Cytochrome P450 2D6 phenotype and genotype in hypertensive patients on long-term therapy with metoprolol.

OBJECTIVE: The aim was to compare cytochrome P450 2D6 phenotype and genotype using metoprolol as a probe drug. Further, to investigate the influence of P450 2D6 activity on metoprolol pharmacokinetics and pharmacodynamics in patients on metoprolol therapy. BACKGROUND: Cytochrome P450 2D6 is a highly polymorphic enzyme that contributes to the variability of metoprolol. However, environmental factors also modify drug disposition. METHODS: Forty-nine hypertensive patients were enrolled. Serum metoprolol and α-hydroxymetoprolol concentrations, resting heart rate were measured before, 1, 3 and 4 hours post-dose. RESULTS: Significantly higher normalized metoprolol serum concentrations, normalized metoprolol AUC0-4 and metoprolol oral clearance were observed in patients with lower P450 2D6 metabolic activity. A trend towards a lower resting heart rate before metoprolol intake was also observed in this group of patients. The differences in metoprolol disposition were more expressed when P450 2D6 phenotype instead of genotype was determined. CONCLUSION: Significant variations exist in metoprolol disposition in hypertensive patients. Both genotyping and phenotyping provides a valuable method in determining the enzymatic activity and in optimising metoprolol therapy (Tab. 3, Fig. 8, Ref. 35).

Cyclosporine A area-under-time-concentration-curve in rheumatologic patients after the first dose. Which of the sparse sampling strategies will predict the best?

OBJECTIVE: The aim of the present study was to validate the limited sampling strategies (LSS:s) for prediction of AUC of cyclosporine A (CsA) after the first dose in rheumatologic patients. METHODS: 22 patients suffering from rheumathoid arthritis, systemic lupus erythematodus, ankylosing spondylitis dermato(poly)myositis or seronegative spondylarthritis were treated with Neoral® (female/male: 11/3, mean ± SD: age 49 ± 14 y, body weight 75 ± 12 kg, height 166 ± 7 cm, dose 71 ± 25 mg, dose per kg 1.0 ± 0.3 mg/kg), or Consupren® (7/1, 78 ± 36, 175 ± 8, 82 ± 22, 1.1 ± 0.3). Two patients whose C12h were missing were excluded from the AUC0-12 calculation. Whole blood levels of CsA were analyzed with HPLC. Blood samples were collected at 0, 0.5, 1, 1.5, 2, 3, 4, 6, 8, and 12 hours after taking the first dose. Altogether 115 LSS:s obtained from the literature were validated. A linear trapezoidal rule was used as a reference method. Mean percentage prediction error (%PE) < ± 15% and maximal one value of absolute %PE > 30% were considered to be acceptable. The root mean squared error (RMSE) was evaluated for equations that passed the criteria. RESULTS: The best performance with all values of the absolute %PE < 30% was found in three LSS:s for AUC0-12 and two for AUC0-8: AUC0-12 = 123.792 + 1.165 × C1h + 3.021 × C3h + 7.33 × C8h; 97.6 + 1.27 × C1h + 3.14 × C3h + 4.06 × C6h; or 124.3 + 1.34 × C1h - 0.16 × C2h + 3.27 × C3h + 3.96 × C6h; AUC0-8 = -19.8 + 1.99 × C2h + 2.38 × C4h + 3.15 × C6h or -22.4 + 2.51 × C2h + 5.49 × C6h. Validation criteria were further fulfilled in AUC0-12 = 24 + 3.66 × C0h + 2.11 × C1.5h + 4.54 × C4h or 0.2 + 2 × C2h + 10.2 × C6h; AUC0-8 = 55.37 + 2.89 × C0h + 1.08 × C1 + 0.9 × C2h + 2.23 × C3h; and AUC0-4 = -41 + 1.17 × C1h + 1.85 × C2h. Only one equation proposed for AUC0-6 did not pass the validation criteria. CONCLUSIONS: Equations validated for prediction of AUC0-12, AUC0-8 and AUC0-4 might be used for LSS:s of CsA independently of the length of treatment, indication, dosage or galenic formulation.

