ABSTRACT
The objective of this study was to describe the pharmacokinetics (PK) of micafungin in plasma and peritoneal fluid in septic patients with intra-abdominal infections. Twelve patients with secondary peritonitis in septic shock receiving 100 mg micafungin once daily were included. Total micafungin plasma and peritoneal fluid were subjected to a population pharmacokinetic analysis using Pmetrics. Monte Carlo simulations were performed considering the total area under the curve from 0 to 24 h (AUC0–24)/MIC ratios in plasma. Micafungin concentrations in both plasma and the peritoneal exudate were best described by a three-compartmental PK model with the fat-free mass (FFM) as a covariate of clearance (CL) and the volume of the central compartment (Vc). The mean parameter estimates (standard deviations [SD]) were 1.18 (0.40) liters/h for CL and 12.85 (4.78) liters for Vc. The mean peritoneal exudate/plasma ratios (SD) of micafungin were 25% (5%) on day 1 and 40% (8%) between days 3 and 5. Dosing simulations supported the use of standard 100-mg daily dosing for Candida albicans (FFM, <60 kg), C. glabrata (FFM, <50 kg), and C. tropicalis (FFM, <30 kg) on the second day of therapy. There is a moderate penetration of micafungin into the peritoneal cavity (25 to 40%). For empirical treatment, a dose escalation of at least a loading dose of 150 mg depending on the FFM of patients and the Candida species is suggested to be effective from the first day of therapy.
KEYWORDS: micafungin, pharmacokinetics, Monte Carlo simulations, intensive care unit, septic shock, secondary peritonitis, peritoneal fluid
INTRODUCTION
Septic shock represents up to 30% of intensive care unit (ICU) admissions (1). Fungal infections are incriminated in 20% of these infections, mostly due to Candida species (2, 3). Fungal-related septic shock greatly increases morbidity and mortality in ICU patients (4–6). Moreover, Candida species, especially Candida albicans, are the first or second most frequently isolated pathogens in cases of secondary peritonitis (7). Delayed administration of appropriate antifungal therapy and inadequate source control have been identified as risk factors correlating with poor outcomes under these conditions (5), suggesting that adequate initial anti-infective treatment is essential to improve the prognosis of ICU patients with invasive fungal infections (4, 6).
In critically ill patients with invasive candidiasis, candidemia, or peritonitis, echinocandins such as micafungin are recommended as a first-line treatment (8, 9). Micafungin is a selective inhibitor of 1,3-beta-d-glucan synthesis and inhibits the production of the fungal cell wall. It exhibits broad-spectrum fungicidal activity against Candida species and a prolonged postantifungal effect (8). Although Candida albicans is still the leading cause of intra-abdominal candidiasis, an increase in non-albicans Candida species-related infections has been observed in ICUs (10). Five species of Candida are commonly identified: C. albicans, C. parapsilosis, C. glabrata, C. tropicalis, and C. krusei (5, 11, 12). For these species, the most relevant pharmacokinetic/pharmacodynamic (PK/PD) target is the ratio of the 24-h total drug exposure (AUC0–24 [area under the curve from 0 to 24 h]) above the MIC (AUC0–24/MIC ratio), with specific species- and drug-related targets defined previously (13).
In critically ill patients, the PK of drugs can be altered by several pathophysiological changes (sepsis, hepatic and/or renal dysfunction, extracorporeal organ support, hypoalbuminemia, altered capillary permeability, and fluid balance) (2, 14), potentially reducing the probability of achieving any PK/PD target. In intra-abdominal infections, impaired tissue penetration and the presence of indwelling surgical drains may further alter micafungin PK. Changes contributing to the high PK variability may significantly alter drug dosing requirements (15, 16). Furthermore, Sinnollareddy et al. (2) reported that non-target attainment with standard dosing of echinocandins may contribute to the emergence of Candida resistance to echinocandins. In addition, Shields et al. (17) found that intra-abdominal candidiasis plays a role as a hidden reservoir for the emergence of echinocandin resistance. Together, these results suggest that conventional dosing regimens may result in inappropriate drug exposure (2, 16).
To date, only one study has investigated micafungin PK in plasma and peritoneal fluid in ICU patients with intra-abdominal fungal infections (18). This study reported a low probability of PK/PD target attainment using standard dosing regimens for less susceptible pathogens.
Thus, the goal of our study was to describe the PK of micafungin in both plasma and peritoneal fluid from ICU patients with septic shock and secondary peritonitis. Population PK analyses and Monte Carlo simulations were performed to determine the optimal micafungin dosing regimen in ICU patients with septic shock and secondary peritonitis.
