Voriconazole is characterized by nonlinear pharmacokinetics due to saturation of its metabolism, resulting in unpredictable exposure with standard dosing regimens. Furthermore, it exhibits substantial inter-and intrapatient variability (88 to 100%), as many physiological, pathological, and pharmacological variables affect its serum concentrations (1). A correlation between serum concentrations and toxicity or response has been reported (2), whereas the benefit of therapeutic-drug monitoring (TDM) of voriconazole in the clinical setting has been demonstrated in many clinical studies, including a randomized clinical trial (3-7). Therefore, TDM of voriconazole is an important tool in individualized therapy, leading to dosage optimization in order to maximize the therapeutic effect and minimize toxicity.The clinical use and value of TDM are related to accurate, rapid, and cost-effective assays. Specifically, voriconazole levels in body fluids are often determined by using chromatographic or microbiological methods. Although high-performance liquid chromatography (HPLC) is still considered the gold standard, bioassays are frequently adopted and routinely performed because of their relative technical simplicity and low consumable and equipment costs, while there are several data indicating the concordance of results between the two methods (8-13). Nevertheless, current microbiological assays are lacking specificity in cases of antifungal combination therapy, as they do not allow the separation and simultaneous quantification of each individual compound. In light of the recent encouraging data from a large prospective randomized clinical trial on antifungal combination therapy (14), voriconazole may be combined with echinocandins in order to increase efficacy and overcome the limitations of voriconazole monotherapy, such as the long time to reach steady state, subtherapeutic levels, and difficult-to-treat infections (e.g., central nervous system [CNS] infections and those caused by azoleresistant pathogens) (15, 16). We therefore developed and validated an agar diffusion bioassay to determine voriconazole concentrations in the serum samples from patients on combination therapy with echinocandins.
Objectives To apply therapeutic drug monitoring and dose-individualization of intravenous Busulfan to paediatric patients and evaluate the impact of syringe-pump induced Busulfan infusion lag-time after in vitro estimation. Methods 76 children and adolescents were administered 2 h intravenous Busulfan infusion every 6 h (16 doses). Busulfan plasma levels, withdrawn by an optimized sampling scheme and measured by a validated HPLC–PDA method, were used to estimate basic PK parameters, AUC, Cmax, kel, t1/2, applying Non-Compartmental Analysis. In vivo infusion lag-time was simulated in vitro and used to evaluate its impact on AUC estimation. Key findings Mean (%CV) Busulfan AUC, Cmax, clearance and t1/2 for pediatric population were found 962.3 μm × min (33.1), 0.95 mg/L (41.4), 0.27 L/h/kg (33.3), 2.2 h (27.8), respectively. TDM applied to 76 children revealed 6 (7.9%) being above and 25 (32.9%) below therapeutic-range (AUC: 900–1350 μm × min). After dose correction, all patients were measured below toxic levels (AUC < 1500 μm × min), no patient below 900 μm × min. Incorporation of infusion lag-time revealed lower AUCs with 17.1% more patients and 23.1% more younger patients, with body weight <16 kg, being below the therapeutic-range. Conclusions TDM, applied successfully to 76 children, confirmed the need for Busulfan dose-individualization in paediatric patients. Infusion lag-time was proved clinically significant for younger, low body-weight patients and those close to the lower therapeutic-range limit.
