Pharmacological impacts on laboratory biomarkers: A guide for nurses and laboratory professionals
Keywords:
Pharmacology, drug-biomarker interactions, test performance, biomarkers, pharmacokinetic interference, clinical utilization, medications, prescribersAbstract
Background: Clinical biomarkers are very essential for diagnosing, assessing and, managing diseases within the laboratory setting. Nevertheless, these biomarkers can be modified through medications, whether prescribed, purchased at a pharmacy, or obtained from a local health food store, making clinical interpretation of the assay results possible only with increased uncertainty. Aim: The main objective of this study is to review the various processes as to how drugs and biomarkers interact, establish the role of the drug-biomarker relationship in the diagnosis of diseases, and analyze how the relationship can be best managed to enhance diagnosis precision and treatment efficacy. Methods: The review of the literature and clinical trials allowed for the analysis of the most widespread drugs that affect biomarkers depending on the pathology; liver function, renal status, and cardiovascular condition biomarkers were included in this category. Results: Consequently, a type of pharmacodynamic effect, the study established that biomarkers under consideration can be increased or decreased by a range of medications including antibiotics, diuretics, steroids, and chemotherapy preparations thus complicating diagnosis. The effects on liver enzymes, renal function index, and glucose levels were of great interest.
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Califf, R. M. (2018). Biomarker definitions and their applications. Experimental Biology and Medicine, 243(2), 213-221. https://doi.org/10.1177/1535370217742027 DOI: https://doi.org/10.1177/1535370217750088
FDA-NIH Biomarker Working Group. (2018). BEST (Biomarkers, Endpoints, and other Tools) Resource. Food and Drug Administration; National Institutes of Health. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK32679
Cescon, D., & Siu, L. L. (2017). Cancer clinical trials: The rear-view mirror and the crystal ball. Cell, 168(3), 575-578. https://doi.org/10.1016/j.cell.2017.01.013 DOI: https://doi.org/10.1016/j.cell.2017.01.027
Ciardiello, D., Vitiello, P. P., Cardone, C., et al. (2019). Immunotherapy of colorectal cancer: Challenges for therapeutic efficacy. Cancer Treatment Reviews, 76, 22-32. https://doi.org/10.1016/j.ctrv.2019.05.003 DOI: https://doi.org/10.1016/j.ctrv.2019.04.003
Suh, K., Carlson, J. J., Xia, F., et al. (2019). Comparative effectiveness of larotrectinib versus entrectinib for the treatment of metastatic NTRK gene fusion cancers. Journal of Clinical Oncology, 37(30), 2640-2648. https://doi.org/10.1200/JCO.19.01930
Nevado-Holgado, A. J., Ribe, E., Thei, L., et al. (2019). Genetic and real-world clinical data, combined with empirical validation, nominate Jak-Stat signaling as a target for Alzheimer's disease therapeutic development. Cells, 8(5), 425. https://doi.org/10.3390/cells8050425 DOI: https://doi.org/10.3390/cells8050425
Pietrantonio, F., Fucà, G., Morano, F., et al. (2018). Biomarkers of primary resistance to trastuzumab in HER2-positive metastatic gastric cancer patients: The AMNESIA case-control study. Clinical Cancer Research, 24(5), 1082-1089. https://doi.org/10.1158/1078-0432.CCR-17-1634 DOI: https://doi.org/10.1158/1078-0432.CCR-17-2781
Hrebien, S., Citi, V., Garcia-Murillas, I., et al. (2019). Early ctDNA dynamics as a surrogate for progression-free survival in advanced breast cancer in the BEECH trial. Annals of Oncology, 30(6), 945-952. https://doi.org/10.1093/annonc/mdz098 DOI: https://doi.org/10.1093/annonc/mdz085
McGill, M. R., & Jaeschke, H. (2018). Biomarkers of drug-induced liver injury: Progress and utility in research, medicine, and regulation. Expert Review of Molecular Diagnostics, 18(9), 797-807. https://doi.org/10.1080/14737159.2018.1502551 DOI: https://doi.org/10.1080/14737159.2018.1508998
Michel, L., Rassaf, T., & Totzeck, M. (2018). Biomarkers for the detection of apparent and subclinical cancer therapy-related cardiotoxicity. Journal of Thoracic Disease, 10(10), S4282-S4295. https://doi.org/10.21037/jtd.2018.06.04 DOI: https://doi.org/10.21037/jtd.2018.08.15
