Ribba, B. (B.)

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    A population pharmacodynamic model for lactate dehydrogenase and neuron specific enolase to predict tumor progression in small cell lung cancer patients
    (Springer Link, 2014) Martin-Algarra, S. (Salvador); Ribba, B. (B.); Troconiz, I.F. (Iñaki F.); Lopez-Picazo, J.M. (José M.); Buil-Bruña, N (Núria); Moreno-Jimenez, M. (Marta)
    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.
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    A review of mixed-effects models of tumor growth and effects of anticancer drug treatment used in population analysis
    (Wiley, 2014) Elishmereni, M. (M.); Holford, N.H. (N.H.); Sarr, C. (C.); Magni, P. (Paolo); Girard, P. (Pascal.); Ribba, B. (B.); Troconiz, I.F. (Iñaki F.); Gueorguieva, I. (I.); Kloft, C. (C.); Friberg, L. (Lena)
    Population modeling of tumor size dynamics has recently emerged as an important tool in pharmacometric research. A series of new mixed-effects models have been reported recently, and we present herein a synthetic view of models with published mathematical equations aimed at describing the dynamics of tumor size in cancer patients following anticancer drug treatment. This selection of models will constitute the basis for the Drug Disease Model Resources (DDMoRe) repository for models on oncology.