Skip to main content
SpringerLink
Log in

A Population Pharmacodynamic Model for Lactate Dehydrogenase and Neuron Specific Enolase to Predict Tumor Progression in Small Cell Lung Cancer Patients

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

Abstract

The development of individualized therapies poses a major challenge in oncology. Significant hurdles to overcome include better disease monitoring and early prediction of clinical outcome. Current clinical practice consists of using Response Evaluation Criteria in Solid Tumors (RECIST) to categorize response to treatment. However, the utility of RECIST is restricted due to limitations on the frequency of measurement and its categorical rather than continuous nature. We propose a population modeling framework that relates circulating biomarkers in plasma, easily obtained from patients, to tumor progression levels assessed by imaging scans (i.e., RECIST categories). We successfully applied this framework to data regarding lactate dehydrogenase (LDH) and neuron specific enolase (NSE) concentrations in patients diagnosed with small cell lung cancer (SCLC). LDH and NSE have been proposed as independent prognostic factors for SCLC. However, their prognostic and predictive value has not been demonstrated in the context of standard clinical practice. Our model incorporates an underlying latent variable (“disease level”) representing (unobserved) tumor size dynamics, which is assumed to drive biomarker production and to be influenced by exposure to treatment; these assumptions are in agreement with the known physiology of SCLC and these biomarkers. Our model predictions of unobserved disease level are strongly correlated with disease progression measured by RECIST criteria. In conclusion, the proposed framework enables prediction of treatment outcome based on circulating biomarkers and therefore can be a powerful tool to help clinicians monitor disease in SCLC.

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
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Eisenhauer E, Therasse P, Bogaerts J, Schwartz L, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228–47.

    Article  CAS  PubMed  Google Scholar 

  2. Ratain MJ, Eckhardt SG. Phase II studies of modern drugs directed against new targets: if you are fazed, too, then resist RECIST. J Clin Oncol. 2004;22(22):4442–5.

    Article  PubMed  Google Scholar 

  3. Villaruz LC, Socinski MA. The clinical viewpoint: definitions, limitations of RECIST, practical considerations of measurement. Clin Cancer Res. 2013;19(10):2629–36.

    Article  PubMed  Google Scholar 

  4. Bender B, Schindler E, Friberg L. Population pharmacokinetic pharmacodynamic modelling in oncology: a tool for predicting clinical response. Br J Clin Pharmacol. 2013;18(2):e86.

    Google Scholar 

  5. Danhof M, Alvan G, Dahl SG, Kuhlmann J, Paintaud G. Mechanism-based pharmacokinetic–pharmacodynamic modeling—a new classification of biomarkers. Pharm Res. 2005;22(9):1432–7.

    Article  CAS  PubMed  Google Scholar 

  6. Romero E, de Mendizabal NV, Cendrós J, Peraire C, Bascompta E, Obach R, et al. Pharmacokinetic/pharmacodynamic model of the testosterone effects of triptorelin administered in sustained release formulations in patients with prostate cancer. J Pharmacol Exp Ther. 2012;342(3):788–98.

    Article  CAS  PubMed  Google Scholar 

  7. 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(9):2218–27.

    Article  CAS  PubMed  Google Scholar 

  8. Stovold R, Blackhall F, Meredith S, Hou J, Dive C, White A. Biomarkers for small cell lung cancer: neuroendocrine, epithelial and circulating tumour cells. Lung Cancer. 2012;76(3):263–8.

    Article  PubMed  Google Scholar 

  9. Yip D, Harper PG. Predictive and prognostic factors in small cell lung cancer: current status. Lung Cancer. 2000;28(3):173–85.

    Article  CAS  PubMed  Google Scholar 

  10. Wójcik E, Kulpa J, Sas-Korczyńska B, Korzeniowski S, Jakubowicz J. ProGRP and NSE in therapy monitoring in patients with small cell lung cancer. Anticancer Res. 2008;28(5B):3027–33.

    PubMed  Google Scholar 

  11. Holdenrieder S, von Pawel J, Dankelmann E, Duell T, Faderl B, Markus A, et al. Nucleosomes, ProGRP, NSE, CYFRA 21–1, and CEA in monitoring first-line chemotherapy of small cell lung cancer. Clin Cancer Res. 2008;14(23):7813–21.

    Article  CAS  PubMed  Google Scholar 

  12. Lippert MC, Javadpour N. Lactic dehydrogenase in the monitoring and prognosis of testicular cancer. Cancer. 1981;48(10):2274–8.

    Article  CAS  PubMed  Google Scholar 

  13. Scartozzi M, Faloppi L, Bianconi M, Giampieri R, Maccaroni E, Bittoni A, et al. The role of LDH serum levels in predicting global outcome in HCC patients undergoing TACE: implications for clinical management. PLoS One. 2012;7(3):e32653.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Zelen M. Keynote address on biostatistics and data retrieval. Cancer Chemother Rep. 1973;4(2):31–42.

    CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

  17. Lavielle M. MONOLIX (MOdèles NOn LInéaires à effets miXtes). Orsay, France: MONOLIX group; 2005.

    Google Scholar 

  18. Jusko WJ, Ko HC, Ebling WF. Convergence of direct and indirect pharmacodynamic response models. J Pharmacokinet Pharmacodyn. 1995;23(1):5–8.

    Article  CAS  Google Scholar 

  19. Jacqmin P, Snoeck E, Van Schaick E, Gieschke R, Pillai P, Steimer J, et al. Modelling response time profiles in the absence of drug concentrations: definition and performance evaluation of the K–PD model. J Pharmacokinet Pharmacodyn. 2007;34(1):57–85.

    Article  CAS  PubMed  Google Scholar 

  20. 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 Programs Biomed. 2005;79(3):241–57.

    Article  PubMed  Google Scholar 

  21. Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J. 2011;13(2):143–51.

    Article  PubMed Central  PubMed  Google Scholar 

  22. Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39(4):561–77.

    CAS  PubMed  Google Scholar 

  23. Go RS, Adjei AA. Review of the comparative pharmacology and clinical activity of cisplatin and carboplatin. J Clin Oncol. 1999;17(1):409–422.

    CAS  PubMed  Google Scholar 

  24. Savic RM, Karlsson MO. Importance of shrinkage in empirical bayes estimates for diagnostics: problems and solutions. AAPS J. 2009;11(3):558–69.

    Article  PubMed Central  PubMed  Google Scholar 

  25. Lesko LJ, Atkinson Jr A. Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies 1. Annu Rev Pharmacol Toxicol. 2001;41(1):347–66.

    Article  CAS  PubMed  Google Scholar 

  26. 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(1):41–56.

    Article  CAS  PubMed  Google Scholar 

  27. Fizazi K, Cojean I, Pignon J, Rixe O, Gatineau M, Hadef S, et al. Normal serum neuron specific enolase (NSE) value after the first cycle of chemotherapy. Cancer. 1998;82(6):1049–55.

    Article  CAS  PubMed  Google Scholar 

  28. Jørgensen L, Osterlind K, Hansen H, Cooper E. The prognostic influence of serum neuron specific enolase in small cell lung cancer. Br J Cancer. 1988;58(6):805.

    Article  PubMed Central  PubMed  Google Scholar 

  29. Jørgensen L, Osterlind K, Genolla J, Gomm S, Hernandez J, Johnson P, et al. Serum neuron-specific enolase (S-NSE) and the prognosis in small-cell lung cancer (SCLC): a combined multivariable analysis on data from nine centres. Br J Cancer. 1996;74(3):463.

    Article  PubMed Central  PubMed  Google Scholar 

  30. Lassen U, Østerlind K, Hirsch F, Bergman B, Dombernowsky P, Hansen H. Early death during chemotherapy in patients with small-cell lung cancer: derivation of a prognostic index for toxic death and progression. Br J Cancer. 1999;79(3/4):515.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Østerlind K, Andersen PK. Prognostic factors in small cell lung cancer: multivariate model based on 778 patients treated with chemotherapy with or without irradiation. Cancer Res. 1986;46(8):4189.

    PubMed  Google Scholar 

  32. Osterlind K. LDH or NSE or LDH and NSE as pretreatment prognostic factors in small cell lung cancer? A commentary. Lung Cancer. 2000;30(1):51–3.

    Article  CAS  PubMed  Google Scholar 

  33. Pinson P, Joos G, Watripont P, Brusselle G, Pauwels R. Serum neuron-specific enolase as a tumor marker in the diagnosis and follow-up of small-cell lung cancer. Respiration. 2009;64(1):102–7.

    Article  Google Scholar 

  34. Quoix E, Purohit A, Faller-Beau M, Moreau L, Oster J, Pauli G. Comparative prognostic value of lactate dehydrogenase and neuron-specific enolase in small-cell lung cancer patients treated with platinum-based chemotherapy. Lung Cancer. 2000;30(2):127–34.

    Article  CAS  PubMed  Google Scholar 

  35. You B, Tranchand B, Girard P, Falandry C, Ribba B, Chabaud S, et al. Etoposide pharmacokinetics and survival in patients with small cell lung cancer: a multicentre study. Lung Cancer. 2008;62(2):261–72.

