Skip to main content

Advertisement

Log in

Pharmacokinetic and cytokine profiles of melanoma patients with dabrafenib and trametinib-induced pyrexia

  • Original Article
  • Published:
Cancer Chemotherapy and Pharmacology Aims and scope Submit manuscript

Abstract

Purpose

The combination of a BRAF inhibitor dabrafenib and a MEK inhibitor trametinib (CombiDT) has improved outcomes compared with chemotherapy or BRAF inhibitor monotherapy in advanced BRAF V600E/K melanoma. However, CombiDT causes a high incidence of pyrexia and treatment interruptions. Pharmacokinetic analysis may provide an explanation for the pyrexia.

Methods

34 patients with Stage 3 BRAF V600 melanoma were treated with CombiDT on a clinical trial between August 2014 and June 2017. Plasma concentrations of drugs and metabolites were determined using validated LC–MS assays, in addition to analysis of a panel of cytokines.

Results

Pyrexia was experienced by 71% of the patients, with an additional 17% requiring dose interruption related to a pyrexia-like prodrome. Dabrafenib concentrations ranged from 4.0 to 4628 ng/ml and trametinib from 1.0 to 45 ng/ml in 34 patients. N-desmethyl-dabrafenib was the most prevalent metabolite, followed by carboxy- and hydroxy-dabrafenib. No definitive association between pyrexia and AUC or Cmin of the drugs, or metabolites could be observed. The level of IL-1B at the early during treatment (EDT) (as a % of pre-treatment) was higher in the pyrexia group (median 109% (range 32–681%) than in the no-incidence group [56% (26–79%)] (p = 0.029). Similarly, the level of IL-6 at EDT was higher in the pyrexia group [181% (34-3156%) vs 73% (57–101%)] (p = 0.028).

Conclusions

No apparent associations between pyrexia and exposure to the drugs or metabolites could be observed. Greater elevations in IL-1B and IL-6 were observed in patients with pyrexia during the first week of treatment compared to those without pyrexia.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Chapman PB et al (2011) Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med 364(26):2507–2516

    Article  CAS  Google Scholar 

  2. Hauschild A et al (2012) Dabrafenib in BRAF-mutated metastatic melanoma: a multicentre, open-label, phase 3 randomised controlled trial. Lancet 380(9839):358–365

    Article  CAS  Google Scholar 

  3. Flaherty KT et al (2012) Improved survival with MEK inhibition in BRAF-mutated melanoma. N Engl J Med 367(2):107–114

    Article  CAS  Google Scholar 

  4. Flaherty KT et al (2012) Combined BRAF and MEK inhibition in melanoma with BRAF V600 mutations. N Engl J Med 367(18):1694–1703

    Article  CAS  Google Scholar 

  5. Long GV et al (2014) Combined BRAF and MEK inhibition versus BRAF inhibition alone in melanoma. N Engl J Med 371(20):1877–1888

    Article  Google Scholar 

  6. Long GV et al (2015) Dabrafenib and trametinib versus dabrafenib and placebo for Val600 BRAF-mutant melanoma: a multicentre, double-blind, phase 3 randomised controlled trial. Lancet 386(9992):444–451

    Article  CAS  Google Scholar 

  7. Long GV et al (2017) Dabrafenib plus trametinib versus dabrafenib monotherapy in patients with metastatic BRAF V600E/K-mutant melanoma: long-term survival and safety analysis of a phase 3 study. Ann Oncol 28(7):1631–1639

    Article  CAS  Google Scholar 

  8. Robert C et al (2015) Improved overall survival in melanoma with combined dabrafenib and trametinib. N Engl J Med 372(1):30–39

    Article  Google Scholar 

  9. Long GV et al (2017) Adjuvant dabrafenib plus trametinib in stage III BRAF-mutated melanoma. N Engl J Med 377(19):1813–1823

    Article  CAS  Google Scholar 

  10. Menzies AM et al (2015) Characteristics of pyrexia in BRAFV600E/K metastatic melanoma patients treated with combined dabrafenib and trametinib in a phase I/II clinical trial. Ann Oncol 26(2):415–421

    Article  CAS  Google Scholar 

  11. Rousset M et al (2017) Trough dabrafenib plasma concentrations can predict occurrence of adverse events requiring dose reduction in metastatic melanoma. Clin Chim Acta 472:26–29

    Article  CAS  Google Scholar 

  12. Long GV et al (2014) Increased MAPK reactivation in early resistance to dabrafenib/trametinib combination therapy of BRAF-mutant metastatic melanoma. Nat Commun 5:5694

