Novel butyrylcholinesterase inhibitors through pharmacophore modeling, virtual screening and DFT-based approaches along-with design of bioisosterism-based analogues

Unveiling potential repurposed drug candidates for Plasmodium falciparum through in silico evaluation: A synergy of structure-based approaches, structure prediction, and molecular dynamics simulations

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein's crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.

Pharmacophore-based virtual screening and in-silico study of natural products as potential DENV-2 RdRp inhibitors

Dengue fever is a significant public health concern throughout the world, causing an estimated 500,000 hospitalizations and 20,000 deaths each year, despite the lack of effective therapies. The DENV-2 RdRp has been identified as a potential target for the development of new and effective dengue therapies. This research's primary objective was to discover an anti-DENV inhibitor using in silico ligand- and structure-based approaches. To begin, a ligand-based pharmacophore model was developed, and 130 distinct natural products (NPs) were screened. Docking of the pharmacophore-matched compounds were performed to the active site of DENV-2 RdRp protease . Eleven compounds were identified as potential DENV-2 RdRp inhibitors based on docking energy and binding interactions. ADMET and drug-likeness were done to predict their pharmacologic, pharmacokinetic, and drug-likeproperties . Compounds ranked highest in terms of pharmacokinetics and drug-like appearances were then subjected to additional toxicity testing to determine the leading compound. Additionally, MD simulation of the lead compound was performed to confirm the docked complex's stability and the binding site determined by docking. As a result, the lead compound (compound-108) demonstrated an excellent match to the pharmacophore, a strong binding contact and affinity for the RdRp enzyme, favourable pharmacokinetics, and drug-like characteristics. In summary, the lead compound identified in this study could be a possible DENV-2 RdRp inhibitor that may be further studied on in vitro and in vivo models to develop as a drug candidate.Communicated by Ramaswamy H. Sarma.

Network pharmacology based high throughput screening for identification of multi targeted anti-diabetic compound from traditionally used plants

The incurable Type 2 diabetes mellitus (T2DM) has now been considered a pandemic with only supportive care in existence. Due to the adverse effects of available anti-diabetic drugs, there arises a great urgency to develop new drug molecules. One of the alternatives that can be considered for the treatment of T2DM are natural compounds from traditionally used herbal medicine. The present study undertakes, an integrated multidisciplinary concept of Network Pharmacology to evaluate the efficacy of potent anti-diabetic compound from traditionally used anti-diabetic plants of north east India and followed by DFT analysis. In the course of the study, 22 plant species were selected on the basis of their use in traditional medicine for the treatment of T2DM by various ethnic groups of the north eastern region of India. Initially, a library of 1053 compounds derived from these plants was generated. This was followed by network preparation between compounds and targets based on the docking result. The compounds having the best network property were considered for DFT analysis. We have identified that auraptene, a monoterpene coumarin for its activity in the management of Type 2 diabetes mellitus and deciphered its unexplored probable mechanisms. Molecular dynamics simulation of the ligand-protein complexes also reveals the stable binding of auraptene with the target proteins namely, Protein Kinase C θ, Glucocorticoid receptor, 11-β hydroxysteroid dehydrogenase 1 and Aldose Reductase, all of which form uniform interactions throughout the MD simulation trajectory. Therefore, this finding could provide new insights for the development of a new anti-diabetic drug.Communicated by Ramaswamy H. Sarma.

Recent Advances in Virtual Screening for Cholinesterase Inhibitors

Alzheimer's disease (AD) is a significant health crisis, and current treatments provide only limited benefits to cognition at the cost of serious side effects. Recently, virtual screening techniques such as ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) have emerged as powerful drug discovery tools for identifying potential ligands of a biological target from a large database of chemical structures. The cholinesterases are an AD target particularly well suited for drug discovery using virtual screening due to their well-characterized active sites and comprehensive understanding of the structure-activity relationships of existing inhibitors. Over the last 5 years (2015-2020), at least 15 studies have used virtual screening techniques to discover potent new cholinesterase inhibitors. Herein we review how LBVS and SBVS have been applied individually or in tandem to discover novel acetylcholinesterase and butyrylcholinesterase inhibitors for AD, and highlight the need to confirm in vitro activity of screening compounds.

