Pharmacophore model, docking, QSAR, and molecular dynamics simulation studies of substituted cyclic imides and herbal medicines as COX-2 inhibitors

3D-QSAR pharmacophore modeling, virtual screening, molecular docking, MD simulations, in vitro and in vivo studies to identify potential anti-hyperplasia drugs

Psoriasis is a common immune-mediated skin condition characterized by aberrant keratinocytes and cell proliferation. The purpose of this study was to explore the FDA-approved drugs by 3D-QSAR pharmacophore model and evaluate their efficiency by in-silico, in vitro, and in vivo psoriasis animal model. A 3D-QSAR pharmacophore model was developed by utilizing HypoGen algorithm using the structural features of 48 diaryl derivatives with diverse molecular patterns. The model was validated by a test set of 27 compounds, by cost analysis method, and Fischer's randomization test. The correlation coefficient of the best model (Hypo2) was 0.9601 for the training set while it was 0.805 for the test set. The selected model was taken as a 3D query for the virtual screening of over 3000 FDA-approved drugs. Compounds mapped with the pharmacophore model were further screened through molecular docking. The hits that showed the best docking results were screened through in silico skin toxicity approach. Top five hits were selected for the MD simulation studies. Based on MD simulations results, the best two hit molecules, that is, ebastine (Ebs) and mebeverine (Mbv) were selected for in vitro and in vivo antioxidant studies performed in mice. TNF-α and COX pro-inflammatory mediators, biochemical assays, histopathological analyses, and immunohistochemistry observations confirmed the anti-inflammatory response of the selected drugs. Based on these findings, it appeared that Ebs can effectively treat psoriasis-like skin lesions and down-regulate inflammatory responses which was consistent with docking predictions and could potentially be employed for further research on inflammation-related skin illnesses such as psoriasis.

Ligand based pharmacophore modelling and integrated computational approaches in the quest for small molecule inhibitors against hCA IX

Carbonic anhydrase IX is an important biomarker to fight hypoxic tumours in both initial and metastatic stages of many forms of cancer. Overexpression of hCA IX in the hypoxic environment, has an active role in pH maintenance and makes the hCA IX a better target for the inhibitors targeting specific types of cancer stages. Being a member of the carbonic anhydrase family and having sixteen isoforms, it is important to have a selective inhibition of hCA IX to limit the disruption in the biological and metabolic pathways where other isoforms of hCA are localised and to avoid the other toxicity and adverse effects we try to find selective hCA IX inhibitors from a natural derivative. In the process of finding selective hCA inhibitors we developed a pharmacophore model based on existing inhibitors with IC50 values of less than 50 nm, which is then validated with the external decoy set and used for database searching followed by virtual screening to identify the hits based on the pharmacophore fit score and RMSD. Molecular docking studies were performed to identify protein ligand interaction and molecular dynamics simulation studies to analyse the stability of the complex and DFT studies were carried out. The initial screening yielded 43 hits with the RMSD value less than 1, which when subjected to docking exhibited very good interaction with key residues ZN301, HIS94, HIS96 and HIS119. The top 4 compounds in the molecular dynamics simulation studies for 100 ns provided useful insights on the stability of the complex and the DFT studies confirmed the energy variation between HOMO and LUMO is within an acceptable range. An average binding score of -7.8 Kcal mol-1 for the lead compounds and high stability margin in the dynamics study concludes that these lead compounds demonstrated outstanding potential for hCA IX inhibitory action theoretically and that further experimental studies for selective inhibition are inevitable.

Rational design of FXR agonists: a computational approach for NASH therapy

Non-alcoholic fatty liver disease (NAFLD) is a hepatic manifestation of the metabolic syndrome, posing risks to cardiovascular and hepatic health worldwide. Non-alcoholic steatohepatitis (NASH) which is a severe form of NAFLD, has a global prevalence. Therapeutic targets for NASH include THR-β, GLP-1 receptor, PPARα/δ/γ, FGF21 analogs, and FXR, a bile acid nuclear receptor pivotal for regulating bile acid synthesis and excretion. Our study aims to design the non-steroidal FXR agonist for NASH treatment, as FXR's role in the regulation of bile acid processes, rendering it a promising drug target for NASH therapy. Utilizing tropifexor as a reference molecule, we generated a shape-based pharmacophore model with seven features, identifying key binding requirements within the FXR active site. Virtual screening using this model, coupled with molecular docking studies, helped pinpoint potential ligands from diverse small molecule databases. Further analysis via MM/GBSA revealed 12 molecules with binding affinities comparable to tropifexor. Among them, DB15416 exhibited the lowest binding free energy and superior docking scores. To assess its dynamic stability, we subjected DB15416 to molecular dynamics simulations, confirming its suitability as a FXR agonist. These findings suggest that DB15416 holds promise as a FXR agonist for NASH treatment, which can be evaluated by experimental studies.

