Identification of a Family of Glycoside Derivatives Biologically Active against Acinetobacter baumannii and Other MDR Bacteria Using a QSPR Model

As the rate of discovery of new antibacterial compounds for multidrug-resistant bacteria is declining, there is an urge for the search for molecules that could revert this tendency. Acinetobacter baumannii has emerged as a highly virulent Gram-negative bacterium that has acquired multiple resistance mechanisms against antibiotics and is considered of critical priority. In this work, we developed a quantitative structure-property relationship (QSPR) model with 592 compounds for the identification of structural parameters related to their property as antibacterial agents against A. baumannii. QSPR mathematical validation (R-2 = 70.27, R-N = -0.008, a (R-2) = 0.014, and delta K = 0.021) and its prediction ability (Q(LMO)(2)= 67.89, Q(EXT)(2) = 67.75, a (Q(2)) = -0.068, delta Q = 0.0, (r(m)(2))over bar = 0.229, and delta r(m)(2) = 0.522) were obtained with different statistical parameters; additional validation was done using three sets of external molecules (R-2 = 72.89, 71.64 and 71.56). We used the QSPR model to perform a virtual screening on the BIOFACQUIM natural product database. From this screening, our model showed that molecules 32 to 35 and 54 to 68, isolated from different extracts of plants of the Ipomoea sp., are potential antibacterials against A. baumannii. Furthermore, biological assays showed that molecules 56 and 60 to 64 have a wide antibacterial activity against clinically isolated strains of A. baumannii, as well as other multidrug-resistant bacteria, including Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, and Pseudomonas aeruginosa. Finally, we propose 60 as a potential lead compound due to its broad-spectrum activity and its structural simplicity. Therefore, our QSPR model can be used as a tool for the investigation and search for new antibacterial compounds against A. baumannii.

Automated summary

As the rate of discovery of new antibacterial compounds for multidrug-resistant bacteria is declining, there is an urge for the search for molecules that could revert this tendency. In this study, a quantitative structure-property relationship (QSPR) model was developed to identify structural parameters related to antibacterial activity against Acinetobacter baumannii. The QSPR model was validated and used for virtual screening of the BIOFACQUIM natural product database, leading to the discovery of potential antibacterials against A. baumannii. Biological assays confirmed the antibacterial activity of some of the identified molecules, highlighting their potential as lead compounds for further development. The QSPR model proved to be a useful tool for the investigation and search for new antibacterial compounds against A. baumannii.