Design of vilazodone-donepezil chimeric derivatives as acetylcholinesterase inhibitors by QSAR, molecular docking and molecular dynamics simulations

Alzheimer's disease (AD) is a disease that affects the cognitive abilities of older adults, and it is one of the biggest global medical challenges of the 21st century. Acetylcholinesterase (AChE) can increase acetylcholine concentrations and improve cognitive function in patients, and is a potential target to develop small molecule inhibitors for the treatment of Alzheimer's disease (AD). In this study, 29 vilazodone-donepezil chimeric derivatives are systematically studied using 3D-QSAR modeling, and a robust and reliable Topomer CoMFA model was obtained with: q2 = 0.720, r2 = 0.991, F = 287.234, N = 6, and SEE = 0.098. Based on the established model and combined with the ZINC20 database, 33 new compounds with ideal inhibitory activity are successfully designed. Molecular docking and ADMET property prediction also show that these newly designed compounds have a good binding ability to the target protein and can meet the medicinal conditions. Subsequently, four new compounds with good comprehensive ability are selected for molecular dynamics simulation, and the simulation results confirm that the newly designed compounds have a certain degree of reliability and stability. This study provides guidance for vilazodone-donepezil chimeric derivatives as a potential AChE inhibitor and has certain theoretical value.

Development of antidiabetic drugs from benzamide derivatives as glucokinase activator: A computational approach

Hyperglycemia is a condition known for the impairment of insulin secretion and is responsible for diabetes mellitus. Various small molecule inhibitors have been discovered as glucokinase activators. Recent studies on benzamide derivatives showed their importance in the treatment of diabetes as glucokinase activator. The present manuscript showed a computation study on benzamide derivatives to help in the production of potent glucokinase activators. In the present study, pharmacophore development, 3D-QSAR, and docking studies were performed on benzamide derivatives to find out the important features required for the development of a potential glucokinase activator. The generated pharmacophore hypothesis ADRR_1 consisted of essential features required for the activity. The resultant statistical data showed high significant values with R² > 0.99; 0.98 for the training set and Q² > 0.52; 0.71 for test set based on atom-based and field-based models, respectively. The potent compound 15b of the series showed a good docking score via binding with different amino acid residues such as (NH…ARG63), (SO₂…ARG250, THR65), and π-π staking with (phenyl……TYR214). The virtual screening study used 3563 compounds from ZINC database and screened hit compound ZINC08974524, binds with similar amino acids as shown by compound 15b and crystal ligand with docking scores SP (-11.17 kcal/mol) and XP (-8.43 kcal/mol). Compounds were further evaluated by ADME and MMGBSA parameters. Ligands and ZINC hits showed no violation of Lipinski rules. All the screened compounds showed good synthetic accessibility. The present study may be used by researchers for the development of novel benzamide derivatives as glucokinase activator.

Theoretically exploring selective-binding mechanisms of BRD4 through integrative computational approaches

The origin of cancer is related to the dysregulation of multiple signal pathways and of physiological processes. Bromodomain-containing protein 4 (BRD4) has become an attractive target for the development of anticancer and anti-inflammatory agents since it can epigenetically regulate the transcription of growth-promoting genes. The synthesized BRD4 inhibitors with new chemical structures can reduce the drug resistance, but their binding modes and the inhibitory mechanism remain unclear. Here, we initially constructed robust QSAR models based on 68 reported tetrahydropteridin analogues using topomer CoMFA and HQSAR. On the basis of QSAR results, we designed 16 novel tetrahydropteridin analogues with modified structures and carried out docking studies. Instead of significant hydrogen bondings with amino acid residue Asn140 as reported in previous research, the molecular docking modelling suggested a novel docking pose that involves the amino acid residues (Trp81, Pro82, Val87, Leu92, Leu94, Cys136, Asp144, and Ile146) at the active site of BRD4. The MD simulations, free energy calculations, and residual energy contributions all indicate that hydrophobic interactions are decisive factors affecting bindings between inhibitors and BRD4. The current study provides new insights that can aid the discovery of BRD4 inhibitors with enhanced anti-cancer ability.

Synthesis and biological evaluation of novel purinyl quinazolinone derivatives as PI3Kδ-specific inhibitors for the treatment of hematologic malignancies

Phosphatidylinositol 3-kinases (PI3Ks) mediate intracellular signal transduction. Aberrant PI3K signaling is associated with oncogenesis and disease progression in solid tumors and hematologic malignancies. Idelalisib (1), a first-in-class PI3Kδ inhibitor for the treatment of hematologic malignancies, was developed, but its sales were limited by black box warnings due to unexpected adverse effects. Therefore, to overcome these adverse events, various quinazolinone derivatives were synthesized and evaluated in vitro based on their inhibitory activity against the PI3K enzyme and the viability of cell lines such as MOLT and SUDHL. Among them, 6f (IC₅₀ = 0.39 nM) and 6m (IC₅₀ = 0.09 nM) showed excellent enzyme activity, and 6m displayed an approximately four-fold higher selectivity for PI3Kγ/δ compared with Idelalisib (1). Furthermore, in vivo PK experiments with 6f and 6m revealed that 6f (AUC_last = 81.04 h*ng/mL, C_max = 18.34 ng/mL, T_max = 0.5 h, t_1/2 = 10.2 h in 1 mpk dose) had improved PK compared with 1. Finally, further experiments will be conducted with 6f selected as a candidate, and the potential for it to be developed as a treatment with good efficacy for hematologic malignancies will be determined.

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

The present work aims at establishing multi-target chemometric models using the recently launched quantitative structure–activity relationship (QSAR)-Co tool for predicting the activity of inhibitor compounds against different isoforms of phosphoinositide 3-kinase (PI3K) under various experimental conditions. The inhibitors of class I phosphoinositide 3-kinase (PI3K) isoforms have emerged as potential therapeutic agents for the treatment of various disorders, especially cancer. The cell-based enzyme inhibition assay results of PI3K inhibitors were curated from the CHEMBL database. Factors such as the nature and mutation of cell lines that may significantly alter the assay outcomes were considered as important experimental elements for mt-QSAR model development. The models, in turn, were developed using two machine learning techniques as implemented in QSAR-Co: linear discriminant analysis (LDA) and random forest (RF). Both techniques led to models with high accuracy (ca. 90%). Several molecular fragments were extracted from the current dataset, and their quantitative contributions to the inhibitory activity against all the proteins and experimental conditions under study were calculated. This case study also demonstrates the utility of QSAR-Co tool in solving multi-factorial and complex chemometric problems. Additionally, the combination of different in silico methods employed in this work can serve as a valuable guideline to speed up early discovery of PI3K inhibitors.