Advertisement

Cancer Chemotherapy and Pharmacology

, Volume 82, Issue 5, pp 887–898 | Cite as

Gender differences in doxorubicin pharmacology for subjects with chemosensitive cancers of young adulthood

  • Z. Liu
  • J. Martin
  • L. Orme
  • B. Seddon
  • J. Desai
  • W. Nicholls
  • D. Thomson
  • D. Porter
  • G. McCowage
  • C. Underhill
  • N. Cranswick
  • M. Michael
  • M. Zacharin
  • A. Herschtal
  • J. Sivasuthan
  • D. M. Thomas
Original Article

Abstract

Purpose

For many cancers, adolescents and young adults (AYA) have worse outcomes than for children and adults. Many factors may contribute to the AYA survival gap, including differences in biology, therapeutic intent, and adherence to therapy. It has been observed that male AYAs have poorer outcomes than females. The purpose of this work was to test the proposition that gender-related pharmacologic factors may account for a component of the AYA survival gap.

Patients and methods

A prospective, multi-institutional pharmacologic study of 79 patients in total with chemosensitive cancers (Ewing sarcoma, osteosarcoma and Hodgkin lymphoma) was conducted, with conventional doxorubicin treatment. Pharmacokinetic data of 13 children, 40 AYAs and 13 adults were valid for analysis. Population pharmacokinetics models were developed for doxorubicin and its metabolite doxorubicinol based on the data created in this study. Consequently, model-based analysis was conducted to investigate the relevant topics.

Results

The clearance of doxorubicinol (normalized to body surface area), the main active metabolite of doxorubicin, appears faster in male AYAs than female (p = 0.04, 95% CI 0.1–3.9 L/h). The exposure of doxorubicinol (normalized to dose) is lower in male AYA than female (p = 0.03, 95% CI − 0.005 to − 0.0002 h/L). These might be correlated to the observed difference on nadir neutrophil count between male AYA and female (p = 0.027, 95% CI 0.09–1.4).

Conclusion

Gender-related differences in doxorubicin pharmacology may account for worse outcomes for male AYAs with chemosensitive cancers compared to females. These findings may reduce the AYA survival gap compared to other age groups.

Keywords

Doxorubicin Doxorubicinol Pharmacokinetics modelling Adolescent and young adult Gender Hodgkin’s lymphoma Ewing sarcoma Osteosarcoma 

Notes

Acknowledgements

Funding was provided by Victorian Cancer Agency (Grant no. CTPS_08_18).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

