Background: Dosing in obese critically ill patients is challenging due to pathophysiological changes derived from obesity and/or critical illness, and it remains fully unexplored. This study estimated the micafungin probability of reaching adequate 24-h area under the curve (AUC )/minimum inhibitory concentration (MIC) values against 0–24h Candida spp. for an obese/nonobese, critically ill/noncritically ill, large population. Methods: Blood samples for pharmacokinetic analyses were collected from 10 critically ill nonobese patients, 10 noncritically ill obese patients, and 11 critically ill morbidly obese patients under empirical/directed micafungin treatment. Patients received once daily 100–150 mg micafungin at the discretion of the treating physician following the prescribing information and hospital guidelines. Total micafungin concentrations were determined by high- performance liquid chromatography (HPLC). Monte-Carlo simulations were performed and the probability of target attainment (PTA) was calculated using the AUC /MIC cut-offs 285 (C. parapsilosis), 3000 (all Candida spp.), and 0–24 5000 (nonparapsilosis Candida spp.). Intravenous once-daily 100-mg, 150-mg, and 200-mg doses were simulated at different body weights (45, 80, 115, 150, and 185 kg) and age (30, 50, 70 and 90 years old). PTAs ≥ 90% were considered optimal. Fractional target attainment (FTA) was calculated using published MIC distributions. A dosing regimen was considered successful if the FTA was ≥ 90%. Results: Overall, 100 mg of micafungin was once-daily administered for nonobese and obese patients with body 2 2 mass index (BMI) ≤ 45 kg/m and 150 mg for morbidly obese patients with BMI > 45 kg/m (except two noncritically 2 2 ill obese patients with BMI ~ 35 kg/m receiving 150 mg, and one critically ill patient with BMI > 45 kg/m receiving 100 mg). Micafungin concentrations in plasma were best described using a two-compartment model. Weight and age (but not severity score) were significant covariates and improved the model. FTAs > 90% were obtained against C. albicans with the 200 mg/24 h dose for all body weights (up to 185 kg), and with the 150 mg/24 h for body weights < 115 kg, and against C. glabrata with the 200 mg/24 h dose for body weights < 115 kg. (Continued on next page) * Correspondence: firstname.lastname@example.org Equal contributors Department of Anesthesia and Surgical Intensive Care, Hospital Universitario La Paz, Paseo de la Castellana 261, 28046 Madrid, Spain Universidad Autónoma de Madrid, Madrid, Spain Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Maseda et al. Critical Care (2018) 22:94 Page 2 of 9 (Continued from previous page) Conclusion: The lack of adequacy for the 100 mg/24 h dose suggested the need to increase the dose to 150 mg/24 h for C. albicans infections. Further pharmacokinetic/pharmacodynamic studies should address optimization of micafungin dosing for nonalbicans Candida infections. Keywords: Morbid obesity, PK/PD, Monte-Carlo simulation, Intensive care unit, Candida spp., Background critically ill, and morbidly obese critically ill patients Obesity, which is increasing at an alarming rate in treated with micafungin. developed countries, is a significant risk factor for nosocomial infections, especially following surgery due to the immune dysfunction associated with Methods obesity . In addition, pathophysiological changes in A pharmacokinetic study was carried out in patients obese patients (e.g., reduced regional blood flow, under micafungin empirical or directed treatment for altered cardiac output, increased fat and lean mass, invasive candidiasis. The population consisted of 11 etc.) might modify the pharmacokinetic/pharmacody- morbidly obese critically ill adult patients (from the namic(PK/PD) profileofantimicrobials[2, 3]. On the Hospital Universitario La Paz, Madrid, Spain), 10 other hand, critically ill patients also present nonobese critically ill patients, and 10 obese noncriti- pathophysiological changes (hepatic and/or renal cally ill patients (from the Hospital del Mar, dysfunction, hypoalbuminemia or increased capillary Barcelona, Spain). Patients admitted to ICUs were permeability, use of organ support modalities) that those considered to be critically ill. The study protocol can alter antimicrobial clearance and volume of distri- was approved by the Ethics Committee of the Hospital bution . Thus, dosing in obese critically ill patients La Paz (Madrid, Spain) and the Hospital del Mar is a challenging scenario for intensivists that has not (Barcelona, Spain). Written informed consent was been fully explored . obtained from patients (or relatives if the patient was Micafungin is an echinocandin, a lipopeptide that unable to provide due to their critical situation) before exhibits concentration-dependent fungicidal activity blood sampling. against most species of Candida , and is licensed as Demographic and