Association of antiviral drugs and their possible mechanisms with DRESS syndrome using data mining algorithms. Issue 3 (27th March 2023)

• Record Type: Journal Article
• Authors: Sharma, Akash; Roy, Sweta; Sharma, Ruchika; Kumar, Anoop
• Is Part Of: Journal of medical virology. Volume 95:Issue 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-27
• Subjects: antiviral drugs -- data mining algorithms -- docking study -- DRESS; Virology -- Periodicals; 616
• DOI: 10.1002/jmv.28671
• Languages: English
• ISSNs: 0146-6615

Abstract

Abstract: Antiviral drugs are not known for drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome. The current study aims is to find out the association of antiviral drugs and their possible mechanism with DRESS. Data mining algorithms such as proportional reporting ratio that is, PRR (≥2) with associated χ 2 value (>4), reporting odds ratio that is, ROR (≥2) with 95% confidence interval and case count (≥3) were calculated to identify a possible signal. Further, molecular docking studies were conducted to check the interaction of selected antiviral drugs with possible targets. The potential signal of DRESS was found to be associated with abacavir, acyclovir, ganciclovir, lamivudine, lopinavir, nevirapine, ribavirin, ritonavir, and zidovudine among all selected antiviral drugs. Further, subgroup analysis has also shown a potential signal in different age groups and gender. The sensitivity analysis results have shown a decrease in the strength of the signal, however, there was no significant impact on the outcome except for acyclovir. The docking results have indicated the possible involvement of human leukocyte antigen (HLA)*B1502 and HLA*B5801. The positive signal of DRESS was found with selected antiviral drugs except for acyclovir.