Reveal the interaction mechanism of five old drugs targeting VEGFR2 through computational simulations

Discovery of antitumor diterpenoids from Casearia graveolens targeting VEGFR-2 to inhibit angiogenesis

Eight novel clerodane diterpenoids (1-8) were isolated from the twigs of Casearia graveolens. Their structures were elucidated through comprehensive nuclear magnetic resonance (NMR), high-resolution electrospray ionization mass spectrometry (HR-ESI-MS), and electronic circular dichroism (ECD) analyses. In addition to structural determination, surface plasmon resonance (SPR) assays were conducted to investigate molecular interactions, revealing that compound 8 exhibited high affinity for vascular endothelial growth factor receptor 2 (VEGFR2), a key regulator of tumor angiogenesis. Subsequent in vivo experiments demonstrated that compound 8 effectively inhibited angiogenesis and displayed significant antitumor activity by suppressing tumor proliferation and metastasis in zebrafish xenograft models. These findings suggest that compound 8 holds promise as an anticancer lead compound targeting VEGFR-2 to obstruct tumor angiogenesis.

Discovery of Ureido-Substituted 4-Phenylthiazole Derivatives as IGF1R Inhibitors with Potent Antiproliferative Properties

The existing kinase inhibitors for hepatocellular carcinoma (HCC) have conferred survival benefits but are hampered by adverse effects and drug resistance, necessitating the development of novel agents targeting distinct pathways. To discover potent new anti-HCC compounds, we leveraged scaffold hopping from Sorafenib and introduced morpholine/piperidine moieties to develop ureido-substituted 4-phenylthiazole analogs with optimized physicochemical properties and binding interactions. Notably, compound 27 exhibited potent cytotoxicity against HepG2 cells (IC50 = 0.62 ± 0.34 μM), significantly exceeding Sorafenib (IC50 = 1.62 ± 0.27 μM). Mechanistic investigations revealed that compound 27 potently inhibited HCC cell migration and colony formation, and it induced G2/M arrest and early-stage apoptosis. Kinase profiling revealed IGF1R as a key target, which compound 27 potently inhibited (76.84% at 10 μM). Molecular modeling substantiated compound 27's strong binding to IGF1R via multiple hydrogen bonds. Computational predictions indicate favorable drug-like properties for compound 27. These findings provide a promising drug candidate for the treatment of HCC patients.

Exploring novel lead scaffolds for SGLT2 inhibitors: Insights from machine learning and molecular dynamics simulations

Sodium-glucose cotransporter 2 (SGLT2) plays a pivotal role in mediating glucose reabsorption within the renal filtrate, representing a well-known target in type 2 diabetes and heart failure. Recent emphasis has been directed toward designing SGLT2 inhibitors, with C-glycoside inhibitors emerging as front-runners. The architecture of SGLT2 has been successfully resolved using cryo-electron microscopy. However, comprehension of the pharmacophores within the binding site of SGLT2 remains unclear. Here, we use machine learning and molecular dynamics simulations on SGLT2 bound with its inhibitors in preclinical or clinical development to shed light on this issue. Our dataset comprises 1240 SGLT2 inhibitors amalgamated from diverse sources, forming the basis for constructing machine learning models. SHapley Additive exPlanation (SHAP) elucidates the crucial fragments that contribute to inhibitor activity, specifically Morgan_3, 162, 310, 325, 366, 470, 597, 714, 926, and 975. Furthermore, the computed binding free energies and per-residue contributions for SGLT2-inhibitor complexes unveil crucial fragments of inhibitors that interact with residues Asn-75, His-80, Val-95, Phe-98, Val-157, Leu-274, and Phe-453 in the binding site of SGLT2. This comprehensive investigation enhances understanding of the binding mechanism for SGLT2 inhibitors, providing a robust framework for evaluating and discovering novel lead scaffolds within this domain.