    Article  CAS  Google Scholar 

  13. Das Thakur M et al (2013) Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature 494(7436):251–255

    Article  CAS  Google Scholar 

  14. Lee CI et al (2014) Features and management of pyrexia with combined dabrafenib and trametinib in metastatic melanoma. Melanoma Res 24(5):468–474

    Article  CAS  Google Scholar 

  15. Menzies AM et al. (2017) 1220PDPhase 2 study of neoadjuvant dabrafenib + trametinib (D + T) for resectable stage IIIB/C BRAF V600 mutant melanoma. Ann Oncol 28(suppl_5):mdx377.007

    Article  Google Scholar 

  16. Bershas DA et al (2013) Metabolism and disposition of oral dabrafenib in cancer patients: proposed participation of aryl nitrogen in carbon-carbon bond cleavage via decarboxylation following enzymatic oxidation. Drug Metab Dispos 41(12):2215–2224

    Article  CAS  Google Scholar 

  17. 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 Programs Biomed 79(3):241–257

    Article  Google Scholar 

  18. Keizer RJ et al (2011) Pirana and PCluster: a modeling environment and cluster infrastructure for NONMEM. Comput Methods Programs Biomed 101(1):72–79

    Article  Google Scholar 

  19. R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. Accessed 28 June 2018

  20. Ouellet D et al (2014) Population pharmacokinetics of dabrafenib, a BRAF inhibitor: effect of dose, time, covariates, and relationship with its metabolites. J Clin Pharmacol 54(6):696–706

    Article  CAS  Google Scholar 

  21. Ouellet D et al (2016) Population pharmacokinetics and exposure-response of trametinib, a MEK inhibitor, in patients with BRAF V600 mutation-positive melanoma. Cancer Chemother Pharmacol 77(4):807–817

    Article  CAS  Google Scholar 

  22. Houben R et al (2004) Constitutive activation of the Ras-Raf signaling pathway in metastatic melanoma is associated with poor prognosis. J Carcinog 3:6

    Article  Google Scholar 

  23. Liu W et al (2007) Distinct clinical and pathological features are associated with the BRAF(T1799A(V600E)) mutation in primary melanoma. J Invest Dermatol 127(4):900–905

    Article  CAS  Google Scholar 

  24. Viros A et al (2008) Improving melanoma classification by integrating genetic and morphologic features. PLoS Med 5(6):e120

    Article  Google Scholar 

  25. Chang DZ et al (2004) Clinical significance of BRAF mutations in metastatic melanoma. J Transl Med 2(1):46

    Article  Google Scholar 

  26. Ugurel S et al (2007) B-RAF and N-RAS mutations are preserved during short time in vitro propagation and differentially impact prognosis. PLoS One 2(2):e236

    Article  Google Scholar 

  27. Davies H et al (2002) Mutations of the BRAF gene in human cancer. Nature 417(6892):949–954

    Article  CAS  Google Scholar 

  28. Long GV et al (2011) Prognostic and clinicopathologic associations of oncogenic BRAF in metastatic melanoma. J Clin Oncol 29(10):1239–1246

    Article  Google Scholar 

  29. Willmore-Payne C et al (2005) Human malignant melanoma: detection of BRAF- and c-kit-activating mutations by high-resolution amplicon melting analysis. Hum Pathol 36(5):486–493

    Article  CAS  Google Scholar 

  30. Flaherty KT et al (2010) Inhibition of mutated, activated BRAF in metastatic melanoma. N Engl J Med 363(9):809–819

    Article  CAS  Google Scholar 

  31. Rizos H et al (2014) BRAF inhibitor resistance mechanisms in metastatic melanoma: spectrum and clinical impact. Clin Cancer Res 20(7):1965–1977

    Article  CAS  Google Scholar 

  32. Shi H et al (2014) Acquired resistance and clonal evolution in melanoma during BRAF inhibitor therapy. Cancer Discov 4(1):80–93

    Article  CAS  Google Scholar 

  33. Van Allen EM et al (2014) The genetic landscape of clinical resistance to RAF inhibition in metastatic melanoma. Cancer Discov 4(1):94–109

    Article  Google Scholar 

  34. Kulkarni D et al (2016) Pyrexia in dabrafenib-treated melanoma patients is not associated with common genetic variation or HLA polymorphisms. Pharmacogenomics 17(5):459–462

    Article  CAS  Google Scholar 

  35. Falchook GS et al (2014) Dose selection, pharmacokinetics, and pharmacodynamics of BRAF inhibitor dabrafenib (GSK2118436). Clin Cancer Res 20(17):4449–4458

    Article  CAS  Google Scholar 

  36. Suttle AB et al (2015) Assessment of the drug interaction potential and single- and repeat-dose pharmacokinetics of the BRAF inhibitor dabrafenib. J Clin Pharmacol 55(4):392–400

