New In Silico Insights into the Application of (Hydroxy)Chloroquine with Macrolide Antibiotic Co-Crystals against the SARS-CoV-2 Virus

In this in silico study, different pharmaceutical co-crystals based on (hydroxy)chloroquine with macrolide antibiotics (azithromycin, clarithromycin, or erythromycin A) were analyzed for the first time. These findings present a new molecular perspective and therefore suggest that the combination of (hydroxy)chloroquine/azithromycin, in the stoichiometric ratio of 1:1, as model co-crystal systems has less toxicity and is the most effective for inhibiting the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Computational evidence for nitro derivatives of quinoline and quinoline N-oxide as low-cost alternative for the treatment of SARS-CoV-2 infection.

A new and more aggressive strain of coronavirus, known as SARS-CoV-2, which is highly contagious, has rapidly spread across the planet within a short period of time. Due to its high transmission rate and the significant time-space between infection and manifestation of symptoms, the WHO recently declared this a pandemic. Because of the exponentially growing number of new cases of both infections and deaths, development of new therapeutic options to help fight this pandemic is urgently needed. The target molecules of this study were the nitro derivatives of quinoline and quinoline N-oxide. Computational design at the DFT level, docking studies, and molecular dynamics methods as a well-reasoned strategy will aid in elucidating the fundamental physicochemical properties and molecular functions of a diversity of compounds, directly accelerating the process of discovering new drugs. In this study, we discovered isomers based on the nitro derivatives of quinoline and quinoline N-oxide, which are biologically active compounds and may be low-cost alternatives for the treatment of infections induced by SARS-CoV-2.

Conformational fingerprints in the modelling performance of MIA-QSAR: a case for SARS-CoV protease inhibitors.

Multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) has proved to be a high-performance 2D tool for drug design purposes. Nonetheless, MIA-QSAR strategy does not efficiently incorporate conformational information. Therefore, understanding the implications of including this type of data into the MIA-QSAR model, in terms of predictability and interpretability, seems a crucial task. Conformational information was included considering the optimised geometries and the docked structures of a series of disulfide compounds potentially useful as SARS-CoV protease inhibitors. The traditional analysis (based on flat-shape molecules) proved itself as the most effective technique, which means that, despite the undeniable importance of conformation for biomolecular behaviour, this type of information did not bring relevant contributions for MIA-QSAR modelling. Consequently, promising drug candidates were proposed on the basis of MIA-plot analyses, which account for PLS regression coefficients and variable importance in projection scores of the MIA-QSAR model.