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Population Pharmacokinetic Analysis of Anidulafungin, an Echinocandin Antifungal

From Vicuron Pharmaceuticals, King of Prussia, Pennsylvania (Dr. Dowell, Dr. Stogniew, Dr. Krause, Dr. Henkel) and GloboMax, Hanover, Maryland (Dr. Knebel, Dr. Ludden).

Address for reprints: James A. Dowell, PhD, Vicuron Pharmaceuticals, 455 South Gulph Road, King of Prussia, PA 19406.

	ABSTRACT

TOP ABSTRACT METHODS RESULTS DISCUSSION REFERENCES

 
The objective of this analysis was to describe the pharmacokinetic characteristics of anidulafungin in patients with serious fungal disease based on pharmacokinetic data collected during four recently completed or ongoing Phase II/III clinical studies. A total of 600 anidulafungin plasma samples from 225 patients across the four studies were available for analysis. Patients received daily intravenous infusions of 50, 75, or 100 mg anidulafungin, preceded by a loading dose that was twice the daily dose. The analysis population consisted of 129 patients with esophageal candidiasis, 87 with invasive candidiasis, 7 with invasive aspergillosis, and 2 with azole refractory mucosal candidiasis. A population analysis approach was used to develop a steady-state pharmacokinetic model for anidulafungin, assess the significance of possible covariates, and determine the amount of intersubject and random residual variability. A two-compartment model with first-order elimination provided the best fit to the data. The clearance of anidulafungin was influenced by weight and gender, and subjects in the invasive candidiasis study had a typical clearance that was approximately 30% higher than subjects from other studies. Weight was determined to be a predictor of the central volume of distribution. The covariates on clearance accounted for less than 20% of the intersubject variability and therefore are deemed to be of little clinical relevance. There was no evidence that the presence of rifampin or metabolic substrates, inhibitors, or inducers of cytochrome P450 influenced the clearance of anidulafungin. This indicates that dosing adjustments are not necessary when anidulafungin is administered in the presence of medications falling into these classifications.

Key Words: Anidulafungin • pharmacokinetics • echinocandin antifungal • clearance

Studies of the pharmacokinetics of anidulafungin have been conducted in healthy volunteers, renally impaired subjects, and hepatically impaired subjects. Results of these studies demonstrate that anidulafungin has low and variable absorption after oral administration but has predictable pharmacokinetics following intravenous administration that includes systemic exposures that increase linearly with dose and low intersubject variability.^6,7 The volume of distribution at steady state has been estimated to be 30 to 50 L.⁶ Anidulafungin has a unique mechanism of elimination. It is primarily eliminated by slow chemical degradation (> 90% of dose), with less than 10% of the drug eliminated in feces as intact drug.⁸ Based on the unique mechanism of elimination, it is hypothesized that anidulafungin is not subject to typical drug-drug interactions. Anidulafungin is a low-clearance compound, as demonstrated by a total systemic clearance of approximately 1 L/h. The half-life of elimination is approximately 1 day, allowing once-daily dosing. A loading dose on the first day of dosing that is double the daily dose allows steady-state concentrations to be obtained by the second dose.⁷ Studies in subjects with renal and hepatic insufficiency have shown no clinically important changes in the pharmacokinetics that would necessitate a dosing adjustment.⁹

The objective of this analysis was to describe the pharmacokinetic characteristics of anidulafungin in patients with serious fungal disease based on pharmacokinetic data collected during four recently completed or ongoing Phase II/III clinical studies. A population analysis approach was used to develop a pharmacokinetic model for anidulafungin in the target patient population, assess the degree of intersubject and random residual variability, and assess the statistical significance of possible covariates on the population pharmacokinetic model parameters. The effects of demographic factors (age, gender, weight, and race), study, and the use of concomitant medications were evaluated.

