Candida auris is an emergent fungal pathogen that causes severe infectious outbreaks globally. The public health concern when dealing with this pathogen is mainly due to reduced susceptibility to current antifungal drugs. A valuable alternative to overcome this problem is to investigate the efficacy of combination therapy. The aim of this study was to determine the in vitro interactions of isavuconazole with echinocandins against C. auris. Interactions were determined using a checkerboard method, and absorbance data were analyzed with different approaches: the fractional inhibitory concentration index (FICI), Greco universal response surface approach, and Bliss interaction model. All models were in accordance and showed that combinations of isavuconazole with echinocandins resulted in an overall synergistic interaction. A wide range of concentrations within the therapeutic range were selected to perform time-kill curves. These confirmed that isavuconazole–echinocandin combinations were more effective than monotherapy regimens. Synergism and fungistatic activity were achieved with combinations that included isavuconazole in low concentrations (≥0.125 mg/L) and ≥1 mg/L of echinocandin. Time-kill curves revealed that once synergy was achieved, combinations of higher drug concentrations did not improve the antifungal activity. This work launches promising results regarding the combination of isavuconazole with echinocandins for the treatment of C. auris infections.

The high failure rate of central nervous system (CNS) drugs is partly associated with an insufficient understanding of target site exposure. Blood–brain barrier (BBB) permeability evaluation tools are needed to explore drugs’ ability to access the CNS. An outstanding aspect of physiologically based pharmacokinetic (PBPK) models is the integration of knowledge on drug-specific and system-specific characteristics, allowing the identification of the relevant factors involved in target site distribution. We aimed to qualify a PBPK platform model to be used as a tool to predict CNS concentrations when significant transporter activity is absent and human data are sparse or unavailable. Data from the literature on the plasma and CNS of rats and humans regarding acetaminophen, oxycodone, lacosamide, ibuprofen, and levetiracetam were collected. Human BBB permeability values were extrapolated from rats using inter-species differences in BBB surface area. The percentage of predicted AUC and Cmax within the 1.25-fold criterion was 85% and 100% for rats and humans, respectively, with an overall GMFE of <1.25 in all cases. This work demonstrated the successful application of the PBPK platform for predicting human CNS concentrations of drugs passively crossing the BBB. Future applications include the selection of promising CNS drug candidates and the evaluation of new posologies for existing drugs.

Fentanyl is widely used off-label in NICU. Our aim was to investigate its cerebral, cardiovascular and pulmonary effects as well as pharmacokinetics in an experimental model for neonates. Fentanyl (5 µg/kg bolus immediately followed by a 90 minute infusion of 3 µg/kg/h) was administered to six mechanically ventilated newborn piglets. Cardiovascular, ventilation, pulmonary and oxygenation indexes as well as brain activity were monitored from T = 0 up to the end of experiments (T = 225–300 min). Also plasma samples for quantification of fentanyl were drawn. A “reliable degree of sedation” was observed up to T = 210–240 min, consistent with the selected dosing regimen and the observed fentanyl plasma levels. Unlike cardiovascular parameters, which were unmodified except for an increasing trend in heart rate, some of the ventilation and oxygenation indexes as well as brain activity were significantly altered. The pulmonary and brain effects of fentanyl were mostly recovered from T = 210 min to the end of experiment. The newborn piglet was shown to be a suitable experimental model for studying fentanyl disposition as well as respiratory and cardiovascular effects in human neonates. Therefore, it could be extremely useful for further investigating the drug behaviour under pathophysiological conditions.

The application of modeling and simulation (M&S) tools to biological, physiological, and clinical data has great potential to enhance drug development and regulatory decision making. The strategic development of multidisciplinary projects aimed at integrating methodologies from different disciplines may bridge between preclinical and clinical drug development as well as between academic curiosity and clinical practice. Herein we review the history and present the state of M&S approaches as well as our vision for future challenges and applications.