Exploring the Interaction Mechanism of Desmethyl-broflanilide in Insect GABA Receptors and Screening Potential Antagonists by In Silico Simulations

Broflanilide, a novel insecticide, is classified as a negative allosteric modulator (NAM) of insect γ-aminobutyric acid (GABA) receptors (GABARs) as desmethyl-broflanilide (DMBF) allosterically inhibits the GABA-induced responses. The G277M mutation of the Drosophila melanogaster GABAR subunit has been reported to abolish the inhibitory activity of DMBF. The binding mode of DMBF in insect GABARs needs to be clarified to understand the underlying mechanism of this mutation and to develop novel, efficient NAMs of insect GABARs. Here, we found that a hydrogen bond formed between DMBF and G277 of the D. melanogaster GABAR model might be the key interaction for the antagonism of DMBF by in silico simulations. The volume increase induced by the G277M mutation blocks the entrance of the binding pocket, making it difficult for DMBF to enter the binding pocket and thereby decreasing its activity. The following virtual screening and bioassay results identified a novel NAM candidate of insect GABARs. Overall, we reported a possible binding mode of DMBF in insect GABARs and proposed the insensitivity mechanism of the G277M mutant GABAR to DMBF using molecular simulations. The identified NAM candidates might provide more alternatives or potentials for the design of GABAR-targeting insecticides.

A Multi-layered Variable Selection Strategy for QSAR Modeling of Butyrylcholinesterase Inhibitors

Background:

Alzheimer’s disease (AD), a neurological disorder, is the most common cause of senile dementia. Butyrylcholinesterase (BuChE) enzyme plays a vital role in regulating the brain acetylcholine (ACh) neurotransmitter, but in the case of Alzheimer’s disease (AD), BuChE activity gradually increases in patients with a decrease in the acetylcholine (ACh) concentration via hydrolysis. ACh plays an essential role in regulating learning and memory as the cortex originates from the basal forebrain, and thus, is involved in memory consolidation in these sites.

Methods:

In this work, we have developed a partial least squares (PLS)-regression based two dimensional quantitative structure-activity relationship (2D-QSAR) model using 1130 diverse chemical classes of compounds with defined activity against the BuChE enzyme. Keeping in mind the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have tried to select significant descriptors from the large initial pool of descriptors using multi-layered variable selection strategy using stepwise regression followed by genetic algorithm (GA) followed by again stepwise regression technique and at the end best subset selection prior to development of final model thus reducing noise in the input. Partial least squares (PLS) regression technique was employed for the development of the final model while model validation was performed using various stringent validation criteria.

Results:

The results obtained from the QSAR model suggested that the quality of the model is acceptable in terms of both internal (R2= 0.664, Q2= 0.650) and external (R2 Pred= 0.657) validation parameters. The QSAR studies were analyzed, and the structural features (hydrophobic, ring aromatic and hydrogen bond acceptor/donor) responsible for enhancement of the activity were identified. The developed model further suggests that the presence of hydrophobic features like long carbon chain would increase the BuChE inhibitory activity and presence of amino group and hydrazine fragment promoting the hydrogen bond interactions would be important for increasing the inhibitory activity against BuChE enzyme.

Conclusion:

Furthermore, molecular docking studies have been carried out to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. The information obtained from the QSAR models are well corroborated with the results of the docking study.

