In Silico Screening and Experimental Validation of Novel MexAB-OprM Efflux Pump Inhibitors of Pseudomonas aeruginosa.

The emergence of multidrug-resistant Pseudomonas aeruginosa possesses a significant public health concern. Constitutively expressed MexAB-OprM efflux pumps in P. aeruginosa significantly contribute to its resistance to a variety of antibiotics. The development of efflux pump inhibitors (EPIs) has emerged as an attractive strategy in reversing antibiotic resistance. In this study, structure-based virtual screening techniques were used for the identification of new MexAB-OprM efflux inhibitors. The predicted poses were thoroughly filtered by induced fit docking procedures followed by in vitro microbiological assays for the validation of in silico results. Two compounds, NSC-147850 and NSC-112703, were able to restore tetracycline susceptibility in MexAB-OprM overexpressing Pseudomonas aeruginosa ATCC® 27853™ strain. This correlation observed between in silico screening and positive efflux inhibitory activity in vitro suggests that NSC-147850 and NSC-112703 have potential as EPIs and may be effective in combination therapy against drug-resistant strains of P. aeruginosa.

A review of SARS-CoV-2 drug repurposing: databases and machine learning models.

The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.

Identification of Drug Combination Therapies for SARS-CoV-2: A Molecular Dynamics Simulations Approach.

Purpose: The development of effective treatments for coronavirus infectious disease 19 (COVID-19) caused by SARS-Coronavirus-2 was hindered by the little data available about this virus at the start of the pandemic. Drug repurposing provides a good strategy to explore approved drugs' possible SARS-CoV-2 antiviral activity. Moreover, drug synergism is essential in antiviral treatment due to improved efficacy and reduced toxicity. In this work, we studied the effect of approved and investigational drugs on one of SARS-CoV-2 essential proteins, the main protease (Mpro), in search of antiviral treatments and/or drug combinations. Methods: Different possible druggable sites of Mpro were identified and screened against an in-house library of more than 4000 chemical compounds. Molecular dynamics simulations were carried out to explore conformational changes induced by different ligands' binding. Subsequently, the inhibitory effect of the identified compounds and the suggested drug combinations on the Mpro were established using a 3CL protease (SARS-CoV-2) assay kit. Results: Three potential inhibitors in three different binding sites were identified; favipiravir, cefixime, and carvedilol. Molecular dynamics simulations predicted the synergistic effect of two drug combinations: favipiravir/cefixime, and favipiravir/carvedilol. The in vitro inhibitory effect of the predicted drug combinations was established on this enzyme. Conclusion: In this work, we could study one of the promising SARS-CoV-2 viral protein targets in searching for treatments for COVID-19. The inhibitory effect of several drugs on Mpro was established in silico and in vitro assays. Molecular dynamics simulations showed promising results in predicting the synergistic effect of drug combinations.