On-line reversed-phase liquid chromatography hydride generation emission spectrometry: speciation of arsenic in urine of patients intravenously treated with As2O3.

Hydride generation inductively coupled plasma-atomic emission spectrometry (HG ICP-AES) was used as a continuous detection system for the determination of arsenic in the eluate from a high-performance liquid chromatographic (HPLC) system. Four arsenic species [arsenite As(III), arsenate As(V), monomethylarsonate (MMA), and dimethylarsinate (DMA)] present in the urine samples of patients treated intravenously with arsenite, were analyzed separately by HPLC-HG-ICP-AES using a non-polar C18 column. This analytical method allowed the sensitive determination of the arsenic species in the submicrogram per liter range. Urine samples collected on different days after arsenite administration were found to contain arsenite predominantly--monomethylarsonate and dimethylarsinate were also detected.

Implementation and evaluation of a stochastic control strategy for individualizing teicoplanin dosage regimen.

A stochastic control strategy for individualizing teicoplanin dosing schedule in neutropenic patients is proposed and compared to the usual Bayesian approach based on the mode of the posterior density of the model parameters. Teicoplanin disposition is described by a bicompartmental model. Age, body weight, serum creatinine, white blood cell count, and sex can be included as covariates. Posterior density of model parameters is obtained by Bayes theorem under a discrete form from which the posterior density of teicoplanin trough concentrations are computed for any dosing schedule. Optimal maintenance dose is determined by minimizing the cost associated, through a logarithmic risk function, to the concentrations being outside the therapeutic range. In Monte Carlo simulation studies on 300 individuals, stochastic control was more accurate than, and equally precise as the usual Bayesian approach. Two-sample based predictions were not better than one-sample based ones. Inclusion of covariates in the model improved dramatically the performances of both strategies. A small retrospective study based on real data (n = 16 patients) shows that reasonable accuracy (bias of 0.7 mg/L) and precision (3 mg/L) in teicoplanin trough concentration prediction is obtained with both strategies provided that covariates are taken into account.