Abu Rmilah, A. A., Lin, G., Begna, K. H., et al. (2020). Risk of QTc prolongation among cancer patients treated with tyrosine kinase inhibitors. International Journal of Cancer, 147(12), 3160-3167. https://doi.org/10.1002/ijc.33245 DOI: https://doi.org/10.1002/ijc.33119
Hierro, C., Matos, I., Martin-Liberal, J., et al. (2019). Agnostic-histology approval of new drugs in oncology: Are we already there? Clinical Cancer Research, 25(11), 3210-3219. https://doi.org/10.1158/1078-0432.CCR-18-3242 DOI: https://doi.org/10.1158/1078-0432.CCR-18-3694
Durack, J., & Lynch, S. V. (2019). The gut microbiome: Relationships with disease and opportunities for therapy. Journal of Experimental Medicine, 216(1), 20-40. https://doi.org/10.1084/jem.20180483 DOI: https://doi.org/10.1084/jem.20180448
Hampel, H., Goetzl, E. J., Kapogiannis, D., Lista, S., & Vergallo, A. (2019). Biomarker-drug and liquid biopsy co-development for disease staging and targeted therapy: Cornerstones for Alzheimer's precision medicine and pharmacology. Frontiers in Pharmacology, 10, 310. https://doi.org/10.3389/fphar.2019.00310 DOI: https://doi.org/10.3389/fphar.2019.00310
Athauda, D., Gulyani, S., Karnati, H., Li, Y., Tweedie, D., Mustapic, M., et al. (2019). Utility of neuronal-derived exosomes to examine molecular mechanisms that affect motor function in patients with Parkinson disease: A secondary analysis of the Exenatide-PD trial. JAMA Neurology. https://doi.org/10.1001/jamaneurol.2018.4304 DOI: https://doi.org/10.1001/jamaneurol.2018.4304
Bergman, P., Piket, E., Khademi, M., James, T., Brundin, L., Olsson, T., et al. (2016). Circulating miR-150 in CSF is a novel candidate biomarker for multiple sclerosis. Neurology Neuroimmunology Neuroinflammation, 3(1), e219. https://doi.org/10.1212/NXI.0000000000000219 DOI: https://doi.org/10.1212/NXI.0000000000000219
Deng, X., & Nakamura, Y. (2017). Cancer precision medicine: From cancer screening to drug selection and personalized immunotherapy. Trends in Pharmacological Sciences, 38(1), 15-24. https://doi.org/10.1016/j.tips.2016.10.013 DOI: https://doi.org/10.1016/j.tips.2016.10.013
Drilon, A., Laetsch, T. W., Kummar, S., DuBois, S. G., Lassen, U. N., Demetri, G. D., et al. (2018). Efficacy of larotrectinib in TRK fusion-positive cancers in adults and children. New England Journal of Medicine, 378(8), 731-739. https://doi.org/10.1056/NEJMoa1714448 DOI: https://doi.org/10.1056/NEJMoa1714448
Dubal, D. B., & Pleasure, S. J. (2019). Neural-derived extracellular vesicles in clinical trials: Message in a bottle. JAMA Neurology. https://doi.org/10.1001/jamaneurol.2018.4325 DOI: https://doi.org/10.1001/jamaneurol.2018.4325
Fandos, N., Perez-Grijalba, V., Pesini, P., Olmos, S., Bossa, M., Villemagne, V. L., et al. (2017). Plasma amyloid beta 42/40 ratios as biomarkers for amyloid beta cerebral deposition in cognitively normal individuals. Alzheimer's & Dementia, 8(3), 179-187. https://doi.org/10.1016/j.jalz.2017.05.005 DOI: https://doi.org/10.1016/j.dadm.2017.07.004
Ferretti, M. T., Iulita, M. F., Cavedo, E., Chiesa, P. A., Schumacher Dimech, A., Santuccione Chadha, A., et al. (2018). Sex differences in Alzheimer disease – the gateway to precision medicine. Nature Reviews Neurology, 14(7), 457-469. https://doi.org/10.1038/s41582-018-0032-9 DOI: https://doi.org/10.1038/s41582-018-0032-9
Eitan, E., Tosti, V., Suire, C. N., Cava, E., Berkowitz, S., Bertozzi, B., et al. (2017). In a randomized trial in prostate cancer patients, dietary protein restriction modifies markers of leptin and insulin signaling in plasma extracellular vesicles. Aging Cell, 16(6), 1430-1433. https://doi.org/10.1111/acel.12657 DOI: https://doi.org/10.1111/acel.12657
Geerts, H., Gieschke, R., & Peck, R. (2018). Use of quantitative clinical pharmacology to improve early clinical development success in neurodegenerative diseases. Expert Review of Clinical Pharmacology, 11(8), 789-795. https://doi.org/10.1080/17512433.2018.1501555 DOI: https://doi.org/10.1080/17512433.2018.1501555
Gibson, G. (2019). Going to the negative: Genomics for optimized medical prescription. Nature Reviews Genetics, 20(2), 1-2. https://doi.org/10.1038/s41576-018-0061-7 DOI: https://doi.org/10.1038/s41576-018-0061-7