Discovery of Mycobacterium tuberculosis CYP121 New Inhibitor via Structure-based Drug Repurposing

Tuberculosis (TB) remains a serious threat to human health with the advent of multi-drug resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB). The urge to find novel drugs to deal with the appearance of drug-resistant TB and its variants is highly needed. This study aims to find new CYP121 inhibitors by screening 8,773 compounds from the drug repositioning database RepoDB. The selection of CYP121 potential inhibitors was based on two criteria: the new inhibitor should bind to CYP121 with higher affinity than its original ligand and interact with catalytically important residues for the function of CYP121. The ligands were docked onto CYP121 using AutoDock Vina, and the molecular dynamics simulation of the selected ligand was conducted using YASARA Structure. We found that antrafenine, an anti-inflammatory and analgesic agent with high CYP inhibitory promiscuity, was bound to CYP121 with a binding affinity of -12.6 kcal/mol and interacted with important residues at the CYP121 binding site. Molecular dynamics analysis of CYP121 bound to the original ligand and antrafenine showed that both ligands affected the dynamics of residues located distantly from the active site. Antrafenine caused more structural changes to CYP121 than the original ligand, as indicated by a significantly higher number of affected residues and rigid body movements caused by the binding of antrafenine to CYP121.

Identification of Anticancer and Anti-inflammatory Drugs from Drug-target Interaction Descriptors by Machine Learning

Background:

Drug repositioning is an important subject in drug-disease research. In the past, most studies simply used drug descriptors as the feature vector to classify drugs or targets, or used qualitative data about drug-target or drug-disease to predict drug-target interactions. These data provide limited information for drug repositioning.

Objective:

Considering both drugs and targets and constructing quantitative drug-target interaction descriptors as a method of drug characteristics are of great significance to the study of drug repositioning.

Methods:

Taking anticancer and anti-inflammatory drugs as research objects, the interaction sites between drugs and targets were determined by molecular docking. Sixty-seven drug-target interaction descriptors were calculated to describe the drug-target interactions, and 22 important descriptors were screened for drug classification by SVM, LightGBM and MLP.

Results:

The accuracy of SVM, LightGBM and MLP reached 93.29%, 92.68% and 94.51%, their Matthews correlation coefficients reached 0.852, 0.840 and 0.882, and their areas under the ROC curve reached 0.977, 0.969 and 0.968, respectively.

Conclusion:

Using drug-target interaction descriptors to build machine learning models can obtain better results for drug classification. Number of atom pairs, force field, hydrophobic interactions and bSASA are the four types of key features for the classification of anticancer and anti-inflammatory drugs.

Identification of novel allosteric binding sites and multi-targeted allosteric inhibitors of receptor and non-receptor tyrosine kinases using a computational approach

EGFR1, VEGFR2, Bcr-Abl and Src kinases are key drug targets in non-small cell lung cancer (NSCLC), bladder cancer, pancreatic cancer, CML, ALL, colorectal cancer, etc. The available drugs targeting these kinases have limited therapeutic efficacy due to novel mutations resulting in drug resistance and toxicity, as they target ATP binding site. Allosteric drugs have shown promising results in overcoming drug resistance, but the discovery of allosteric drugs is challenging. The allosteric binding pockets are difficult to predict, as they are generally associated with high energy conformations and regulate protein function in yet unknown mechanisms. In addition, the discovery of drugs using conventional methods takes long time and goes through several challenges, putting the lives of many cancer patients at risk. Therefore, the aim of the present work was to apply the most successful, drug repurposing approach in combination with computational methods to identify kinase inhibitors targeting novel allosteric sites on protein structure and assess their potential multi-kinase binding affinity. Multiple crystal structures belonging to EGFR1, VEGFR2, Bcr-Abl and Src tyrosine kinases were selected, including mutated, inhibitor bound and allosteric conformations to identify potential leads, close to physiological conditions. Interestingly the potential inhibitors identified were peptides. The drugs identified in this study could be used in therapy as a single multi-kinase inhibitor or in a combination of single kinase inhibitors after experimental validation. In addition, we have also identified new hot spots that are likely to be druggable allosteric sites for drug discovery of kinase-specific drugs in the future.Communicated by Ramaswamy H. Sarma.