack the substructural features that are critical to drug interactions and are not effective in generalizing across domains and different distribution data.
View Article and Find Full Text PDFJ Med Chem
January 2025
Department of Pharmacokinetics Dynamics & Metabolism, Pfizer Inc., Groton, Connecticut 06340, United States.
assessment of the potential of compounds to affect drug metabolizing enzymes and transporters and perpetrate drug-drug interactions (DDIs) is a common practice in drug research. For the development phase, regulators define an exhaustive list of enzymes and transporters to consider, but DDIs associated with many of these are minor and can be well-managed in the clinic; thus, progression of drug candidates that address unmet medical needs should not be curtailed due to this property. However, some enzymes and transporters are very important in drug disposition, so it is important to avoid/reduce inhibition or induction of these through drug design.
View Article and Find Full Text PDFJ Glob Antimicrob Resist
December 2024
Research Center of Clinical Pharmacy, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China. Electronic address:
Background: Nirmatrelvir-ritonavir is effective in the treatment of SARS-CoV-2 infection. It can cause drug‒drug interactions (DDIs), even several days after withdrawal, due to irreversible inhibition of the cytochrome enzyme.
Methods: Hospitalized patients diagnosed with COVID-19 infection and treated with nirmatrelvir-ritonavir were retrospectively included according to preset criteria.
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