    Article  PubMed  Google Scholar 

  36. Molina R, Filella X, Auge JM. ProGRP: a new biomarker for small cell lung cancer. Clin Biochem. 2004;37(7):505–11.

    Article  CAS  PubMed  Google Scholar 

  37. Warburg O. On the origin of cancer cells. Science. 1956;123(3191):309–14.

    Article  CAS  PubMed  Google Scholar 

  38. Gambhir SS. Molecular imaging of cancer with positron emission tomography. Nat Rev Cancer. 2002;2(9):683–93.

    Article  CAS  PubMed  Google Scholar 

  39. Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324(5930):1029–33.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  40. Zhuang L, Scolyer RA, Murali R, McCarthy SW, Zhang XD, Thompson JF, et al. Lactate dehydrogenase 5 expression in melanoma increases with disease progression and is associated with expression of Bcl-XL and Mcl-1, but not Bcl-2 proteins. Mod Pathol. 2010;23(1):45–53.

    Article  CAS  PubMed  Google Scholar 

  41. Scatena R, Bottoni P, Pontoglio A, Mastrototaro L, Giardina B. Glycolytic enzyme inhibitors in cancer treatment. 2008.

    Google Scholar 

  42. Zhou W, Capello M, Fredolini C, Racanicchi L, Piemonti L, Liotta LA, et al. Proteomic analysis reveals Warburg effect and anomalous metabolism of glutamine in pancreatic cancer cells. J Proteome Res. 2011;11(2):554–63.

    PubMed  Google Scholar 

  43. Giaccone G. Clinical perspectives on platinum resistance. Drugs. 2000;59(4):9–17.

    Article  CAS  PubMed  Google Scholar 

  44. Anderlini P, Przepiorka D, Champlin R, Korbling M. Biologic and clinical effects of granulocyte colony-stimulating factor in normal individuals. Blood. 1996;88(8):2819–25.

    CAS  PubMed  Google Scholar 

  45. Zohlnhöfer D, Ott I, Mehilli J, Schömig K, Michalk F, Ibrahim T, et al. Stem cell mobilization by granulocyte colony-stimulating factor in patients with acute myocardial infarction. JAMA:J Am Med Assoc. 2006;295(9):1003–10.

    Article  Google Scholar 

  46. Lindemann A, Herrmann F, Oster W, Haffner G, Meyenburg W, Souza LM, et al. Hematologic effects of recombinant human granulocyte colony-stimulating factor in patients with malignancy. Blood. 1989;74(8):2644–51.

    CAS  PubMed  Google Scholar 

  47. Anderlini P, Przepiorka D, Seong D, Miller P, Sundberg J, Lichtiger B, et al. Clinical toxicity and laboratory effects of granulocyte‐colony‐stimulating factor (filgrastim) mobilization and blood stem cell apheresis from normal donors, and analysis of charges for the procedures. Transfusion. 1996;36(7):590–5.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

We thank MC Martínez-Cosgaya, I Echenique-Gubía, and MJ Martínez Alvarez-Nava for the kind help in obtaining data from Clinica Universitaria Navarra and Karen Marron for the help with the manuscript. NB-B was supported by a predoctoral fellowship from Asociación de Amigos de la Universidad de Navarra and a grant from INRIA. This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115156, resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in-kind contribution. The DDMoRe project is also supported by financial contribution from academic and SME partners. This work does not necessarily represent the view of all DDMoRe partners.

Author information

Authors and Affiliations

  1. Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, 31009, Spain

    Núria Buil-Bruna & Iñaki F. Trocóniz

  2. Department of Medical Oncology, Clínica Universitaria de Navarra, University of Navarra, Pamplona, Spain

    José-María López-Picazo & Salvador Martín-Algarra

  3. Department of Radiation Oncology, Clínica Universitaria de Navarra, University of Navarra, Pamplona, Spain

    Marta Moreno-Jiménez

  4. Inria, Ecole Normale Supérieure de Lyon, 46 allée d’Italie, 69007, Lyon Cedex 07, France

    Benjamin Ribba

Authors
  1. Núria Buil-Bruna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José-María López-Picazo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marta Moreno-Jiménez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Salvador Martín-Algarra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Benjamin Ribba
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Iñaki F. Trocóniz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iñaki F. Trocóniz.

Additional information

Benjamin Ribba and Iñaki F. Trocóniz share the senior authorship of this paper.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

ESM 1

(JPEG 60 kb)

High resolution image (TIFF 401 kb)

ESM 2

(DOCX 22 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., López-Picazo, JM., Moreno-Jiménez, M. et al. A Population Pharmacodynamic Model for Lactate Dehydrogenase and Neuron Specific Enolase to Predict Tumor Progression in Small Cell Lung Cancer Patients. AAPS J 16, 609–619 (2014). https://doi.org/10.1208/s12248-014-9600-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1208/s12248-014-9600-0

KEY WORDS

Search

Navigation