RESULTS
Study population.
Twelve patients admitted to the ICU for septic shock and secondary peritonitis were included in the study. For 6 out of 12 (50%) patients, peritoneal cultures were positive for C. albicans (n = 4), C. glabrata (n = 1), C. kefyr (n = 1), and C. lusitaniae (n = 1). The demographic, clinical, and biological data are shown in Table 1. One patient (8%) had died by day 28 postinclusion.
TABLE 1.
Data collected from intensive care unit patientsa
| Parameter | Value [no. or median (min–max)] |
|---|---|
| Descriptive data | |
| Sex (male/female patients) | 7/5 |
| Age (yrs) | 71 (32–85) |
| Total body wt (kg) | 64.8 (52–110) |
| Ht (cm) | 171 (150–184) |
| Body mass index (kg/m2) | 22.3 (18.6–40.4) |
| Body surface area (m2) | 1.8 (1.5–2.1) |
| Ideal body wt (kg) | 64.7 (46.9–78) |
| Lean body wt (kg) | 52.9 (38.6–68.1) |
| Fat-free mass (kg) | 52.5 (34.2–67.1) |
| Adjusted body wt (kg) | 68.2 (49.8–81.7) |
| Patients with past medical history of: | |
| Hypertension | 3 |
| COPD | 3 |
| Type 2 diabetes | 2 |
| Others | 7 |
| Clinical data | |
| SAPS II | 40.5 (29–67) |
| SOFA score | |
| Day 1 | 5 (1–9) |
| Days 3–5 | 1 (0–11) |
| Biological data | |
| Albumin (g/liter) | |
| Day 1 | 25.4 (20.7–33.4) |
| Days 3–5 | 25.7 (23.7–31.2) |
| ASAT (IU/liter) | |
| Day 1 | 41 (13–126) |
| Days 3–5 | 51 (17–73) |
| ALAT (IU/liter) | |
| Day 1 | 26 (10–173) |
| Days 3–5 | 24 (10–100) |
| Lactate (mmol/liter) | |
| Day 1 | 1.9 (0.7–3.3) |
| Days 3–5 | 1.4 (1.1–2.8) |
| Serum creatinine (μmol/liter) | |
| Day 1 | 94.5 (54–194) |
| Days 3–5 | 80 (37–601) |
| crCL (ml/min) | |
| Day 1 | 70.3 (15.2–101.3) |
| Days 3–5 | 87.2 (20.9–136) |
ALAT, alanine transferase; ASAT, aspartate transaminase; COPD, chronic obstructive pulmonary disease; crCL, creatinine clearance using the Cockcroft-Gault equation; SAPS II, simplified acute physiology score II; SOFA score, sequential organ failure assessment score.
Observed micafungin concentrations.
Figure 1 shows the observed micafungin concentrations in plasma and peritoneal fluid, while observed individual concentration-time profiles are shown in Fig. S1 in the supplemental material. In plasma, the observed mean maximal concentration (Cmax) (standard deviation [SD]) is 8.45 (3.04) mg/liter on the first day, versus 8.07 (3.31) mg/liter on the sampling day between days 3 and 5, and the observed mean total AUC0–24 (SD) is 91.73 (25.69) mg ·h/liter on the first day, versus 102.64 (49.60) mg ·h/liter on the sampling day between days 3 and 5 (Fig. 1A). In peritoneal fluid, the observed mean Cmax (SD) is 1.79 (0.80) mg/liter on the first day, versus 2.36 (1.07) mg/liter on the sampling day between days 3 and 5, and the observed mean total AUC0–24 (SD) is 21.88 (5.56) mg ·h/liter on the first day, versus 44.90 (16.26) mg ·h/liter on the sampling day between days 3 and 5 (Fig. 1B). Observed Cmax values were reached after the first 2 h in plasma and after the 8th hour in peritoneal fluid.
FIG 1.
Observed concentrations of micafungin over time. (A) Plasma (n = 171 samples for 12 patients, with n = 90 on day 1 and n = 81 on days 3 to 5). (B) Peritoneal fluid (n = 42 samples for 6 patients, with n = 20 on day 1 and n = 22 on days 3 to 5). Solid gray lines indicate CLSI breakpoints, such as 0.064 mg/liter (C. glabrata), 0.25 mg/liter (C. albicans, C. tropicalis, and C. krusei), and 2 mg/liter (C. parapsilosis).
