Comparison of drug–drug interaction checking databases for interactions involving BCR-ABL tyrosine kinase inhibitors

Year 2024, , 32 - 39, 30.04.2024

Ayşe Günay , Eren Demirpolat , Mükerrem Betül Yerer , Ali Ünal

https://doi.org/10.26650/IstanbulJPharm.2024.1207607

Abstract

Background and Aims: BCR-ABL tyrosine kinase inhibitors (TKIs) are used for the treatment of chronic myeloid leukemia and are commonly involved in clinically significant drug–drug interactions (DDIs). In this study, we aimed to evaluate the consensus of DDI checking databases for interactions involving BCR-ABL TKIs.

Methods: We checked DDIs of 100 drugs with six BCR-ABL TKIs—dasatinib, imatinib, nilotinib, ponatinib, bosutinib, and asciminib—in two subscription-based databases (UpToDate and Micromedex) and two open-access databases (Drugs.com and Medscape). Databases were compared in terms of severity ratings, literature support ratings, and general interaction mechanism definitions using Fleiss’ and Cohen’s kappa statistics.

Results: A total of 410 interactions were found. Nilotinib was the most interacted TKI, with 88 interactions. Drugs.com detected the highest number of interactions (n = 355). The overall agreement levels of databases for the severity ratings and general mechanisms were calculated as 0.13 (p = 0) and 0.28 (p = 0), respectively. The Micromedex- UpToDate pair showed the highest agreement level in terms of severity ratings and general mechanism definitions, with kappa values of 0.23 and 0.45, respectively.

Conclusion: The differences among databases for DDIs involving BCR-ABL TKIs were statistically significant. Therefore, healthcare practitioners should check DDIs in multiple databases.

Keywords

Drug-drug interactions, chronic myeloid leukemia, tyrosine kinase inhibitors, TKIs, CML