	METHODS

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Study Design
The analysis included data collected from 225 patients from four recently completed or ongoing Phase II/III anidulafungin clinical studies. Patients received various dosage regimens of anidulafungin consisting of a loading dose on the first day that was twice the daily maintenance dose (50, 75, or 100 mg). Anidulafungin was administered by intravenous infusion at a rate of 1 mg/min. Infusion times ranged from 0.83 to 3.33 hours, depending on the administered loading or maintenance dose. A total of 600 blood samples were collected for anidulafungin plasma concentration measurements across the four studies. All plasma concentrations were collected after at least the third dose so the patients would be at steady state. Each patient had an average of 2 to 3 samples collected at different time points relative to the initiation of dose. The analysis population consisted of 129 patients with esophageal candidiasis, 87 with invasive candidiasis, 7 with invasive aspergillosis, and 2 with fluconazole refractory mucosal candidiasis. The protocols and amendments received institutional review board approval at all investigational sites. The study was conducted according to the Declaration of Helsinki and its amendments, which includes securing written informed consent from all subjects before their enrollment in the study.

Sodium heparinized plasma samples were analyzed for anidulafungin using a validated liquid chromatography tandem mass spectrometry (LC-MS/MS) method. Analyte quantitation was performed using a weighted linear regression (1/concentration²) of peak area ratios versus concentration. The assay was validated over the nominal concentration range of 0.1 to 20 µg/mL. The assay demonstrated acceptable accuracy and precision, with an overall accuracy for the quality control samples between 91.0% and 106%, and a coefficient of variation (%CV) of less than 6%.

The covariates explored for inclusion in the anidulafungin population model are listed in Table I. These included demography (age, gender, weight, and race), study, and concomitant medications. All demographic covariates were recorded at baseline. Concomitant medications were categorized metabolically as substrates, inducers, or inhibitors of cytochrome P450 and evaluated in the model. Rifampin, a known potent inducer, was included as a separate variable. The concomitant medications were categorized using the current referenced list by David A. Flockart, MD, PhD, at the Indiana University School of Medicine.¹⁰

Pharmacokinetic Analysis
Mixed-effects models were evaluated using the first-order (FO) and first-order conditional estimation with interaction (FOCEI) maximum likelihood estimation in the NONMEM program (double precision, Version V, Level 1.1).¹¹

Base Model Selection
One- and two-compartment models were evaluated. Selection of the appropriate base population pharmacokinetic model was based on a graphical evaluation of the plasma anidulafungin concentrations versus time and model diagnostic criteria, including a significant reduction in the objective function value (p < 0.05), a decrease in the residual error, randomness of the individual weighted residuals distribution against the predicted concentration and time, and randomness of the observed concentration distribution versus individual predicted concentration values across the identity line.

The population pharmacokinetic parameters were assumed to be log-normally distributed. Intersubject variability terms were initially included on all of the population model parameters. The importance of the variability parameters was evaluated during base model selection. An additive plus proportional random residual error model was fitted to the data to allow the maximum amount of model flexibility.

Covariate Selection
The relationships between structural model-based Bayesian estimates of the pharmacokinetic parameters and individual covariates were explored graphically and via generalized additive modeling (GAM) analysis.¹² All of the covariates that were significant, based on the Akaike information criterion (AIC) values from the GAM analysis or from the graphical evaluation, were evaluated in NONMEM. An additional exploratory analysis, which consisted of testing specific covariates against the final population model, was performed to investigate covariates of interest that were not detected by the GAM analysis.

Population Pharmacokinetic Model Building
Continuous covariates were entered into the model according to the following equation:

where P is the individual estimate of the parameter, COV is the value of the covariate, is the median value of the covariate in the study population, ₁ represents the typical value of the parameter (when ), and ₂ represents the slope of the effect of the covariate on the parameter (e.g., body weight or age).

Categorical covariates were included in the model using indicator variables, as shown in the following equation:

where P is the individual estimate of the parameter, IND is an indicator variable that has a value of 1 when the covariate is present (otherwise, IND = 0), ₁ represents the typical value of the parameter when IND = 0, and ₂ represents the effect of the covariate (when IND = 1).

All of the significant covariates were incorporated into a "full" model in NONMEM. A backward elimination process was used to determine the final population pharmacokinetic model. During this process, the statistical significance (p < 0.001) of each covariate was tested by setting each covariate to its null value, running the NONMEM estimation step, and recording the objective function. After each model breakdown step, the least significant covariate was removed from the model, and the process was repeated until all of the remaining covariates were significant.

