Comparative pharmacokinetics of the three echinocandins in ICU patients.

BACKGROUND: We conducted a prospective study in ICU patients of two tertiary hospitals in order to determine basic pharmacokinetic (PK) parameters, associated variation and target attainment rates for anidulafungin, micafungin and caspofungin. METHODS: Serum samples from patients treated for 7 days with the standard doses of anidulafungin (N = 13), micafungin (N = 14) or caspofungin (N = 7) were analysed by validated chromatographic methods. PK parameters determined with non-compartmental analysis were correlated with demographic, laboratory and disease severity characteristics. The percentages of patients attaining drug exposures described in the summary of product characteristics (SmPC) documents and preclinical PK/PD targets for stasis were estimated. RESULTS: The median (range) AUC24 was 101.46 (54.95-274.15) mg·h/L for anidulafungin, 79.35 (28.00-149.30) mg·h/L for micafungin and 48.46 (19.44-103.69) mg·h/L for caspofungin. The interindividual variability of anidulafungin, micafungin and caspofungin AUC24 was 46%-58%, attributed mainly to variability in volume of distribution (V), clearance (CL) and in both V and CL, respectively. Significant correlations were found between anidulafungin AUC24 and BMI (rs = -0.670, P = 0.012) and liver enzymes (rs = 0.572-0.665, P = 0.013-0.041) and between caspofungin Cmin and transaminase levels (rs = -0.775 to -0.786, P = 0.036-0.041). Less than 50% of our patients attained the corresponding SmPC median AUC24s and none of the patients attained the PK/PD targets for Candida albicans and Candida parapsilosis. CONCLUSIONS: Anidulafungin exposure in ICU patients was comparable with that reported in non-ICU patients and in healthy volunteers. Micafungin exposure was comparable to that of other patients but ∼30% lower than that in healthy volunteers, whereas caspofungin exposure was rather low (∼50% lower than in healthy volunteers). Larger interindividual variability (50%-60%) was recorded in ICU patients compared with other groups for all three echinocandins.

Estimating parameters of nonlinear dynamic systems in pharmacology using chaos synchronization and grid search.

Bridging fundamental approaches to model optimization for pharmacometricians, systems pharmacologists and statisticians is a critical issue. These fields rely primarily on Maximum Likelihood and Extended Least Squares metrics with iterative estimation of parameters. Our research combines adaptive chaos synchronization and grid search to estimate physiological and pharmacological systems with emergent properties by exploring deterministic methods that are more appropriate and have potentially superior performance than classical numerical approaches, which minimize the sum of squares or maximize the likelihood. We illustrate these issues with an established model of cortisol in human with nonlinear dynamics. The model describes cortisol kinetics over time, including its chaotic oscillations, by a delay differential equation. We demonstrate that chaos synchronization helps to avoid the tendency of the gradient-based optimization algorithms to end up in a local minimum. The subsequent analysis illustrates that the hybrid adaptive chaos synchronization for estimation of linear parameters with coarse-to-fine grid search for optimal values of non-linear parameters can be applied iteratively to accurately estimate parameters and effectively track trajectories for a wide class of noisy chaotic systems.

Commentary on the MID3 Good Practices Paper.

During the last 10 years the European Medicines Agency (EMA) organized a number of workshops on modeling and simulation, working towards greater integration of modeling and simulation (M&S) in the development and regulatory assessment of medicines. In the 2011 EMA - European Federation of Pharmaceutical Industries and Associations (EFPIA) Workshop on Modelling and Simulation, European regulators agreed to the necessity to build expertise to be able to review M&S data provided by companies in their dossier. This led to the establishment of the EMA Modelling and Simulation Working Group (MSWG). Also, there was agreement reached on the need for harmonization on good M&S practices and for continuing dialog across all parties. The MSWG acknowledges the initiative of the EFPIA Model-Informed Drug Discovery and Development (MID3) group in promoting greater consistency in practice, application, and documentation of M&S and considers the paper is an important contribution towards achieving this objective.