Limited-sampling strategy models for itraconazole and hydroxy-itraconazole based on data from a bioequivalence study

Associations between plasma hydroxylated metabolite of itraconazole and serum creatinine in patients with a hematopoietic or immune-related disorder

PURPOSE

Serum markers of renal function have not been characterized in patients treated with itraconazole (ITZ). This study aimed to evaluate the associations between plasma ITZ and its hydroxylated metabolite (OH-ITZ) concentrations and serum markers of renal function in patients with hematopoietic or immune-related disorder.

METHODS

This study enrolled 40 patients with hematopoietic or immune-related disorder who are receiving oral ITZ solution. Plasma concentrations of ITZ and OH-ITZ at 12 h after dosing were determined at steady state. Their relationships with serum levels of creatinine and cystatin C and their estimated glomerular filtration rate (eGFR) were evaluated.

RESULTS

The free plasma concentration of ITZ had no correlation with serum creatinine and serum creatinine-based estimated glomerular filtration rate (eGFR-cre). The free plasma concentration of OH-ITZ was positively and negatively correlated with serum creatinine and eGFR-cre, respectively. The free plasma concentrations of ITZ and OH-ITZ had no association with serum cystatin C and serum cystatin C-based eGFR. Serum creatinine was higher by 16% after than before starting ITZ treatment, while eGFR-cre was lower by 9.3%. The serum creatinine ratio after/before ITZ treatment was positively correlated with the free plasma concentration of OH-ITZ. The patients co-treated with trimethoprim-sulfamethoxazole had higher serum creatinine. Concomitant glucocorticoid administration did not significantly alter serum cystatin C.

CONCLUSIONS

Patients with hematopoietic or immune-related disorder treated with oral ITZ had a higher level of serum creatinine. Although serum creatinine potentially increases in conjunction with the free plasma concentration of OH-ITZ, concomitant ITZ administration has a slight impact on the eGFR-cre level in clinical settings.

Limited sampling strategy for determining metformin area under the plasma concentration-time curve

AIM

The aim was to develop and validate limited sampling strategy (LSS) models to predict the area under the plasma concentration-time curve (AUC) for metformin.

METHODS

Metformin plasma concentrations (n = 627) at 0-24 h after a single 500 mg dose were used for LSS development, based on all subsets linear regression analysis. The LSS-derived AUC(0,24 h) was compared with the parameter 'best estimate' obtained by non-compartmental analysis using all plasma concentration data points. Correlation between the LSS-derived and the best estimated AUC(0,24 h) (r(2) ), bias and precision of the LSS estimates were quantified. The LSS models were validated in independent cohorts.

RESULTS

A two-point (3 h and 10 h) regression equation with no intercept estimated accurately the individual AUC(0,24 h) in the development cohort: r(2)  = 0.927, bias (mean, 95% CI) -0.5, -2.7-1.8% and precision 6.3, 4.9-7.7%. The accuracy of the two point LSS model was verified in study cohorts of individuals receiving single 500 or 1000 mg (r(2)  = -0.933-0.934) or seven 1000 mg daily doses (r(2)  = 0.918), as well as using data from 16 published studies covering a wide range of metformin doses, demographics, clinical and experimental conditions (r(2)  = 0.976). The LSS model reproduced previously reported results for effects of polymorphisms in OCT2 and MATE1 genes on AUC(0,24 h) and renal clearance of metformin.

CONCLUSIONS

The two point LSS algorithm may be used to assess the systemic exposure to metformin under diverse conditions, with reduced costs of sampling and analysis, and saving time for both subjects and investigators.

Therapeutic drug monitoring for triazoles: A needs assessment review and recommendations from a Canadian perspective

Invasive fungal infections cause significant morbidity and mortality in patients with concomitant underlying immunosuppressive diseases. The recent addition of new triazoles to the antifungal armamentarium has allowed for extended-spectrum activity and flexibility of administration. Over the years, clinical use has raised concerns about the degree of drug exposure following standard approved drug dosing, questioning the need for therapeutic drug monitoring (TDM). Accordingly, the present guidelines focus on TDM of triazole antifungal agents. A review of the rationale for triazole TDM, the targeted patient populations and available laboratory methods, as well as practical recommendations based on current evidence from an extended literature review are provided in the present document.

Antifungal use and therapeutic monitoring of plasma concentrations of itraconazole in heart and lung transplantation patients

BACKGROUND

The prophylactic use of itraconazole has dramatically reduced the incidence of fungal infections in patients after solid-organ transplantation. To further reduce this incidence, it has been suggested that plasma concentrations of itraconazole be monitored and maintained above a putative minimum target concentration of 500 ng/mL.

