Abstract
Purpose
Ceritinib 750 mg/day was approved for the treatment of patients with untreated anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC) based on ASCEND-4 study. The objective of this article is to introduce the use of time-dependent modeling approach in the updated exposure–efficacy analysis of ceritinib for the first-line indication.
Methods
Exposure–efficacy analyses, including data from 156 patients, were first conducted using time-independent logistic regression model for response of complete or partial response and Cox regression model for progression-free survival (PFS). The exposure measure used was average Ctrough, which is defined as the geometric mean of all evaluable Ctrough for each patient. To further investigate the impact of exposure measure on exposure–efficacy analyses, a time-dependent modeling approach was used, where exposure at different time intervals was associated with the corresponding response endpoints in a longitudinal manner.
Results
With exposure measure being average Ctrough, it was observed that higher exposure was associated with reduced efficacy in terms of response (odds ratio = 0.77) and PFS [hazard ratio (HR) = 1.12]. These time-independent models do not account for the impact of time-varying concentration due to dose modifications. Subsequently, a new time-dependent modeling approach was used, where exposure and efficacy were associated longitudinally in the analyses. The results showed that the odds ratio of response became 1.07, and the HR of PFS became 1.04, indicating no apparent reverse relationship between exposure and efficacy across the exposure range studied.
Conclusion
The drug effect on efficacy in clinical trials could be better characterized using time-dependent exposure–response models.
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References
Soda M, Choi YL, Enomoto M et al (2007) Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature 448:561–566. https://doi.org/10.1038/nature05945
Shaw AT, Yeap BY, Mino-Kenudson M et al (2009) Clinical features and outcome of patients with non-small-cell lung cancer who harbor EML4-ALK. J Clin Oncol 27:4247–4253. https://doi.org/10.1200/JCO.2009.22.6993
Shaw AT, Kim D-W, Mehra R et al (2014) Ceritinib in ALK-rearranged non-small-cell lung cancer. N Engl J Med 370:1189–1197. https://doi.org/10.1056/NEJMoa1311107
US Food and Drug Administration. Zykadia prescribing information. https://www.pharma.us.novartis.com/sites/www.pharma.us.novartis.com/files/zykadia.pdf (2017). Accessed 13 Dec 2017
Soria JC, Tan DSW, Chiari R et al (2017) First-line ceritinib versus platinum-based chemotherapy in advanced ALK-rearranged non-small-cell lung cancer (ASCEND-4): a randomised, open-label, phase 3 study. Lancet 389:917–929. https://doi.org/10.1016/S0140-6736(17)30123-X
US Food and Drug Administration. Guidance for industry: exposure-response relationships-study design, data analysis, and regulatory applications. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM072109.pdf (2003). Accessed 29 Jan 2019
US Food and Drug Administration (2014) Clinical pharmacology and biopharmaceutics review(s) of ceritinib. http://www.accessdata.fda.gov/drugsatfda_docs/nda/2014/205755Orig1s000ClinPharmR.pdf. Accessed 13 Dec 2017
Khozin S, Blumenthal GM, Zhang L et al (2015) FDA approval: ceritinib for the treatment of metastatic anaplastic lymphoma kinase-positive non-small cell lung cancer. Clin Cancer Res 21:2436–2439. https://doi.org/10.1158/1078-0432.CCR-14-3157
US Food and Drug Administration (2014) Medical review(s) of ceritinib. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2014/205755Orig1s000MedR.pdf. Accessed 13 Dec 2017
US Food and Drug Administration (2014) Clinical pharmacology and biopharmaceutics review(s) of panobinostat. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2015/205353Orig1s000ClinPharmR.pdf. Accessed 13 Dec 2017
Liu C (2016) Dose adjustment integrated exposure-response analysis (DAIER) for dose optimization lenvatinib in renal cell carcinoma FDA-AACR Oncology Dose Finding Workshop. http://www.aacr.org/AdvocacyPolicy/GovernmentAffairs/Documents/6.13.16%20FDA-AACR%20Dose%20Finding%20for%20Online.pdf. Accessed 11 May 2018
Anderson JR, Cain KC, Gelber RD (1983) Analysis of survival by tumor response. J Clin Oncol 11:710–719. https://doi.org/10.1200/JCO.1983.1.11.710
Anderson JR, Cain KC, Gelber RD (2008) Analysis of survival by tumor response and other comparisons of time-to-event by outcome variables. J Clin Oncol 26:3913–3915. https://doi.org/10.1200/JCO.2008.16.1000
Giobbie-Hurder A, Gelber RD, Regan MM (2013) Challenges of guarantee-time bias. J Clin Oncol 31:2963–2969. https://doi.org/10.1200/JCO.2013.49.5283
Heudi O, Vogel D, Lau YY, Picard F, Kretz O (2014) Liquid chromatography tandem mass spectrometry method for the quantitative analysis of ceritinib in human plasma and its application to pharmacokinetic studies. Anal Bioanal Chem 406:7389–7396. https://doi.org/10.1007/s00216-014-8125-9
Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247. https://doi.org/10.1016/j.ejca.2008.10.026
Hong Y, Passos VQ, Huang PH, Lau YY (2017) Population pharmacokinetics of ceritinib in adult patients with tumors characterized by genetic abnormalities in anaplastic lymphoma kinase. J Clin Pharmacol 57:652–662. https://doi.org/10.1002/jcph.849
Bruno R, Claret L (2009) On the use of change in tumor size to predict survival in clinical oncology studies: toward a new paradigm to design and evaluate phase II studies. Clin Pharmacol Ther 86:136–138. https://doi.org/10.1038/clpt.2009.97
Claret L, Bruno R (2014) Assessment of tumor growth inhibition metrices to predict overall survival. Clin Pharmacol Ther 96:135–137. https://doi.org/10.1038/clpt.2014.112
Acknowledgements
The authors thank Zhe Chen for his contribution to the exploratory data analysis. Medical editorial assistance for this manuscript was provided by Shiva Krishna Rachamadugu, Novartis Healthcare Pvt. Ltd.
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This study was funded by Novartis Pharmaceutical Corporation.
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YY Lau, W Gu, YY Ho, X Zhang and P Urban are employees of Novartis Pharmaceutical Corporation. YY Lau, YY Ho, X Zhang and W Gu also own Novartis stocks. Y Hong declares no conflicts of interest.
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
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Ying Hong was an employee of Novartis at the time of analysis and initiation of the manuscript.
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Lau, Y.Y., Gu, W., Ho, YY. et al. Application of time-dependent modeling for the exposure–efficacy analysis of ceritinib in untreated ALK-rearranged advanced NSCLC patients. Cancer Chemother Pharmacol 84, 501–511 (2019). https://doi.org/10.1007/s00280-019-03830-5
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DOI: https://doi.org/10.1007/s00280-019-03830-5