Advertisement

Metabolomics

, 14:3 | Cite as

Targeted and global pharmacometabolomics in everolimus-based immunosuppression: association of co-medication and lysophosphatidylcholines with dose requirement

  • Dorothea Lesche
  • Vilborg Sigurdardottir
  • Alexander B. Leichtle
  • Christos T. Nakas
  • Uwe Christians
  • Lars Englberger
  • Martin Fiedler
  • Carlo R. Largiadèr
  • Paul MohacsiEmail author
  • Johanna Sistonen
Original Article

Abstract

Introduction

The immunosuppressive therapy with everolimus (ERL) after heart transplantation is characterized by a narrow therapeutic window and a substantial variability in dose requirement. Factors explaining this variability are largely unknown.

Objectives

Our aim was to evaluate factors affecting ERL metabolism and to identify novel metabolites associated with the individual ERL dose requirement to elucidate mechanisms underlying ERL dose response variability.

Method

We used liquid chromatography coupled with mass spectrometry for quantification of ERL metabolites in 41 heart transplant patients and evaluated the effect of clinical and genetic factors on ERL pharmacokinetics. Non-targeted plasma metabolic profiling by ultra-performance liquid chromatography and high resolution quadrupole-time-of-flight mass spectrometry was used to identify novel metabolites associated with ERL dose requirement.

Results

The determination of ERL metabolites revealed differences in metabolite patterns that were independent from clinical or genetic factors. Whereas higher ERL dose requirement was associated with co-administration of sodium-mycophenolic acid and the CYP3A5 expressor genotype, lower dose was required for patients receiving vitamin K antagonists. Global metabolic profiling revealed several novel metabolites associated with ERL dose requirement. One of them was identified as lysophosphatidylcholine (lysoPC) (16:0/0:0). Subsequent targeted analysis revealed that high levels of several lysoPCs were significantly associated with higher ERL dose requirement.

Conclusion

For the first time, this study describes distinct ERL metabolite patterns in heart transplant patients and detected potentially new drug–drug interactions. The global metabolic profiling facilitated the discovery of novel metabolites associated with ERL dose requirement that might represent new clinically valuable biomarkers to guide ERL therapy.

Keywords

Pharmacometabolomics Everolimus metabolism Heart transplantation Dose requirement 

Notes

Acknowledgements

We thank Barbara Rindlisbacher and Roland Geyer form the Clinical Metabolomics Facility at the Centre of Laboratory Medicine (Bern University Hospital) and Bjoern Schniedewind from the iC42 Clinical Research and Development Facility in Denver for technical support, as well as the personnel of the heart failure outpatient clinic at the Bern University Hospital for assistance in patient recruitment.

Funding

The study was funded by grants from the Foundation for Pathobiochemistry and Molecular Diagnostics of the German Society of Clinical Chemistry and Laboratory Medicine to J.S., from the United States National Institutes of Health (NICHD R01 HD070511) to U.C., and from the Katharina Huber-Steiner foundation to P.M.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest directly or indirectly related to the research presented in this manuscript.

Supplementary material

11306_2017_1294_MOESM1_ESM.docx (642 kb)
Supplementary material 1 (DOCX 641 KB)

