Synthesis and antibacterial activity of some heterocyclic chalcone analogues alone and in combination with antibiotics

A series of simple heterocyclic chalcone analogues have been synthesized by Claisen Schmidt condensation reactions between substituted benzaldehydes and heteroaryl methyl ketones and evaluated for their antibacterial activity. The structures of the synthesized chalcones were established by IR and 1H-NMR analysis. The biological data shows that compounds p?, f? and t? had strong activities against both susceptible and resistant Staphylococcus aureus strains, but not activity against a vancomycin and methicillin resistant Staphylococcus aureus isolated from a human sample. The structure and activity relationships confirmed that compounds f?, f? and t? are potential candidates for future drug discovery and development.     

Discovery of small molecular inhibitors for interleukin-33/ST2 protein–protein interaction: a virtual screening,molecular dynamics simulations and binding free energy calculations

The interleukin-1 receptor like ST2 has emerged as a potential drug discovery target since it was identified as the receptor of the novel cytokine IL-33, which is involved in many inflammatory and autoimmune diseases. For the treatment of such IL-33-related disorders, efforts have been made to discover molecules that can inhibit the protein–protein interactions (PPIs) between IL-33 and ST2, but to date no drug has been approved. Although several anti-ST2 antibodies have entered clinical trials, the exploration of small molecular inhibitors is highly sought-after because of its advantages in terms of oral bioavailability and manufacturing cost. The aim of this study was to discover ST2 receptor inhibitors based on its PPIs with IL-33 in crystal structure (PDB ID: 4KC3) using virtual screening tools with pharmacophore modeling and molecular docking. From an enormous chemical space ZINC, a potential series of compounds has been discovered with stronger binding affinities than the control compound from a previous study. Among them, four compounds strongly interacted with the key residues of the receptor and had a binding free energy?<????20 kcal/mol. By intensive calculations using data from molecular dynamics simulations, ZINC59514725 was identified as the most potential candidate for ST2 receptor inhibitor in this study.

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Structure-Based Discovery of ABCG2 Inhibitors: A Homology Protein-Based Pharmacophore Modeling and Molecular Docking Approach

ABCG2 is an ABC membrane protein reverse transport pump, which removes toxic substances such as medicines out of cells. As a result, drug bioavailability is an unexpected change and negatively influences the ADMET (absorption, distribution, metabolism, excretion, and toxicity), leading to multi-drug resistance (MDR). Currently, in spite of promising studies, screening for ABCG2 inhibitors showed modest results. The aim of this study was to search for small molecules that could inhibit the ABCG2 pump. We first used the WISS MODEL automatic server to build up ABCG2 homology protein from 655 amino acids. Pharmacophore models, which were con-structed based on strong ABCG2 inhibitors (IC₅₀ < 1 μM), consist of two hydrophobic (Hyd) groups, two hydrogen bonding acceptors (Acc2), and an aromatic or conjugated ring (Aro|PiR). Using molecular docking method, 714 substances from the DrugBank and 837 substances from the TCM with potential to inhibit the ABCG2 were obtained. These chemicals maybe favor synthesized or extracted and bioactivity testing.     

Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds

The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure–activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed good performance on both the diverse training set and the validation sets. The applicability domain and the prediction quality of the developed models were also judged using the state-of-the-art methods and tools. In our structure-based approach, the P-gp structure and its binding region were predicted for a docking study to determine possible interactions between the ligands and the receptor. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening using prediction models and molecular docking in an attempt to restore cancer cell sensitivity to cytotoxic drugs.     

QSAR modeling on benzo[c]phenanthridine analogues as topoisomerase I inhibitors and anti-cancer agents

Benzo[c]phenanthridine (BCP) derivatives were identified as topoisomerase I (TOP-I) targeting agents with pronounced antitumor activity. In this study, hologram-QSAR, 2D-QSAR and 3D-QSAR models were developed for BCPs on topoisomerase I inbibitory activity and cytotoxicity against seven tumor cell lines including RPMI8402, CPT-K5, P388, CPT45, KB3-1, KBV-1and KBH5.0. The hologram, 2D, and 3D-QSAR models were obtained with the square of correlation coefficient R2 = 0.58-0.77, the square of the crossvalidation coefficient q2 = 0.41-0.60 as well as the external set's square of predictive correlation coefficient r2 = 0.5-0.80. Moreover, the assessment method based on reliability test with confidence level of 95% was used to validate the predictive power of QSAR models and to prevent over-fitting phenomenon of classical QSAR models. Our QSAR model could be applied to design new analogues of BCPs with higher antitumor and topoisomerase I inhibitory activity.