Population Pharmacokinetic Model Evaluation
The ability of the final anidulafungin population pharmacokinetic model to describe the observed data was investigated via Monte Carlo simulation in NONMEM. The final anidulafungin population pharmacokinetic model, including final fixed-effect and random-effect parameters, was used to simulate 100 replications of the observed data set. The simulated data were sorted by observation times, and the 90th, 80th, 50th (median), 20th, and 10th quantiles of the simulated data were calculated for each time point. The observed data were plotted against the quantiles of the pooled, simulated data to ensure that the majority of observed data fell within the boundaries of the 90th and 10th quantiles of the simulated data. The results of these 100 simulations were used to provide evidence that the derived population pharmacokinetic model accurately described the observed data.

	RESULTS

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Pharmacokinetic Analysis
Base Model
Review of the diagnostic plots and the minimum objective function indicated that the two-compartment model resulted in a better fit to the data. In addition, a plot of plasma anidulafungin concentrations versus time demonstrated two-compartment characteristics. The two-compartment model was parameterized in terms of clearance, central volume of distribution, intercompartmental clearance, peripheral volume of distribution, and a diagonal omega matrix.

The base model population pharmacokinetic parameter estimates for anidulafungin are presented in Table II. The clearance of anidulafungin in the analysis population was estimated to be 0.946 L/h, and the central volume of distribution was 9.97 L. The volume of distribution at steady state, calculated as central plus peripheral volume of distribution, was 33.2 L. The diagnostic plots were unbiased, and the percent relative standard error for the pharmacokinetic parameters ranged from 2.8% to 35.2%.

Intersubject variability on clearance was estimated to be 34.9%. During base model selection, the intersubject variability terms for the central and peripheral volumes of distribution were estimated to be approximately zero. For this reason, the intersubject variability terms on the volume of distribution parameters were excluded from the model. In a subsequent NONMEM estimation step, the intersubject variability term for intercompartmental clearance was excluded from the model due to an inability to estimate model parameter standard errors, which may have occurred due to model overparameterization. Intersubject variability was supported only on clearance in the anidulafungin base model. The random residual error model was proportional and was estimated to be 24.6%.

Population Model
Exploratory graphical evaluation and GAM analysis indicated that weight, gender, and the effect of being in the invasive candidiasis study (Study) might be related to the clearance of anidulafungin. The effects of weight, gender, and Study on clearance and weight on the central and peripheral volumes of distribution were included in the "full" NONMEM model.

The final population pharmacokinetic model contained effects for weight, gender, and Study on anidulafungin clearance and weight on the anidulafungin central volume of distribution. The equations describing the relationship between the significant covariates and the anidulafungin clearance and central volume of distribution were determined to be as follows:

where CL = anidulafungin clearance in L/h, V1 = anidulafungin central volume of distribution in L, WT is the body weight in kg, Gender = 1 for males and 0 for females, and Study = 1 for patients in the invasive candidiasis study and 0 for all other studies.

The volume of the peripheral compartment was estimated to be 19.6 L, and the intercompartmental clearance was 20.3 L/h. The inclusion of weight as a covariate on the peripheral volume of distribution was not supported in the final model. The volume of distribution at steady state, calculated as central plus peripheral volume, was 33.4 L. Terminal half-life values were calculated from the individual parameter estimates for each patient. This yielded a mean terminal half-life of 25.6 hours for anidulafungin.

An additional exploratory analysis was performed to evaluate the effects of the concomitant medications (substrates, inducers, inhibitors, and rifampin). Each of these potential covariates was tested against the final anidulafungin population pharmacokinetic model. None of these covariates resulted in a significant change in the objective function or had significant changes in the individual parameter estimates (Figure 1). Therefore, these potential covariates were not retained in the final model.

View larger version (14K): [in this window] [in a new window]   Figure 1. Distribution of clearance (CL) when concomitant medication categories were included into the anidulafungin population pharmacokinetic model. Categories consist of patients who received no known interacting medications (None) and patients who received known P450 inhibitors, inducers, or rifampin while being treated with anidulafungin. For each box plot, the center line with the open circle represents the median, the bounds are the difference between the first and third quartiles, and the whiskers represent either the extreme range of data or 1.5 times the interquartile range. The notched shaded areas represent the confidence intervals, and outliers are represented as solid circles.