clinical data prior to initiation of a first-line treatment for invasive candidiasis . The antifungal treatment were collected. Severity (Simpli- recently published study EUROBACT was conducted fied AcutePhysiologyScore (SAPS) II), Sequen- in 162 intensive care units (ICUs) in 24 countries. It tial Organ Failure Assessment (SOFA) score , and showed that, among patients with candidemia, risk for invasive candidiasis (Candida score) (ex- Candida albicans was the most frequent fungi isolated cept for patients with microbiologically documented (57.1%), followed by Candida glabrata (15.3%), infections) were calculated. Patients received dosage Candida parapsilosis (10.2%), and Candida tropicalis regimens of once-daily 100 mg or 150 mg micafungin (6.1%) . (Astellas Pharma S.A., Spain) diluted in 100 ml Altered serum concentrations of micafungin associ- isotonic saline solution that was intravenously infused ated with morbid obesity in critically ill patients over 60 min at the discretion of the treating physician might impact the achievement of therapeutic drug (following the prescribing information and hospital exposures as defined by the area under the serum treatment guidelines). On day 3, blood samples were concentration curve over a 24-h period (AUC )/ collected at baseline (predose) and after 1, 3, 5, 8, 18, 0–24h minimum inhibitory concentration (MIC), the and 24 h. Additional blood samples at day 0 and day pharmacodynamic index linked to clinical efficacy for 7 were collected when feasible. micafungin [9, 10]. A previous study conducted by our group showed cumulative fraction responses > 90% for micafungin at the standard dose (100 mg) Sample handling and storage against C. albicans and C. glabrata in a special popu- Blood samples were immediately placed on ice and lation of critically ill patients on continuous venove- centrifuged at 3000 rpm for 10 min. Following on, nous hemofiltration . they were stored at −80 °C. The samples were The aim of this study was to estimate the micafungin transported by a commercial courier company to the probability of achieving adequate AUC /MIC values Burns Trauma and Critical Care Research Centre, The 0–24h against Candida spp. for a large population using University of Queensland,Australia,for further Monte-Carlo simulations  and data from obese, analysis. Maseda et al. Critical Care (2018) 22:94 Page 3 of 9 Drug assay were checked for normal distribution characteristics and Total micafungin concentrations in plasma were mea- trends in data errors . sured by a validated ultra-high-performance liquid chro- matography (UHPLC)-tandem mass spectrometry (MS/ Probability of target attainment (PTA) MS) method, from 0.2 to 30 μg/ml, on a Shimadzu Nexera Monte-Carlo simulations (n = 1000) were employed using 2 UHPLC system coupled to a Shimadzu 8030+ triple Pmetrics software to determine the PTA of achieving the quadruple mass spectrometer (Shimadzu, Kyoto, Japan) PK/PD target of AUC /MIC (285 for C. parapsilosis, 0–24 . Clinical samples were assayed alongside plasma 3000 for all Candida spp., and 5000 for nonparapsilosis calibrators and quality controls and met batch acceptance Candida spp.)  for varying MICs (0.008 to 1 μg/ml). criteria . Intravenous once-daily doses of 100 mg, 150 mg, and 200 mg were simulated at different body weight (45, 80, 115, 150, and 185 kg) and age (30, 50, 70, and 90 years Generation of large population data old). PTAs ≥ 90% were considered optimal. Population pharmacokinetic modeling To describe total micafungin concentrations, one- and Fractional target attainment (FTA) calculation two-compartment models were developed with the non- Published MIC distribution data of C. parapsilosis,non- parametric adaptive grid algorithm within the freely avail- parapsilosis Candida spp., and all Candida spp. from the able Pmetrics software package for R (Los Angeles, CA, SENTRY study  were used to determine the FTA, USA) [17, 18]. Elimination from the central compartment, which identifies the potential success of the treatment by and intercompartmental distribution into the peripheral comparing the pharmacodynamic exposure (i.e, PTA) compartment (two-compartment model), were modeled against an MIC distribution. Specifically, PTA values as first-order processes. The discrimination between determined at each MIC were multiplied by the fraction different models resulted from the comparison of the −2 of isolates found at that MIC, and the sum of the products log-likelihood (−2LL). A p value of < 0.05 was considered equaled the FTA. A value of FTA ≥ 90% against a popula- statistically significant. tion of organisms was considered optimal. Population pharmacokinetics covariate screening Statistical analysis Age, gender, body weight, body mass index (BMI), Acute Correlations were assessed by means of scatter graphs Physiology and Chronic Health Evaluation (APACHE) II, and the Pearson correlation