Discovery of novel chemotypes for competitive AMPA receptor antagonists as potential antiepileptic agents through structure-based virtual screening of natural products library

In silico investigation of potential phytoconstituents against ligand- and voltage-gated ion channels as antiepileptic agents

The most promising anticonvulsant phytocompounds were explored in this work using docking, molecular dynamic (MD) simulation, and Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) approaches. A total of 70 phytochemicals were screened against α-amino-3-hydroxyl-5-methyl-4-isoxazole propionic acid (AMPA), N-methyl-d-aspartate (NMDA), voltage-gated sodium ion channels (VGSC), and carbonic anhydrase enzyme II (CA II) receptors, and the docking results were compared to the reference drug phenytoin. Amentoflavone displayed the highest affinity for AMPA and VGSC receptors, with docking scores of - 10.4 and - 10.1 kcal/mol, respectively. Oliganthin H-NMDA and epigallocatechin-3-gallate-CA II complexes showed docking scores of - 10.9 and - 6.9 kcal/mol, respectively. All four complexes depicted a high dock score compared to the phenytoin complex at the binding site of the corresponding proteins. The MD simulation investigated the stabilities and favorable conformation of apoproteins and ligand/reference-bound complexes. The results revealed that proteins AMPA, VGSC, and CA II were more efficiently stabilized by lead phytochemicals than phenytoin binding. Additionally, principal component analysis and MM-PBSA results suggested that these lead phytocompounds have good compactness and strong binding free energy. Further, physicochemical and pharmacokinetic studies revealed that these final lead phytochemicals would be suitable for oral intake, have sufficient intestinal permeability, and have the ability to cross the blood-brain barrier (BBB). Comprehensively, this study predicted amentoflavone as the best lead phytochemical out of the 70 anticonvulsant phytocompounds that can be used to treat epilepsy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03948-1.

Discovery of novel PARP-1 inhibitors using tandem in silico studies: integrated docking, e-pharmacophore, deep learning based de novo and molecular dynamics simulation approach

Cancer accounts for the majority of deaths worldwide, and the increasing incidence of breast cancer is a matter of grave concern. Poly (ADP-ribose) polymerase-1 (PARP-1) has emerged as an attractive target for the treatment of breast cancer as it has an important role in DNA repair. The focus of the study was to identify novel PARP-1 inhibitors using a blend of tandem structure-based screening (Docking and e-pharmacophore-based screening) and artificial intelligence (deep learning)-based de novo approaches. The scrutiny of compounds having good binding characteristics for PARP-1 was carried out using a tandem mode of screening along with parameters such as binding energy and ADME analysis. The efforts afforded compound Vab1 (PubChem ID 129142036), which was chosen as a seed for obtaining novel compounds through a trained artificial intelligence (AI)-based model. Resultant compounds were assessed for PARP-1 inhibition; binding affinity prediction and interaction pattern analysis were carried out using the extra precision (XP) mode of docking. Two best hits, Vab1-b and Vab1-g, exhibiting good dock scores and suitable interactions, were subjected to 100 nanoseconds (ns) of molecular dynamics simulation in the active site of PARP-1 and compared with the reference Protein-Ligand Complex. The stable nature of PARP-1 upon binding to these compounds was revealed through MD simulation.Communicated by Ramaswamy H. Sarma.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.