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The effect of three new fluoroquinolones on theophylline kinetics and the urinary excretion of metabolites was studied in 5 healthy subjects (3 male, 2 female). All subjects received serial, single i.v. infusions of theophylline (aminophylline, 250 mg) over 60 min after 200 mg doses of a quinolone (enoxacin, ofloxacin, norfloxacin) every 8 h for 3 consecutive days, the quinolone being administered up to the day following theophylline administration. Pretreatment with ofloxacin and norfloxacin did not influence theophylline disposition, but theophylline clearance fell from 0.054 to 0.027 l.h-1.kg-1 in the presence of enoxacin, without a change in the apparent volume of distribution. Enoxacin, too, was the sole compound to increase the urinary excretion of theophylline (33.2 vs 43.9 mg, before vs after treatment), and significantly to decrease the excretion of 3-methylxanthine (3-MX), 1-methyluric acid (1-MU) and 1,3-dimethyluric acid (1,3-DMU) in 24-h urine samples (from 19.8 to 7.16 mg, from 28.3 to 10.3 mg and from 68.8 to 49.5 mg, respectively). The effect of the quinolones on hepatic drug metabolizing enzyme activity was investigated in each subject using the ratios of 6-hydroxycortisol to total 17-hydroxycorticosteroids and to free cortisol in 24-h urines as an index of the hepatic P-450-dependent enzyme system. No significant difference in ratio was observed between control and other treatments. It is concluded that the theophylline-enoxacin interaction was largely due to inhibition of a metabolic system other than the common hepatic P-450 system.

The present paper reports theoretical equations for the predictive performance of the Bayesian forecasting method. The precision of parameter estimates and predicted concentrations for an individual was described by general equations with the aid of a variance-covariance matrix of parameter estimates that involved the Bayes theorem. The equations were applied to assess the predictive performance of the one-point Bayesian method in association with blood sampling time, the population parameters, and the pharmacostatistical model. The simulation study showed that the prediction error in parameter estimates essentially depended upon the sampling time but the magnitude of dependency was affected by the size of inter- and intraindividual variances. With a smaller value of interindividual variance, the dependency on sampling time was less apparent. Effects of sampling time were further examined using clinical data obtained from 20 patients taking theophylline, and the results were in good agreement with the theoretical consideration. The present general equations are useful to investigate the sampling strategy as well as structural and variance modeling on the predictive performance of the Bayesian method.

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The population pharmacokinetics of theophylline were studied in 55 patients with stable chronic airway obstruction. Two hundred and seventy six theophylline serum concentrations after intravenous short infusion were analyzed using a nonlinear mixed-effect model. The influence of hepatic dysfunction, smoking habit, age and the measurement of arterial blood gases (oxygen tension: PaO2, carbon dioxide tension: PaCO2, blood pH) and clinical laboratory tests (serum albumin concentration, haematocrit) on the pharmacokinetic parameters of theophylline was examined by the likelihood ratio test. Assessment of each factor was made by a forward selection method. In the final regression model, the total body clearance (CL, l/h/kg) was related to the value of PaCO2 as well as to the presence of hepatic dysfunction, and the volume of distribution (Vd, l/kg) was related with the PaCO2 value as expressed in the following equations: CL = exp(-3.78 - 0.525.HF + 0.0233.PaCO2) and Vd = exp(-1.12 + 0.00934.PaCO2), where HF is a categorical variable with a value of unity if a patient has hepatic dysfunction otherwise zero. The interactions among blood gas measurements were observed and the CL and Vd of theophylline would be inversely correlated with PaO2 or pH, if we selected PaO2 or blood pH to be a more important factor than PaCO2. The inter-individual variabilities in CL and Vd were 38.5% and 12.5%, respectively, and the residual variability in theophylline serum concentrations was 10.6% as a coefficient of variation. This final model and the population parameters of theophylline will be useful for individualization of a drug dosage regimen by means of the Bayesian method.(ABSTRACT TRUNCATED AT 250 WORDS)