From patients with both peritoneal and plasma concentrations available, the observed AUC0–24 peritoneal fluid/plasma ratio indicated mean (SD) micafungin penetrations into the peritoneal fluid of 25% (5%) on the first day and 40% (8%) on the sampling day between days 3 and 5 (Fig. 1C). All observed micafungin concentrations in plasma and peritoneal fluid are higher than the CLSI breakpoints (19) considering 0.064 mg/liter for C. glabrata and 0.25 mg/liter for C. albicans, C. tropicalis, and C. krusei, against 90% of observations in plasma and only 40% in peritoneal fluid considering the CLSI breakpoint of 2 mg/liter for C. parapsilosis (Fig. 1A and B).
PK model.
The time course of plasma and peritoneal fluid concentrations of micafungin (Fig. 1) was best described by a three-compartment linear PK model with first-order distribution and clearance (CL) and a proportional-error model (Fig. S2) compared to a two-compartment model (with a −2-log-likelihood [−2LL] decrease of 15.3; P < 0.05). Interoccasion variability (IOV) on PK parameters did not improve the model and therefore was not implemented. The only covariate retained in the final model was FFM normalized to the median population value (to the power of β) on the total micafungin CL and volume of the central compartment (Vc). The included covariate was statistically significant at >3.84 (P < 0.05 by a chi-square test), with a −2LL decrease of 10.8 (Fig. S4). The final model was described as follows: micafungin CL = typical value of total CL × (FFM/52.5)1.25, and micafungin Vc = typical value of Vc × (FFM/52.5)0.66.
The PK parameter estimates are shown in Table 2, and the support point, covariance matrix, and correlation matrix are shown in Fig. S3. The goodness-of-fit plots for the final model are shown in Fig. 2A and B (plasma and peritoneal fluid data, respectively), and the visual predictive check (VPC) (plasma data) is shown in Fig. 2C. However, the small sample size did not allow us to perform a VPC for peritoneal data. Internal validation shows that 7% of the observed concentrations were outside the 90% prediction interval (P = 0.30 by an exact binomial test), and the median concentration in plasma was closely estimated (4.90 mg/liter and 4.95 mg/liter for predicted and observed concentrations, respectively). Although a VPC for peritoneal data was not feasible, the median concentration in peritoneal fluid (90% confidence interval [CI]) was also closely estimated: 1.06 (0.55 to 3.30) mg/liter versus 1.09 (0.63 to 2.63) mg/liter for predicted and observed concentrations, respectively. All these criteria confirmed the acceptable performance of the model.
TABLE 2.
Population pharmacokinetic parameter estimates of the final modela
| Parameter | Estimate |
|||||
|---|---|---|---|---|---|---|
| Mean | SD | CV | Var | Bootstrap analysis |
||
| WtMed (95% CI) | MAWD (95% CI) | |||||
| CL (liters/h) | 1.18 | 0.40 | 33.47 | 0.16 | 1.03 (0.80–1.81) | 0.13 (0.00–0.44) |
| Vc (liters) | 12.85 | 4.78 | 37.16 | 22.81 | 11.45 (8.63–20.26) | 1.49 (0.00–5.81) |
| Vpe (liters) | 4.82 | 1.53 | 31.62 | 2.32 | 5.11 (3.46–6.45) | 0.74 (0.00–1.50) |
| Vp (liters) | 3.86 | 1.87 | 48.50 | 3.51 | 3.67 (2.00–6.65) | 0.83 (0.00–2.12) |
| Qpe (liters/h) | 0.04 | 0.01 | 25.54 | 0.00 | 0.04 (0.03–0.06) | 0.00 (0.00–0.01) |
| Qp (liters/h) | 5.89 | 2.61 | 44.33 | 6.82 | 7.63 (2.00–8.00) | 0.31 (0.00–2.96) |
| β.CL | 1.25 | 0.59 | 46.95 | 0.35 | 1.51 (0.75–1.65) | 0.25 (0.00–0.74) |
| β.Vc | 0.66 | 0.57 | 86.13 | 0.32 | 0.81 (0.00–0.89) | 0.08 (0.00–0.63) |
CI, confidence interval; CL, total clearance; CV, coefficient of variation; MAWD, median absolute weighted deviation; Qp, intercompartment clearance between the central and peripheral compartments; Qpe, intercompartment clearance between the central and peritoneal compartments; SD, standard deviation; Var, variance; Vc, volume of distribution of the central compartment; Vp, volume of distribution of the peripheral compartment; Vpe, volume of distribution of the peritoneal compartment; WtMed, weighted median; β.CL, parameter factor of CL; β.Vc, parameter factor of Vc.