References

  1. 1.
    Bleyer A, Viny A, Barr R (2006) Cancer in 15- to 29-year-olds by primary site. Oncologist 11:590–601CrossRefPubMedGoogle Scholar
  2. 2.
    Jemal A, Siegel R, Ward E, Murray T, Xu J, Thun MJ (2007) Cancer statistics, 2007. CA Cancer J Clin 57:43–66CrossRefPubMedGoogle Scholar
  3. 3.
    Albritton K, Bleyer WA (2003) The management of cancer in the older adolescent. Eur J Cancer 39:2584–2599CrossRefPubMedGoogle Scholar
  4. 4.
    Bleyer A (2002) Older adolescents with cancer in North America deficits in outcome and research. Pediatr Clin North Am 49:1027–1042CrossRefPubMedGoogle Scholar
  5. 5.
    Bleyer A (2005) The adolescent and young adult gap in cancer care and outcome. Curr Probl Pediatr Adolesc Health Care 35:182–217CrossRefPubMedGoogle Scholar
  6. 6.
    Mitchell AE et al (2004) Cancer in adolescents and young adults: treatment and outcome in Victoria. Med J Aust 180:59–62PubMedGoogle Scholar
  7. 7.
    Khamly KK, Thursfield VJ, Fay M, Desai J, Toner GC, Choong PF, Ngan SY, Powell GJ, Thomas DM (2009) Gender-specific activity of chemotherapy correlates with outcomes in chemosensitive cancers of young adulthood. Int J Cancer 125:426–431CrossRefPubMedGoogle Scholar
  8. 8.
    Klimm B, Reineke T, Haverkamp H, Behringer K, Eich HT, Josting A, Pfistner B, Diehl V, Engert A (2005) Role of hematotoxicity and sex in patients with Hodgkin’s lymphoma: an analysis from the German Hodgkin Study Group. J Clin Oncol 23:8003–8011CrossRefPubMedGoogle Scholar
  9. 9.
    Bacci G, Longhi A, Ferrari S, Mercuri M, Versari M, Bertoni F (2006) Prognostic factors in non-metastatic Ewing’s sarcoma tumor of bone: an analysis of 579 patients treated at a single institution with adjuvant or neoadjuvant chemotherapy between 1972 and 1998. Acta Oncol 45:469–475CrossRefPubMedGoogle Scholar
  10. 10.
    Gehan EA, Nesbit ME Jr, Burgert EO Jr, Viettit J, Tefft M, Perez CA, Kissane J, Hempel C. (1981) Prognostic factors in children with Ewing’s sarcoma. Natl Cancer Inst Monogr 56:273–278Google Scholar
  11. 11.
    Collins M et al (2013) Benefits and adverse events in younger versus older patients receiving neoadjuvant chemotherapy for osteosarcoma: findings from a meta-analysis. J Clin Oncol 31:2303–2312CrossRefPubMedGoogle Scholar
  12. 12.
    Smeland S, Muller C, Alvegard TA, Wiklund T, Wiebe T, Bjork O, Stenwig AE, Willen H, Holmstrom T, Folleras G, Brosjo O, Kivioja A et al (2003) Scandinavian Sarcoma Group Osteosarcoma Study SSG VIII: prognostic factors for outcome and the role of replacement salvage chemotherapy for poor histological responders. Eur J Cancer 39:488–494CrossRefPubMedGoogle Scholar
  13. 13.
    Bielack SS, Kempf-Bielack B, Delling G, Exner GU, Flege S, Helmke K, Kotz R, Salzer-Kuntschik M, Werner M, Winkelmann W, Zoubek A, Jurgens H et al (2002) Prognostic factors in high-grade osteosarcoma of the extremities or trunk: an analysis of 1,702 patients treated on neoadjuvant cooperative osteosarcoma study group protocols. J Clin Oncol 20:776–790CrossRefPubMedGoogle Scholar
  14. 14.
    Martin JH, Phillips E, Thomas DM, Somogyi A (2015) Adding the ‘medicines’ back in personaolized medicine to improve cancer treatment outcomes. Br J Clin Pharmacol 80:929–931CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Kansara M, Teng W, Smyth M, Thomas DM (2014) Translational Biology of osteosarcoma. Nat Rev Cancer 14:722–735CrossRefPubMedGoogle Scholar
  16. 16.
    Gaspar N, Hawkins DS, Dirksen U, Lewis IJ, Ferrari S, Le Deley MC, Kovar H, Grimer R, Whelan J, Claude L, Delattre O, Paulussen M, Picci P, Sundby Hall K, van den Berg H, Ladenstein R, Michon J, Hjorth L, Judson I, Luksch R, Bernstein ML, Marec-Bérard P, Brennan B, Craft AW, Womer RB, Juergens H, Oberlin O (2015) Ewing sarcoma: current management and future approaches through collaboration. J Clin Oncol 33:3036–3046CrossRefPubMedGoogle Scholar
  17. 17.
    Kahn JM, Kelly KM (2018) Adolescent and young adult Hodgkin lymphoma: raising the bar through collaborative science and multidisciplinary care. Pediatr Blood Cancer 30:e27033CrossRefGoogle Scholar
  18. 18.
    Gewirtz DA (1999) A critical evaluation of the mechanisms of action proposed for the antitumor effects of the anthracycline antibiotics adriamycin and daunorubicin. Biochem Pharmacol 57:727–741CrossRefPubMedGoogle Scholar
  19. 19.
    Boucek RJ, Olson RD, Brenner DE, Ogunbunmi EM, Inui M, Fleischer S (1987) The major metabolite of doxorubicin is a potent inhibitor of membrane-associated ion pumps. A correlative study of cardiac muscle with isolated membrane fractions. J Biol Chem 262:15851–15856PubMedGoogle Scholar
  20. 20.
    Gilbert CM, McGeary RP, Filippich LJ, Norris RL, Charles BG (2005) Simultaneous liquid chromatographic determination of doxorubicin and its major metabolite doxorubicinol in parrot plasma. J Chromatogr B Anal Technol Biomed Life Sci 826(1–2):273–276CrossRefGoogle Scholar
