Skip to main content
SpringerLink
Log in

Establishing the Quantitative Relationship Between Lanreotide Autogel®, Chromogranin A, and Progression-Free Survival in Patients with Nonfunctioning Gastroenteropancreatic Neuroendocrine Tumors

  • Research Article
  • Published:
The AAPS Journal Aims and scope Submit manuscript

Abstract

The objective of this work was to establish the quantitative relationship between Lanreotide Autogel® (LAN) on serum chromogranin A (CgA) and progression-free survival (PFS) in patients with nonfunctioning gastroenteropancreatic neuroendocrine tumors (GEP-NETs) through an integrated pharmacokinetic/pharmacodynamic (PK/PD) model. In CLARINET, a phase III, randomized, double-blind, placebo-controlled study, 204 patients received deep subcutaneous injections of LAN 120 mg (n = 101) or placebo (n = 103) every 4 weeks for 96 weeks. Data for 810 LAN and 1298 CgA serum samples (n = 632 placebo and n = 666 LAN) were used to develop a parametric time-to-event model to relate CgA levels and PFS (76 patients experienced disease progression: n = 49 placebo and n = 27 LAN). LAN serum profiles were described by a one-compartment disposition model. Absorption was characterized by two parallel pathways following first- and zero-order kinetics. As PFS data were considered informative dropouts, CgA and PFS responses were modeled jointly. The LAN-induced decrease in CgA levels was described by an inhibitory E MAX model. Patient age and target lesions at baseline were associated with an increment in baseline CgA. Weibull model distribution showed that decreases in CgA from baseline reduced the hazard of disease progression significantly (P < 0.001). Covariates of tumor location in the pancreas and tumor hepatic tumor load were associated with worse prognosis (P < 0.001). We established a semimechanistic PK/PD model to better understand the effect of LAN on a surrogate endpoint (serum CgA) and ultimately the clinical endpoint (PFS) in treatment-naive patients with nonfunctioning GEP-NETs.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Yao JC, Hassan M, Phan A, Dagohoy C, Leary C, Mares JE, et al. One hundred years after “carcinoid”: epidemiology of and prognostic factors for neuroendocrine tumors in 35,825 cases in the United States. J Clin Oncol. 2008;26:3063–72.

    Article  PubMed  Google Scholar 

  2. Ramage JK, Davies AH, Ardill J, Bax N, Caplin M, Grossman A, et al. Guidelines for the management of gastroenteropancreatic neuroendocrine (including carcinoid) tumours. Gut. 2005;54 Suppl 4:iv1–16.

    PubMed  PubMed Central  Google Scholar 

  3. Arnold R, Wilke A, Rinke A, Mayer C, Kann PH, Klose K, et al. Plasma chromogranin A as marker for survival in patients with metastatic endocrine gastroenteropancreatic tumors. Clin Gastroenterol Hepatol. 2008;6:820–7.

    Article  CAS  PubMed  Google Scholar 

  4. Kaltsas GA, Besser GM, Grossman AB. The diagnosis and medical management of advanced neuroendocrine tumors. Endocr Rev. 2004;25:458–511.

    Article  CAS  PubMed  Google Scholar 

  5. Modlin IM, Lye KD, Kidd M. A 5‐decade analysis of 13,715 carcinoid tumors. Cancer. 2003;97:934–59.

    Article  PubMed  Google Scholar 

  6. Caplin ME, Pavel M, Ćwikła JB, Phan AT, Raderer M, Sedláčková E, et al. Lanreotide in metastatic enteropancreatic neuroendocrine tumors. N Engl J Med. 2014;371:224–33.

    Article  PubMed  Google Scholar 

  7. SOMATULINE DEPOT labeling revision 12/22/2014 reference ID: 3677425. http://www.accessdata.fda.gov/drugsatfda_docs/label/2014/022074s010lbl.pdf.

  8. Somatuline® autogel®. Summary of product characteristics (SmPC). Available from: https://www.medicines.org.uk/emc/medicine/25104.

  9. Oberg K, Akerstrom G, Rindi G, Jelic S, ESMO Guidelines Working Group. Neuroendocrine gastroenteropancreatic tumours: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2010;21 Suppl 5:v223–7.