Background We conducted a prospective study in ICU patients of two tertiary hospitals in order to determine basic pharmacokinetic (PK) parameters, associated variation and target attainment rates for anidulafungin, micafungin and caspofungin. Methods Serum samples from patients treated for 7 days with the standard doses of anidulafungin (N = 13), micafungin (N = 14) or caspofungin (N = 7) were analysed by validated chromatographic methods. PK parameters determined with non-compartmental analysis were correlated with demographic, laboratory and disease severity characteristics. The percentages of patients attaining drug exposures described in the summary of product characteristics (SmPC) documents and preclinical PK/PD targets for stasis were estimated. Results The median (range) AUC24 was 101.46 (54.95–274.15) mg·h/L for anidulafungin, 79.35 (28.00–149.30) mg·h/L for micafungin and 48.46 (19.44–103.69) mg·h/L for caspofungin. The interindividual variability of anidulafungin, micafungin and caspofungin AUC24 was 46%–58%, attributed mainly to variability in volume of distribution (V), clearance (CL) and in both V and CL, respectively. Significant correlations were found between anidulafungin AUC24 and BMI (rs = −0.670, P = 0.012) and liver enzymes (rs = 0.572–0.665, P = 0.013–0.041) and between caspofungin Cmin and transaminase levels (rs = −0.775 to −0.786, P = 0.036–0.041). Less than 50% of our patients attained the corresponding SmPC median AUC24s and none of the patients attained the PK/PD targets for Candida albicans and Candida parapsilosis. Conclusions Anidulafungin exposure in ICU patients was comparable with that reported in non-ICU patients and in healthy volunteers. Micafungin exposure was comparable to that of other patients but ∼30% lower than that in healthy volunteers, whereas caspofungin exposure was rather low (∼50% lower than in healthy volunteers). Larger interindividual variability (50%–60%) was recorded in ICU patients compared with other groups for all three echinocandins.
Aims The population pharmacokinetics (PK) of anidulafungin in critically ill patients hospitalized in intensive care units (ICUs) was explored with the intention of evaluating and optimizing dosing regimens. Methods A PK study was conducted in a cohort of 13 patients treated with anidulafungin using intensive sampling during multiple periods per patient and the high‐performance liquid chromatography method for drug quantification. A population PK model was developed to describe the concentration‐time course of anidulafungin and the inter‐individual (IIV) and interoccasion variability (IOV) of the PK parameters. Model‐based PK simulations have been performed to estimate the probability of target attainment (PTA), given the pharmacokinetic/pharmacodynamic target of free 24‐hour area under the free drug concentration‐time curve over minimum inhibitory concentration for several dosing regimens. Results A two‐compartment PK model, with first‐order elimination, best described the data with population clearance (CL) and central/peripheral volume of distribution (V1/V2) of 0.778 L/h and 10.2/21.1 L, respectively, and a mean ± s.d. AUC0‐24 of 119.97 ± 46.23 mg·h/L. Pronounced IIV and IOV variability was found for CL (38% and 31%) and V1 (47% and 30%), respectively. Sequential Organ Failure Assessment (SOFA) and Body Mass Index (BMI) were found to be covariates on CL and V1, respectively. Low PTA values were calculated for borderline Clinical & Laboratory Standards Institute (CLSI)‐susceptible Candida strains. Conclusions Although anidulafungin exposure was found comparable to that in healthy volunteers, elevated interindividual and significant interoccasion variability was found in critically ill ICU patients, which resulted in reduced PTA values in these patients.
Background: Rising antimicrobial resistance has led to a revived interest in inhaled colistin treatment in the critically ill patient with ventilator-associated respiratory infection (VARI). Nebulization via vibrating mesh nebulizers (VMNs) is considered the current standard-of-care, yet the use of generic jet nebulizers (JNs) is more widespread. Few data exist on the intrapulmonary pharmacokinetics of colistin when administered through VMNs, while there is a complete paucity regarding the use of JNs. Methods: In this study, 18 VARI patients who received 2 million international units of inhaled colistimethate sodium (CMS) through a VMN were pharmacokinetically compared with six VARI patients who received the same drug dose through a JN, in the absence of systemic CMS administration. Results: Surprisingly, VMN and JN led to comparable formed colistin exposures in the epithelial lining fluid (ELF) (median (IQR) AUC0–24: 86.2 (46.0–185.9) mg/L∙h with VMN and 91.5 (78.1–110.3) mg/L∙h with JN). The maximum ELF concentration was 10.4 (4.7–22.6) mg/L and 7.4 (6.2–10.3) mg/L, respectively. Conclusions: Based on our results, JN might be considered a viable alternative to the theoretically superior VMN. Therapeutic drug monitoring in the ELF can be advised due to the observed low exposure, high variability, and appreciable systemic absorption.
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