Microbes are hardly seen as planktonic species and are most commonly found as biofilm communities in cases of chronic infections. Biofilms are regarded as a biological condition, where a large group of microorganisms gets adhered to a biotic or abiotic surface. In this context, Pseudomonas aeruginosa, a Gram-negative nosocomial pathogen is the main causative organism responsible for life-threatening and persistent infections in individuals affected with cystic fibrosis and other lung ailments. The bacteria can form a strong biofilm structure when it adheres to a surface suitable for the development of a biofilm matrix. These bacterial biofilms pose higher natural resistance to conventional antibiotic therapy due to their multiple tolerance mechanisms. This prevailing condition has led to an increasing rate of treatment failures associated with P. aeruginosa biofilm infections. A better understanding of the effect of a diverse group of antibiotics on established biofilms would be necessary to avoid inappropriate treatment strategies. Hence, the search for other alternative strategies as effective biofilm treatment options has become a growing area of research. The current review aims to give an overview of the mechanisms governing biofilm formation and the different strategies employed so far in the control of biofilm infections caused by P. aeruginosa. Moreover, this review can also help researchers to search for new antibiofilm agents to tackle the effect of biofilm infections that are currently imprudent to conventional antibiotics.

Pseudomonas aeruginosa, an opportunistic human pathogen, causes fatal effects in patients with cystic fibrosis and immunocompromised individuals and leads to around 1000 deaths annually. The quorum sensing mechanism of P. aeruginosa plays a major role in promoting biofilm formation and expression of virulent genes. Hence, quorum sensing inhibition is a promising novel approach to treat these bacterial infections as these organisms show a wide range of antibiotic resistance. Among the interconnected quorum sensing network of P. aeruginosa, targeting the las system is of increased interest as its principal receptor protein LasR is the earliest activated gene. It is also shown to be involved in the regulation of other virulence-associated genes. In this study, we have applied high-throughput virtual screening, an in silico computational method to identify a new class of LasR inhibitors that could serve as potent antagonists to treat P. aeruginosa-associated infections. Three-tire structure-based virtual screening was performed on the Schrödinger small molecule database, which resulted in 12 top hit compounds with docking scores lesser than −11.0 kcal/mol. Three of these best-scored compounds CACPD2011a-0001928786 (C1), CACPD2011a-0001927437 (C2), and CACPD2011a-0000896051 (C3) were further analyzed. The binding free energies of these compounds in complex with the target protein LasR (3IX4) were evaluated, and the pharmacokinetic properties were determined. The stability of the docked complexes was assessed by running a molecular dynamics simulation for 100 ns. Molecular dynamics simulation analysis revealed that all three compounds were found to be in stable contact with the protein over the entire simulation period. The antagonistic effect of these compounds was validated using the LasR reporter gene assay in the presence of acyl homoserine lactone. Significant reduction in the β-galactosidase enzyme activity was achieved at 100 nM concentration for all three compounds pursued. Hence, the present study provides strong evidence that these three compounds could serve as quorum sensing inhibitors of P. aeruginosa LasR protein and can be a probable candidate to treat Pseudomonas-associated infections.

Gastric cancer (GC) is an aggressive malignancy with increased mortality rate and low treatment options. Increasing evidence suggests that network pharmacology will be a novel method for identifying the systemic mechanism of therapeutic compounds in diseases like cancer. The current study aimed to use a network pharmacology approach to establish the predictive targets of prunetin-5-O-glucoside (PG) against gastric cancer and elucidate its biological mechanisms. Primarily, genes associated with the pathogenesis of GC was identified from the DiGeNET database and targets of PG was obtained from the Swiss target prediction database. In total, 65 correlative hits were identified as anti-gastric cancer targets of PG. Functional enrichment and pathway analysis revealed significant biological mechanisms of the targets. Interaction of protein network and cluster analysis using STRING resulted in three crucial interacting hub targets namely, HSP90AA1, CDK2, and MMP1. Additionally, the in vitro cytotoxic potential of PG was assessed on three gastric cancer cells (AGS, MKN-28, and SNU-484). Furthermore, the crucial targets were validated using molecular docking, followed by their expressions being evaluated by western blot and Human Protein Atlas. The findings indicate that the pharmacological action of PG against GC might be associated with the regulation of three core targets: HSP90AA1, CDK2, and MMP1. Thus, the network pharmacology undertaken in the current study established the core active targets of PG, which may be extensively applied with further validations for treatment in GC.

Pseudomonas aeruginosa, a virulent pathogen affects patients with cystic fibrosis and nosocomial infections. Quorum sensing (QS) mechanism plays a crucial role in causing these ailments by mediating biofilm formation and expressing virulent genes. A novel approach to circumvent this bacterial infection is by hindering its QS network. Targeting LasR of las system serves beneficial as it holds the top position in QS system cascade. Here, we have integrated machine learning, pharmacophore based virtual screening, molecular docking and simulation studies to look for new leads as inhibitors for LasR. Support vector machine (SVM) learning algorithm was used to generate QSAR models from 66 antagonist dataset. The top three models resulted in correlation coefficient (R²) values of 0.67, 0.86, and 0.91, respectively. The correlation coefficient (R²_test) values on external test set were found to be 0.62, 0.57, and 0.55, respectively. A four-point pharmacophore model was developed. The pharmacophore hypothesis AAAD_1 was used to screen for potential leads against MolPort database in ZincPharmer. The leads which showed predicted pIC50 value of >8.00 by SVM models were subjected to docking analysis that reranked the compounds based on docking scores. Four top leads namely ZINC3851967 N-[3,5-bis(trifluoromethyl)phenyl]-5-tert-butyl-6-chloropyrazine-2-carboxamide, ZINC4024175 4-Amino-1-[(2R,3S,4S,5S)-3,4-dihydroxy-5-(hydroxymethyl)oxolan-2-yl]-2-oxopyrimidine-5-carbonitrile, ZINC2125703 N-[(5-Methoxy-4,7-dimethyl-2-oxo-2H-chromen-3-yl)acetyl]-beta-alanine, and ZINC3851966 N-[3,5-Bis(trifluoromethyl)phenyl]5-tert-butylpyrazine-2-carboxamide were selected. These compounds were checked for its stability by performing a molecular dynamics simulation for a period of 100 ns. The ADME properties of the leads were also determined. Hence, the compounds identified in this study can be used as possible leads for developing a novel inhibitor for LasR. Communicated by Ramaswamy H. Sarma