Population Pharmacokinetics of Anidulafungin in Critically Ill Patients

TEXT

The 2009 Infectious Diseases Society of America treatment guidelines for candidemia recommend the use of an echinocandin as initial therapy for critically ill patients (1). Anidulafungin is commonly used for the treatment of diseases caused by Candida spp. in critically ill patients. However, there are relatively limited population pharmacokinetic (PK) data for this patient population (1, 2). A deep understanding of PK-pharmacodynamic relationships underpins the design of safe and effective regimens and highlights circumstances where a standard fixed regimen may fail. Herein, we describe the population PK of anidulafungin in critically ill patients and evaluate the probability of achieving target area under the concentration-time curve from 0 to 24 h (AUC_0–24)/MIC values at steady state against Candida albicans and Candida glabrata with the currently licensed regimen.

A total of 23 critically ill patients with proven or suspected invasive fungal infection (from Hospital del Mar, Barcelona, Spain) who were receiving anidulafungin were recruited. The study was approved by the Ethics Committee of Parc de Salut Mar (2016/6987/I) in Barcelona, Spain, and written informed consent was obtained from patients or their legal representatives before enrollment.

All patients received a loading dose of 200 mg of anidulafungin (Ecalta) followed by a maintenance dose of 100 mg/24 h infused over 1 h. Sampling occurred after day 3 of treatment, and blood was collected preinfusion and 1, 3, 5, 8, 18, and 24 h postadministration in most of the patients. Anidulafungin concentrations were measured using a previously described validated high-pressure liquid chromatography method (3).

Population PK modeling was performed using Pmetrics (4, 5). One- and two-compartment models were fitted to the data. The elimination from the central compartment and intercompartmental distribution were modeled as first-order processes. Age, sex, total body weight (TBW), acute physiology and chronic health evaluation (APACHE) severity score, and liver cirrhosis were evaluated as covariates using stepwise linear regression. Potential covariates were entered separately into the model and retained if their inclusion resulted in a statistically significant improvement in the log-likelihood value and/or improvements in the observed-predicted plots.

The fit of each model to the data was assessed using linear regression of observed-predicted values before and after the Bayesian step. The mean prediction error and the mean bias-adjusted squared prediction error were used to assess bias and imprecision, respectively. Models were compared by calculating twice the difference in log-likelihood values, which was then assessed against χ² distribution using the appropriate degrees of freedom (i.e., difference in number of parameters for each model). To further assess the predictive accuracy of the final model, a visual predictive check (VPC) was performed.

Monte Carlo simulations (n = 1,000) of plasma concentrations were used to calculate the free AUC_0–24/MIC at steady state (i.e., from 144 to 168 h after treatment initiation). From the 1,000 simulated concentration-time profiles, a probability of target attainment (PTA) against C. albicans and C. glabrata was calculated using free AUC_0–24/MIC targets of 20 and 7, respectively. These targets have been associated with the stasis endpoint by using a preclinical model of disseminated candidiasis with CLSI methodology (6). A range of MIC values (0.002 to 16 mg/liter) and a range of TBWs (70 to 150 kg) were examined. Human protein binding of 99% was used to estimate free-drug concentrations of anidulafungin (7).

The demographics of the study population were as follows: 10 patients (43.5%) were male, median age was 65 years (range, 28 to 81 years), TBW was 75 kg (range, 54 to 168 kg), and median APACHE severity score was 21 (range, 10 to 48). Nine patients (39.1%) had liver cirrhosis with Child-Pugh scores of A (n = 1), B (n = 3), and C (n = 5). The median estimated AUC_0–24 was 102.19 mg · h/liter (range, 51.22 to 185.64 mg · h/liter). The concentration-time profiles of anidulafungin in patients are shown in Fig. 1.

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FIG 1

Anidulafungin concentration-time profile of patients receiving a loading dose of 200 mg intravenously followed by a maintenance dose of 100 mg intravenously every 24 h. Intensive sampling was performed after the day 3 of treatment.

The final model was a two-compartment model. Estimates for central tendency, dispersion, and 95% credibility limits for the population PK parameters are shown in Table 1. TBW was the only covariate that explained any portion of the observed variance. In the final model, the clearance (CL) of anidulafungin was described using a power function [CL = CL1 · (TBW/70)^0.75]. Figure 2 shows the observed-predicted values before and after the Bayesian step. After maximum a posteriori probability-Bayesian estimation, the observed-versus-predicted plot had an intercept and slope of −0.432 and 1.03, respectively, and an r² value of 0.967. The bias and imprecision were both acceptable (bias, 0.0729 mg/liter; imprecision, 0.982 mg/liter). The predictive value of the model was further confirmed using a VPC plot (Fig. 3).

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FIG 2

Population (A) and individual (B) predicted anidulafungin concentrations versus observed concentrations of anidulafungin. Broken line, line of identity (observed = predicted concentrations).

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FIG 3

Visual predictive check of anidulafungin plasma concentrations versus time for the final model. Gray shading, confidence bound around each simulated centile; open circles, observed concentrations of anidulafungin.

Patients with greater TBWs who received a standard dosage of anidulafungin developed less drug exposure than smaller patients. The difference in predicted MIC coverage between patients weighing 70 and 150 kg was a single MIC dilution. For C. albicans isolates, a PTA of ≥90% was achieved for patients with a TBW of ≤70 kg for C. albicans isolates with MIC values of ≤0.032 mg/liter. For heavier patients, the coverage of C. albicans MIC was not as extensive, and high PTAs were only achieved for isolates with MIC values of ≤0.016 mg/liter. This difference was mitigated by an increase in the maintenance dose to 150 mg/day in heavier patients (data not shown). For C. glabrata isolates, a PTA of ≥90% was achieved for MIC values of ≤0.064 mg/liter for patients with a TBW up to 150 kg who received the standard anidulafungin dosage (Fig. 4). When the same dosage increase was simulated, a PTA of ≥90% was achieved for MIC values of ≤0.125 and ≤0.064 mg/liter for patients with TBWs of 70 and 150 kg, respectively (data not shown).

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FIG 4

PTA of anidulafungin for patients with different total body weights (70 and 150 kg) against C. albicans and C. glabrata infections and MIC distributions according to CLSI methodology (11).

The finding that TBW had an influence on anidulafungin clearance is consistent with a significant body of evidence supporting this observation for the echinocandin class in general (1, 8–10). Both linear and exponential relationships have been used to describe the effect of weight on clearance (10). Regardless of the function that is ultimately used, heavier patients require progressively higher absolute dosages to achieve drug exposures comparable to those observed in smaller patients. For C. albicans and C. glabrata isolates, a TBW of 150 kg resulted in the loss of an MIC dilution that could be covered by using the current licensed regimen compared with that in 70-kg patients. Critically ill patients with high TBWs may require higher dosages of anidulafungin for the treatment of C. albicans or C. glabrata infections to avoid potential clinical failures. Further prospectively conducted studies are warranted.