A Population Pharmacodynamic Model for Lactate Dehydrogenase and Neuron Specific Enolase to Predict Tumor Progression in Small Cell Lung Cancer Patients
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.
KEY WORDSbiomarkers lung cancer mixed-effect model pharmacodynamics population model
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.
- 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
- 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.PubMedCrossRefGoogle Scholar
- 14.Zelen M. Keynote address on biostatistics and data retrieval. Cancer Chemother Rep. 1973;4(2):31–42.Google Scholar
- 16.Bauer R. NONMEM users guide introduction to NONMEM 7.2. 0. ICON Development Solutions Ellicott City, MD 2011.Google Scholar
- 17.Lavielle M. MONOLIX (MOdèles NOn LInéaires à effets miXtes). Orsay, France: MONOLIX group; 2005.Google Scholar
- 41.Scatena R, Bottoni P, Pontoglio A, Mastrototaro L, Giardina B. Glycolytic enzyme inhibitors in cancer treatment. 2008.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.PubMedCrossRefGoogle Scholar