  21. 21.
    Beal SSL, Boekmann A, Bauer RJ (2009) NONMEM’s user’s guides. ICON Development Solutions, Ellicott CityGoogle Scholar
  22. 22.
    Lindbom L, Pihlgren P, Jonsson EN (2004) Perl-speaks-NONMEM (PsN)--a Perl module for NONMEM related programming. Comput Methods Progr Biomed 75(2):85–94CrossRefGoogle Scholar
  23. 23.
    Lindbom L, Pihlgren P, Jonsson EN (2005) PsN-Toolkit—a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput Methods Progr Biomed 79(3):241–257CrossRefGoogle Scholar
  24. 24.
    http://www.pltsoft.com. Accessed 10 Sept 2018
  25. 25.
    R_Core_Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  26. 26.
    Kontny NE, Wurthwein G, Joachim B, Boddy AV, Krischke M, Fuhr U, Thompson PA, Jorger M, Schellens JH, Hempel G (2013) Population pharmacokinetics of doxorubicin: establishment of a NONMEM model for adults and children older than 3 years. Cancer Chemother Pharmacol 71(3):749–763.  https://doi.org/10.1007/s00280-013-2069-1 CrossRefPubMedGoogle Scholar
  27. 27.
    Voller S, Boos J, Krischke M, Wurthwein G, Kontny NE, Boddy AV, Hempel G (2015) Age-dependent pharmacokinetics of doxorubicin in children with cancer. Clin Pharmacokinet.  https://doi.org/10.1007/s40262-015-0272-4 CrossRefPubMedGoogle Scholar
  28. 28.
    Frost BM, Eksborg S, Bjork O, Abrahamsson J, Behrendtz M, Castor A, Forestier E, Lonnerholm G (2002) Pharmacokinetics of doxorubicin in children with acute lymphoblastic leukemia: multi-institutional collaborative study. Med Pediatr Oncol 38(5):329–337.  https://doi.org/10.1002/mpo.10052 CrossRefPubMedGoogle Scholar
  29. 29.
    Kunarajah K, Hennig S, Norris RL, Lobb M, Charles BG, Pinkerton R, Moore AS (2017) Population pharmacokinetic modelling of doxorubicin and doxorubicinol in children with cancer: is there a relationship with cardiac troponin profiles?. Cancer Chemother Pharmacol 80:15–25CrossRefPubMedGoogle Scholar
  30. 30.
    Beal SL (2001) Ways to fit a PK model with some data below the quantification limit. J Pharmacokinet Pharmacodyn 28(5):481–504CrossRefPubMedGoogle Scholar
  31. 31.
  32. 32.
    Parke J, Holford NH, Charles BG (1999) A procedure for generating bootstrap samples for the validation of nonlinear mixedeffects population models. Comput Methods Progr Biomed 59(1):19–29CrossRefGoogle Scholar
  33. 33.
    Beal SL (2001) Ways to fit a PK model with some data below the quantification limit. J 532 Pharmacokinet Pharmacodyn 28:481–504CrossRefGoogle Scholar
  34. 34.
    Pérez-Blanco JS, Santos-Buelga D, Fernández De Gatta MDM et al (2016) Population pharmacokinetic of doxorubicin and doxorubicinol in patients diagnosed with non-Hodgkin´s lymphoma. Br J Clin Pharmacol 82:1517–1527CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Callies S, de Alwis DP, Wright JG, Sandler A, Burgess M, Aarons L (2003) A population pharmacokinetic model for doxorubicin and doxorubicinol in the presence of a novel MDR modulator, zosuquidar trihydrochloride (LY335979). Cancer Chemother Pharmacol 51:107–118PubMedGoogle Scholar
  36. 36.
    Veal GJ, Hartford CM, Stewart CF (2010) Clinical pharmacology in the adolescent oncology patient. J Clin Oncol 28:4790–4799CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Cusack BJ, Young SP, Driskell J, Olson RD (1993) Doxorubicin and doxorubicinol pharmacokinetics and tissue concentrations following bolus injection and continuous infusion of doxorubicin in the rabbit. Cancer Chemother Pharmacol 32:53–58CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Z. Liu
    • 1
    • 2
  • J. Martin
    • 1
  • L. Orme
    • 3
    • 4
  • B. Seddon
    • 5
  • J. Desai
    • 3
  • W. Nicholls
    • 6
    • 7
  • D. Thomson
    • 7
  • D. Porter
    • 8
  • G. McCowage
    • 9
  • C. Underhill
    • 10
  • N. Cranswick
    • 2
  • M. Michael
    • 3
  • M. Zacharin
    • 2
  • A. Herschtal
    • 3
  • J. Sivasuthan
    • 3
  • D. M. Thomas
    • 11
  1. 1.School of Medicine and Public HealthUniversity of NewcastleCallaghanAustralia
  2. 2.Clinical Pharmacology and Department of MedicineThe Royal Children’s HospitalMelbourneAustralia
  3. 3.Division of Cancer Medicine, Peter MacCallum Cancer CentreUniversity of MelbourneMelbourneAustralia
  4. 4.Royal Children’s HospitalBrisbaneAustralia
  5. 5.University College London HospitalLondonUK
  6. 6.Brisbane Children’s HospitalBrisbaneAustralia
  7. 7.Princess Alexandra HospitalBrisbaneAustralia
  8. 8.Starship Children’s HospitalAucklandNew Zealand
  9. 9.Children’s Hospital WestmeadSydneyAustralia
  10. 10.Border Medical OncologyAlbury-WodongaAustralia
  11. 11.Cancer DivisionGarvan Institute of Medical ResearchDarlinghurstAustralia

Personalised recommendations