The final population pharmacokinetic parameter estimates for anidulafungin are presented in Table III. The population pharmacokinetic parameters for the final anidulafungin population model were estimated with good precision. The percent relative standard errors for all of the fixed-effect parameters were below 25%, with the exception of weight on clearance (26.9%) and gender on clearance (25.4%). The random-effect parameters were estimated with similar precision. The diagnostic plots demonstrated a lack of bias and a good fit of the model to the anidulafungin plasma concentrations (Figure 2).

View larger version (18K): [in this window] [in a new window]   Figure 2. Diagnostic plots for the final anidulafungin population pharmacokinetic model. Observed versus predicted plasma anidulafungin concentrations (left panel) and weighted residuals versus predicted plasma anidulafungin concentrations (right panel). Solid line is unity.

Model Evaluation
The ability of the final model to describe the observed data was evaluated via Monte Carlo simulations within NONMEM. A total of 100 data sets were simulated using the fixed- and random-effect parameters from the final population model presented in Table III. The observed data were plotted against the 90th, 80th, 50th, 20th, and 10th quantiles of the pooled, simulated data (Figure 3). The majority of the observed anidulafungin plasma concentrations fall within the range of the model-predicted data for each dose group, providing evidence that the derived population pharmacokinetic model accurately described the observed data.

View larger version (14K): [in this window] [in a new window]   Figure 3. Model evaluation of anidulafungin final population pharmacokinetic model by dose group. Data points are the observed anidulafungin plasma concentration versus time data. The short dashed line represents the median; solid lines represent the 10th (lower) and 90th (upper) percentiles of the simulated anidulafungin plasma concentrations versus time data; long dashed lines represent the 20th and 80th percentiles of the simulated anidulafungin plasma concentrations versus time data.

	DISCUSSION

TOP ABSTRACT METHODS RESULTS DISCUSSION REFERENCES

 
The estimated pharmacokinetic parameters for the patients in this analysis were similar to those determined in healthy volunteers receiving anidulafungin at similar doses.⁷ The results of this analysis indicate that the clearance of anidulafungin was influenced by body weight, gender, and the effect of being in the invasive candidiasis study, and that anidulafungin distribution was influenced by body weight. Specifically, anidulafungin clearance was estimated to increase with increasing body weight, to be slightly increased in males, and to be greater in patients in the invasive candidiasis study. The anidulafungin central volume of distribution was estimated to increase with increasing body weight.

The inclusion of covariates on clearance explained approximately 20% of the variability in anidulafungin clearance. The difference in clearance in the patients in the invasive candidiasis study could be related to physiologic differences since these patients were older, hospitalized, and more acutely ill than patients in the esophageal candidiasis study, while the patients in the esophageal candidiasis study were more ambulatory and leaner and had a higher rate of HIV and AIDS. Based on the point estimates of anidulafungin clearance by Study and Gender in Table III, the clearance of male and female patients in the invasive candidiasis study would be expected to be approximately 30% and 36% higher, respectively, than male and female patients in the other three studies. A histogram of anidulafungin clearance by Study, with study defined as the invasive candidiasis study versus all others, and a histogram of clearance by Gender are displayed in Figure 4 and Figure 5, respectively. A histogram of clearance for males in the invasive candidiasis study versus all other studies and a histogram of clearance for females in the invasive candidiasis study versus all other studies are displayed in Figure 6 and Figure 7, respectively. In all four histograms, there is a large degree of overlap in the clearance values. The large degree of overlap in the clearance values, as well as the small amount of variability explained by the inclusion of covariates on clearance, suggests that even though the subgroups were large enough to detect a statistical difference, the effect of these covariates on the clearance of anidulafungin is minimal.

View larger version (31K): [in this window] [in a new window]   Figure 4. Histogram of clearance (CL) by Study. Downward hatch bars are individual CL values from patients in the invasive candidiasis study, and upward hatch bars are from patients in all other studies.

View larger version (35K): [in this window] [in a new window]   Figure 5. Histogram of clearance (CL) by Gender. Downward hatch bars are individual CL values from males, and upward hatch bars are from females.

