The Synergistic Effect of Azoles and Fluoxetine against Resistant Candida albicans Strains Is Attributed to Attenuating Fungal Virulence.

This study evaluated the synergistic effects of the selective serotonin reuptake inhibitor, fluoxetine, in combination with azoles against Candida albicans both in vitro and in vivo and explored the underlying mechanism. MICs, sessile MICs, and time-kill curves were determined for resistant C. albicans Galleria mellonella was used as a nonvertebrate model for determining the efficacy of the drug combinations against C. albicans in vivo For the mechanism study, gene expression levels of the SAP gene family were determined by reverse transcription (RT)-PCR, and extracellular phospholipase activities were detected in vitro by the egg yolk agar method. The combinations resulted in synergistic activity against C. albicans strains, but the same effect was not found for the non-albicans Candida strains. For the biofilms formed over 4, 8, and 12 h, synergism was seen for the combination of fluconazole and fluoxetine. In addition, the time-kill curves confirmed the synergism dynamically. The results of the G. mellonella studies agreed with the in vitro analysis. In the mechanism study, we observed that fluconazole plus fluoxetine caused downregulation of the gene expression levels of SAP1 to SAP4 and weakened the extracellular phospholipase activities of resistant C. albicans The combinations of azoles and fluoxetine showed synergistic effects against resistant C. albicans may diminish the virulence properties of C. albicans.

Evaluation of nine formulas for estimating the body surface area of children with hematological malignancies.

Objectives: Body surface area (BSA) is an important parameter in clinical practice for children. To find out the most accurate BSA formula for Chinese children, nine formulas were compared. Methods: This single-center study comprised children who were diagnosed with acute lymphoblastic leukemia and treated with anticancer agents in a specialized children's hospital in China from January 2017 to December 2020. The BSA values were calculated using the formulas from Boyd, Banerjee and Bhattacharya, Costeff, Fujimoto and Watanabe, Haycock, Gehan and George, Mosteller, Stevenson and a Pediatrics textbook. The arithmetic mean of formulas was calculated as the "gold standard" for comparison. Results: A total of 666 children (389 males and 277 females) were included. All nine formulas showed a strong positive correlation with the "gold standard." Underestimation was observed with the Banerjee and Bhattacharya, Fujimoto and Watanabe formulas. The Gehan and George formula showed overestimation. Values estimated from the Haycock and Mosteller formulas were the closest to the mean BSA. Conclusion: The Haycock and Mosteller formulas are the most recommended formulas for Chinese children with hematological malignancies.

Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning.

Objective: To establish an optimal model to predict the teicoplanin trough concentrations by machine learning, and explain the feature importance in the prediction model using the SHapley Additive exPlanation (SHAP) method. Methods: A retrospective study was performed on 279 therapeutic drug monitoring (TDM) measurements obtained from 192 patients who were treated with teicoplanin intravenously at the First Affiliated Hospital of Army Medical University from November 2017 to July 2021. This study included 27 variables, and the teicoplanin trough concentrations were considered as the target variable. The whole dataset was divided into a training group and testing group at the ratio of 8:2, and predictive performance was compared among six different algorithms. Algorithms with higher model performance (top 3) were selected to establish the ensemble prediction model and SHAP was employed to interpret the model. Results: Three algorithms (SVR, GBRT, and RF) with high R 2 scores (0.676, 0.670, and 0.656, respectively) were selected to construct the ensemble model at the ratio of 6:3:1. The model with R 2 = 0.720, MAE = 3.628, MSE = 22.571, absolute accuracy of 83.93%, and relative accuracy of 60.71% was obtained, which performed better in model fitting and had better prediction accuracy than any single algorithm. The feature importance and direction of each variable were visually demonstrated by SHAP values, in which teicoplanin administration and renal function were the most important factors. Conclusion: We firstly adopted a machine learning approach to predict the teicoplanin trough concentration, and interpreted the prediction model by the SHAP method, which is of great significance and value for the clinical medication guidance.

In vivo activity of fluconazole/tetracycline combinations in Galleria mellonella with resistant Candida albicans infection.

OBJECTIVES: Treatment of azole-resistant Candida albicans infections continues to pose significant challenges. With limited options of licensed agents, drug combinations may be a practical treatment alternative. In our previous studies, the combinations minocycline/fluconazole (MINO/FLC) and doxycycline/fluconazole (DOXY/FLC) shown synergistic effects in vitro. It is necessary to explore their appropriate dosage, potential toxicity and in vivo efficacy. METHODS: The Galleria mellonella infection model was employed to study the in vivo efficacy of MINO/FLC and DOXY/FLC by survival analysis, quantification of C. albicans fungal burden and histological studies. RESULTS: The survival rates of G. mellonella larvae infected with lethal doses of resistant C. albicans CA10 increased significantly when treated with the drug combinations compared with FLC treatment alone, and the fungal burden was reduced by almost four-fold. The histopathological study showed that fewer infected areas in larvae were observed and the destructive degree was less when larvae were exposed to the drug combinations. CONCLUSIONS: These findings suggest that combination of a tetracycline antibiotic (MINO or DOXY) with FLC has antifungal activity against azole-resistant C. albicans in vivo. This is in agreement with several previous in vitro studies and provides preliminary in vivo evidence that such a combination might be useful therapeutically.

Synergistic Effect of Fluconazole and Calcium Channel Blockers against Resistant Candida albicans.

Candidiasis has increased significantly recently that threatens patients with low immunity. However, the number of antifungal drugs on the market is limited in comparison to the number of available antibacterial drugs. This fact, coupled with the increased frequency of fungal resistance, makes it necessary to develop new therapeutic strategies. Combination drug therapy is one of the most widely used and effective strategy to alleviate this problem. In this paper, we were aimed to evaluate the combined antifungal effects of four CCBs (calcium channel blockers), amlodipine (AML), nifedipine (NIF), benidipine (BEN) and flunarizine (FNZ) with fluconazole against C. albicans by checkerboard and time-killing method. In addition, we determined gene (CCH1, MID1, CNA1, CNB1, YVC1, CDR1, CDR2 and MDR1) expression by quantitative PCR and investigated the efflux pump activity of resistant candida albicans by rhodamine 6G assay to reveal the potential mechanisms. Finally, we concluded that there was a synergy when fluconazole combined with the four tested CCBs against resistant strains, with fractional inhibitory concentration index (FICI) <0.5, but no interaction against sensitive strains (FICI = 0.56 ~ 2). The mechanism studies revealed that fluconazole plus amlodipine caused down-regulating of CNA1, CNB1 (encoding calcineurin) and YVC1 (encoding calcium channel protein in vacuole membrane).