    Article  PubMed  Google Scholar 

  10. Eriksson B, Oberg K, Stridsberg M. Tumor markers in neuroendocrine tumors. Digestion. 2000;62 Suppl 1:33–8.

    Article  CAS  PubMed  Google Scholar 

  11. Janson ET, Holmberg L, Stridsberg M, Eriksson B, Theodorsson E, Wilander E, et al. Carcinoid tumors: analysis of prognostic factors and survival in 301 patients from a referral center. Ann Oncol. 1997;8:685–90.

    Article  CAS  PubMed  Google Scholar 

  12. Granberg D, Wilander E, Stridsberg M, Granerus G, Skogseid B, Oberg K. Clinical symptoms, hormone profiles, treatment, and prognosis in patients with gastric carcinoids. Gut. 1998;43:223–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kulke MH, Siu LL, Tepper JE, Fisher G, Jaffe D, Haller DG, et al. Future directions in the treatment of neuroendocrine tumors: consensus report of the National Cancer Institute neuroendocrine tumor clinical trials planning meeting. J Clin Oncol. 2011;29:934–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Italiano A. Prognostic or predictive? It’s time to get back to definitions! J Clin Oncol. 2011;29:4718. author reply 4718–9.

    Article  PubMed  Google Scholar 

  15. Sargent DJ, Conley BA, Allegra C, Collette L. Clinical trial designs for predictive marker validation in cancer treatment trials. J Clin Oncol. 2005;23:2020–7.

    Article  PubMed  Google Scholar 

  16. Pape UF, Berndt U, Muller-Nordhorn J, Bohmig M, Roll S, Koch M, et al. Prognostic factors of long-term outcome in gastroenteropancreatic neuroendocrine tumours. Endocr Relat Cancer. 2008;15:1083–97.

    Article  PubMed  Google Scholar 

  17. Klimstra DS, Modlin IR, Coppola D, Lloyd RV, Suster S. The pathologic classification of neuroendocrine tumors: a review of nomenclature, grading, and staging systems. Pancreas. 2010;39:707–12.

    Article  PubMed  Google Scholar 

  18. Johanson V, Tisell LE, Olbe L, Wangberg B, Nilsson O, Ahlman H. Comparison of survival between malignant neuroendocrine tumours of midgut and pancreatic origin. Br J Cancer. 1999;80:1259–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Rinke A, Muller HH, Schade-Brittinger C, Klose KJ, Barth P, Wied M, et al. Placebo-controlled, double-blind, prospective, randomized study on the effect of octreotide LAR in the control of tumor growth in patients with metastatic neuroendocrine midgut tumors: a report from the PROMID study group. J Clin Oncol. 2009;27:4656–63.

    Article  CAS  PubMed  Google Scholar 

  20. Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, Rubinstein L, et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst. 2000;92:205–16.

    Article  CAS  PubMed  Google Scholar 

  21. Lindstrom ML, Bates DM. Nonlinear mixed effects models for repeated measures data. Biometrics. 1990;46:673–87.

    Article  CAS  PubMed  Google Scholar 

  22. Bauer R. NONMEM users guide introduction to NONMEM 7.2. 0. ICON Development Solutions Ellicott City, MD. 2011.

  23. Buil-Bruna N, Garrido M, Dehez M, Manon A, Nguyen T, Trocóniz I. Population pharmacokinetic analysis of lanreotide depot/autogel in the treatment of neuroendocrine tumors: pooled analysis of four clinical trials. Clin Pharmacokinet. 2016. doi:10.1007/s40262-015-0329-4.

  24. Box GEP, Cox DR. An analysis of transformations. J R Stat Soc Ser B Methodol. 1964;26:211–52.

    Google Scholar 

  25. Fox J, Weisberg S. An R companion to applied regression. 2nd ed. Thousand Oaks: Sage; 2011.

    Google Scholar 

  26. Post TM, Freijer JI, DeJongh J, Danhof M. Disease system analysis: basic disease progression models in degenerative disease. Pharm Res. 2005;22:1038–49.