References

  1. Andreassen, A. K., Andersson, B., Gustafsson, F., Eiskjaer, H., Radegran, G., Gude, E., et al. (2014). Everolimus initiation and early calcineurin inhibitor withdrawal in heart transplant recipients: a randomized trial. American Journal of Transplant, 14(8), 1828–1838.  https://doi.org/10.1111/ajt.12809.CrossRefGoogle Scholar
  2. Azimzadeh, A. M., Lees, J. R., Ding, Y., & Bromberg, J. S. (2011). Immunobiology of transplantation: impact on targets for large and small molecules. Clinical Pharmacology & Therapeutics, 90(2), 229–242.  https://doi.org/10.1038/clpt.2011.106.CrossRefGoogle Scholar
  3. Boernsen, K. O., Egge-Jacobsen, W., Inverardi, B., Strom, T., Streit, F., Schiebel, H. M., et al. (2007). Assessment and validation of the MS/MS fragmentation patterns of the macrolide immunosuppressant everolimus. Journal of Mass Spectrometry, 42(6), 793–802.  https://doi.org/10.1002/jms.1215.CrossRefPubMedGoogle Scholar
  4. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference (1st edn.). New York: Springer.Google Scholar
  5. Chapman, T. M., & Perry, C. M. (2004). Everolimus. Drugs, 64(8), 861–872 (discussion 873–864).CrossRefPubMedGoogle Scholar
  6. Eisen, H. J., Tuzcu, E. M., Dorent, R., Kobashigawa, J., Mancini, D., Valantine-von Kaeppler, H. A., et al. (2003). Everolimus for the prevention of allograft rejection and vasculopathy in cardiac-transplant recipients. New England Journal of Medicine, 349(9), 847–858.  https://doi.org/10.1056/NEJMoa022171.CrossRefPubMedGoogle Scholar
  7. Everett, J. R. (2016). From metabonomics to pharmacometabonomics: The role of metabolic profiling in personalized medicine. Frontiers in Pharmacology.  https://doi.org/10.3389/fphar.2016.00297.Google Scholar
  8. Excoffier, L., & Lischer, H. E. (2010). Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources, 10(3), 564–567.  https://doi.org/10.1111/j.1755-0998.2010.02847.x.CrossRefPubMedGoogle Scholar
  9. Fahy, E., Sud, M., Cotter, D., & Subramaniam, S. (2007). LIPID MAPS online tools for lipid research. Nucleic Acids Research, 35, W606–W612.  https://doi.org/10.1093/nar/gkm324.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Fang, Z. Z., & Gonzalez, F. J. (2014). LC-MS-based metabolomics: An update. Archives of Toxicology, 88(8), 1491–1502.  https://doi.org/10.1007/s00204-014-1234-6.CrossRefPubMedGoogle Scholar
  11. Furger, P., & Suter, T. M. (2009). SURFmed: Guidelines internal medicine (2nd ed.). Neuhausen: Editions D&F GmbH.Google Scholar
  12. Hernandez-Ruedas, M. A., Arroyo-Rodriguez, V., Meave, J. A., Martinez-Ramos, M., Ibarra-Manriquez, G., Martinez, E., et al. (2014). Conserving tropical tree diversity and forest structure: the value of small rainforest patches in moderately-managed landscapes. PLoS ONE, 9(6), e98931,  https://doi.org/10.1371/journal.pone.0098931.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Hiemann, N. E., Wellnhofer, E., Lehmkuhl, H. B., Knosalla, C., Hetzer, R., & Meyer, R. (2011). Everolimus prevents endomyocardial remodeling after heart transplantation. Transplantation, 92(10), 1165–1172.  https://doi.org/10.1097/TP.0b013e3182332886.CrossRefPubMedGoogle Scholar
  14. Hollis, I. B., Reed, B. N., & Moranville, M. P. (2015). Medication management of cardiac allograft vasculopathy after heart transplantation. Pharmacotherapy, 35(5), 489–501. doi: https://doi.org/10.1002/phar.1580.CrossRefPubMedGoogle Scholar
  15. Jasiak, N. M., & Park, J. M. (2016). Immunosuppression in solid-organ transplantation: Essentials and practical tips. Critical Care Nursing Quarterly, 39(3), 227–240.  https://doi.org/10.1097/CNQ.0000000000000117.CrossRefPubMedGoogle Scholar
  16. Ji, Y., Hebbring, S., Zhu, H., Jenkins, G. D., Biernacka, J., Snyder, K., et al. (2011). Glycine and a glycine dehydrogenase (GLDC) SNP as citalopram/escitalopram response biomarkers in depression: Pharmacometabolomics-informed pharmacogenomics. Clinical Pharmacology & Therapeutics, 89(1), 97–104.  https://doi.org/10.1038/clpt.2010.250.CrossRefGoogle Scholar
  17. Kaddurah-Daouk, R., & Weinshilboum, R. & Pharmacometabolomics-Research-Network (2015). Metabolomic signatures for drug response phenotypes: Pharmacometabolomics enables precision medicine. Clinical Pharmacology & Therapeutics, 98(1), 71–75,  https://doi.org/10.1002/cpt.134.CrossRefGoogle Scholar
  18. Kessey, A., Lewin, A., & Strimmer, K. (2015). Optimal whitening and decorrelation. arXiv:1512.00809v1.Google Scholar