METHODS

A retrospective audit was undertaken of patients who had had a heart or lung transplant over a 14-month period (between January 1, 2010 and March 31, 2011). The itraconazole prophylaxis regimen (dose, time of last dose, time of blood collection) and plasma concentrations were recorded together with the use of concomitant antacid medication. Details of breakthrough fungal infections were documented.

RESULTS

Eighty-four heart or lung organ transplantations were undertaken in the study period; 57 were treated prophylactically with itraconazole. Plasma concentrations of itraconazole were monitored in 56% (n = 32) of these cases. Considerable interpatient (range, 50-2000 ng/mL) and intrapatient variability in plasma concentrations was observed. The putative target was not achieved consistently in the majority of cases. All patients were taking a proton pump inhibitor. Six of the cohort developed an invasive fungal infection. None of the 3 patients for whom plasma concentrations were monitored was above the target concentration.

CONCLUSIONS

Further clinical studies, involving monitoring of the active metabolite and attention to the importance of the stereoisomers of itraconazole, may give better insight into the appropriateness of the currently suggested minimum target concentration, whose validity remains uncertain. Formulations with improved absorption characteristics could reduce the variability of absorption with the goal of further reducing the incidence of infrequent, but life-threatening, invasive fungal infections.

Limited-sampling strategies for anti-infective agents: systematic review

BACKGROUND

Area under the concentration-time curve (AUC) is a pharmacokinetic parameter that represents overall exposure to a drug. For selected anti-infective agents, pharmacokinetic-pharmacodynamic parameters, such as AUC/MIC (where MIC is the minimal inhibitory concentration), have been correlated with outcome in a few studies. A limited-sampling strategy may be used to estimate pharmacokinetic parameters such as AUC, without the frequent, costly, and inconvenient blood sampling that would be required to directly calculate the AUC.

OBJECTIVE

To discuss, by means of a systematic review, the strengths, limitations, and clinical implications of published studies involving a limited-sampling strategy for anti-infective agents and to propose improvements in methodology for future studies.

METHODS

The PubMed and EMBASE databases were searched using the terms "anti-infective agents", "limited sampling", "optimal sampling", "sparse sampling", "AUC monitoring", "abbreviated AUC", "abbreviated sampling", and "Bayesian". The reference lists of retrieved articles were searched manually. Included studies were classified according to modified criteria from the US Preventive Services Task Force.

RESULTS

Twenty studies met the inclusion criteria. Six of the studies (involving didanosine, zidovudine, nevirapine, ciprofloxacin, efavirenz, and nelfinavir) were classified as providing level I evidence, 4 studies (involving vancomycin, didanosine, lamivudine, and lopinavir-ritonavir) provided level II-1 evidence, 2 studies (involving saquinavir and ceftazidime) provided level II-2 evidence, and 8 studies (involving ciprofloxacin, nelfinavir, vancomycin, ceftazidime, ganciclovir, pyrazinamide, meropenem, and alpha interferon) provided level III evidence. All of the studies providing level I evidence used prospectively collected data and proper validation procedures with separate, randomly selected index and validation groups. However, most of the included studies did not provide an adequate description of the methods or the characteristics of included patients, which limited their generalizability.

CONCLUSIONS

Many limited-sampling strategies have been developed for anti-infective agents that do not have a clearly established link between AUC and clinical outcomes in humans. Future studies should first determine if there is an association between AUC monitoring and clinical outcomes. Thereafter, it may be worthwhile to prospectively develop and validate a limited-sampling strategy for the particular anti-infective agent in a similar population.

A possible simplification for the estimation of area under the curve (AUC₀₋₁₂) of enteric-coated mycophenolate sodium in renal transplant patients receiving tacrolimus

Enteric-coated mycophenolate sodium (EC-MPS) is widely used in renal transplantation. With a delayed absorption profile, it has not been possible to develop limited sampling strategies to estimate area under the curve (mycophenolic acid [MPA] AUC₀₋₁₂), which have limited time points and are completed in 2 hours. We developed and validated simplified strategies to estimate MPA AUC₀₋₁₂ in an Indian renal transplant population prescribed EC-MPS together with prednisolone and tacrolimus. Intensive pharmacokinetic sampling (17 samples each) was performed in 18 patients to measure MPA AUC₀₋₁₂. The profiles at 1 month were used to develop the simplified strategies and those at 5.5 months used for validation. We followed two approaches. In one, the AUC was calculated using the trapezoidal rule with fewer time points followed by an extrapolation. In the second approach, by stepwise multiple regression analysis, models with different time points were identified and linear regression analysis performed. Using the trapezoidal rule, two equations were developed with six time points and sampling to 6 or 8 hours (8hrAUC[₀₋₁₂exp]) after the EC-MPS dose. On validation, the 8hrAUC(₀₋₁₂exp) compared with total measured AUC₀₋₁₂ had a coefficient of correlation (r²) of 0.872 with a bias and precision (95% confidence interval) of 0.54% (-6.07-7.15) and 9.73% (5.37-14.09), respectively. Second, limited sampling strategies were developed with four, five, six, seven, and eight time points and completion within 2 hours, 4 hours, 6 hours, and 8 hours after the EC-MPS dose. On validation, six, seven, and eight time point equations, all with sampling to 8 hours, had an acceptable r with the total measured MPA AUC₀₋₁₂ (0.817-0.927). In the six, seven, and eight time points, the bias (95% confidence interval) was 3.00% (-4.59 to 10.59), 0.29% (-5.4 to 5.97), and -0.72% (-5.34 to 3.89) and the precision (95% confidence interval) was 10.59% (5.06-16.13), 8.33% (4.55-12.1), and 6.92% (3.94-9.90), respectively. Of the eight simplified approaches, inclusion of seven or eight time points improved the accuracy of the predicted AUC compared with the actual and can be advocated based on the priority of the user.