The possibility of using the specific RIA method for the area-under-time-concentration curve sparse sampling calculation of cyclosporin A despite a large post-dose overestimation.

OBJECTIVE: To find limited sampling strategies (LSS) for prediction of the real AUC using the RIA analytical method. METHOD: Blood samples of 40 male renal transplant patients taken pre-dose and after 0.5, 1, 1.5, 2, 3, 5, 8, and 12 h in the steady-state were analyzed with HPLC and the specific RIA method. I. Eight equations for AUC0-12 and one for AUC0-8 obtained from the literature, that produced the mean percentage prediction error (%PE) < ± 15% and absolute %PE < 30% in 95% of predictions, were analyzed for possibility to predict the real AUC of CsA. II. Multiple regression analysis (MRA) was provided for the AUC equation proposal. Patients were divided into two groups according to the AUC0-12. Group I was used for LSS : s proposals while Group II for validation. The bias and precision were expressed as %PE, r2 and RMSE. The relationship of %PE interassay and with LSS:s was expressed as Pearson correlation r. GraphPad InStatt Software was used for MRA and Pearson r calculation. RESULTS: None of the equations described in the literature predicts AUC of CsA proprietarily. Seven equations for AUC0-12 and five for AUC0-8 were proposed with MRA for prediction of real AUC from RIA values. CONCLUSIONS: LSS:s can moderate the interassay %PE in AUC of CsA. New patients should be tested with both RIA and HPLC for the level of overestimation. The conversion factors should be calculated for patients with an overestimation higher than 90%. Our equation 251.09 + 0.5195 × C1h + 4.926 × C3h or 196.13 + 4.526 Â× C0h + 2.089 × C1.5h for AUC0-12, and 171.80 + 0.4759 × C1h + 4.132 × C3h for AUC0-8 may be used in patients with medium or low RIA and HPLC differences. Repeated analysis with HPLC is thus suggested in cases with AUC:s results close to the lower or upper margin of the therapeutic window.

Significant discrepancy in cyclosporin A post-dose concentrations when analyzed with specific RIA and HPLC method.

C(2) or AUC sparse sampling methods are widely recommended for therapeutic monitoring of cyclosporin A (CsA). One additional reason for promoting the C(2) sampling time in place of commonly used C(0) is that the C(2) level may actually provide more accurate measurement of parent drug concentration by immunoassays, as lower portion of metabolites has been formed 2 hours post-dose than at the steady-state trough time point. HPLC and RIA whole blood levels of CsA and its main metabolites AM1, AM9 and AM4N were compared during 12 hours profile after chronic administration. 40 stable renal transplant male patients (age 49 +/- 6 years, body weight 76 +/- 7 kg) were treated with CsA (Sandimmun Neoral, Novartis s.r.o, Prague, Czech Republic) in doses 198 +/- 56 mg twice daily. Samples were collected in steady state (after 2 weeks of regular treatment regimen) as follows: pre-dose, 0.5, 1, 1.5, 2, 3, 5, 8 and 12 hours after dose. CsA concentrations were determined both specific RIA assay (Cyclo-Trac SP Whole, Dia Sorin) and HPLC method, where concentrations of metabolites AM1, AM9 and AM4N were simultaneously analyzed. The AUC(0-12) was calculated by the linear trapezoidal rule. The percentage prediction error defined as [(RIA value-HPLC value)/HPLC value] x 100 was used for estimation of differences. C(max), t(max), and C(avg) were compared using Student's t-test. RIA produced significantly higher CsA levels than HPLC method in the period of 0.5 - 5 hours after application. The greatest differences (43 - 56%) occurred between 1 and 3 hours after dose. AUC(0-12), C(max) a C(avg) calculated from RIA results were consequently significantly higher. Only t(max) remained unchanged. The ratio of metabolites/parent drug after CsA intake is decreasing but their absolute concentrations are significantly increasing. Mean levels at C(0)/C(2) were CsA-RIA 82/612, CsA-HPLC 89/425, AM1 121/179, AM9 4.1/81.4, AM4N 9.5/21.0 ng/ml. TDM using C(2) and AUC sparse sampling may cause misleading interpretation using both methods alternately for the same patient.