    Article  CAS  PubMed  Google Scholar 

  27. Buil-Bruna N, López-Picazo J, Moreno-Jiménez M, Martín-Algarra S, Ribba B, Trocóniz IF. A population pharmacodynamic model for lactate dehydrogenase and neuron specific enolase to predict tumor progression in small cell lung cancer patients. AAPS J. 2014;16:609–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sheiner LB, Stanski DR, Vozeh S, Miller RD, Ham J. Simultaneous modeling of pharmacokinetics and pharmacodynamics: application to d-tubocurarine. Clin Pharmacol Ther. 1979;25:358–71.

    Article  CAS  PubMed  Google Scholar 

  29. Dayneka NL, Garg V, Jusko WJ. Comparison of four basic models of indirect pharmacodynamic responses. J Pharmacokinet Biopharm. 1993;21:457–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Hu C, Sale M. A joint model for nonlinear longitudinal data with informative dropout. J Pharmacokinet Pharmacodyn. 2003;30:83–103.

    Article  PubMed  Google Scholar 

  31. Collett D. Modelling survival data in medical research. Boca Raton: CRC; 2003.

    Google Scholar 

  32. Lindbom L, Pihlgren P, Jonsson N. PsN-toolkit—a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput Methods Prog Biomed. 2005;79:241–57.

    Article  Google Scholar 

  33. Nyberg J, Karlsson KE, Jönsson S, Simonsson USH, Karlsson MO, Hooker AC. Simulating large time-to-event trials in NONMEM. PAGE 23 (2014) Abstr 3166 [www.page-meeting.org/?abstract=3166].

  34. Schoemaker RC, Van Gerven JM, Cohen AF. Estimating potency for the E max-model without attaining maximal effects. J Pharmacokinet Biopharm. 1998;26:581–93.

    Article  CAS  PubMed  Google Scholar 

  35. Bonate PL, Suttle B. Effect of censoring due to progressive disease on tumor size kinetic parameter estimates. AAPS J. 2013;15:832–9.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Panzuto F, Nasoni S, Falconi M, Corleto VD, Capurso G, Cassetta S, et al. Prognostic factors and survival in endocrine tumor patients: comparison between gastrointestinal and pancreatic localization. Endocr Relat Cancer. 2005;12:1083–92.

    Article  PubMed  Google Scholar 

  37. Kouvaraki MA, Ajani JA, Hoff P, Wolff R, Evans DB, Lozano R, et al. Fluorouracil, doxorubicin, and streptozocin in the treatment of patients with locally advanced and metastatic pancreatic endocrine carcinomas. J Clin Oncol. 2004;22:4762–71.

    Article  CAS  PubMed  Google Scholar 

  38. Nikou G, Marinou K, Thomakos P, Papageorgiou D, Sanzanidis V, Nikolaou P, et al. Chromogranin a levels in diagnosis, treatment and follow-up of 42 patients with non-functioning pancreatic endocrine tumours. Pancreatology. 2008;8:510–9.

    Article  CAS  PubMed  Google Scholar 

  39. NCCN clinical practice guidelines in oncology/neuroendocrine tumors. Version 1.2015 [Internet]. [Cited accessed March 2015]. Available from: http://www.nccn.org/professionals/physician_gls/PDF/neuroendocrine.pdf.

  40. Almufti R, Wilbaux M, Oza A, Henin E, Freyer G, Tod M, et al. A critical review of the analytical approaches for circulating tumor biomarker kinetics during treatment. Ann Oncol. 2014;25:41–56.

    Article  CAS  PubMed  Google Scholar 

  41. Keizer RJ, Schellens JH, Beijnen JH, Huitema AD. Pharmacodynamic biomarkers in model-based drug development in oncology. Curr Clin Pharmacol. 2011;6:30–40.

    Article  CAS  PubMed  Google Scholar 

  42. Kogan Y, Halevi-Tobias K, Elishmereni M, Vuk-Pavlović S, Agur Z. Reconsidering the paradigm of cancer immunotherapy by computationally aided real-time personalization. Cancer Res. 2012;72:2218–27.

