Antibiotic-resistant bacteria may result in serious infections which are difficult to treat. In addition, the poor antibiotic pipeline has contributed to the crisis. Recently, a complex of furosemide and silver (Ag-FSE) has been reported as a potential antibacterial agent. However, its poor aqueous solubility is limiting its activity. The purpose of this study was to encapsulate Ag-FSE into chitosan nanoparticles (CSNPs) and evaluate antibacterial efficacy. Ag-FSE CSNPs were prepared using an ionic gelation technique. The particle size, polydispersity index, and zeta potential of Ag-FSE CSNPs were 197.1 ± 3.88 nm 0.234 ± 0.018 and 36.7 ± 1.78 mV, respectively. Encapsulation efficiency was 66.72 ± 4.14%. In vitro antibacterial activity results showed that there was 3- and 6-fold enhanced activity with Ag-FSE CSNPs against E. coli and S. aureus, respectively. Results also confirmed that Ag-FSE CSNPs showed ~44% release within 4 h at pH 5.5 and 6.5. Moreover, release from the CSNPs was sustained with a cumulative release of ~75% over a period of 24 h. In conclusion, encapsulation of Ag-FSE into CSNPs resulted in significant improvement of antibacterial efficacy with a sustained and pH-sensitive release. Therefore, Ag-FSE CSNPs can be considered as a potential novel antibacterial agent against bacterial infections.

Signaling bias is a feature of many G protein-coupled receptor (GPCR) targeting drugs with potential clinical implications. Whether it is therapeutically advantageous for a drug to be G protein biased or β-arrestin biased depends on the context of the signaling pathway. Here, we explored GPCR ligands that exhibit biased signaling to gain insights into scaffolds and pharmacophores that lead to bias. More specifically, we considered BiasDB, a database containing information about GPCR biased ligands, and focused our analysis on ligands which show either a G protein or β-arrestin bias. Five different machine learning models were trained on these ligands using 15 different sets of features. Molecular fragments which were important for training the models were analyzed. Two of these fragments (number of secondary amines and number of aromatic amines) were more prevalent in β-arrestin biased ligands. After training a random forest model on HierS scaffolds, we found five scaffolds, which demonstrated G protein or β-arrestin bias. We also conducted t-SNE clustering, observing correspondence between unsupervised and supervised machine learning methods. To increase the applicability of our work, we developed a web implementation of our models, which can predict bias based on user-provided SMILES, drug names, or PubChem CID. Our web implementation is available at: drugdiscovery.utep.edu/biasnet.

<p>Signaling bias is a feature of many G–protein coupled receptor (GPCR) modulating drugs with clinical implications. Whether it is therapeutically advantageous for a drug to be G Protein biased or <i>β</i>-Arrestin (<i>β</i>-Arr) biased, depends on the context of the signaling pathway. Here, we explored GPCR ligands that exhibit biased signaling to gain insights into scaffolds and pharmacophores that leads to bias. More specifically, we used BiasDB, a database containing information about GPCR biased ligands and all ligands which show a (<i>β</i>-Arr) / G protein bias or a G protein / <i>β</i>-Arr bias are considered for the study. Four machine learning models were trained on these ligands to classify them. The features which were most important for training the models were analyzed. Two of these features (number of secondary amines and number of aromatic amines) were more prevalent in <i>β</i>-Arr biased ligands. After training a Random Forest model on HierS scaffolds, we found five scaffolds which demonstrated G protein or <i>β</i>-Arr bias. We also conducted t-SNE clustering, observing correspondence between unsupervised and supervised machine learning methods. To increase the applicability of our work, we developed a web implementation of our models which can predict bias based on a user-provided SMILES patterns. Our web implementation is available at: drugdiscovery.utep.edu/biasnet.</p>

<p>Signaling bias is a feature of many G–protein coupled receptor (GPCR) modulating drugs with clinical implications. Whether it is therapeutically advantageous for a drug to be G Protein biased or <i>β</i>-Arrestin (<i>β</i>-Arr) biased, depends on the context of the signaling pathway. Here, we explored GPCR ligands that exhibit biased signaling to gain insights into scaffolds and pharmacophores that leads to bias. More specifically, we used BiasDB, a database containing information about GPCR biased ligands and all ligands which show a (<i>β</i>-Arr) / G protein bias or a G protein / <i>β</i>-Arr bias are considered for the study. Four machine learning models were trained on these ligands to classify them. The features which were most important for training the models were analyzed. Two of these features (number of secondary amines and number of aromatic amines) were more prevalent in <i>β</i>-Arr biased ligands. After training a Random Forest model on HierS scaffolds, we found five scaffolds which demonstrated G protein or <i>β</i>-Arr bias. We also conducted t-SNE clustering, observing correspondence between unsupervised and supervised machine learning methods. To increase the applicability of our work, we developed a web implementation of our models which can predict bias based on a user-provided SMILES patterns. Our web implementation is available at: drugdiscovery.utep.edu/biasnet.</p>