A limited sampling strategy for tacrolimus in renal transplant patients

AIMS

To develop and validate limited sampling strategy (LSS) equations to estimate area under the curve (AUC(0-12)) in renal transplant patients.

METHODS

Twenty-nine renal transplant patients (3-6 months post transplant) who were at steady state with respect to tacrolimus kinetics were included in this study. The blood samples starting with the predose (trough) and collected at fixed time points for 12 h were analysed by microparticle enzyme immunoassay. Linear regression analysis estimated the correlations of tacrolimus concentrations at different sampling time points with the total measured AUC(0-12). By applying multiple stepwise linear regression analysis, LSS equations with acceptable correlation coefficients (R(2)), bias and precision were identified. The predictive performance of these models was validated by the jackknife technique.

RESULTS

Three models were identified, all with R(2) > or = 0.907. Two point models included one with trough (C(0)) and 1.5 h postdose (C(1.5)), another with trough and 4 h postdose. Increasing the number of sampling time points to more than two increased R(2) marginally (0.951 to 0.990). After jackknife validation, the two sampling time point (trough and 1.5 h postdose) model accurately predicted AUC(0-12). Regression coefficient R(2) = 0.951, intraclass correlation = 0.976, bias [95% confidence interval (CI)] 0.53% (-2.63, 3.69) and precision (95% CI) 6.35% (4.36, 8.35).

CONCLUSION

The two-point LSS equation [AUC(0-12) = 19.16 + (6.75.C(0)) + (3.33.C1.5)] can be used as a predictable and accurate measure of AUC(0-12) in stable renal transplant patients prescribed prednisolone and mycophenolate.

Clinical study of solid dispersions of itraconazole prepared by hot-stage extrusion

The aim of this study was to investigate the performance of three new solid dispersion formulations of itraconazole in human volunteers in comparison with Sporanox, the marketed form. Solid dispersions made up of itraconazole (40%, w/w) and HPMC 2910, Eudragit E100 or a mixture of Eudragit E100-PVPVA64 were manufactured by hot-stage extrusion and filled in gelatin capsules. The formulations were tested in eight human volunteers in a double blind, single dose, and cross-over study. Concentrations of the drug and its metabolite hydroxyitraconazole in the plasma were determined using HPLC. The in vivo performance was evaluated by comparing the mean area under the plasma concentration-time curves (AUC), the mean maximum plasma concentration (C(max)), and the mean time to reach C(max) (T(max)). The mean bioavailability of itraconazole was comparable after administration of the HPMC solid dispersion, compared to Sporanox, while it was lower after administration of the Eudragit E100 or Eudragit E100-PVPVA64 dispersions. Due to high variability, a significant decrease in AUC and C(max) was only observed for the Eudragit E100-PVPVA formulation. Although the solid dispersions showed different in vitro dissolution behaviour, T(max) values were comparable. The same observations with respect to AUC, C(max) and T(max) could be made for hydroxyitraconazole. The present results indicate that hot-stage extrusion can be considered as a valuable alternative for manufacturing solid dispersions of itraconazole.