    Article  CAS  PubMed  Google Scholar 

  43. You B, Harvey R, Henin E, Mitchell H, Golfier F, Savage P, et al. Early prediction of treatment resistance in low-risk gestational trophoblastic neoplasia using population kinetic modelling of hCG measurements. Br J Cancer. 2013;108:1810–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kronik N, Kogan Y, Elishmereni M, Halevi-Tobias K, Vuk-Pavlović S, Agur Z. Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models. PLoS One. 2010;5, e15482.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hansson E, Ma G, Amantea M, French J, Milligan P, Friberg L, et al. PKPD modeling of predictors for adverse effects and overall survival in sunitinib-treated patients with GIST. CPT Pharmacometrics Syst Pharmacol. 2013;2, e85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hansson E, Amantea M, Westwood P, Milligan P, Houk B, French J, et al. PKPD modeling of VEGF, sVEGFR-2, sVEGFR-3, and sKIT as predictors of tumor dynamics and overall survival following sunitinib treatment in GIST. CPT Pharmacometrics Syst Pharmacol. 2013;2, e84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Buil-Bruna N, Sahota T, Lopez-Picazo JM, Moreno-Jimenez M, Martin-Algarra S, Ribba B, et al. Early prediction of disease progression in small cell lung cancer: toward model-based personalized medicine in oncology. Cancer Res. 2015;75:2416–25.

    Article  CAS  PubMed  Google Scholar 

  48. Wilbaux M, Hénin E, Oza A, Colomban O, Pujade-Lauraine E, Freyer G, et al. Dynamic modeling in ovarian cancer: an original approach linking early changes in modeled longitudinal CA-125 kinetics and survival to help decisions in early drug development. Gynecol Oncol. 2014;133:460–6.

    Article  CAS  PubMed  Google Scholar 

  49. Bonate PL, Suttle AB. Modeling tumor growth kinetics after treatment with pazopanib or placebo in patients with renal cell carcinoma. Cancer Chemother Pharmacol. 2013;72:231–40.

    Article  CAS  PubMed  Google Scholar 

  50. Claret L, Girard P, Hoff PM, Van Cutsem E, Zuideveld KP, Jorga K, et al. Model-based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumor dynamics. J Clin Oncol. 2009;27:4103–8.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Nicholas Brown, Senior Publications Officer, Ipsen Biopharm Ltd.

Author information

Authors and Affiliations

  1. Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Irunlarrea 1, 31080, Pamplona, Spain

    Núria Buil-Bruna & Iñaki F. Trocóniz

  2. IdiSNA Navarra Institute for Health Research, Pamplona, Spain

    Núria Buil-Bruna & Iñaki F. Trocóniz

  3. Clinical Pharmacokinetics, Pharmacokinetics and Drug Metabolism, Ipsen Innovation, Les Ulis, France

    Marion Dehez, Amandine Manon & Thi Xuan Quyen Nguyen

Authors
  1. Núria Buil-Bruna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marion Dehez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amandine Manon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thi Xuan Quyen Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Iñaki F. Trocóniz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iñaki F. Trocóniz.

Ethics declarations

Conflict of Interest

This work was funded by Ipsen Pharma. Núria Buil-Bruna was supported by a predoctoral fellowship from Asociación de Amigos de la Universidad de Navarra. Marion Dehez, Amandine Manon, and Quyen Nguyen are employees of Ipsen which is the marketing authorization holder of Somatuline®, and Iñaki F. Trocóniz received research funding from Ipsen.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 16.7 kb)

Supplementary Fig. S1

Observed CgA levels distributions represented by histograms (upper panels) and Q-Q plots (lower panels) for nontransformed (natural scale), log-transformed and Box-Cox transformed CgA levels. (GIF 196 kb)

High resolution image (TIF 133 kb)

Supplementary Fig. S2

Relationship between observed baseline CgA levels and main covariates that showed statistical significance in the CgA model, including patient age, primary tumor location and number of lesions. Gray line shows the smooth curve fitted by the loess function. (GIF 266 kb)

High resolution image (TIF 231 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buil-Bruna, N., Dehez, M., Manon, A. et al. Establishing the Quantitative Relationship Between Lanreotide Autogel®, Chromogranin A, and Progression-Free Survival in Patients with Nonfunctioning Gastroenteropancreatic Neuroendocrine Tumors. AAPS J 18, 703–712 (2016). https://doi.org/10.1208/s12248-016-9884-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1208/s12248-016-9884-3

KEY WORDS

Search

Navigation