Prevalence and significance of potential drug-drug interactions among cancer patients receiving chemotherapy.

BACKGROUND: Cancer patients often receive multiple drugs to maximize their therapeutic benefit, treat co-morbidities and counter the adverse effects of chemotherapy. Concomitant administration of multiple drugs increases the risk of drug interactions leading to compromised therapeutic efficacy or safety of therapy. The purpose of this study was to identify the prevalence, levels and predictors of potential drug-drug interactions (pDDIs) among cancer patients. METHODS: Six hundred and 78 patients receiving chemotherapy from two tertiary care hospitals were included in this cross-sectional study. Patient medication profiles were screened for pDDIs using the Micromedex® database. Logistic regression analysis was performed to identify the predictors of pDDIs. RESULTS: The overall prevalence of pDDIs was 78%, majority of patients had 1-2 pDDIs (39.2%). A total of 1843 pDDIs were detected. Major-pDDIs were most frequent (67.3%) whereas, a significant association of pDDIs was found between > 7 all prescribed drugs (p < 0.001) and ≥ 3 anti-cancer drugs (p < 0.001). Potential adverse outcomes of these interactions include reduced therapeutic effectiveness, QT interval prolongation, tendon rupture, bone marrow suppression and neurotoxicity. CONCLUSIONS: Major finding of this study is the high prevalence of pDDIs signifying the need of strict patient monitoring for pDDIs among cancer patients. Patients at higher risk to pDDIs include those prescribed with > 7 any types of drugs or ≥ 3 anticancer drugs. Moreover, list of most frequently identified major and moderate interactions will aid health care professional in timely identification and prevention of pDDIs.

Association of H1-antihistamines with torsade de pointes: a pharmacovigilance study of the food and drug administration adverse event reporting system.

Background: This study aimed to measure the association of various H1-antihistamines (H1A) with Torsade de Pointes (TdP), and present a comprehensive overview of H1A-induced TdP cases reported to the Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: All H1A-induced TdP cases (n = 406) were retrieved from the FAERS database using the preferred term 'Torsade de Pointes' of MedDRA version-22 from 1990 to 2019. Four data-mining algorithms were used for disproportionality analysis: Reporting Odds Ratio (ROR); Proportional Reporting Ratio (PRR), Empirical Bayes Geometric Mean (EBGM), and Information Content (IC). H1A with >3 TdP cases were included. Results: A total of 12 signals (Astemizole, cetirizine, chlorpheniramine, clemastine, desloratadine, diphenhydramine, hydroxyzine, loratadine, meclizine, promethazine, terfenadine, and trimeprazine) were identified including six new signals (cetirizine, chlorpheniramine, clemastine, desloratadine, loratadine, and meclizine). The number of risk factors (p = 0.031) and concomitant QT-prolonging drugs (p = <0.001) were significantly lower among new signals vs old signals. Moreover, new signals were strongly associated with QT-prolongation, cardiac reactions, and electrolyte abnormalities as compared with old signals. Conclusions: Our study found the increased torsadogenic potential of new signals compared with previously known old signals, hence necessitating clinical studies to determine the actual torsadogenic potential of newly identified signals.