FIG 2.
Internal validation: diagnostic plots for the final model. (A) Observed versus population and individual predicted micafungin concentrations in plasma. (B) Observed versus population and individual predicted micafungin concentrations in peritoneal fluid. (C) Visual predictive check (VPC) for plasma concentrations. For panels A and B, the dashed line is the identity line, the filled lines is the regression line, and circles are the calculated values. For panel C, solid lines are the 5th, 50th, and 95th percentiles of the prediction interval; the gray area indicates the confidence intervals around the 5th, 50th, and 95th percentiles; and circles are the observed concentrations.
According to the model, the predicted mean Cmax (SD) in plasma is 7.16 (2.90) mg/liter on the first day, versus 8.34 (3.58) mg/liter on the sampling day between days 3 and 5, and the predicted mean total AUC0–24 (SD) in plasma is 87.57 (21.66) mg · h/liter on the first day, versus 117.96 (39.67) mg · h/liter on the sampling day between days 3 and 5.
Dosing simulations and fractional target attainment in plasma.
Figure S5 shows the simulated PK profiles in plasma for the different tested micafungin dosing regimens. Steady-state concentrations are reached earlier using a reduced dosing interval (2nd dose, at 12 h) or with a loading dose (2nd dose, at 24 h) than with the standard daily dose (3rd dose, at 48 h). Probabilities of target attainment (PTAs) based on the targeted total AUC0–24/MIC ratio of 3,000 are shown in Fig. 3, and fractional target attainment (FTA) values are displayed in Table 3. Optimal dosing regimens based on FFM values and targeted Candida species are illustrated in Table 4. For empirical treatment, a micafungin loading dose of 150 mg followed by a maintenance dose of 100 mg is sufficient for patients with an FFM of ≤30 kg, whereas a loading dose of 200 mg followed by a maintenance dose of 150 mg is sufficient for patients with an FFM of between 30 kg and 50 kg, or 200 mg with a reduced dosing interval (every 12 h [q12h]) on the first day is required for patients with an FFM of >50 kg.
FIG 3.
Probability of PK/PD target attainment (PTA) over the first 48 h of treatment for different dosing regimens and different fat-free masses (FFMs): 30 kg (black) and 70 kg (gray). The PTA was a total AUC0–24 (area under the curve from 0 to 24 h)/MIC ratio of >3,000. LD, loading dose; MD, maintenance dose. The MIC was 0.004 to 0.250 mg/liter). A micafungin dosing regimen is successful if the FTA is >90%. EUCAST MIC distributions for C. albicans (susceptibility [S], 0.004 to 0.016 mg/liter), C. glabrata (S, 0.004 to 0.032 mg/liter), C. tropicalis (S, 0.004 to 0.064 mg/liter), C. krusei (S, 0.032 to 0.250 mg/liter), and C. parapsilosis (S, 0.250 to 2.000 mg/liter) were used.
TABLE 3.
Fractional target attainment for different dosing regimens for each Candida speciesa
| Dosing regimen(s) and FFM (kg) | Target attainment |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|
C. albicans
|
C. glabrata
|
C. tropicalis
|
C. krusei
|
C. parapsilosis
|
||||||
| Day 1 | Day 2 | Day 1 | Day 2 | Day 1 | Day 2 | Day 1 | Day 2 | Day 1 | Day 2 | |
| 100 mg q24h | ||||||||||
| 30 | + | + | + | + | − | + | − | − | − | − |
| 40 | + | + | + | + | − | − | − | − | − | − |
| 50 | + | + | − | + | − | − | − | − | − | − |
| 60 | − | + | − | − | − | − | − | − | − | − |
| 70 | − | − | − | − | − | − | − | − | − | − |
| 150 mg q24h | ||||||||||
| 30 | + | + | + | + | + | + | − | − | − | − |
| 40 | + | + | + | + | − | + | − | − | − | − |
| 50 | + | + | + | + | − | − | − | − | − | − |
| 60 | + | + | + | + | − | − | − | − | − | − |
| 70 | + | + | − | + | − | − | − | − | − | − |
| 200 mg q24h | ||||||||||
| 30 | + | + | + | + | + | + | − | − | − | − |
