타이로신키나아제 억제제의 임상적으로 유의한 약물상호작용 정보 일관성 분석
Evaluation of Information Consistency of Clinically Significant Drug Interactions in Tyrosine Kinase Inhibitors
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Background: Drug-drug interactions (DDIs) in patients using oral anticancer treatment are more common than in those using injectable anticancer agents. In addition, DDIs related to anticancer treatment are known to cause clinically significant outcomes, such as treatment failure and severe toxicity. To prevent these negative outcomes, significant DDIs are monitored and managed using the information provided in drug databases. We aimed to evaluate the consistency of information on clinically significant DDIs for tyrosine kinase inhibitors (TKIs) between representative drug databases. Methods: We selected clinically significant DDIs involving medications that are co-prescribed with TKIs and met the following criteria: the severity level of DDIs was equal or greater than “D” in Lexicomp® or “major” in Micromedex®. We then analyzed the consistency of the severity classification and evidence level between the drug databases. Spearman’s correlation coefficient was used to identify the relationship between DDI information in the drug databases. Results: In total, 627 DDI pairs were identified as clinically significant; information on these was provided by Lexicomp® and Micromedex® for 571 and 438 pairs, respectively, and both drug databases provided information on 382 DDI pairs. There was no correlation between the severity and evidence level of DDIs provided in the two databases; Spearman’s correlation coefficient for Lexicomp® and Micromedex® was -0.009 (p=0.861) and -0.064 (p=0.209), respectively. Conclusion: To judge the significance of DDIs, healthcare providers should consider that the information on DDIs may be different between drug information databases; hence, clinical factors must be considered concurrently.
Abstract
Background: Drug-drug interactions (DDIs) in patients using oral anticancer treatment are more common than in those using injectable anticancer agents. In addition, DDIs related to anticancer treatment are known to cause clinically significant outcomes, such as treatment failure and severe toxicity. To prevent these negative outcomes, significant DDIs are monitored and managed using the information provided in drug databases. We aimed to evaluate the consistency of information on clinically significant DDIs for tyrosine kinase inhibitors (TKIs) between representative drug databases. Methods: We selected clinically significant DDIs involving medications that are co-prescribed with TKIs and met the following criteria: the severity level of DDIs was equal or greater than “D” in Lexicomp® or “major” in Micromedex®. We then analyzed the consistency of the severity classification and evidence level between the drug databases. Spearman’s correlation coefficient was used to identify the relationship between DDI information in the drug databases. Results: In total, 627 DDI pairs were identified as clinically significant; information on these was provided by Lexicomp® and Micromedex® for 571 and 438 pairs, respectively, and both drug databases provided information on 382 DDI pairs. There was no correlation between the severity and evidence level of DDIs provided in the two databases; Spearman’s correlation coefficient for Lexicomp® and Micromedex® was -0.009 (p=0.861) and -0.064 (p=0.209), respectively. Conclusion: To judge the significance of DDIs, healthcare providers should consider that the information on DDIs may be different between drug information databases; hence, clinical factors must be considered concurrently.
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