  19. Kirchner, G. I., Meier-Wiedenbach, I., & Manns, M. P. (2004). Clinical pharmacokinetics of everolimus. Clinical Pharmacokinetics, 43(2), 83–95.  https://doi.org/10.2165/00003088-200443020-00002.CrossRefPubMedGoogle Scholar
  20. Kovarik, J. M., Eisen, H., Dorent, R., Mancini, D., Vigano, M., Rouilly, M., et al. (2003). Everolimus in de novo cardiac transplantation: Pharmacokinetics, therapeutic range, and influence on cyclosporine exposure. The Journal of Heart and Lung Transplantation, 22(10), 1117–1125.CrossRefPubMedGoogle Scholar
  21. Lavi, S., McConnell, J. P., Rihal, C. S., Prasad, A., Mathew, V., Lerman, L. O., et al. (2007). Local production of lipoprotein-associated phospholipase A2 and lysophosphatidylcholine in the coronary circulation: association with early coronary atherosclerosis and endothelial dysfunction in humans. Circulation, 115(21), 2715–2721.  https://doi.org/10.1161/CIRCULATIONAHA.106.671420.CrossRefPubMedGoogle Scholar
  22. Lemaitre, F., Bezian, E., Goldwirt, L., Fernandez, C., Farinotti, R., Varnous, S., et al. (2012). Population pharmacokinetics of everolimus in cardiac recipients: comedications, ABCB1, and CYP3A5 polymorphisms. Therapeutic Drug Monitoring, 34(6), 686–694.  https://doi.org/10.1097/FTD.0b013e318273c899.CrossRefPubMedGoogle Scholar
  23. Lesche, D., Sigurdardottir, V., Setoud, R., Englberger, L., Fiedler, G. M., Largiader, C. R., et al. (2015). Influence of CYP3A5 genetic variation on everolimus maintenance dosing after cardiac transplantation. Cliniacl Transplantation, 29(12), 1213–1220.  https://doi.org/10.1111/ctr.12653.CrossRefGoogle Scholar
  24. Levey, A. S., Stevens, L. A., Schmid, C. H., Zhang, Y. L., Castro, A. F., Feldman, H. I., et al. (2009). A new equation to estimate glomerular filtration rate. Annals of Internal Medicine, 150(9), 604–612.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Matsumoto, T., Kobayashi, T., & Kamata, K. (2007). Role of lysophosphatidylcholine (LPC) in atherosclerosis. Current Medicinal Chemistry, 14(30), 3209–3220.CrossRefPubMedGoogle Scholar
  26. Moes, D. J., Swen, J. J., den Hartigh, J., van der Straaten, T., van der Heide, J. J., Sanders, J. S., et al. (2014). Effect of CYP3A4*22, CYP3A5*3, and CYP3A combined genotypes on cyclosporine, everolimus, and tacrolimus pharmacokinetics in renal transplantation. CPT Pharmacometrics & Systems Pharmacology, 3, e100.  https://doi.org/10.1038/psp.2013.78.CrossRefGoogle Scholar
  27. Otagiri, M., Fleitman, J. S., & Perrin, J. H. (1980). Investigations into the binding of phenprocoumon to albumin using fluorescence spectroscopy. Journal of Pharmacy and Pharmacology, 32(7), 478–482.CrossRefPubMedGoogle Scholar
  28. Patti, G. J., Yanes, O., & Siuzdak, G. (2012). Innovation: Metabolomics: The apogee of the omics trilogy. Nature Reviews Molecular Cell Biology, 13(4), 263–269.  https://doi.org/10.1038/nrm3314.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Raichlin, E., & Kushwaha, S. S. (2008). Proliferation signal inhibitors and cardiac allograft vasculopathy. Current Opinion in Organ Transplantation, 13(5), 543–550.  https://doi.org/10.1097/MOT.0b013e32830fdf70.CrossRefPubMedGoogle Scholar
  30. Ramautar, R., Berger, R., van der Greef, J., & Hankemeier, T. (2013). Human metabolomics: Strategies to understand biology. Current Opinion in Chemical Biology, 17(5), 841–846.  https://doi.org/10.1016/j.cbpa.2013.06.015.CrossRefPubMedGoogle Scholar
  31. Rosing, K., Fobker, M., Kannenberg, F., Gunia, S., Dell’Aquila, A. M., Kwiecien, R., et al. (2013). Everolimus therapy is associated with reduced lipoprotein-associated phospholipase A2 (Lp-Pla2) activity and oxidative stress in heart transplant recipients. Atherosclerosis, 230(1), 164–170.  https://doi.org/10.1016/j.atherosclerosis.2013.07.007.CrossRefPubMedGoogle Scholar
  32. Schmitz, G., & Ruebsaamen, K. (2010). Metabolism and atherogenic disease association of lysophosphatidylcholine. Atherosclerosis, 208(1), 10–18.  https://doi.org/10.1016/j.atherosclerosis.2009.05.029.CrossRefPubMedGoogle Scholar
  33. Schniedewind, B., Niederlechner, S., Galinkin, J. L., Johnson-Davis, K. L., Christians, U., & Meyer, E. J. (2015). Long-term cross-validation of everolimus therapeutic drug monitoring assays: the Zortracker study. Therapeutic Drug Monitoring, 37(3), 296–303.  https://doi.org/10.1097/FTD.0000000000000191.CrossRefPubMedPubMedCentralGoogle Scholar