coefficient (r). serum creatinine concentration, measured creatinine clear- ance, Cockroft-Gault estimated creatinine clearance, and Results serum albumin concentration were evaluated as covariates. Table 1 shows the demographic data, baseline analytical Covariate selection was performed using a stepwise linear parameters, and clinical scores for the patients distrib- regression from R on all covariates and Bayesian posterior uted by obese/nonobese and critically/noncritically ill parameters. Potential covariates were separately entered categorization. Overall, the standard 100-mg dose of into the model and statistically tested by use of the –2LL micafungin was once-daily administered for nonobese values. If inclusion of the covariate resulted in a statistically 2 and obese patients with BMI ≤ 45 kg/m . The 150-mg significant improvement in the LL values (p < 0.05) and in dose was administered for morbidly obese patients with an improvement of the goodness-of-fit plots, then the 2 BMI > 45 kg/m , with the exception of two noncritically covariate was retained in the final model. 2 ill obese patients with BMI of around 35 kg/m who received the 150-mg dose, and one critically ill patient Model diagnostics with BMI > 45 kg/m who received the 100-mg dose. Goodness-of-fit was assessed by linear regression, with Figure 1 shows the mean observed concentration-time an observed-predicted plot, coefficients of determin- profile of micafungin concentrations for the study ation, and LL values. Predictive performance evaluation population. was based on mean error of prediction (bias) and mean bias-adjusted squared error of prediction (imprecision) Pharmacokinetic model of the population and individual prediction models. The A two-compartment linear model (including zero order internal validity of the population pharmacokinetic input of drug into the central compartment) best described model was assessed by the bootstrap resampling method thetime courseof242 totalplasma concentrations of mica- (n = 1000) and normalized prediction distribution errors fungin. The goodness-of-fit of the model was improved (NPDE) . Using a visual predictive check method, (p < 0.05) by the inclusion of the covariate body weight parameters obtained from the bootstrap method were (normalized to 70 kg) and age (normalized to 60 years old plotted with the observed concentrations. NPDE plots to an exponential value of 0.75) for micafungin clearance. Maseda et al. Critical Care (2018) 22:94 Page 4 of 9 Table 1 Demographic and clinical data Variable Total Critically morbidly obese Noncritically obese Critically nonobese Dose (mg) 100 150 100 150 100 n 31 74 73 10 Age (years), median (range) 58 (27–85) 45 (27–73) 53.5 (44–63) 58 (48–85) 58 (43–73) 72 (43–85) % Females 71.0 100 50.0 85.7 100 40 a 2 BMI (kg/m ), median (range) 34.7 (19.6–60.0) 44.2 (40.3–51.7) 52.8 (47.4–60.0) 27.7 (25.2–34.7) 35.5 (34.7–52.4) 23.1 (19.6–38.5) Weight (kg), median (range) 95 (44–193) 113 (95–121) 157.5 (142–170) 84.1 (62–105) 105 (105–193) 65 (44.0–92.5) Creatinine (mg/dl), median (range) 1.0 (0.4–3.9) 0.7 (0.6–1.5) 1.3 (0.6–1.7) 1.1 (0.7–1.4) 0.8 (0.7–1.5) 1.0 (0.4–3.0) a 2 CrCl (ml/min/1.73m ) 93.4 ± 51.4 133.1 ± 44.9 112.5 ± 53.2 62.7 ± 38.4 105.4 ± 63.9 75.9 ± 46.1 Albumin (g/dl), median (range) 3 (1.2–4.0) 2.7 (1.9–3.5) 3.1 (2.4–4) 3.5 (2.6–3.9) 3.6 (3.4–3.7) 2.5 (1.2–3.3) b c d Candida score, median (range) 3 (2–4) 3(2–4) 3.5 (3–4) DT DT 3(2–4) SOFA score, median (range) 6 (0–12) 6 (2–8) 7 (5–10) 5 (2–10) 6 (5–7) 4.5 (0–12) SAPS II, median (range) 34 (9–57) 40 (8–57) 34.5 (25–45) 47 (9–53) 41 (18–44) 26 (11–42) Data are expressed as mean ± standard deviation, except where stated BMI, Body mass index; CrCl, creatinine clearance; SAPS, Simplified Acute Physiology Score; SOFA, Sequential Organ Failure Assessment For 21 patients Directed treatment (DT): three candidemia, three osteoarticular infections, and one urinary tract infection DT: one peritonitis, one urinary tract infection, and one osteoarticular infection Use of this exponent on age improved the model better Where TVCL is the typical value of micafungin clear- than either covariate added as a linear function alone and ance, Wt is the total body weight (kg) and Age is the reflected the likely nonlinear effect of the increasing body patient’s age (years). weight and age on micafungin clearance. Addition of body The mean ± standard deviation (SD) population phar- weight or age alone did not statistically improve the model macokinetic parameter estimates for the final covariate when compared with the structural model (− 2LL value, model are shown in Table 2. The diagnostic plots 595.2 vs 596.4 for weight inclusion, p = 0.0586; 587.5 vs confirmed the appropriateness of the model as shown in 596.4 for age inclusion, p = 0.597). When both the covari- Fig. 2. The final covariate model was then used for ates body weight and age were included, the log