These findings suggest that public health policies based on national treatment guidelines should rigorously include the monitoring of quality control of available antimicrobial products. In the absence of such measures, specific treatment strategies are likely to fail and to generate drug resistance.

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The availability of personal computer programs to individualize drug regimens has stimulated interest in modeling population pharmacokinetics. This study used the NPEM2 software to determine cyclosporine population pharmacokinetic parameter values and distributions in a first group of 25 recipients of liver transplants during their first postoperative week. On a second group of 25 patients, the authors used these values to evaluate Bayesian predictive performance of cyclosporine blood concentrations with the USC*PACK PC program. During the study period, all the patients have been treated by continuous intravenous infusion. The one-compartment model pharmacokinetic parameter-the slope of volume to body weight (Vs) and the elimination rate constant (Kel) values found (mean values: Vs = 2.177 l/kg, Kel = 0.235 h(-1); median values: Vs = 1.559 l/kg, Kel = 0.163 h(-1); the percent coefficient of variation (Vs = 92%, Kel = 79%) appear reasonable and show the ability of NPEM2 to deal with sparse data. When the predictions were studied with day 1, day 2, or day 3 concentrations, predictive bias was respectively -0.030, -0.013, and 0.013 microg/ml, suggesting a greater clearance of cyclosporine immediately after surgery, the clearance decreasing in the days after. With the first three blood levels and the Bayesian fitting procedure, it was possible to predict at least half the subsequent measured blood levels of each patient accurately (within 20%) in more than three-quarters (76%) of the second group of recipients of transplants, and for 40% of patients the authors obtained accurate predictions in 100% of the subsequent blood levels. For a few patients (12%) they found quite poor predictions. The reason for this is unclear. The results suggest that this population model and the Bayesian fitting procedure using two or three blood levels can be reasonably and carefully used to control, in real time, cyclosporine blood levels in a majority of new patients with liver transplants.

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