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Therapeutic drug monitoring (TDM) of valproate (VAL) is important in the optimization of its therapy. The aim of the present work was to evaluate the ability of TDM using model-based, goal-oriented Bayesian adaptive control for help in planning, monitoring, and adjusting individualized VAL dosing regimens. USC*PACK software and routine TDM data were used to estimate population and individual pharmacokinetics of two commercially available VAL formulations in epileptic adult and pediatric patients on chronic VAL monotherapy. The population parameter values found were in agreement with values reported earlier. A statistically significant (P < 0.001) difference in median values of the absorption rate constant was found between enteric-coated and sustained-release VAL formulations. In our patients (aged 0.25-53 years), VAL clearance declined with age until adult values were reached at about age 10. Because of the large interindividual variability in PK behavior, the median population parameter values gave poor predictions of the observed VAL serum concentrations. In contrast, the Bayesian individualized models gave good predictions for all subjects in all populations. The Bayesian posterior individualized PK models were based on the population models described here and where most patients had two (a peak and a trough) measured serum concentrations. Repeated consultations and adjusted dosage regimens with some patients allowed us to evaluate any possible influence of dose-dependent VAL clearance on the precision of total VAL concentration predictions based on TDM data and the proposed population models. These nonparametric expectation maximization (NPEM) population models thus provide a useful tool for planning an initial dosage regimen of VAL to achieve desired target peak and trough serum concentration goals, coupled with TDM soon thereafter, as a peak-trough pair of serum concentrations, and Bayesian fitting to individualize the PK model for each patient. The nonparametric PK parameter distributions in these NPEM population models also permit their use by the new method of 'multiple model' dosage design, which allows the target goals to be achieved specifically with maximum precision. Software for both types of Bayesian adaptive control is now available to employ these population models in clinical practice.

Background:Resistance toward chemotherapeutics is one of the main obstacles on the way to effective cancer treatment. Personalization of chemotherapy could improve clinical outcome. However, despite preclinical significance, most of the potential markers have failed to reach clinical practice partially due to the inability of numerous studies to estimate the marker’s impact on resistance properly.Objective:The analysis of drug resistance mechanisms to chemotherapy in cancer cells, and the proposal of study design to identify bona fide markers.Methods:A review of relevant papers in the field. A PubMed search with relevant keywords was used to gather the data. An example of a search request: drug resistance AND cancer AND paclitaxel.Results:We have described a number of drug resistance mechanisms to various chemotherapeutics, as well as markers to underlie the phenomenon. We also proposed a model of a rational-designed study, which could be useful in determining the most promising potential biomarkers.Conclusion:Taking into account the most reasonable biomarkers should dramatically improve clinical outcome by choosing the suitable treatment regimens. However, determining the leading biomarkers, as well as validating of the model, is a work for further investigations.