likelihood Monte-Carlo dosing simulations. value decreased significantly (−2LL, 415.6; p = 0.0238) and the goodness-of-fit of the model also showed an improve- ment. The final covariate model was as follows: Dosing simulations PTAs for AUC /MIC of 285, 3000, or 5000 for 0–24 0:75 0:75 different micafungin doses (100 mg, 150 mg, 200 mg) Micafungin CL ¼ TVCLðÞ Wt=70 ðÞ Age=60 and body weights (from 45 kg to 185 kg) for patients with a medium age of 70 years old (no significant changes were observed in simulations with different patient ages) are described in Table 3.The Monte- Carlo simulations showed that increases in the micafungin dose resulted in increased PTAs. For nonparapsilosis Candida (AUC/MIC > 5000) this Table 2 Estimated micafungin parameters Parameters Mean ± SD Coefficient of Variance Median variation (%) Clearance (l/h) 0.80 ± 0.49 61.78 0.24 0.73 Central volume (l) 16.34 ± 5.87 35.95 34.49 16.34 −1 kcp (h ) 0.38 ± 0.37 97.76 0.14 0.26 −1 kpc (h ) 0.32 ± 0.31 96.51 0.09 0.14 Parameter estimates for micafungin from the final two-compartment covariate population pharmacokinetic model kcp, rate constant for drug distribution from the central to peripheral Fig. 1 Micafungin concentrations. Mean observed micafungin compartment; kpc, rate constant for drug distribution from the peripheral to concentration-time profiles (error bars represent standard deviation) central compartment Maseda et al. Critical Care (2018) 22:94 Page 5 of 9 Fig. 2 Diagnostic plots for the final population pharmacokinetic covariate model. a Observed micafungin concentrations versus population predicted concentrations. b Observed micafungin concentrations versus individual predicted concentrations. c Visual predictive check (circles represent observed data). Concentrations are expressed as μg/ml target attainment was only obtained with > 90% FTAs for the simulated PTAs against MIC distributions probability with doses of 150 mg and 200 mg against for C. albicans, C. glabrata, C. parapsilosis,and C. tropicalis isolates with MICs up to 0.008 μg/ml, regardless of are shown in Table 4. FTAs > 90% were obtained against C. the patient’sweight. albicans with the 200 mg/24 h dose for all body weights, Maseda et al. Critical Care (2018) 22:94 Page 6 of 9 Table 3 Probability of target attainment (PTA) for micafungin AUC /MIC of 285 for AUC /MIC of 3000 for AUC /MIC of 5000 for 0–24 0–24 0–24 body weight (kg) equal to: body weight (kg) equal to: body weight (kg) equal to: Dose (mg/24 h) MIC (μg/ml) 45 80 115 150 185 45 80 115 150 185 45 80 115 150 185 100 0.008 100 100 100 100 100 99.0 98.9 98.8 98.5 97.8 83.7 78.4 72.3 64.0 55.1 0.016 100 100 100 100 100 72.0 58.8 52.2 40.9 31.1 9.2 3.7 1.4 0.5 0.2 0.032 100 100 100 100 100 23.0 0.7 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.064 99.9 99.7 99.7 99.7 99.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.125 93.8 85.6 79.0 75.8 71.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.25 23.4 9.8 4.8 1.9 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 150 0.008 100 100 100 100 100 100 100 100 99.9 99.8 98.8 98.4 98.1 97.2 91.3 0.016 100 100 100 100 100 96.7 95.5 89.6 80.8 78.6 57.3 49.2 35.4 25.3 17.6 0.032 100 100 100 100 100 38.5 22.2 12.3 6.9 3.0 0.9 0.1 0.1 0.0 0.0 0.064 100 100 100 100 100 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.125 99.1 98.9 98.9 98.6 98.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.25 72.8 59.8 52.8 42.7 32.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 2.5 0.8 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 200 0.008 100 100 100 100 100 100 100 100 100 100 99.7 99.7 99.6 99.4 99.1 0.016 100 100 100 100 100 99.0 98.9 98.8 98.5 97.8 83.7 78.4 72.3 64.0 55.1 0.032 100 100 100 100 100 72.0 58.8 52.2 40.9 31.1 9.2 3.7 1.4 0.5 0.2 0.064 100 100 100 100 100 2.3 0.7 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.125 99.9 99.8 99.7 99.7 99.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.25 93.8 85.6 79.0 75.8 71.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 23.4 9.8 4.8 1.9 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Micafungin PTA for different target values of area under the serum concentration curve over a 24-h period divided by the minimum inhibitory concentration (AUC /MIC), body weights, and once-daily doses 0–24 and with the 150 mg/24 h dose for body weights of 45 kg, treated with micafungin estimated the micafungin probabil- 80 kg, and 115 kg, and against C. glabrata for body weights ity of achieving adequate AUC /MIC values against 0–24h of 45 kg, 80 kg, and 115 kg with the 200 mg/24 h dose. No Candida spp. for a large population. Our results showed FTAs > 90% were obtained with the 100 mg/24 h dose the lack of adequate micafungin exposure (in terms of regardless of the species or the patient’sweight. FTAs) with the 100 mg/24 h dose regardless of the Candida species or the patient’s weight. Against C. albi- Discussion