Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies

Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules. Further, the molecules were segregated into active and inactive groups through six machine learning (ML) matrices. The active molecules found in each ML model were used for in silico pharmacokinetics and toxicity assessments. A total of five molecules followed the acceptable pharmacokinetics and toxicity profiles. Several potential binding interactions between the proposed molecules and CYP3A5 were observed. The dynamic behavior of the selected molecules in the CYP3A5 was explored through a molecular dynamics (MD) simulation study. Several parameters obtained from the MD simulation trajectory explained the stability of the protein–ligand complexes in dynamic states. The high binding affinity of each molecule was revealed by the binding free energy calculation through the MM-GBSA methods. Therefore, it can be concluded that the proposed molecules might be potential CYP3A5 molecules for therapeutic application in cardiovascular diseases subjected to in vitro/in vivo validations.

Network pharmacology analysis and experimental verification reveal the mechanism of the traditional Chinese medicine YU-Pingfeng San alleviating allergic rhinitis inflammatory responses

YU-Pingfeng San (YPFS) can regulate inflammatory response to alleviate the symptoms of nasal congestion and runny rose in allergic rhinitis (AR). However, the mechanism of action remains unclear. In this study, 30 active ingredients of three effective herbs included in YPFS and 140 AR/YPFS-related genes were identified by database analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the targets were mainly enriched in immune inflammatory-related biological processes and pathways. Finally, three hub gene targeting epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), and protein kinase B1 (AKT1) related to YPFS and AR were identified by network pharmacology analysis. YPFS treatment decreased the expression of EGFR, MAPK1, and AKT1 in ovalbumin (OVA)-induced AR mice and impaired the production of inflammatory factors interleukin (IL)-4, IL-5, and IL-13, thus alleviating immunoglobulin E (IgE) production and the symptoms of scratching nose in AR. Through molecular docking analysis, we found that the active ingredients decursin, anomalin, and wogonin of YPFS could bind to EGFR, MAPK1, and AKT1 proteins. Moreover, decursin treatment impaired the expression of IL-4 and IL-5 in human PBMCs. These results suggested that YPFS could alleviate the AR inflammatory responses by targeting EGFR, MAPK1, and AKT1, showing the mechanism of action of YPFS in AR treatment.

Identification of potential matrix metalloproteinase-2 inhibitors from natural products through advanced machine learning-based cheminformatics approaches

Matrix metalloproteinase-2 (MMP-2) is capable of degrading Collage TypeIV in the vascular basement membrane and extracellular matrix. Studies have shown that MMP-2 is tightly associated with the biological behavior of malignant tumors. Therefore, the identification of inhibitors targeting MMP-2 could be effective in treating the disease by maintaining extracellular matrix homeostasis. In the pharmaceutical and biomedical fields, many computational tools are widely used, which improve the efficiency of the whole process to some extent. Apart from the conventional cheminformatics approaches (e.g., pharmacophore model and molecular docking), virtual screening strategies based on machine learning also have promising applications. In this study, we collected 2871 compound activity data against MMP-2 from the ChEMBL database and divided the training and test sets in a 3:1 ratio. Four machine learning algorithms were then selected to construct the classification models, and the best-performing model, i.e., the stacking-based fusion model with the highest AUC value in both training and test datasets, was used for the virtual screening of ZINC database. Next, we screened 17 potential MMP-2 inhibitors from the results predicted by the machine learning model via ADME/T analysis. The interactions between these compounds and the target protein were explored through molecular docking calculations, and the results showed that ZINC712249, ZINC4270723, and ZINC15858504 had lower binding free energies than the co-crystal ligand. To further examine the binding stability of the complexes, we performed molecular dynamics simulations and finally identified these three hits as the most promising natural products for MMP-2 inhibitors.