| 40 | + | + | + | + | + | + | − | − | − | − |
| 50 | + | + | + | + | + | + | − | − | − | − |
| 60 | + | + | + | + | − | − | − | − | − | − |
| 70 | + | + | + | + | − | − | − | − | − | − |
| 100 mg q12h on day 1 and then 100 mg q24h on day n + 1 | ||||||||||
| 30 | + | + | + | + | + | + | − | − | − | − |
| 40 | + | + | + | + | + | − | − | − | − | − |
| 50 | + | + | + | + | − | − | − | − | − | − |
| 60 | + | + | + | − | − | − | − | − | − | − |
| 70 | + | − | + | − | − | − | − | − | − | − |
| 150 mg q12h on day 1 and then 150 mg q24h on day n + 1 | ||||||||||
| 30 | + | + | + | + | + | + | − | − | − | − |
| 40 | + | + | + | + | + | + | − | − | − | − |
| 50 | + | + | + | + | + | + | − | − | − | − |
| 60 | + | + | + | + | + | − | − | − | − | − |
| 70 | + | + | + | + | − | − | − | − | − | − |
| 200 mg q12h on day 1 and then 200 mg q24h on day n + 1 | ||||||||||
| 30 | + | + | + | + | + | + | − | − | − | − |
| 40 | + | + | + | + | + | + | − | − | − | − |
| 50 | + | + | + | + | + | + | − | − | − | − |
| 60 | + | + | + | + | + | + | − | − | − | − |
| 70 | + | + | + | + | + | − | − | − | − | − |
| LD of 150 mg and MD of 100 mg q24h | ||||||||||
| 30 | + | + | + | + | + | + | − | − | − | − |
| 40 | + | + | + | + | − | − | − | − | − | − |
| 50 | + | + | + | + | − | − | − | − | − | − |
| 60 | + | + | + | − | − | − | − | − | − | − |
| 70 | + | − | − | − | − | − | − | − | − | − |
| LD of 200 mg and MD of 100 mg q24h | ||||||||||
| 30 | + | + | + | + | + | + | − | − | − | − |
| 40 | + | + | + | + | + | − | − | − | − | − |
| 50 | + | + | + | + | + | − | − | − | − | − |
| 60 | + | + | + | − | − | − | − | − | − | − |
| 70 | + | − | + | − | − | − | − | − | − | − |
| LD of 200 mg and MD of 150 mg q24h | ||||||||||
| 30 | + | + | + | + | + | + | − | − | − | − |
| 40 | + | + | + | + | + | + | − | − | − | − |
| 50 | + | + | + | + | + | − | − | − | − | − |
| 60 | + | + | + | + | − | − | − | − | − | − |
| 70 | + | + | + | + | − | − | − | − | − | − |
Fractional target attainment (FTA) is the sum of the probability of target attainment (PTA) values determined at each MIC and the fraction of isolates found at that MIC. The target was a total AUC0–24/MIC ratio of >3,000. A micafungin dosing regimen is successful if the FTA is >90%. EUCAST MIC distributions for C. albicans (susceptibility [S], 0.004 to 0.016 mg/liter), C. glabrata (S, 0.004 to 0.032 mg/liter), C. tropicalis (S, 0.004 to 0.064 mg/liter), C. krusei (S, 0.032 to 0.250 mg/liter), and C. parapsilosis (S, 0.250 to 2.000 mg/liter) were used. LD, loading dose; MD, maintenance dose. Shading indicates FTA values of >90%.
TABLE 4.
Optimal dosing regimens for different fat-free masses and for each Candida speciesa
FFM, fat-free mass; C.spp., Candida species; DIx, dosing interval (day 1, x milligrams q12h; day n + 1, x milligrams q24h); LD×1-MD×2, loading dose (×1 mg) and maintenance dose q24h (×2 mg); S, susceptibility.
DISCUSSION
This study describes micafungin PK in plasma and peritoneal fluid in ICU patients with proven or suspected septic shock and secondary peritonitis. High PK variability and moderate penetration (25 to 40%) of micafungin into the peritoneal cavity are observed. Also, the population PK analysis demonstrates that the standard 100-mg dosing regimen of micafungin is sufficient to achieve the PK/PD target on the second day of treatment only for the lowest FFM values (FFM of ≤60 kg for C. albicans, FFM of ≤50 kg for C. glabrata, and FFM of ≤30 kg for C. tropicalis). Still, a dose escalation with a loading dose of 150 mg or higher depending on the FFM of patients and the Candida species is suggested to be effective from the first day of therapy. Micafungin dose optimization can be achieved either using a loading dose or by reducing the dosing interval with the same dose.