cans, micafungin exposure was adequate with the 150 mg/ The present Monte-Carlo simulation using data from 24 h dose for patients weighing up to 115 kg and with the obese, critically ill, and morbidly obese critically ill patients 200 mg/24 h dose for those surpassing such a weight. Table 4 Fractional target attainment (FTA) for micafungin 100 mg/24 h 150 mg/24 h 200 mg/24 h Body weight (kg) 45 80 115 150 185 45 80 115 150 185 45 80 115 150 185 C. albicans 74.6 62.6 56.6 46.4 37.4 97.0 95.9 90.6 82.6 80.6 99.1 99.0 98.9 98.6 98.0 C. glabrata 62.0 52.3 47.5 39.3 32.2 86.4 82.6 76.5 69.3 67.0 94.2 91.7 90.4 88.1 85.8 C. tropicalis 23.9 19.3 17.1 15.9 11.0 49.6 40.8 34.1 28.8 26.1 67.9 60.8 57.3 51.4 46.1 C. parapsilosis 1.6 1.2 1.0 0.9 0.9 3.6 2.7 2.4 2.2 1.9 6.6 4.6 3.7 3.3 3.0 Micafungin FTA calculated using MIC distributions  for the different Candida species Maseda et al. Critical Care (2018) 22:94 Page 7 of 9 As in previous studies, plasma concentrations of mica- Increasing the dose to 200 mg/24 h would overcome the fungin were best described using a two-compartment problem caused by being overweight for C. albicans;how- model and, as mentioned, weight was a significant covariate ever, such an increase would not solve the problem for . Unlike the introduction of the severity score as a other Candida species, requiring higher exposures. Since covariate, introducing the patient’s age improved the model. a previous study indicated that the maximum tolerated The influence of severity scores on micafungin exposure in dose of micafungin in patients undergoing hematopoietic severely ill patients is controversial among studies in the stem cell transplantation was at least up to 8 mg/kg/24 h literature; while one study considered SOFA as a relevant , strategies including individualized dosing have been covariate , another study did not find a correlation of advocated as a great opportunity to further improve the APACHE II or SOFA with exposure, suggesting the possi- efficacy of micafungin , an antifungal with reported bility of being ruled out as a cause of low drug exposure 70–80% efficacy in the treatment of candidemia with the . The reason for this could be the high interindividual current dosing of 100 mg/24 h [27, 30, 37, 38]. variability found in studies investigating micafungin expos- The results of this study are of high importance due to ure in critically ill patients [23–25]incontrasttodatafrom the very limited information available on the pharmaco- healthy volunteers or patients under continuous venove- kinetics and efficacy of echinocandins in obese critically nous hemofiltration [11, 24], which represent more uniform ill patients, especially in those with morbid obesity. Most populations. studies have been performed with caspofungin. In agree- On the contrary, weight has been described as markedly ment with our results, pharmacokinetic studies with influencing micafungin clearance bothinpatients caspofungin showed lower exposure in overweight and weighing > 66.3 kg  and in healthy volunteers in a study obese patients, whether critically ill  or not , and including subjects with BMI < 25, 25–40, and > 40 kg/m also showed the benefits of increasing the dose in [27, 28]. In the present simulation, the inclusion of weight morbidly obese patients . Similarly, the limited data as a covariate improved the model. According to the results in the literature regarding the influence of obesity on the of our model, for MIC values > 0.008 μg/ml, the 100 mg/24 pharmacokinetics of anidulafungin confirm the lower h dose failed to achieve the optimal ratio threshold of anidulafungin exposure in patients with morbid obesity AUC /MIC of 3000. This cut-off was associated with compared with nonobese patients  and the need for 0–24h therapeutic outcome in animal models for disseminated increasing the dose in a critically ill morbid obese candidiasis by C. albicans , and it was extrapolated to patient . humans  using data from clinical trials of invasive The present study is the first population assessment of candidiasis/candidemia . Theincreaseinmicafungin micafungin in critically ill nonobese, noncritically ill obese, exposure provided by the doses of 150 or 200 mg/24 h and critically ill morbidly obese patients. Several limita- markedly improved the coverage encompassing MICs of 0. tions and challenges must be kept in mind in this respect. 