  34. Schoeppler, K. E., Aquilante, C. L., Kiser, T. H., Fish, D. N., & Zamora, M. R. (2014). The impact of genetic polymorphisms, diltiazem, and demographic variables on everolimus trough concentrations in lung transplant recipients. Clinical Transplantation, 28(5), 590–597.  https://doi.org/10.1111/ctr.12350.CrossRefPubMedGoogle Scholar
  35. Schweiger, M., Stiegler, P., Puntschart, A., Sereinigg, M., Prenner, G., Wasler, A., et al. (2012). Everolimus in different combinations as maintenance immunosuppressive therapy in heart transplant recipients. Experimental and Clinical Transplantation, 10(3), 273–277.CrossRefPubMedGoogle Scholar
  36. Serna, J., Garcia-Seisdedos, D., Alcazar, A., Lasuncion, M. A., Busto, R., & Pastor, O. (2015). Quantitative lipidomic analysis of plasma and plasma lipoproteins using MALDI-TOF mass spectrometry. Chemistry and Physics of Lipids, 189, 7–18.  https://doi.org/10.1016/j.chemphyslip.2015.05.005.CrossRefPubMedGoogle Scholar
  37. Smith, C. A., O’Maille, G., Want, E. J., Qin, C., Trauger, S. A., Brandon, T. R., et al. (2005). METLIN: A metabolite mass spectral database. Therapeutic Drug Monitoring, 27(6), 747–751.CrossRefPubMedGoogle Scholar
  38. Strom, T., Haschke, M., Boyd, J., Roberts, M., Arabshahi, L., Marbach, P., et al. (2007a). Crossreactivity of isolated everolimus metabolites with the Innofluor Certican immunoassay for therapeutic drug monitoring of everolimus. Therapeutic Drug Monitoring, 29(6), 743–749.  https://doi.org/10.1097/FTD.0b013e31815b3cbf.CrossRefPubMedGoogle Scholar
  39. Strom, T., Haschke, M., Zhang, Y. L., Bendrick-Peart, J., Boyd, J., Roberts, M., et al. (2007b). Identification of everolimus metabolite patterns in trough blood samples of kidney transplant patients. Therapeutic Drug Monitoring, 29(5), 592–599.  https://doi.org/10.1097/FTD.0b013e3181570830.CrossRefPubMedGoogle Scholar
  40. Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., et al. (2007). Proposed minimum reporting standards for chemical analysis. Metabolomics, 3(3), 211–221.  https://doi.org/10.1007/s11306-007-0082-2.CrossRefPubMedPubMedCentralGoogle Scholar
  41. Tellis, C. C., & Tselepis, A. D. (2009). The role of lipoprotein-associated phospholipase A2 in atherosclerosis may depend on its lipoprotein carrier in plasma. Biochim Biophys Acta, 1791(5), 327–338.  https://doi.org/10.1016/j.bbalip.2009.02.015.CrossRefPubMedGoogle Scholar
  42. Ufer, M., Svensson, J. O., Krausz, K. W., Gelboin, H. V., Rane, A., & Tybring, G. (2004). Identification of cytochromes P450 2C9 and 3A4 as the major catalysts of phenprocoumon hydroxylation in vitro. European Journal of Clinical Pharmacology, 60(3), 173–182.  https://doi.org/10.1007/s00228-004-0740-5.CrossRefPubMedGoogle Scholar
  43. Wessler, J. D., Grip, L. T., Mendell, J., & Giugliano, R. P. (2013). The P-glycoprotein transport system and cardiovascular drugs. Journal of the American College of Cardiology, 61(25), 2495–2502.  https://doi.org/10.1016/j.jacc.2013.02.058.CrossRefPubMedGoogle Scholar
  44. Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., et al. (2013). HMDB 3.0–The human metabolome database in 2013. Nucleic Acids Research, 41, D801–D807.  https://doi.org/10.1093/nar/gks1065.CrossRefPubMedGoogle Scholar
  45. Wolf, S., Schmidt, S., Muller-Hannemann, M., & Neumann, S. (2010). In silico fragmentation for computer assisted identification of metabolite mass spectra. BMC Bioinformatics, 11, 148.  https://doi.org/10.1186/1471-2105-11-148.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Dorothea Lesche
    • 1
    • 2
  • Vilborg Sigurdardottir
    • 3
  • Alexander B. Leichtle
    • 1
  • Christos T. Nakas
    • 1
    • 4
  • Uwe Christians
    • 5
  • Lars Englberger
    • 6
  • Martin Fiedler
    • 1
  • Carlo R. Largiadèr
    • 1
  • Paul Mohacsi
    • 6
    Email author
  • Johanna Sistonen
    • 1
  1. 1.University Institute of Clinical Chemistry, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
  2. 2.Graduate School for Cellular and Biomedical SciencesUniversity of BernBernSwitzerland
  3. 3.Department of Cardiology, Swiss Cardiovascular Centre, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland
  4. 4.Laboratory of BiometryUniversity of ThessalyVolosGreece
  5. 5.iC42 Clinical Research and DevelopmentUniversity of ColoradoAuroraUSA
  6. 6.Department of Cardiovascular Surgery, Swiss Cardiovascular Centre, Inselspital, Bern University HospitalUniversity of BernBernSwitzerland

Personalised recommendations