Real time cell analysis (RTCA) is an impedance-based technology which tracks various living cell characteristics over time, such as their number, morphology or adhesion to the extra cellular matrix. However, there is no consensus about how RTCA data should be used to quantitatively evaluate pharmacodynamic parameters which describe drug efficacy or toxicity. The purpose of this work was to determine how RTCA data can be analyzed with mathematical modeling to explore and quantify drug effect in vitro. The pharmacokinetic-pharmacodynamic erlotinib concentration profile predicted by the model and its effect on the human epidermoïd carcinoma cell line A431 in vitro was measured through RTCA output, designated as cell index. A population approach was used to estimate model parameter values, considering a plate well as the statistical unit. The model related the cell index to the number of cells by means of a proportionality factor. Cell growth was described by an exponential model. A delay between erlotinib pharmacokinetics and cell killing was described by a transit compartment model, and the effect potency, by an E max function of erlotinib concentration. The modeling analysis performed on RTCA data distinguished drug effects in vitro on cell number from other effects likely to modify the relationship between cell index and cell number. It also revealed a time-dependent decrease of erlotinib concentration over time, described by a mono-exponential pharmacokinetic model with nonspecific binding.
Several findings suggest that patient outcome would be improved with individualized doses. The aim of this paper is to describe major approaches, methods and underlying basic foundations implemented, in clinical practice, for dosage individualization. Also we propose a new method codified by kinetic nomograms as reliable alternative to traditional Bayesian methods. Clinical and simulation data were reported to evaluate performances of the proposed methods. Real examples of therapeutic drug monitoring were selected. Bayesian methods were used to individualize high-dose methotrexate rate infusion and amikacin dosage regimen, and kinetic nomograms to adjust sirolimus doses. 1) Using only few measurements, Bayesian method resulted in accurate estimates of individual pharmacokinetic parameters of high dose methotrexate infusion. Targeting a pre-defined end-of-infusion level, infusion rate was individualized according to the previously obtained pharmacokinetic parameters. 2) With the same reasoning, individual pharmacokinetic parameters of amikacin were obtained by Bayesian estimation using three individual samples. Subsequent dosage adjustment allowed achievement of therapeutic goals at steady state. 3) Without computing individual pharmacokinetic parameters, nor using pharmacokinetic software, kinetic nomograms steered individual sirolimus blood levels within its therapeutic window with only two samples and in the first week after starting treatment. This contribution relates traditional Bayesian methods developed in 80's but not yet fully integrated in clinical context because of their complexity. The contribution focuses on recent developments based on population approaches, rendering the dosage adjustment methodology a simple and quick bedside application.
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