016 μg/ml for body weights up to 80 kg (with the 150 mg/ Despite the relatively large sample size in this study, the 24 h dose) or for all body weights (200 mg/24 h dose). distribution of patients resulted in a low number of indi- When considering recent MIC distributions for C. albicans, viduals in some of the groups. In addition, the present themostfrequentisolatedspecies, our results indicate that, study is a pharmacodynamic modeling not designed to to obtain adequate coverage (FTA ≥ 90%), the micafungin examine the effect of micafungin exposure on patient dose should be increased to 150 mg/24 h for nonobese outcome; clinical trials should address this issue from a patients (≤ 115 kg) and to 200 mg/24-h for those with body clinical perspective. weight > 115 kg. This finding is consistent with previous reports showing the need for an increase in doses of anti- Conclusion fungals in obese patients [5, 15, 31, 32] due to inadequate The results of this study indicate that micafungin expos- exposure with standards doses, as described with micafun- ure was adequate with the 150 mg/24 h dose for patients gin [26, 33]. In this sense, there is a report suggesting inad- weighing up to 115 kg and with the 200 mg/24 h dose for equate exposure with micafungin 100 mg/24 h in an obese those surpassing such weight to cover C. albicans.The critically ill patient weighing 230 kg . Exposure may be 200 mg/24 h dose covered C. glabrata for patients weigh- crucial in morbidly obese critically ill patients when ing up to 115 kg. Since other species of Candida were not compared with the ICU population or obesity alone . A successfully covered, further PK/PD studies should timely and sufficiently high exposure to the appropriate address this point to optimize dosing for nonalbicans antifungal agent is essential for the eradication of the Candida infections. pathogen. This acquires importance since, worldwide, mean Abbreviations weight, both in men and women, has been increasing over APACHE: Acute Physiology and Chronic Health Evaluation; AUC: Area under the last decades. In the USA, during 2013 and 2014, the the curve; BMI: Body mass index; FTA: Fractional target attainment; overall age-adjusted prevalence of obesity was 37.7% . ICU: Intensive care unit; LL: Log-likelihood; MIC: Minimum inhibitory Maseda et al. Critical Care (2018) 22:94 Page 8 of 9 concentration; MS/MS: Tandem mass spectrometry; NPDE: Normalized Received: 25 January 2018 Accepted: 26 March 2018 prediction distribution errors; PK/PD: Pharmacokinetic/pharmacodynamic; PTA: Probability of target attainment; SAPS: Simplified Acute Physiology Score; SD: Standard deviation; SOFA: Sequential Organ Failure Assessment; References UHPLC: Ultra-high-performance liquid chromatography 1. Milner JJ, Beck MA. The impact of obesity on the immune response to infection. Proc Nutr Soc. 2012;71:298–306. 2. Knibbe CA, Brill MJ, van Rongen A, Diepstraten J, van der Graaf PH, Danhof Acknowledgements M. Drug disposition in obesity: toward evidence-based dosing. Annu Rev The authors thank A. López-Tofiño and C. Hernández Gancedo (Department Pharmacol Toxicol. 2015;55:149–67. of Anesthesia and Surgical Intensive Care, Hospital Universitario La Paz, Madrid, 3. Jain R, Chung SM, Jain L, Khurana M, Lau SW, Lee JE, et al. Implications Spain) for their support during the study, and L. Aguilar (PRISM-AG) for his of obesity for drug therapy: limitations and challenges. Clin Pharmacol critical review of the manuscript. Ther. 2011;90:77–89. 4. Roberts JA, Lipman J. Pharmacokinetic issues for antibiotics in the critically Funding ill patient. Crit Care Med. 2009;37:840–51. This work was supported in part by an unrestricted grant from Astellas Pharma 5. Alobaid AS, Hites M, Lipman J, Taccone FS, Roberts JA. Effect of obesity on S.A. (Madrid, Spain). The funder had no role in the study design, data collection the pharmacokinetics of antimicrobials in critically ill patients: a structured and interpretation, or the decision to submit the work for publication. JAR is review. Int J Antimicrob Agents. 2016;47:259–68. funded by a Career Development Fellowship from the National Health and 6. Chandrasekar PH, Sobel JD. Micafungin: a new echinocandin. Clin Infect Dis. Medical Research Council of Australia (APP1048652). 2006;42:1171–8. 7. Pappas PG, Kauffman CA, Andes DR, Clancy CJ, Marr KA, Ostrosky-Zeichner L, et al. Executive summary: clinical practice guideline for the management Availability of data and materials of candidiasis: 2016 update by the Infectious Diseases Society of America. Data from patients are recorded in their corresponding medical records Clin Infect Dis. 2016;62:409–17. at participating hospitals. The anonymous datasets used and/or analyzed 8. Paiva JA, Pereira JM, Tabah A, Mikstacki A, de Carvalho FB, Koulenti D, et al. during the current study are available from the corresponding author on Characteristics and risk factors for 28-day mortality of hospital acquired reasonable request and with permission of the corresponding hospital. fungemias in ICUs: data from the EUROBACT study. Crit Care. 2016;20:53. 9. Andes D, Ambrose PG, Hammel JP, Van Wart SA, Iyer V, Reynolds DK, et al. Use of pharmacokinetic-pharmacodynamic analyses to optimize therapy Authors’ contributions with the systemic antifungal micafungin for invasive candidiasis or EM, SG, and JAR designed the study. EM, M-PC-M, AS-d-l-R, AM-F, CAG-B, candidemia. Antimicrob Agents Chemother. 