View larger version (34K): [in this window] [in a new window]   Figure 6. Histogram of clearance (CL) for males in the invasive candidiasis study versus all other males. Downward hatch bars are individual CL values from males in the invasive candidiasis study, and upward hatch bars are from males in all other studies.

View larger version (27K): [in this window] [in a new window]   Figure 7. Histogram of clearance (CL) for females in the invasive candidiasis study versus all other females. Downward hatch bars are individual CL values from females in the invasive candidiasis study, and upward hatch bars are from females in all other studies.

Estimation of intersubject variability was not supported on either volume term or intercompartmental clearance. However, body weight was determined to be a predictor of the anidulafungin central volume of distribution. The inability to estimate intersubject variability on the volume terms and on inter-compartmental clearance may be due to the sparse sampling scheme employed during data collection or the difficulty in estimating intersubject variability for a drug with inherently low intersubject variability. The relatively small amount of variability explained by weight in the model supports an anidulafungin dosage that is not based on body weight.

The inclusion of other possible covariates (age, race, metabolic substrates, metabolic inhibitors, metabolic inducers, or rifampin) on clearance in the model was not supported. Based on the route of anidulafungin elimination, mainly via chemical degradation, the lack of inclusion of covariates representing the presence of concomitant medications was expected. For most of the concomitant medication categories (rifampin, inducer, inhibitor), there were adequate numbers of patients (> 10% per category) to have observed any consistent, clinically meaningful changes.

In conclusion, the clearance of anidulafungin was influenced by body weight, gender, and the effect of being in the invasive candidiasis study. However, these covariates accounted for less than 20% of the intersubject variability of anidulafungin clearance and therefore are deemed to have little clinical relevance. The clearance of anidulafungin was not influenced by the presence of rifampin or metabolic substrates, inhibitors, or inducers of cytochrome P450, indicating that dosing adjustments are not necessary when anidulafungin is administered in the presence of medications falling into these classifications.

	FOOTNOTES

 
DOI: 10.1177/0091270004265644

Submitted for publication October 29, 2003; Revised version accepted March 21, 2004.

	REFERENCES

TOP ABSTRACT METHODS RESULTS DISCUSSION REFERENCES

 

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3. Arevalo MP, Carrillo-Munoz AJ, Salgado J, Cardenes D, Brio S, Quindos G, et al: Antifungal activity of the echinocandin anidulafungin against yeast pathogens: a comparative study with M27-A microdilution method. J Antimicrob Chemother 2003; 51(1): 163-166.[Abstract/Free Full Text]

4. Moore CB, Oakley KL, Denning DW: In vitro activity of a new echinocandin, LY303366, and comparison with fluconazole, flucytosine and amphotericin B against Candida species. Clin Microbiol Infect 2001;7(1): 11-16.[CrossRef]

5. Bartlett MS, Current WL, Goheen MP, Boylan CJ, Lee CH, Shaw MM, et al: Semisynthetic echinocandins affect cell wall deposition of Pneumocystis carinii in vitro and in vivo. Antimicrob Agents Chemother 1996;40(8): 1811-1816.[Abstract]

6. Brown GL, White RJ, Taubel J: Phase 1 dose optimization study for V-echinocandin [poster 1105]. Poster presented at the 40th Annual ICAAC Conference, Toronto, September 2000.

7. Thye D, Shepherd B, White RJ, Weston IE, Henkel T: Anidulafungin: a phase 1 study to identify the maximum tolerated dose in healthy volunteers [poster 36]. Poster presented at the 41st Annual ICAAC Conference, Chicago, December 2001.

8. Stogniew M, Pu F, Henkel T, Dowell J: Anidulafungin biotransformation in humans is by degradation not metabolism. Clin Microbiol Infect 2003;9(Suppl.): 291.

9. Dowell J, Stogniew M, Krause D: Anidulafungin dosage adjustments are not required for patients with hepatic and/or renal impairment. Clin Microbiol Infect 2003;9(Suppl.): 290-291.

10. Flockhart DA: Drugs metabolized by known P450's [reference list]. Indiana School of Medicine, January 2002. Retrieved from http://medicine.iupui.edu/flockhart/

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12. Jonnson EN, Karlsson MO: Xpose 2.0 User's Manual. Sweden: Uppsala University, Department of Pharmacy, 1997.
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