Friberg, L. (Lena)

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    Predictive ability of a semi-mechanistic model for neutropenia in the development of novel anti-cancer agents: two case studies
    (Boston Springer US, 2011) Wanders, J. (Jantien); Peraire, C. (Concepción); Keizer, R. (Ron); Huitema, A.D.R (Aldwin D. R.); Karlsson, M.O. (Mats O.); Troconiz, I.F. (Iñaki F.); Soto, E. (Elena); Friberg, L. (Lena); Schellens, J.H.M (Jan H.M.); Beijnen, J.H. (Jos H.); Obach, R. (Rosendo); Cendros, J.M. (Josep Maria)
    Abstract In cancer chemotherapy neutropenia is a common dose-limiting toxicity. An ability to predict the neutropenic effects of cytotoxic agents based on proposed trial designs and models conditioned on previous studies would be valuable. The aim of this study was to evaluate the ability of a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for myelosuppression to predict the neutropenia observed in Phase I clinical studies, based on parameter estimates obtained from prior trials. Pharmacokinetic and neutropenia data from 5 clinical trials for diflomotecan and from 4 clinical trials for indisulam were used. Data were analyzed and simulations were performed using the population approach with NONMEM VI. Parameter sets were estimated under the following scenarios: (a) data from each trial independently, (b) pooled data from all clinical trials and (c) pooled data from trials performed before the tested trial. Model performance in each of the scenarios was evaluated by means of predictive (visual and numerical) checks. The semi-mechanistic PK/PD model for neutropenia showed adequate predictive ability for both anti-cancer agents. For diflomotecan, similar predictions were obtained for the three scenarios. For indisulam predictions were better when based on data from the specific study, however when the model parameters were conditioned on data from trials performed prior to a specific study, similar predictions of the drug related-neutropenia profiles and descriptors were obtained as when all data were used. This work provides further indication that modeling and simulation tools can be applied in the early stages of drug development to optimize future trials.
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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.