2011;55:2113–21. and FG collected data. SG, SL, and JAR analyzed blood samples and analyzed 10. Turnidge J, Paterson DL. Setting and revising antibacterial susceptibility data. EM, SG, JAR, and M-JG analyzed results and prepared the manuscript. breakpoints. Clin Microbiol Rev. 2007;20:391–408. All authors read and approved the final manuscript. 11. Maseda E, Grau S, Villagran MJ, Hernandez-Gancedo C, Lopez-Tofiño A, Roberts JA, et al. Micafungin pharmacokinetic/pharmacodynamic adequacy for the treatment of invasive candidiasis in critically ill Ethics approval and consent to participate patients on continuous venovenous haemofiltration. J Antimicrob The study protocol was approved by the Ethics Committee of Hospital La Paz Chemother. 2014;69:1624–32. (Madrid, Spain) and Hospital del Mar (Barcelona, Spain). Written informed consent 12. Le Gall JR, Lemeshow S, Saulnier F. A new simplified acute physiologic was obtained from patients (or relatives if the patient was unable to provide due score (SAPS-II) based on a European/North-American multicenter study. to their critical situation) before blood sampling. JAMA. 1993;270:2957–63. 13. Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ Competing interests dysfunction/failure. Intensive Care Med. 1996;22:707–10. EM has received consultancy fees and payment for lectures from Astellas 14. León C, Ruiz-Santana S, Saavedra P, Almirante B, Nolla-Salas J, Alvarez-Lerma Pharma S.A. (Madrid, Spain), Pfizer, Novartis, Angellini, and Merck Sharp F, et al. A bedside scoring system (“Candida score”) for early antifungal and Dohme. SG has received grants for research from Astellas Pharma S.A. treatment in nonneutropenic critically ill patients with Candida colonization. (Madrid, Spain), Pfizer, Roche Pharma, and Angelini Pharma, and funds for Crit Care Med. 2006;34:730–7. speaking at symposia organized by Pfizer, Astellas Pharma S.A., and Merck 15. Alobaid AS, Wallis SC, Jarrett P, Starr T, Stuart J, Lassig-Smith M, et al. Sharp and Dohme. SL has received travel grants from Astellas Pharma for Population pharmacokinetics of piperacillin in nonobese, obese, and medical conference attendance. CAG-B has received travel grants from morbidly obese critically ill patients. Antimicrob Agents Chemother. Astellas Pharma for Sepsis Valladolid 2014 Congress attendance. The 2017;61:e01276–316. remaining authors declare that they have no competing interests. 16. Food and Drug Administration. Guidance for industry: bioanalytical method validation. Rockville: U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Publisher’sNote Center for Veterinary Medicine (CVM); 2001. Springer Nature remains neutral with regard to jurisdictional claims in 17. Tatarinova T, Neely M, Bartroff J, van Guilder M, Yamada W, Bayard D, et al. published maps and institutional affiliations. Two general methods for population pharmacokinetic modelling: non- parametric adaptive grid and non-parametric Bayesian. J Pharmacokinet Author details Pharmacodyn. 2013;40:189–99. Department of Anesthesia and Surgical Intensive Care, Hospital Universitario 18. Neely MN, van Guilder MG, Yamada WM, Schumitzky A, Jelliffe RW. Accurate La Paz, Paseo de la Castellana 261, 28046 Madrid, Spain. Universidad detection of outliers and subpopulations with Pmetrics, a nonparametric Autónoma de Madrid, Madrid, Spain. Pharmacy Department, Hospital del and parametric pharmacometric modeling and simulation package for R. Mar, Barcelona, Spain. Institut Hospital del Mar d’Investigacions Mèdiques Ther Drug Monit. 2012;34:467–76. (IMIM), Barcelona, Spain. Universitat Autónoma de Barcelona, Barcelona, 19. Mentré F, Escolano S. Prediction discrepancies for the evaluation of nonlinear 6 7 Spain. PRISM-AG, Madrid, Spain. Anesthesiology Department, Hospital del mixed-effects models. J Pharmacokinet Pharmacodyn. 2006;33:345–67. Mar, Barcelona, Spain. Department of Molecular and Clinical Pharmacology, 20. Pfaller MA, Messer SA, Woosley LN, Jones RN, Castanheira M. Echinocandin University of Liverpool, Liverpool, UK. Burns, Trauma and Critical Care and triazole antifungal susceptibility profiles for clinical opportunistic yeast Research Centre, The University of Queensland, Brisbane, Australia. and mold isolates collected from 2010 to 2011: application of new CLSI Department of Intensive Care Medicine, Royal Brisbane and Women’s clinical breakpoints and epidemiological cutoff values for characterization of Hospital, Brisbane, Australia. Pharmacy Department, Royal Brisbane and geographic and temporal trends of antifungal resistance. J Clin Microbiol. Women’s Hospital, Brisbane, Australia. 2013;51:2571–81. Maseda et al. Critical Care (2018) 22:94 Page 9 of 9 21. Wasmann RE, Muilwijk EW, Burger DM, Verweij PE, Knibbe CA, Brüggemann RJ. Clinical pharmacokinetics and pharmacodynamics of micafungin. Clin Pharmacokinet. 2017; https://doi.org/10.1007/s40262-017-0578-5. [Epub ahead of print]. 