In vitro synergistic effects of fluoxetine and paroxetine in combination with amphotericin B against Cryptococcus neoformans

Antifungal and antibiofilm effect of duloxetine hydrochloride against Cryptococcus neoformans and Cryptococcus gattii

Cryptococcosis is an invasive mycosis caused mainly by Cryptococcus gattii and C. neoformans and is treated with amphotericin B (AMB), fluconazole and 5-fluorocytosine. However, antifungal resistance, limited and toxic antifungal arsenal stimulate the search for therapeutic strategies such as drug repurposing. Among the repurposed drugs studied, the selective serotonin reuptake inhibitors (SSRIs) have shown activity against Cryptococcus spp. However, little is known about the antifungal effect of duloxetine hydrochloride (DH), a selective serotonin and norepinephrine reuptake inhibitor (SSNRI), against C. neoformans and C. gattii. In this study, DH inhibited the growth of several C. neoformans and C. gattii strains at concentrations ranging from 15.62 to 62.50 µg/mL. In addition, DH exhibited fungicidal activity ranging from 15.62 to 250 µg/mL. In biofilm, DH treatment reduced Cryptococcus spp. biomass at a level comparable to AMB, with a significant reduction (85%) for C. neoformans biofilms. The metabolic activity of C. neoformans and C. gattii biofilms decreased significantly (99%) after treatment with DH. Scanning electron micrographs confirmed the anti-biofilm activity of DH, as isolated cells could be observed after treatment. In conclusion, DH showed promising antifungal activity against planktonic cells and biofilms of C. neoformans and C. gattii, opening perspectives for further studies with DH in vivo.

The Repurposing of FDA-Approved Drugs as FtsZ Inhibitors against Mycobacterium tuberculosis: An In Silico and In Vitro Study

Mycobacterium tuberculosis (Mtb), the causative pathogen of tuberculosis, remains one of the leading causes of death from a single infectious agent. Furthermore, the growing evolution to multi-drug-resistant (MDR) strains requires de novo identification of drug targets for evaluating candidates or repurposing drugs. Hence, targeting FtsZ, an essential cell division protein, is a promising target.

METHODS

Using an in silico pharmacological repositioning strategy, four FDA-based drugs that bind to the catalytic site FtsZ were selected. The Alamar Blue colorimetric assay was used to assess antimicrobial activity and the effect of drugs on Mtb growth through growth curves. Bacterial load was determined with an in vitro infection model using colony-forming units (CFU)/mL, and cytotoxicity on human monocyte-derived macrophages (MDMhs) was assessed by flow cytometry.

RESULTS

Paroxetine and nebivolol exhibited antimycobacterial activity against both reference TB and MDR strains at a concentration of 25 µg/mL. Furthermore, both paroxetine and nebivolol demonstrated a significant reduction (p < 0.05) in viable bacteria compared to the untreated group in the in vitro infection model.

CONCLUSIONS

Collectively, our findings demonstrate that the use of paroxetine and nebivolol is a promising strategy to help in the control of tuberculosis infection.

piscesCSM: prediction of anticancer synergistic drug combinations

While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic drug combinations has been a challenging venture. Computational methods have emerged in this context as a promising tool for prioritizing drug combinations for further evaluation, though they have presented limited performance, utility, and interpretability. Here, we propose a novel predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures to accurately predict drug combinations with favourable anticancer synergistic effects against one or multiple cancer cell lines. Leveraging these insights, we developed a general supervised machine learning model to guide the prediction of anticancer synergistic drug combinations in over 30 cell lines. It achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.89 on independent non-redundant blind tests, outperforming state-of-the-art approaches on both large-scale oncology screening data and an independent test set generated by AstraZeneca (with more than a 16% improvement in predictive accuracy). Moreover, by exploring the interpretability of our approach, we found that simple physicochemical properties and graph-based signatures are predictive of chemotherapy synergism. To provide a simple and integrated platform to rapidly screen potential candidate pairs with favourable synergistic anticancer effects, we made piscesCSM freely available online at https://biosig.lab.uq.edu.au/piscescsm/ as a web server and API. We believe that our predictive tool will provide a valuable resource for optimizing and augmenting combinatorial screening libraries to identify effective and safe synergistic anticancer drug combinations. SCIENTIFIC CONTRIBUTION: This work proposes piscesCSM, a machine-learning-based framework that relies on well-established graph-based representations of small molecules to identify and provide better predictive accuracy of syngenetic drug combinations. Our model, piscesCSM, shows that combining physiochemical properties with graph-based signatures can outperform current architectures on classification prediction tasks. Furthermore, implementing our tool as a web server offers a user-friendly platform for researchers to screen for potential synergistic drug combinations with favorable anticancer effects against one or multiple cancer cell lines.

In vitro effects of selective serotonin reuptake inhibitors on Cryptococcus gattii capsule and biofilm

Infections caused by Cryptococcus gattii mainly affect immunocompetent individuals and the treatment presents important limitations. This study aimed to validate the efficacy of selective serotonin reuptake inhibitors (SSRI), fluoxetine hydrochloride (FLH), and paroxetine hydrochloride (PAH) in vitro against C. gattii. The antifungal activity of SSRI using the microdilution method revealed a minimal inhibitory concentration (MIC) of 31.25 µg/ml. The combination of FLH or PAH with amphotericin B (AmB) was analyzed using the checkerboard assay and the synergistic effect of SSRI in combination with AmB was able to reduce the SSRI or AmB MIC values 4-8-fold. When examining the effect of SSRI on the induced capsules, we observed that FLH and PAH significantly decreased the size of C. gattii capsules. In addition, the effects of FLH and PAH were evaluated in biofilm biomass and viability. The SSRI were able to reduce biofilm biomass and biofilm viability. In conclusion, our results indicate the use of FLH and PAH exhibited in vitro anticryptococcal activity, representing a possible future alternative for the cryptococcosis treatment.