Goetzl, E. J., Mustapic, M., Kapogiannis, D., Eitan, E., Lobach, I. V., Goetzl, L., et al. (2016). Cargo proteins of plasma astrocyte-derived exosomes in Alzheimer's disease. FASEB Journal, 30, 3853-3859. https://doi.org/10.1096/fj.201600756R DOI: https://doi.org/10.1096/fj.201600756R
Goetzl, E. J., Schwartz, J. B., Mustapic, M., Lobach, I. V., Daneman, R., Abner, E. L., et al. (2017). Altered cargo proteins of human plasma endothelial cell-derived exosomes in atherosclerotic cerebrovascular disease. FASEB Journal, 31, 3689-3694. https://doi.org/10.1096/fj.201700149 DOI: https://doi.org/10.1096/fj.201700149
Goetzl, E. J., Abner, E. L., Jicha, G. A., Kapogiannis, D., & Schwartz, J. B. (2018a). Declining levels of functionally specialized synaptic proteins in plasma neuronal exosomes with progression of Alzheimer's disease. FASEB Journal, 32, 888-893. https://doi.org/10.1096/fj.201700731R DOI: https://doi.org/10.1096/fj.201700731R
Goetzl, E. J., Schwartz, J. B., Abner, E. L., Jicha, G. A., & Kapogiannis, D. (2018b). High complement levels in astrocyte-derived exosomes of Alzheimer disease. Annals of Neurology, 83, 544-552. https://doi.org/10.1002/ana.25172 DOI: https://doi.org/10.1002/ana.25172
Goldberg, K. B., Blumenthal, G. M., McKee, A. E., & Pazdur, R. (2018). The FDA oncology center of excellence and precision medicine. Experimental Biology & Medicine, 243, 308-312. https://doi.org/10.1177/1535370217740861 DOI: https://doi.org/10.1177/1535370217740861
Hampel, H., O'Bryant, S. E., Molinuevo, J. L., Zetterberg, H., Masters, C. L., Lista, S., et al. (2018a). Blood-based biomarkers for Alzheimer disease: Mapping the road to the clinic. Nature Reviews Neurology, 14, 639-652. https://doi.org/10.1038/s41582-018-0079-7 DOI: https://doi.org/10.1038/s41582-018-0079-7
Hampel, H., Toschi, N., Baldacci, F., Zetterberg, H., Blennow, K., Kilimann, I., et al. (2018b). Alzheimer's disease biomarker-guided diagnostic workflow using the added value of six combined cerebrospinal fluid candidates: Aβ1-42, total-tau, phosphorylated-tau, NFL, neurogranin, and YKL-40. Alzheimer's & Dementia, 14, 492-501. https://doi.org/10.1016/j.jalz.2017.11.015 DOI: https://doi.org/10.1016/j.jalz.2017.11.015
Hampel, H., Vergallo, A., Aguilar, L. F., Benda, N., Broich, K., Cuello, A. C., et al. (2018c). Precision pharmacology for Alzheimer's disease. Pharmacology Research, 130, 331-365. https://doi.org/10.1016/j.phrs.2018.02.014 DOI: https://doi.org/10.1016/j.phrs.2018.02.014
Heitzer, E., Haque, I. S., Roberts, C. E. S., & Speicher, M. R. (2019). Current and future perspectives of liquid biopsies in genomics-driven oncology. Nature Reviews Genetics, 20, 71-88. https://doi.org/10.1038/s41576-018-0071-5 DOI: https://doi.org/10.1038/s41576-018-0071-5
Jack, C. R. J., Bennett, D. A., Blennow, K., Carrillo, M. C., Feldman, H. H., Frisoni, G. B., et al. (2016). A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology, 87, 539-547. https://doi.org/10.1212/WNL.0000000000002923 DOI: https://doi.org/10.1212/WNL.0000000000002923
Jorgensen, J. T., & Hersom, M. (2016). Companion diagnostics—a tool to improve pharmacotherapy. Annals of Translational Medicine, 4, 482. https://doi.org/10.21037/atm.2016.12.26 DOI: https://doi.org/10.21037/atm.2016.12.26
Haller, S., Deindl, P., Cassini, A., et al. (2016). Neurological sequelae of healthcare-associated sepsis in very-low-birthweight infants: Umbrella review and evidence-based outcome tree. Euro Surveillance, 21(8). DOI: https://doi.org/10.2807/1560-7917.ES.2016.21.8.30143
Page, G. G., Corwin, E. J., Dorsey, S. G., Redeker, N. S., & Jo, D. (2018). Biomarkers as common data elements for symptom and self-management science. Journal of Nursing Scholarship, 50(3), 276–286. DOI: https://doi.org/10.1111/jnu.12378
Corwin, E. J., & Ferranti, E. P. (2016). Integration of biomarkers to advance precision nursing interventions for family research across the life span. Nursing Outlook, 64(4), 292–298. DOI: https://doi.org/10.1016/j.outlook.2016.04.007
Martin, J. B., & Badeaux, J. E. (2017). Interpreting laboratory tests in infection: Making sense of biomarkers in sepsis and systemic inflammatory response syndrome for intensive care unit patients. Critical Care Nursing Clinics, 29(1), 119–130. DOI: https://doi.org/10.1016/j.cnc.2016.09.004
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