    Article  CAS  Google Scholar 

  37. Falchook GS et al (2012) Dabrafenib in patients with melanoma, untreated brain metastases, and other solid tumours: a phase 1 dose-escalation trial. Lancet 379(9829):1893–1901

    Article  CAS  Google Scholar 

  38. Lawrence SK et al (2014) The metabolic drug-drug interaction profile of Dabrafenib: in vitro investigations and quantitative extrapolation of the P450-mediated DDI risk. Drug Metab Dispos 42(7):1180–1190

    Article  Google Scholar 

  39. Ouellet D et al (2013) Effects of particle size, food, and capsule shell composition on the oral bioavailability of dabrafenib, a BRAF inhibitor, in patients with BRAF mutation-positive tumors. J Pharm Sci 102(9):3100–3109

    Article  CAS  Google Scholar 

  40. Rowland A et al (2018) Physiologically based pharmacokinetic modeling to identify physiological and molecular characteristics driving variability in drug exposure. Clin Pharmacol Ther. https://doi.org/10.1002/cpt.1076

    Article  PubMed  PubMed Central  Google Scholar 

  41. Infante JR et al (2012) Safety, pharmacokinetic, pharmacodynamic, and efficacy data for the oral MEK inhibitor trametinib: a phase 1 dose-escalation trial. Lancet Oncol 13(8):773–781

    Article  CAS  Google Scholar 

  42. Cox DS et al (2013) Evaluation of the effects of food on the single-dose pharmacokinetics of trametinib, a first-in-class MEK inhibitor, in patients with cancer. J Clin Pharmacol 53(9):946–954

    Article  CAS  Google Scholar 

  43. Yamazaki N et al (2018) Phase 1/2 study assessing the safety and efficacy of dabrafenib and trametinib combination therapy in Japanese patients with BRAF V600 mutation-positive advanced cutaneous melanoma. J Dermatol 45(4):397–407

    Article  CAS  Google Scholar 

  44. Ho MY et al (2014) Trametinib, a first-in-class oral MEK inhibitor mass balance study with limited enrollment of two male subjects with advanced cancers. Xenobiotica 44(4):352–368

    Article  CAS  Google Scholar 

  45. Zhang JM, An J (2007) Cytokines, inflammation, and pain. Int Anesthesiol Clin 45(2):27–37

    Article  CAS  Google Scholar 

  46. Castell JV et al (1988) Recombinant human interleukin-6 (IL-6/BSF-2/HSF) regulates the synthesis of acute phase proteins in human hepatocytes. FEBS Lett 232(2):347–350

    Article  CAS  Google Scholar 

  47. Kozak W et al (1998) IL-6 and IL-1 beta in fever. Studies using cytokine-deficient (knockout) mice. Ann NY Acad Sci 856:33–47

    Article  CAS  Google Scholar 

  48. Harden LM et al (2008) Interleukin (IL)-6 and IL-1 beta act synergistically within the brain to induce sickness behavior and fever in rats. Brain Behav Immun 22(6):838–849

    Article  CAS  Google Scholar 

  49. Netterberg I et al (2018) The risk of febrile neutropenia in breast cancer patients following adjuvant chemotherapy is predicted by the time course of interleukin-6 and C-reactive protein by modelling. Br J Clin Pharmacol 84(3):490–500

    Article  CAS  Google Scholar 

  50. Cheng X et al (2018) Interleukin-6 producing pheochromocytoma as a new reason for fever of unknown origin: a retrospective study. Endocr Pract 24(6):507–511

    Article  CAS  Google Scholar 

  51. Andres BM et al (2003) Postoperative fever after total knee arthroplasty: the role of cytokines. Clin Orthop Relat Res 415:221–231

    Article  Google Scholar 

Download references

Acknowledgements

This work was funded by the University of Sydney, and is a sub-analysis of the trial (NCT01972347) funded by Novartis. We also acknowledge Melanoma Institute Australia for their provision of the patient samples.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hannah Yejin Kim.

Ethics declarations

Conflict of interest

A.M. Menzies is a consultant/advisor for Bristol-Myers Squibb, MSD, Novartis, Pierre-Fabre, and Roche. G.V. Long is a consultant/advisor for Amgen, Array, Bristol-Myers Squibb, Merck, Novartis, Pierre-Fabre, and Roche. All other authors have declared no conflicts of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 712 KB)

Supplementary material 2 (DOCX 139 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, H.Y., Duong, J.K., Gonzalez, M. et al. Pharmacokinetic and cytokine profiles of melanoma patients with dabrafenib and trametinib-induced pyrexia. Cancer Chemother Pharmacol 83, 693–704 (2019). https://doi.org/10.1007/s00280-019-03780-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00280-019-03780-y

Keywords

Navigation