Pharmacokinetic profile of SKP-1041, a modified release formulation of zaleplon.

OBJECTIVES: Two investigations aimed to define the pharmacokinetic profile of a modified-release preparation of zaleplon (SKP-1041). METHODS: Protocol SOM001 was a 5-way crossover, double-blind, randomized trial comparing three novel modified-release formulations of zaleplon 15 mg (SKP-1041A, SKP-1041B, SKP-1041C) to placebo and immediate-release zaleplon 10 mg. Protocol SOM002 was a randomized, crossover, open-label trial to compare the pharmacokinetics of SKP-1041B after day and night administration. In SOM001, study drug was administered at 9:00 a.m. (fasted); blood samples were obtained beginning 1 h predose through 12 h postdose. In study SOM002, study drug was administered at 9:00 a.m. or 10:30 p.m.; blood samples were obtained beginning 1 h predose through 12 h postdose. Subjects were 19 (SOM001) and 23 (SOM002) healthy adults between ages 20-46. RESULTS: Dose-normalized total AUCs for modified-release preparations A, B, C and immediate-release zaleplon were not significantly different; peak plasma concentrations were similar for A and B, and both were significantly higher than C. Time to peak plasma concentration for A, B, and C were 4-5 h compared to 1.5 h for immediate-release zaleplon; mean terminal phase half-life was in the range 1-2 h for A, B and immediate-release zaleplon. No significant differences were noted between day and night administration in the SOM002 study. CONCLUSIONS: Zaleplon, 15 mg, in a novel, modified-release formulation (SKP-1041) had a time to peak plasma concentrations at 4-5 h postdose compared to 1.5 h for immediate-release zaleplon, 10 mg. The pharmacokinetic profile suggests this formulation may be useful for treating middle-of-the-night awakening.

Serum concentrations and clinical effects of atorvastatin in patients taking grapefruit juice daily.

AIM: To determine whether customary exposure to grapefruit juice (GFJ) alters serum concentrations, effectiveness, and potential adverse effects of atorvastatin in patients requiring the medication. METHODS: Patients receiving extended treatment with atorvastatin (10, 20 or 40 mg day(-1)) at a stable dose received 300 ml day(-1) of 100% GFJ for a period of 90 days. One cohort of patients (arm A, n= 60) continued on their current dose of atorvastatin; the second cohort (arm B, n= 70) reduced the daily dose by 50%. Serum atorvastatin, lipid profile, liver functions, and creatine phosphokinase (CPK) were measured at baseline and at 30, 60, and 90 days after starting GFJ. RESULTS: In Arm A patients, co-ingestion of GFJ significantly elevated serum atorvastatin by 19% to 26% compared with baseline. Changes in lipid profile relative to baseline were negligible. There were no adverse effects on liver function tests or CPK. In arm B patients, serum atorvastatin declined by 12% to 25% compared to baseline, with a small but significant unfavourable effect in serum lipid profile. There were no adverse effects on liver function tests or CPK. CONCLUSION: In patients on extended stable atorvastatin treatment, addition of daily GFJ in typical quantities slightly elevates serum atorvastatin concentrations, but has no meaningful effect on the serum lipid profile, and causes no detectable adverse liver or muscle effects. Reduction of atorvastatin dosage when moderate amounts of GFJ are co-ingested does not appear to be necessary.

Mechanism of cytochrome P450-3A inhibition by ketoconazole.

OBJECTIVES: Ketoconazole is extensively used as an index inhibitor of cytochrome P450-3A (CYP3A) activity in vitro and in vivo, but the mechanism of ketoconazole inhibition of CYP3A still is not clearly established. METHODS: Inhibition of metabolite formation by ketoconazole (seven concentrations from 0.01 to 1.0 µm) was studied in human liver microsomes (n = 4) at six to seven substrate concentrations for triazolam, midazolam, and testosterone, and at two substrate concentrations for nifedipine. KEY FINDINGS: Analysis of multiple data points per liver sample based on a mixed competitive-noncompetitive model yielded mean inhibition constant K(i) values in the range of 0.011 to 0.045 µm. Ketoconazole IC50 increased at higher substrate concentrations, thereby excluding pure noncompetitive inhibition. For triazolam, testosterone, and midazolam α-hydroxylation, mean values of α (indicating the 'mix' of competitive and noncompetitive inhibition) ranged from 2.1 to 6.3. However, inhibition of midazolam 4-hydroxylation was consistent with a competitive process. Determination of K(i) and α based on the relation between 50% inhibitory concentration values and substrate concentration yielded similar values. Pre-incubation of ketoconazole with microsomes before addition of substrate did not enhance inhibition, whereas inhibition by troleandomycin was significantly enhanced by pre-incubation. CONCLUSIONS: Ketoconazole inhibition of triazolam α- and 4-hydroxylation, midazolam α-hydroxylation, testosterone 6ß-hydroxylation, and nifedipine oxidation appeared to be a mixed competitive-noncompetitive process, with the noncompetitive component being dominant but not exclusive. Quantitative estimates of K(i) were in the low nanomolar range for all four substrates.

Sources of variability in ketoconazole inhibition of human cytochrome P450 3A in vitro.

Despite the extensive use of ketoconazole as an index inhibitor of human cytochrome P450 3A (CYP3A) isoforms in vitro, literature reports of the quantitative inhibitory potency of ketoconazole are highly variable. In 51 published studies reporting 76 values of ketoconazole inhibition constants (K(i)) versus in vitro clearance of 31 different CYP3A substrates, the K(i) values ranged from 0.001 µM to 25 µM. The geometric mean was 0.1 µM (90% confidence interval: 0.07 to 0.15 µM), and the median was 0.08 µM. Even for one specific substrate metabolized to one specific metabolite (midazolam α-hydroxylation), variability was still extensive (K(i) range: 0.004-0.18 µM). Only about 20% of overall variability in K(i) was explained by a combination of incubation, duration, and microsomal protein concentration. The remaining variation is unexplained, but could be attributable to factors such as: in vitro clearance by non-CYP3A pathways; incorrect assignment of inhibition mechanism; and variable relative content of CYP3A4 and CYP3A5 in different microsomal preparations. However, the role of these factors still is not established. Until sources of variation are more clearly defined, variability can be minimized by use of low microsomal protein concentrations, short incubation periods, and data analysis procedures that use untransformed reaction velocities and inhibition models that allow for mixed competitive-noncompetitive mechanisms.