22. Jullien V, Azoulay E, Schwebel C, Le Saux T, Charles PE, Cornet M, et al. Population pharmacokinetics of micafungin in ICU patients with sepsis and mechanical ventilation. J Antimicrob Chemother. 2017;72:181–9. 23. Lempers VJ, Schouten JA, Hunfeld NG, Colbers A, van Leeuwen HJ, Burger DM, et al. Altered micafungin pharmacokinetics in intensive care unit patients. Antimicrob Agents Chemother. 2015;59:4403–9. 24. Martial LC, Ter Heine R, Schouten JA, Hunfeld NG, van Leeuwen HJ, Verweij PE, et al. Population pharmacokinetic model and pharmacokinetic target attainment of micafungin in intensive care unit patients. Clin Pharmacokinet. 2017; https://doi.org/10.1007/s40262-017-0509-5. [Epub ahead of print]. 25. Chang CC, Slavin MA, Chen SC. New developments and directions in the clinical application of the echinocandins. Arch Toxicol. 2017;91:1613–21. 26. Gumbo T, Hiemenz J, Ma L, Keirns JJ, Buell DN, Drusano GL. Population pharmacokinetics of micafungin in adult patients. Diagn Microbiol Infect Dis. 2008;60:329–31. 27. Hall RG, Swancutt MA, Gumbo T. Fractal geometry and the pharmacometrics of micafungin in overweight, obese, and extremely obese people. Antimicrob Agents Chemother. 2011;55:5107–12. 28. Pasipanodya JP, Hall RG 2nd, Gumbo T. In silico-derived bedside formula for individualized micafungin dosing for obese patients in the age of deterministic chaos. Clin Pharmacol Ther. 2015;97:292–7. 29. Andes D, Diekema DJ, Pfaller MA, Bohrmuller J, Marchillo K, Lepak A. In vivo comparison of the pharmacodynamic targets for echinocandin drugs against Candida species. Antimicrob Agents Chemother. 2010;54:2497–506. 30. Kuse ER, Chetchotisakd P, da Cunha CA, Ruhnke M, Barrios C, Raghunadharao D, et al. Micafungin versus liposomal amphotericin B for candidaemia and invasive candidosis: a phase III randomised double-blind trial. Lancet. 2007;369:1519–27. 31. Alobaid AS, Wallis SC, Jarrett P, Starr T, Stuart J, Lassig-Smith M, et al. Effect of obesity on the population pharmacokinetics of fluconazole in critically ill patients. Antimicrob Agents Chemother. 2016;60:6550–7. 32. Lempers VJ, van Rongen A, van Dongen EP, van Ramshorst B, Burger DM, Aarnoutse RE, et al. Does weight impact anidulafungin pharmacokinetics? Clin Pharmacokinet. 2016;55:1289–94. 33. Yang Q, Wang T, Xie J, Wang Y, Zheng X, Chen L, et al. Pharmacokinetic/ pharmacodynamic adequacy of echinocandins against Candida spp. in intensive care unit patients and general patient populations. Int J Antimicrob Agents. 2016;47:397–402. 34. Zomp A, Bookstaver PB, Ahmed Y, Turner JE, King C. Micafungin therapy in a critically ill, morbidly obese patient. J Antimicrob Chemother. 2011;66:2678–80. 35. Flegal KM, Kruszon-Moran D, Carroll MD, Fryar CD, Ogden CL. Trends in obesity among adults in the United States, 2005 to 2014. JAMA. 2016; 315:2284–91. 36. Sirohi B, Powles RL, Chopra R, Russell N, Byrne JL, Prentice HG, et al. A study to determine the safety profile and maximum tolerated dose of micafungin (FK463) in patients undergoing haematopoietic stem cell transplantation. Bone Marrow Transplant. 2006;38:47–51. 37. Pappas PG, Rotstein CM, Betts RF, Nucci M, Talwar D, De Waele JJ, et al. Micafungin versus caspofungin for treatment of candidemia and other forms of invasive candidiasis. Clin Infect Dis. 2007;45:883–93. 38. Goto N, Hara T, Tsurumi H, Ogawa K, Kitagawa J, Kanemura N, et al. Efficacy and safety of micafungin for treating febrile neutropenia in hematological Submit your next manuscript to BioMed Central malignancies. Am J Hematol. 2010;85:872–6. 39. Nguyen TH, Hoppe-Tichy T, Geiss HK, Rastall AC, Swoboda S, Schmidt J, and we will help you at every step: et al. Factors influencing caspofungin plasma concentrations in patients of a • We accept pre-submission inquiries surgical intensive care unit. J Antimicrob Chemother. 2007;60:100–6. 40. Hall RG, Swancutt MA, Meek C, Leff R, Gumbo T. Weight drives caspofungin � Our selector tool helps you to ﬁnd the most relevant journal pharmacokinetic variability in overweight and obese people: fractal power � We provide round the clock customer support signatures beyond two-thirds or three-fourths. Antimicrob Agents � Convenient online submission Chemother. 2013;57:2259–64. 41. Ferriols-Lisart R, Aguilar G, Pérez-Pitarch A, Puig J, Ezquer-Garín C, Alós M. � Thorough peer review Plasma concentrations of caspofungin in a critically ill patient with morbid � Inclusion in PubMed and all major indexing services obesity. Crit Care. 2017;21:200. � Maximum visibility for your research 42. Liu P, Ruhnke M, Meersseman W, Paiva JA, Kantecki M, Damle B. Pharmacokinetics of anidulafungin in critically ill patients with candidemia/ Submit your manuscript at invasive candidiasis. Antimicrob Agents Chemother. 2013;57:1672–6. www.biomedcentral.com/submit
Critical Care – Springer Journals
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