Pharmacophore-Based Virtual Screening Toward the Discovery of Novel Anti-echinococcal Compounds

Echinococcosis is a serious helminthic zoonosis with a great impact on human health and livestock husbandry. However, the clinically used drugs (benzimidazoles) have a low cure rate, so alternative drugs are urgently needed. Currently, drug screenings for echinococcosis are mainly phenotype-based, and the efficiency of identifying active compounds is very low. With a pharmacophore model generated from the structures of active amino alcohols, we performed a virtual screening to discover novel compounds with anti-echinococcal activity. Sixty-two compounds from the virtual screening were tested on Echinococcus multilocularis protoscoleces, and 10 of these compounds were found to be active. After further evaluation of their cytotoxicity, S6 was selected along with two active amino alcohols for in vivo pharmacodynamic and pharmacokinetic studies. At the two tested doses (50 and 25 mg/kg), S6 inhibited the growth of E. multilocularis in mice (14.43 and 9.53%), but no significant difference between the treatment groups and control group was observed. Treatment with BTB4 and HT3 was shown to be ineffective. During the 28 days of treatment, the death of mice in the mebendazole, HT3, and BTB4 groups indicated their toxicity. The plasma concentration of S6 administered by both methods was very low, with the C_max being only 1 ng/ml after oral administration and below the detection limit after intramuscular administration. In addition, the plasma concentrations of BTB4 and HT3 in vitro did not reach high enough levels to kill the parasites. The toxicities of these two amino alcohols indicated that they are not suitable for further development as anti-echinococcal drugs. However, further attempts should be made to increase the bioavailability of S6 and modify its structure. In this study, we demonstrate that pharmacophore-based virtual screenings with high drug identification efficiency could be used to find novel drugs for treating echinococcosis.

Pharmacophore-based drug design for the identification of novel butyrylcholinesterase inhibitors against Alzheimer's disease

BACKGROUND

Alzheimer's disease is a severe neurodegenerative disease of the central nervous system in the elderly.

HYPOTHESIS/PURPOSE

In our study, we aimed to find the best potential small molecule for AD treatment.

STUDY DESIGN

We used many models in Discovery Studio 2016 to find new potential inhibitors of butyrylcholinesterase (BChE), including pharmacophore model, virtual screening model, molecular docking model, de novo evolution model.

METHODS

Ligand-based pharmacophore models were used to identify the critical chemical features of BChE inhibitors using the module of 3D QSAR Pharmacophore Generation in Discovery Studio 2016. The best pharmacophore model was then validated by cost analysis, Fischer's randomization method, 3D-QSAR Method of the training set and test set. The compounds that match the best pharmacophore model with the predicted activity <1 μM filtered by Lipinski's rule of five were subjected to molecular docking.

RESULT

After virtual screening, 35 compounds filtered by Lipinski's rule of five and ADMET analysis were subjected to molecular docking and then the number were narrowed down on 10 compounds based on -CDOCKER_ENERGY. Finally, we obtained and modified the best potential candidate ENA739155.

CONCLUSION

Ultimately, ENA739155_Evo with -CDOCKER_ENERGY of 47.12, estimate activity of 0.012, fit value of 10.02 could be further subjected to drug development and forwarded as better alternatives to the current batch of medicines used for the treatment of AD.

Integrating pharmacophore mapping, virtual screening, density functional theory, molecular simulation towards the discovery of novel apolipoprotein (apoE ε4) inhibitors

AIM

An integrated protocol of virtual screening involving molecular docking, pharmacophore probing, and simulations was established to identify small novel molecules targeting crucial residues involved in the variant apoE ε4 to mimic its behavior as apoE2 thereby eliminating the amyloid plaque accumulation and facilitating its clearance.

MATERIALS AND METHODS

An excellent ligand-based and structure-based approach was made to identify common pharmacophoric features involving structure-based docking with respect to apoE ε4 leading to the development of apoE ε4 inhibitors possessing new scaffolds. An effort was made to design multiple-substituted triazine derivatives series bearing a novel scaffold. A structure-based pharmacophore mapping was developed to explore the binding sites of apoE ε4 which was taken into consideration. Subsequently, virtual screening, ADMET, DFT searches were at work to narrow down the proposed hits to be forwarded as a potential drug likes candidates. Further, the binding patterns of the best-proposed hits were studied and were forwarded for molecular dynamic simulations of 10 ns for its structural optimization.

RESULTS

Selectivity profile for the most promising candidates was studied, revealing significantly C13 and C15 to be the most potent compounds. The proposed hits can be forwarded for further study against apoE ε4 involved in neurological disorder Alzheimer's.