Altered PK has been commonly reported in ICU patients for many drugs, due to pathophysiological changes (2, 16). Similarly, in our study, higher clearance (CL) (1.18 liters/h) and volume of distribution (Vc) (12.85 liters) values have been observed with lower drug exposure than in healthy subjects of 70 kg (CL, 0.73 liters/h; V, 14 liters) (8). The micafungin clearance in our cohort was similar to the clearance reported for cohorts of ICU patients on continuous venovenous hemofiltration (0.88 liters/h) or mechanical ventilation (1.34 liters/h) or with an intra-abdominal infection (1.09 and 1.27 liters/h) (18, 20–22).
Conventional dosing regimens used in the present study were associated with moderate (25 to 40%) micafungin penetration into the peritoneum. The magnitude of drug diffusion into the peritoneum mainly depends on drug plasma protein binding and lipophilicity. These physicochemical properties determine the rates and extents of tissue penetration and bioavailability within a tissue, organ, or fluid. Given its high protein binding, micafungin is likely to poorly diffuse into tissues. However, in the context of inflammation and sepsis, increased permeability may be observed. Some amount of drug bound to proteins that usually do not penetrate tissue could diffuse through the peritoneal membrane in patients with peritonitis, adding to the higher penetration ratio in this population. Otherwise, these results are consistent with micafungin peritoneal penetration reported in a previous study by Grau et al. (18). The micafungin peritoneal penetration reported in the present study is also similar to the peritoneal penetration of anidulafungin but slightly higher than that of caspofungin (23). However, PK/PD target (AUC0–24/MIC ratio) values have been defined in plasma (13), whereas to our knowledge, no PK/PD target for peritoneal fluid has been determined. Thus, we considered micafungin peritoneal concentrations above the MICs of the most common Candida species as the minimal targeted tissue concentrations at the site of infection. In the present study, all observed concentrations were above the CLSI breakpoints (19) of 0.064 mg/liter for C. glabrata and 0.25 mg/liter for C. albicans, C. tropicalis, and C. krusei in plasma and peritoneal fluid. However, fewer than half of the concentrations were higher than the CLSI breakpoint of 2 mg/liter for C. parapsilosis in peritoneal fluid. The lack of well-defined PK/PD targets together with the moderate penetration of micafungin into peritoneal fluid may contribute to inadequate drug exposure at the site of infection in addition to promoting the emergence of resistance of Candida species (2, 17, 24–26). The next-generation echinocandin agent rezafungin (CD101) should be investigated in intra-abdominal fungal infections as results from animal models show good penetration of this echinocandin in tissues, including abdominal abscesses, versus micafungin (27).
Previous studies found total body weight as a determinant covariate to explain interindividual micafungin variability (18, 20, 22, 28). Conversely, in the present study, FFM better related size to PK than total body weight. As FFM depends on sex, body mass, and height, it could represent an interesting alternative parameter to adjust drug dosing in critically ill populations (29).
According to our simulation analyses, the standard dose of 100 mg daily reached the FTA of >90% on the second day of treatment for C. albicans (FFM, 60 kg), C. glabrata (FFM, 50 kg), and C. tropicalis (FFM, 30 kg). However, none of the dosing regimens tested reached the FTA of >90% for C. krusei and C. parapsilosis (MIC distributions of 0.032 to 0.250 mg/liter and 0.250 to 2.000 mg/liter, respectively), as shown in previous studies (30–32). Also, the suggested dosing regimens of micafungin should not reach an FTA of >90% for rare species such as C. kefyr and C. lusitaniae considering their equal or high MIC compared to C. krusei (33). It is of note that despite a specific lower AUC/MIC ratio target for C. parapsilosis, no species-related differences in treatment outcomes and high success rates, including for intra-abdominal infections caused by C. parapsilosis, have been reported with the 100-mg standard dose (34). Still, careful attention is required for C. krusei and C. tropicalis in regard to their higher mortality rates (11, 12). Hence, a dose escalation is needed to reach PK/PD targets in plasma and the peritoneal cavity, where higher concentrations would be achieved. Dose adjustments have been established in previous studies (8, 20, 28, 31, 35, 36). The findings of the present study bring new insights into micafungin dose optimization for suspected intra-abdominal fungal infections to ensure adequate concentrations in the abdominal cavity and to prevent the emergence of resistance due to an inadequate initial antifungal regimen (2, 17, 24–26).