Synergistic effect of the verapamil and amphotericin B against Cryptococcus neoformans

Cryptococcus neoformans is an encapsulated yeast that can cause cryptococcosis and cryptococcal meningitis, which conventional treatment involves antifungal drugs such as polyenes, flucytosine, azoles, and their combinations. However, the high cost, toxicity, and increase in fungi resistance to antifungal agents stimulate the search for therapeutic strategies such as drug repurposing and combination therapy. This study evaluated the activity of the antihypertensive verapamil (VEH) alone and combined with amphotericin B (AmB) against C. neoformans. VEH exhibited antifungal activity against C. neoformans with minimum inhibitory concentration and minimum fungicidal concentration of 118 µg per mL. The combination of VEH and AmB exhibited synergism, reducing at least eightfold both drugs' concentrations. Moreover, the combination decreased the size and glucuronoxylomannnan content of C. neoformans capsule. However, no difference was observed in ergosterol levels of C. neoformans after treatment with VEH and AmB in combination. Altogether, VEH in combination with AmB exhibits potential as a candidate as for the development of anti-cryptococcal drug.

Antifungal activity of selective serotonin reuptake inhibitors against Cryptococcus spp. and their possible mechanism of action

Fungal infections caused by Cryptococcus spp. pose a threat to health, especially in immunocompromised individuals. The available arsenal of drugs against cryptococcosis is limited, due to their toxicity and/or lack of accessibility in low-income countries, requiring more therapeutic alternatives. Selective serotonin reuptake inhibitors (SSRIs), through drug repositioning, are a promising alternative to broaden the range of new antifungals against Cryptococcus spp. This study evaluates the antifungal activity of three SSRIs, sertraline, paroxetine, and fluoxetine, against Cryptococcus spp. strains, as well as assesses their possible mechanism of action. Seven strains of Cryptococcus spp. were used. Sensitivity to SSRIs, fluconazole, and itraconazole was evaluated using the broth microdilution assay. The interactions resulting from combinations of SSRIs and azoles were investigated using the checkerboard assay. The possible action mechanism of SSRIs against Cryptococcus spp. was evaluated through flow cytometry assays. The SSRIs exhibited in vitro antifungal activity against Cryptococcus spp. strains, with minimum inhibitory concentrations ranging from 2 to 32 μg/mL, and had synergistic and additive interactions with azoles. The mechanism of action of SSRIs against Cryptococcus spp. involved damage to the mitochondrial membrane and increasing the production of reactive oxygen species, resulting in loss of cellular viability and apoptotic cell death. Fluoxetine also was able to cause significant damage to yeast DNA. These findings demonstrate the in vitro antifungal potential of SSRIs against Cryptococcus spp. strains.

Effect of fluoxetine on planktonic and biofilm growth and the antimicrobial susceptibility of Burkholderia pseudomallei

Aim: This study evaluated the effect of fluoxetine (FLU) on planktonic and biofilm growth and the antimicrobial susceptibility of Burkholderia pseudomallei. Materials & methods: The minimum inhibitory concentrations (MICs) for FLU were determined by broth microdilution. Its effect on growing and mature biofilms and its interaction with antibacterial drugs were evaluated by assessing biofilm metabolic activity, biomass and structure through confocal microscopy. Results: The FLU MIC range was 19.53-312.5 μg/ml. FLU eradicated growing and mature biofilms of B. pseudomallei at 19.53-312.5 μg/ml and 1250-2500 μg/ml, respectively, with no structural alterations and enhanced the antibiofilm activity of antimicrobial drugs. Conclusion: These results bring perspectives for the use of FLU in the treatment of melioidosis, requiring further studies to evaluate its applicability.

Searching for new antifungals for the treatment of cryptococcosis

There is a consensus that the antifungal repertoire for the treatment of cryptococcal infections is limited. Standard treatment involves the administration of an antifungal drug derived from natural sources (i.e., amphotericin B) and two other drugs developed synthetically (i.e., flucytosine and fluconazole). Despite treatment, the mortality rates associated with fungal cryptococcosis are high. Amphotericin B and flucytosine are toxic, require intravenous administration, and are usually unavailable in low-income countries because of their high cost. However, fluconazole is cost-effective, widely available, and harmless with regard to its side effects. However, fluconazole is a fungistatic agent that has contributed considerably to the increase in fungal resistance and frequent relapses in patients with cryptococcal meningitis. Therefore, there is an unquestionable need to identify new alternatives or adjuvants to conventional drugs for the treatment of cryptococcosis. A potential antifungal agent should be able to kill cryptococci and "bypass" the virulence mechanism of the yeast. Furthermore, it should have fungicidal action, low toxicity, high selectivity, easily penetrate the central nervous system, and widely available. In this review, we describe cryptococcosis, its conventional therapy, and failures arising from the use of drugs traditionally considered to be the reference standard. Additionally, we present the approaches used for the discovery of new drugs to counteract cryptococcosis, ranging from the conventional screening of natural products to the inclusion of structural modifications to optimize anticryptococcal activity, as well as drug repositioning and combined therapies.

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

MOTIVATION

Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network has recently shown remarkable performance in the prediction of compound-protein interactions, but it has not been applied to the screening of drug combinations.

RESULTS

In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multilayer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS (Deep Learning for Drug-Drug Synergy prediction) with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation.

AVAILABILITY AND IMPLEMENTATION

Source code and data are available at https://github.com/Sinwang404/DeepDDS/tree/master.