Prednisolone

To develop limited-sampling strategy (LSS) models for estimating prednisolone's area under plasma concentration versus time curve (AUC(0-infinity)), its maximum concentration in plasma (C(max)), and total clearance (CL/F). Healthy subjects (n = 24), enrolled in a bioequivalence study, received 20 mg PO of the prodrug prednisone as reference and test tablets, and plasma prednisolone concentrations (n = 576) were measured by a validated HPLC assay. A linear regression analysis of AUC(0-infinity), C(max), CL/F, and log(CL/F) against the plasma prednisolone concentrations for the reference formulation was carried out to develop LSS models to estimate these parameters. The LSS models were validated on the test formulation data sets and on simulated sets generated by the software ADAPT II. LSS models based on a single [1.5 hours for C(max) and 7 hours for AUC(0-infinity), CL/F, and log(CL/F)] plasma sample, accurately estimated (R2 = 0.84-0.97, mean bias < 1%; mean precision < 10%) these pharmacokinetic parameters. Validation tests indicated that the most informative single-point LSS models developed for the reference formulation provide precise estimates (R(2) > 0.83; mean bias < 3%; mean precision < 10%) of the corresponding pharmacokinetic parameters for the test formulation. LSS models based on the two most informative sampling points (1.5 and 7 hours) were required for accurate estimates (R(2) > 0.87; mean bias < 6%; mean precision < 8%) of prednisolone's C(max), AUC(0-infinity), CL/F, and log(CL/F) for the simulated data sets. Finally, bioequivalence assessment of the prednisone formulations, based on LSS-derived AUC(0-infinity) and C(max) values provided results identical to those obtained using the original values for these parameters. One- and 2-point LSS models provided accurate estimates of prednisolone's C(max), AUC(0-infinity), and CL/F, following single oral doses of prednisone, and allowed correct assessment of bioequivalence between two prednisone formulations.

Limited-sampling strategy models for estimating the pharmacokinetic parameters of 4-methylaminoantipyrine, an active metabolite of dipyrone

Bioanalytical data from a bioequivalence study were used to develop limited-sampling strategy (LSS) models for estimating the area under the plasma concentration versus time curve (AUC) and the peak plasma concentration (Cmax) of 4-methylaminoantipyrine (MAA), an active metabolite of dipyrone. Twelve healthy adult male volunteers received single 600 mg oral doses of dipyrone in two formulations at a 7-day interval in a randomized, crossover protocol. Plasma concentrations of MAA (N = 336), measured by HPLC, were used to develop LSS models. Linear regression analysis and a "jack-knife" validation procedure revealed that the AUC(0-infinity) and the Cmax of MAA can be accurately predicted (R2>0.95, bias <1.5%, precision between 3.1 and 8.3%) by LSS models based on two sampling times. Validation tests indicate that the most informative 2-point LSS models developed for one formulation provide good estimates (R2>0.85) of the AUC(0-infinity) or Cmax for the other formulation. LSS models based on three sampling points (1.5, 4 and 24 h), but using different coefficients for AUC(0-infinity) and Cmax, predicted the individual values of both parameters for the enrolled volunteers (R2>0.88, bias = -0.65 and -0.37%, precision = 4.3 and 7.4%) as well as for plasma concentration data sets generated by simulation (R2>0.88, bias = -1.9 and 8.5%, precision = 5.2 and 8.7%). Bioequivalence assessment of the dipyrone formulations based on the 90% confidence interval of log-transformed AUC(0-infinity) and Cmax provided similar results when either the best-estimated or the LSS-derived metrics were used.

Development and validation of limited-sampling strategies for predicting amoxicillin pharmacokinetic and pharmacodynamic parameters

Amoxicillin plasma concentrations (n = 1,152) obtained from 48 healthy subjects in two bioequivalence studies were used to develop limited-sampling strategy (LSS) models for estimating the area under the concentration-time curve (AUC), the maximum concentration of drug in plasma (C(max)), and the time interval of concentration above MIC susceptibility breakpoints in plasma (T>MIC). Each subject received 500-mg amoxicillin, as reference and test capsules or suspensions, and plasma concentrations were measured by a validated microbiological assay. Linear regression analysis and a "jack-knife" procedure revealed that three-point LSS models accurately estimated (R(2), 0.92; precision, <5.8%) the AUC from 0 h to infinity (AUC(0-infinity)) of amoxicillin for the four formulations tested. Validation tests indicated that a three-point LSS model (1, 2, and 5 h) developed for the reference capsule formulation predicts the following accurately (R(2), 0.94 to 0.99): (i) the individual AUC(0-infinity) for the test capsule formulation in the same subjects, (ii) the individual AUC(0-infinity) for both reference and test suspensions in 24 other subjects, and (iii) the average AUC(0-infinity) following single oral doses (250 to 1,000 mg) of various amoxicillin formulations in 11 previously published studies. A linear regression equation was derived, using the same sampling time points of the LSS model for the AUC(0-infinity), but using different coefficients and intercept, for estimating C(max). Bioequivalence assessments based on LSS-derived AUC(0-infinity)'s and C(max)'s provided results similar to those obtained using the original values for these parameters. Finally, two-point LSS models (R(2) = 0.86 to 0.95) were developed for T>MICs of 0.25 or 2.0 microg/ml, which are representative of microorganisms susceptible and resistant to amoxicillin.