In vitro effects of tacrolimus on human cytochrome P450.

Tacrolimus, a potent immunosuppressive drug, is known to be metabolized predominantly in the liver by cytochrome P450 3A (CYP3A). In order to determine the potential of tacrolimus to inhibit the metabolism of other drugs, we have investigated its inhibitory effects on specific cytochrome reactions. Specific substrates for the seven cytochromes (CYPs) 1A2, 2A6, 2C9, 2C19, 2D6, 2E1 and 3A4/5 were incubated with human hepatic microsome preparations with or without specific inhibitors or tacrolimus and the metabolites were detected by high-pressure liquid chromatography (HPLC) or fluorimetric methods. All the specific inhibitors reduced or abolished the specific CYP activity. Tacrolimus had no effect on any CYP at concentrations below 1 microM, while at higher concentrations it had a mild inhibitory effect on CYP3A4 and 3A5. These observations suggest that tacrolimus is unlikely to potentiate the effect of coadministered drugs through inhibition of their metabolism in the liver.

Comparison of two methods to obtain a desired first isepamicin peak in intensive care patients.

A randomized multicenter study in intensive care unit (ICU) patients, evaluated the capacity of a Bayesian method to obtain an optimal first isepamicin (ISP) peak of 80 mg/L in comparison to a fixed loading dose (LD). Patients (n=236) over 18 years of age were enrolled from 6 September 1997 to 17 July 1999 and randomly assigned to received ISP in a calculated dose (CD) or a loading dose (LD) of 25 mg/kg body weight. The CD was estimated using a specific population model with Bayesian methodology implemented in the PKS program (Abbott PKS, Abbott Diagnostics, Rungis, France). The data required included age, body weight, height, gender and serum creatinine. ISP disposition is described by a one-compartment model. Blood samples were drawn 1 and 24 h after the start of infusion for fluorescence polarization immunoassay measurement of serum ISP concentrations. The predictive performance was assessed by computing bias and precision. Peak concentrations were significantly higher in CD group than the LD group (84.2 +/- 28.6 vs. 74.7 +/- 24.1 mg/L, respectively; P=0.008), but trough levels were comparable. The optimal ISP peak was attained by a significantly higher percentage of CD patients (P=0.018), and by significantly more CD patients on mechanical ventilation (P=0.025), and with simplified acute physiological scores (SAPS) > 35 (P=0.002). Pharmacokinetic parameters were similar for the two groups with large interindividual variations. Mean (+/- SD) volume of distribution of ventilated patients (72%) was significantly higher than of nonventilated patients (23.31 +/- 7.35 vs. 20.60 +/- 6.30 L, respectively; P=0.001). No relationship was found between the volume of distribution and SAPS. Total clearance was significantly correlated with estimated CLCR (creatinine clearance) (P=0.0001). Precision (RMSE) is better for CD than for LD strategy, respectively 27.96 and 28.66 mg/L. The Bayesian method was significantly more accurate and performed particularly well in ventilated patients and patients with high SAPS, compare to an LD of 25 mg/kg to obtain a first ISP peak of 80 mg/L in ICU patients. Therefore, a fixed dose of 28.5 mg/kg would be also adequate to reach a peak of 80 mg/L.

Digoxin Toxicity : Evaluation in Clinical Practice with Pharmacokinetic Correlations.

OBJECTIVE: To evaluate digoxin pharmacokinetic parameters using Bayesian estimation in 60 patients, and to identify factors that appeared to affect the risk of digoxin toxicity. PATIENTS AND METHODS: 60 patients with serum digoxin concentrations were evaluated retrospectively. We collected demographic, clinical and laboratory data, and information on concurrent medications and clinical and electrocardiographic features of digoxin toxicity. The incidence of digoxin toxicity was evaluated in 50 patients. Serum digoxin concentrations were measured with fluorescence polarisation immunoassay Individual pharmacokinetic parameters were estimated by Bayesian method using Abbottbase Pharmacokinetic Systems. RESULTS: Signs of digoxin toxicity were present in 23 patients (46%). Patients without signs of digoxin toxicity had a significantly lower mean serum digoxin concentration than patients with signs, 1.99 ± 0.9 µg/L vs 2.7 ± 1.5 µ.g/L, respectively (p = 0.047). Patients with serum digoxin concentrations >2.2 µg/L differed significantly from those with values ≤2.2 µg/L, respectively, for the following parameters: age (82.0 ± 8.0 vs 72.0 ± 16.0 years; p = 0.005), serum creatinine levels (133.0 ± 55.0 vs 106.0 ± 26.0 µmo1/L; p = 0.012), bodyweight (57.4 ± 12.8 vs 69.2 ± 17.8kg; p = 0.01), volume of distribution (208.5 ± 89.5 vs 315.7 ± 91.2L; p = 0.0001), total clearance (1.60 ± 0.65 vs 3.4 ± 1.5 L/h; p = 0.0001), and elimination half-life (94.2 ± 28.6 vs 72.4 ± 16.7h; p = 0.001). Estimation of optimal dose showed that the doses recommended in intoxicated patients should be 3.5 times lower to reach the therapeutic range. CONCLUSION: Digoxin concentrations were higher in patients with toxicity. Older age enhanced the risk of digoxin toxicity. Monitoring digoxin concentrations may help to confirm suspected digitalis toxicity.

[Impact of medical prescription computerisation on the incidence of adverse drug effects]. / Impact d'une prescription médicale informatisée sur la réduction du mésusage thérapeutique.

INTRODUCTION: Adverse drug effects are a significant public health problem. Prescription errors are responsible for a significant proportion of these adverse effects. METHODS: We have aimed to improve the link between generation of and interpretation of a prescription through computerisation. The prescription sheet, which is generated daily, was organised to allow care planning without the need to re-copy out treatments on the sheet. A prescription aid was available which was based on a core group of drugs commonly used in our respiratory service. The aim of the study was to compare the various types of errors observed during 6 weeks of computerized prescriptions (229 files) to a retrospective series of handwritten prescriptions of the service at an identical time (184 files) the previous year. The case-mix was identical for both analysed periods. RESULTS: The total number of technical prescribing errors in the 1,599 handwritten lines (49.27% error) was significantly higher (p<0.001) than the 1,805 computerized prescriptions lines (42.88% error). The errors of copying (p<0.001), eligibility (p<0.001) and incorrect spelling (p<0.05) were the main sources of error which were significantly reduced by computerisation. CONCLUSION: Computerised prescription is likely to reduce the incidence of prescribing errors and adverse drug effects.