There are several limitations to this study. First, the small cohort of patients may have prevented us from finding correlations between covariates (serum albumin level and sequential organ failure assessment [SOFA] score) and PK parameters and adequately investigating interoccasion variability. Hence, further larger multicenter studies focusing on peritoneal concentrations of micafungin in ICU patients are required to adequately investigate correlations between covariates and PK parameters on one hand and peritoneal concentrations and patient outcomes on the other hand. Second, peritoneal samples are more challenging to collect, leading to a smaller sample size than for plasma samples. Nevertheless, population PK analysis enables sparse sampling, and the model adequately handled peritoneal data. Third, given the high protein binding of micafungin, measuring unbound concentrations of micafungin may be informative. However, even when using very sensitive ultrahigh-pressure liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), such assays remain highly challenging and were not available for this study. Fourth, as no predefined PK/PD target in the peritoneum has been defined, we chose an arbitrary target of peritoneal concentrations above the CLSI breakpoints of different Candida species. Defining infection site-specific PK/PD targets for echinocandins is urgently needed.
In conclusion, there is moderate penetration of micafungin into the peritoneal cavity (25 to 40%). Therapeutic exposure in both plasma and peritoneal fluid is needed to ensure favorable outcomes and prevent the emergence of resistance. Dose escalation (by either increasing the loading dose or reducing the dosing interval) depending on the FFM of patients and the Candida species is suggested by dosing simulations in septic patients with intra-abdominal infections.
MATERIALS AND METHODS
Setting.
This was a prospective, single-center PK trial (ClinicalTrials.gov identifier NCT02805049). According to French law, ethics approval was obtained from the local ethics committee of Nîmes (Comité de Protection des Personnes Sud-Méditerranée III 2012.02.05) (37). Written informed consent was obtained from either the patient or their designated substitute decision-maker.
Study population.
Patient eligibility criteria for the study were (i) ICU admission for septic shock with secondary peritonitis and (ii) clinical indication for micafungin therapy. Exclusion criteria were (i) pregnancy, (b) a history of allergy to or contraindication for micafungin, or (iii) known seropositivity for human immunodeficiency viruses (HIVs) or hepatitis C virus or known history of having contracted tuberculosis (for limiting laboratory assay contamination risks).
Study protocol.
A dosing regimen of 100 mg micafungin once daily was administered intravenously over 1 h as part of empirical antimicrobial therapy for septic shock with proven or suspected intra-abdominal fungal infection. Both blood samples and peritoneal fluid samples collected from surgical drains were drawn on day 1 and between day 3 and day 5 of therapy. Before each peritoneal sample was obtained, the reservoir of the drain was emptied, and fresh peritoneal fluid was drained into it. These samples were used to determine total plasma and peritoneal micafungin concentrations at baseline (predose) and 1, 1.5, 2, 4, 6, 12, and 24 h after the start of micafungin infusion.
Sample handling and storage.
Blood samples (n = 171 for 12 patients, with n = 90 on day 1 and n = 81 on days 3 to 5) and peritoneal fluid samples (n = 42 for 6 patients, with n = 20 on day 1 and n = 22 on days 3 to 5) were immediately placed on ice, centrifuged at 3,000 rpm for 10 min, and then stored at −80°C. Samples were transported (with a temperature log on dry ice) by a commercial courier company to The University of Queensland Centre of Clinical Research, The University of Queensland, Brisbane, Queensland, Australia, for analysis.
Drug assay.
Total micafungin concentrations in plasma and peritoneal fluid were measured by a validated ultrahigh-pressure liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method, from 0.1 to 20 mg/liter, on a Shimadzu Nexera 2 UHPLC device coupled to a Shimadzu 8050 triple-quadrupole mass spectrometer (Shimadzu, Kyoto, Japan). The stationary phase was a C8 Kinetex 50- by 2.1-mm, 1.7-μm column (Phenomenex, Torrance, CA, USA) operated at room temperature. Mobile phase A was 0.1% (vol/vol) formic acid in 10 mM ammonium formate, and mobile phase B was 100% acetonitrile with 0.1% (vol/vol) formic acid. The mobile phase was delivered with a gradient from 5% to 60% mobile phase B at a flow rate of 0.4 ml/min for a 3.7-min run time and produced a back pressure of approximately 6,000 lb/in2. Calibrators were prepared by diluting micafungin (Toronto Research Chemicals, Toronto, Canada) in plasma at concentrations of 0.1, 0.2, 0.5, 1, 2, 5, 10, and 20 μg/ml. Quality controls (QCs) were prepared by diluting a separate weighing of micafungin in a separate batch of plasma at concentrations of 0.3, 3, and 15 μg/ml. Calibrators and QCs were stored in individual tubes at −80°C and discarded after use. Calibrators and QCs in duplicate were included in every batch, and acceptance criteria were applied (38). The overall intra- and interday precisions and accuracies were within 15%. The assay was linear over the concentration range of 0.1 to 20 mg/liter, with 0.1 mg/liter being the lower limit of quantification (LLOQ) of the method. These concentrations, obtained at the beginning of therapy, were considered equal to 0 mg/liter.
