Identification of Human Leukotriene A4 Hydrolase Inhibitors Using Structure-Based Pharmacophore Modeling and Molecular Docking

Exploring the Antibacterial Potential of Artemisia judaica Compounds Targeting the Hydrolase/Antibiotic Protein in Klebsiella pneumoniae: In Vitro and In Silico Investigations

Carbapenem antibiotic resistance is an emerging medical concern. Bacteria that possess the Klebsiella pneumoniae carbapenemase (KPC) protein, an enzyme that catalyzes the degradation of carbapenem antibiotics, have exhibited remarkable resistance to traditional and even modern therapeutic approaches. This study aimed to identify potential natural drug candidates sourced from the leaves of Artemisia judaica (A. judaica). The phytoconstituents present in A. judaica dried leaves were extracted using ethanol 80%. A reasonable amount of the extract was used to identify these phytochemicals via gas chromatography/mass spectrometry (GC/MS). One hundred twenty-two bioactive compounds from A. judaica were identified and subjected to docking analysis against the target bacterial protein. Four compounds (PubChem CID: 6917974, 159099, 628694, and 482788) were selected based on favorable docking scores (-9, -7.8, -7.7, and -7.5 kcal/mol). This computational investigation highlights the potential of these four compounds as promising antibacterial candidates against the specific KPC protein. Additionally, in vitro antibacterial assays using A. judaica extracts were conducted. The minimum inhibitory concentration (MIC) against the bacterium K. pneumonia was 125 μg/mL. Well-disk diffusion tests exhibited inhibition zones ranging from 10.3 ± 0.5 mm to 17 ± 0.5 mm at different concentrations, and time-kill kinetics at 12 h indicated effective inhibition of bacterial growth by A. judaica leaf extracts. Our findings have revealed the pharmaceutical potential of Artemisia judaica as a natural source for drug candidates against carbapenem-resistant pathogens.

Current Status of the Use of Multifunctional Enzymes as Anti-Cancer Drug Targets

Fighting cancer is one of the major challenges of the 21st century. Among recently proposed treatments, molecular-targeted therapies are attracting particular attention. The potential targets of such therapies include a group of enzymes that possess the capability to catalyze at least two different reactions, so-called multifunctional enzymes. The features of such enzymes can be used to good advantage in the development of potent selective inhibitors. This review discusses the potential of multifunctional enzymes as anti-cancer drug targets along with the current status of research into four enzymes which by their inhibition have already demonstrated promising anti-cancer effects in vivo, in vitro, or both. These are PFK-2/FBPase-2 (involved in glucose homeostasis), ATIC (involved in purine biosynthesis), LTA₄H (involved in the inflammation process) and Jmjd6 (involved in histone and non-histone posttranslational modifications). Currently, only LTA₄H and PFK-2/FBPase-2 have inhibitors in active clinical development. However, there are several studies proposing potential inhibitors targeting these four enzymes that, when used alone or in association with other drugs, may provide new alternatives for preventing cancer cell growth and proliferation and increasing the life expectancy of patients.

Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods

Leukotriene A4 hydrolase (LTA4H) is an important anti-inflammatory target which can convert leukotriene A4 (LTA4) into pro-inflammatory substance leukotriene B4 (LTB4). In this paper, we built 18 classification models for 463 LTA4H inhibitors by using support vector machine (SVM), random forest (RF) and K-Nearest Neighbour (KNN). The best classification model (Model 2A) was built from RF and MACCS fingerprints. The prediction accuracy of 88.96% and the Matthews correlation coefficient (MCC) of 0.74 had been achieved on the test set. We also divided the 463 LTA4H inhibitors into six subsets using K-Means. We found that the highly active LTA4H inhibitors mostly contained diphenylmethane or diphenyl ether as the scaffold and pyridine or piperidine as the side chain. In addition, six quantitative structure-activity relationship (QSAR) models for 172 LTA4H inhibitors were built by multiple linear regression (MLR) and SVM. The best QSAR model (Model 6A) was built by using SVM and CORINA Symphony descriptors. The coefficients of determination of the training set and the test set were equal to 0.81 and 0.79, respectively. Classification and QSAR models could be used for subsequent virtual screening, and the obtained fragments that were important for highly active inhibitors would be helpful for designing new LTA4H inhibitors.