Multi-conformational frame from molecular dynamics as a structure-based pharmacophore model for mapping, screening and identifying ligands against PPAR-γ: a new protocol to develop promising candidates

Design, Synthesis, and Biological Evaluation of Novel Quercetin Derivatives as PPAR-γ Partial Agonists by Modulating Epithelial-Mesenchymal Transition in Lung Cancer Metastasis

Epithelial-to-mesenchymal transition (EMT) is responsible for driving metastasis of multiple cancer types including lung cancer. Peroxisome proliferator-activated receptor (PPAR)-γ, a ligand-activated transcription factor, controls expression of variety of genes involved in EMT. Although several synthetic compounds act as potent full agonists for PPAR-γ, their long term application is restricted due to serious adverse effects. Therefore, partial agonists involving reduced and balanced PPAR-γ activity are more effective and valued. A previous study discerned the efficacy of quercetin and its derivatives to attain favorable stabilization with PPAR-γ. Here this work is extended by synthesizing five novel quercetin derivatives (QDs) namely thiosemicarbazone (QUETSC)) and hydrazones (quercetin isonicotinic acid hydrazone (QUEINH), quercetin nicotinic acid hydrazone (QUENH), quercetin 2-furoic hydrazone (QUE2FH), and quercetin salicyl hydrazone (QUESH)) and their effects are analyzed in modulating EMT in lung cancer cell lines via PPAR-γ partial activation. QDs-treated A549 cells diminish cell proliferation strongly at nanomolar concentration compared to NCI-H460 cells. Of the five screened derivatives, QUETSC, QUE2FH, and QUESH exhibit the property of partial activation as compared to the overexpressive level of rosiglitazone. Consistently, these QDs also suppress EMT process by markedly downregulating the levels of mesenchymal markers (Snail, Slug, and zinc finger E-box binding homeobox 1) and concomitant upregulation of epithelial marker (E-cadherin).

Dynamacophore model for breast cancer estrogen receptor alpha as an effective lead generation screening technique

Regardless to overwhelming quantum of cancer research worldwide, there are few drugs on the market to treat disease conditions. This is owing to multiple process inferences of drug targets in integrated pathways for invasion, growth, and metastasis. Over the past years, the death rate due to breast cancer has been increasing, that set the stage for improved better treatment. Therefore, there is a persistent and vital demand for innovative development of drugs to treat breast cancer. Many studies have reported that more than 60% of breast cancers are Estrogen receptor-α (ERα)-positive tumours and a key transcription factor, Estrogen receptor-α (ERα) was believed to promote proliferation of breast cancer cells. In this study, 150 ns of molecular dynamics was performed for protein-ligand complex to retrieve the potential stable conformations. The most populated dynamics cluster of 4-Hydroxytamoxifen intact with active site amino acid was selected to generate dynamacophore model (dynamic pharmacophore). Further, internal model validation with AU-ROC values ∼0.93 indicate the best model to screen library. The refined hits are funnelled in pharmacokinetics/dynamics, CDOCKER molecular docking, MM-GBSA and density functional theory to identify the promising ERα ligand candidates.Communicated by Ramaswamy H. Sarma.

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Structure-based pharmacophore modeling, virtual screening, and molecular dynamics simulation studies for identification of Plasmodium falciparum 5-aminolevulinate synthase inhibitors

Plasmodium falciparum (Pf) 5-aminolevulinic acid synthase (5-ALAS) is an essential enzyme with high selectivity during liver stage development, signifying its potential as a prophylactic antimalarial drug target. The aim of this study was to identify important potential lead compounds which can serve as inhibitors of Pf 5-ALAS using pharmacophore modeling, virtual screening, qualitative structural assessment, in silico ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) evaluation and molecular dynamics simulation. The best model of the tertiary structure of Pf 5-ALAS was obtained using MolProbity, while the following databases were explored for the pharmacophore-based virtual screening: CHEMBL, ChemDiv, ChemSpace, MCULE, MCULE-ULTIMATE, MolPort, NCI Open Chemical Repository, LabNetwork and ZINC databases. 2,621 compounds were screened against the modeled Pf 5-ALAS using AutoDock vina. The post-screening analysis was carried out using Discovery Studio while molecular dynamics simulation was performed on the best hits using NAMD-VMD and Galaxy Europe platform. Compound CSMS00081585868 was observed as the best hit with a binding affinity of -9.9 kcal/mol and predicted Ki of 52.10 nM, engaging in seven hydrogen bonds with the target's active site amino acid residues. The in silico ADMET prediction showed that all ten best hits possessed relatively good pharmacokinetic properties. The qualitative structural assessment of the best hit, CSMS00081585868, revealed that the presence of two pyridine scaffolds bearing hydroxy and fluorine groups linked by a pyrrolidine scaffold contributed significantly to its ability to have a strong binding affinity with the receptor. The best hit also showed stability in the active site of Pf 5-ALAS as confirmed from the RMSD obtained during the MD simulation.

Advances in computer-aided drug design for type 2 diabetes

INTRODUCTION

The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing.

AREAS COVERED

In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies.

EXPERT OPINION

There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.