Data collection.
For all patients included, the following demographic and clinical data were recorded: age, sex, total body weight, height, simplified acute physiology score II (SAPS II) (39) at ICU admission, modified sequential organ failure assessment (SOFA) score at ICU admission and on the sampling day, past medical history, biological data (serum albumin concentration, serum lactate concentration, serum alanine and aspartate aminotransferase [ALAT and ASAT, respectively] concentrations, serum creatinine concentration, and timed urinary creatinine clearance [crCL] calculation), and infection-related data.
PK analyses.
The concentration-time data were analyzed using a nonparametric adaptive grid (NPAG) algorithm within the freely available Pmetrics software package for R (40, 41). PK models with two and three compartments were tested, including additive and proportional-error models. Interoccasion variability (IOV) was tested on CL, Vc, Vpe, (volume of distribution of the peritoneal compartment), and Vp (volume of distribution of the peripheral compartment). The total AUC0–24 was determined from the posterior pharmacokinetic parameter estimates using the noncompartmental function in Pmetrics.
Sample size.
Based on the findings in similar population PK studies (18), a sample population size of ≥10 was found to be necessary to provide robust and realistic PK prediction (42) with bias and precision of PK predictions below 25% (predictions were considered acceptable if bias/precision for the mean and variability of PK parameters were <25%). A sample size of 12 patients was considered appropriate to take into account sample dropout.
Population PK covariates.
Age, total body weight, SOFA score on sampling days, serum albumin concentration, serum lactate concentration, serum ALAT and ASAT concentrations, and size descriptors (body mass index, body surface area, ideal body weight, lean body weight, fat-free mass [FFM], and adjusted body weight) (43) were evaluated as clinically relevant and physiologically plausible covariates. The selection of these covariates was performed using a stepwise multivariate linear regression analysis, with forward inclusion and backward elimination, to quantify the correlation between covariates and PK parameters.
Population PK model diagnostics.
Model selection was initially based on the objective function value computed as the −2-log likelihood (−2LL) or on the Akaike information criterion (AIC), where a decrease in the −2LL and/or AIC of ≥3.84 was considered significant (P < 0.05 by a chi-squared test [1 degree of freedom]). In addition, the goodness of fit was assessed by linear regression, with observed-predicted plots and coefficients of variation of PK parameters. Evaluation of the predictive performance was based on the mean prediction error (bias) and the mean bias-squared prediction error (imprecision) of the population and individual prediction models. Finally, internal validation of the model was performed using bootstrap resampling methods (n = 1,000) and visual predictive checks (VPCs) in order to obtain predicted median concentrations and 90% prediction intervals.
Monte Carlo simulation of plasma concentrations and probability of target attainment.
Micafungin dosing regimens were assessed using the probability of PK/PD target attainment (PTA) for the first 48 h of therapy (6, 12). According to the preclinical model, the following threshold has been identified as the PK/PD target for efficacy: a non-species-related target of a total AUC0–24/MIC ratio of >3,000 for all Candida species (1-log kill/24 h) (13). Monte Carlo simulations of plasma micafungin concentrations (n = 1,000) were performed using the final PK model according to several FFM values (30, 40, 50, 60, and 70 kg) and for nine dosing regimens: 100/150/200 mg q24h, 100/150/200 mg q12h on day 1 followed by 100/150/200 mg q24h, a loading dose of 150/200 mg followed by a maintenance dose of 100 mg, or a loading dose of 200 mg followed by a maintenance dose of 150 mg q24h.
Fractional target attainment.
Fractional target attainment (FTA) was performed by comparing the PTA against the EUCAST MIC distributions for C. albicans (susceptibility [S], 0.004 to 0.016 mg/liter), C. glabrata (S, 0.004 to 0.032 mg/liter), C. tropicalis (S, 0.004 to 0.064 mg/liter), C. krusei (S, 0.032 to 0.250 mg/liter), and C. parapsilosis (S, 0.250 to 2.000 mg/liter) (https://mic.eucast.org/). A dosing regimen was considered optimal if the fractional target attainment was ≥90%.
ACKNOWLEDGMENTS
We have received academic funding from the Nîmes University Hospital (LOCAL/2016/CR-01) to conduct this work. Jeffrey Lipman received honoraria from MSD.
Footnotes
Supplemental material is available online only.
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