Supporting Institution

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References

• An, X., Tiwari, A. K., Sun, Y., Ding, P. R., Ashby, C. R., Jr, & Chen, Z. S. (2010). BCR-ABL tyrosine kinase inhibitors in the treatment of Philadelphia chromosome positive chronic myeloid leukemia: a review. Leukemia Research, 34(10), 1255-1268. google scholar
• Bossaer, J. B., & Thomas, C. M. (2017). Drug Interaction Database Sensitivity With Oral Antineoplastics: An Exploratory Analysis. Journal of Oncology Practice, 13(3), e217-e222. google scholar
• Cancer.org. (2022, Nov 1). Targeted Therapies for Chronic Myeloid Leukemia. Retrieved from https://www.cancer.org/cancer/chronic-myeloid-leukemia/treating/targeted-therapies.html google scholar
• Cascorbi I. (2012). Drug interactions-principles, examples and clini-cal consequences. Deutsches Arzteblatt International, 109(33-34), 546-556. google scholar
• Corrie, K., Hardman, J. G. (2011). Mechanisms of drug interactions: pharmacodynamics and pharmacokinetics. Anaesthesia and In-tensive Care Medicine, 12(4), 156-159. google scholar
• Deeks E. D. (2022). Asciminib: First Approval. Drugs, 82(2), 219-226. google scholar
• Drugs.com. (2022, Sep 7). Drug Interactions Checker. Retrieved from https://www.drugs.com/drug_interactions.html google scholar
• Haider, S. I., Johnell, K., Thorslund, M., & Fastbom, J. (2007). Trends in polypharmacy and potential drug-drug interactions across edu-cational groups in elderly patients in Sweden for the period 1992 -2002. International Journal of Clinical Pharmacology and Ther-apeutics, 45(12), 643-653. google scholar
• Hohl, C. M., Dankoff, J., Colacone, A., & Afilalo, M. (2001). Polyphar-macy, adverse drug-related events, and potential adverse drug in-teractions in elderly patients presenting to an emergency depart-ment. Annals of Emergency Medicine, 38(6), 666-671. google scholar
• Hsieh, Y. C., Kirschner, K., & Copland, M. (2021). Improving out-comes in chronic myeloid leukemia through harnessing the im-munological landscape. Leukemia, 35(5), 1229-1242. google scholar
• IBM Micromedex. (2022, Sep 7). Drug Interactions. Retrieved from https://www.micromedexsolutions.com/home/dispatch google scholar
• Kennedy, J. A., & Hobbs, G. (2018). Tyrosine Kinase Inhibitors in the Treatment of Chronic-Phase CML: Strategies for Front-line Decision-making. Current Hematologic Malignancy Reports, 13(3), 202-211. google scholar
• Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. google scholar
• Luskin, M. R., & DeAngelo, D. J. (2018). How to treat chronic myeloid leukemia (CML) in older adults. Journal of Geriatric Oncology, 9(4), 291-295. google scholar
• Marcath, L. A., Xi, J., Hoylman, E. K., Kidwell, K. M., Kraft, S. L., & Hertz, D. L. (2018). Comparison of Nine Tools for Screening Drug-Drug Interactions of Oral Oncolytics. Journal of Oncology Practice, 14(6), e368-e374. google scholar
• Medscape. (2022, Sep 7). Drug Interaction Checker. Retrieved from https://reference.medscape.com/drug-interactionchecker google scholar
• World Health Organization. (2022, Sep 1). Collaborating Centre for Drug Statistics Methodology. ATC/DDD Index 2022 Retrieved from https://www.whocc.no/atc_ddd_index/ google scholar
• Monteith, S., Glenn, T., Gitlin, M., & Bauer, M. (2020). Potential Drug interactions with Drugs used for Bipolar Disorder: A Comparison of 6 Drug Interaction Database Programs. Pharmacopsychiatry, 53(5), 220-227. google scholar
• Osman, A. E. G., & Deininger, M. W. (2021). Chronic Myeloid Leukemia: Modern therapies, current challenges and future di-rections. Blood Reviews, 49, 100825. google scholar
• Osorio, S., Escudero-Vilaplana, V., Gomez-Centurion, I., Perez-Lopez, R., Ayala, R., Vall-Llovera, F., ... CML Spanish Group (GELMC) (2018). Drug-to-drug interactions of tyrosine kinase inhibitors in chronic myeloid leukemia patients. Is it a real problem?. Annals of Hematology, 97(11), 2089-2098. google scholar
• Patel, R. I., & Beckett, R. D. (2016). Evaluation of resources for analyz-ing drug interactions. Journal of the Medical Library Association : JMLA, 104(4), 290-295. google scholar
• Reis, A. M., & Cassiani, S. H. (2010). Evaluation of three brands of drug interaction software for use in intensive care units. Pharmacy World & Science : PWS, 32(6), 822-828. google scholar
• Riechelmann, R. P., & Del Giglio, A. (2009). Drug interactions in oncology: how common are they?. Annals of Oncology, 20(12), 1907-1912. google scholar
• Saydam, G., Ali, R., Demir, A. M., Eskazan, A. E., Guvenc, B., Haznedaroglu, I. C., . . . Ilhan, O. (2022). The effect of comor-bidities on the choice of tyrosine kinase inhibitors in patients with chronic myeloid leukemia. International Journal of Hematologic Oncology, 11(1), JJH38. google scholar
• Suriyapakorn, B., Chairat, P., Boonyoprakarn, S., Rojanarattanangkul, P., Pisetcheep, W., Hunsakunachai, N., . . . Khemawoot, P. (2019). Comparison of potential drug-drug interactions with metabolic syndrome medications detected by two databases. PloS One, 14(11), e0225239. google scholar
• Uptodate. (2022, Sep 7). Lexicomp Drug Interactions. Retrieved from https://www.uptodate.com/drug-interactions/?source=responsive_home#di-druglist google scholar
• van Leeuwen, R. W., van Gelder, T., Mathijssen, R. H., & Jansman, F.G. (2014). Drug-drug interactions with tyrosine-kinase inhibitors: a clinical perspective. The Lancet. Oncology, 15(8), e315-e326. google scholar
• van Leeuwen, R. W., Brundel, D. H., Neef, C., van Gelder, T., Mathi-jssen, R. H., Burger, D. M., & Jansman, F. G. (2013). Prevalence of potential drug-drug interactions in cancer patients treated with oral anticancer drugs. British Journal of Cancer, 108(5), 1071-1078. google scholar
• van Leeuwen, R. W. F., Jansman, F. G. A., Hunfeld, N. G., Peric, R., Reyners, A. K. L., Imholz, A. L. T., . . . Mathjssen, R. H. J. (2017). Tyrosine Kinase Inhibitors and Proton Pump Inhibitors: An Evaluation of Treatment Options. Clinical Pharmacokinetics, 56(7), 683-688. google scholar
• Vonbach, P., Dubied, A., Krahenbühl, S., & Beer, J. H. (2008). Eval-uation of frequently used drug interaction screening programs. Pharmacy World & Science : PWS, 30(4), 367-374. google scholar