Rabdosichuanin C inhibits productions of pro-inflammatory mediators regulated by NF-κB signaling in LPS-stimulated RAW264.7 cells

Chronic pharyngitis (CP) is an inflammatory disease of the pharyngeal mucosa and its lymphatic tissues that is difficult to treat clinically. However, research on the exact therapeutic agents and molecular mechanisms of CP is still unclear. In this study, we investigated Rabdosichuanin C (RC) to attenuate lipopolysaccharide (LPS)-induced inflammatory damage in RAW264.7 cells by a combination of targeted virtual screening and in vitro activity assay and further clarified its molecular mechanism of action centering on the IκB/nuclear factor kappa B (NF-κB) pathway. Molecular docking and pharmacophore simulation methods were used to screen compounds with IκB inhibitory effects. Expression of genes and proteins related to the IκB/NF-κB signaling pathway by RC in LPS-induced inflammatory injury model of RAW264.7 cells was detected by PCR, enzyme-linked immunosorbent assay, and Western blot. The docking of RC with IκB protein showed good binding energy, and pharmacophore simulations further confirmed the active effect of RC in inhibiting IκB protein. RC intervention in LPS-induced RAW264.7 cells significantly reduced the expression levels of inflammatory factors tumor necrosis factor-α, interleukins-6, iNOS, and CD-86 at the messenger RNA and protein levels, downregulated IκB, p65 protein phosphorylation levels, and significantly inhibited IκB/NF-κB signaling pathway activation. Virtual screening provided us with an effective method to rapidly identify compounds RC that target inhibit the action of IκB, and the activity results showed that RC inhibits NF-κB signaling pathway activation. It is suggested that RC may play a role in the treatment of CP by inhibiting the IκB/NF-κB signaling pathway.

Identification of novel dual acting ligands targeting the adenosine A2A and serotonin 5-HT1A receptors

GPCRs are a family of transmembrane receptors that are profoundly linked to various neurological disorders, among which is Parkinson's disease (PD). PD is the second most ubiquitous neurological disorder after Alzheimer's disease, characterized by the depletion of dopamine in the central nervous system due to the impairment of dopaminergic neurons, leading to involuntary movements or dyskinesia. The current standard of care for PD is Levodopa, a dopamine precursor, yet the chronic use of this agent can exacerbate motor symptoms. Recent studies have investigated the effects of combining A2AR antagonist and 5-HT1A agonist on dyskinesia and motor complications in animal models of PD. It has been proved that the drug combination has significantly improved involuntary movements while maintaining motor activity, highlighting as a result new lines of therapy for PD treatments, through the regulation of both receptors. Using a combination of ligand-based pharmacophore modelling, virtual screening, and molecular dynamics simulation, this study intends on identifying potential dual-target compounds from IBScreen. Results showed that the selected models displayed good enrichment metrics with a near perfect receiver operator characteristic (ROC) and Area under the accumulation curve (AUAC) values, signifying that the models are both specific and sensitive. Molecular docking and ADMET analysis revealed that STOCK2N-00171 could be potentially active against A2AR and 5-HT1A. Post-MD analysis confirmed that the ligand exhibits a stable behavior throughout the simulation while maintaining crucial interactions. These results imply that STOCK2N-00171 can serve as a blueprint for the design of novel and effective dual-acting ligands targeting A2AR and 5-HT1A.Communicated by Ramaswamy H. Sarma.

Identification of novel curcumin derivatives against pancreatic cancer: a comprehensive approach integrating 3D-QSAR pharmacophore modeling, virtual screening, and molecular dynamics simulations

Pancreatic cancer, known as the "silent killer," poses a daunting challenge in cancer therapy. The dysregulation of the PI3Kα signaling pathway in pancreatic cancer has attracted considerable interest as a promising target for therapeutic intervention. In this regard, the use of curcumin derivatives as inhibitors of PI3Kα has emerged, providing a novel and promising avenue for developing effective treatments for this devastating disease. Computational approaches were employed to explore this potential and investigate 58 curcumin derivatives with cytotoxic activity against the Panc-1 cell line. Our approach involved ligand-based pharmacophore modeling and atom-based 3D-QSAR analysis. The resulting QSAR model derived from the best-fitted pharmacophore hypothesis (AAHRR_1) demonstrated remarkable performance with high correlation coefficients (R2) of 0.990 for the training set and 0.977 for the test set. The cross-validation coefficient (Q2) of 0.971 also validated the model's predictive power. Tropsha's recommended criteria, including the Y-randomization test, were employed to ensure its reliability. Furthermore, an enrichment study was conducted to evaluate the model's performance in identifying active compounds. AAHRR_1 was used to screen a curated PubChem database of curcumin-related compounds. Two molecules (CID156189304 and CID154728220) exhibited promising pharmacokinetic properties and higher docking scores than Alpelisib, warranting further investigation. Extensive molecular dynamics simulations provided crucial insights into the conformational dynamics within the binding site, validating their stability and behavior. These findings contribute to our understanding of the potential therapeutic effectiveness of these compounds as PI3Kα inhibitors in pancreatic cancer.Communicated by Ramaswamy H. Sarma.

Exploring the role of COX-2 in Alzheimer's disease: Potential therapeutic implications of COX-2 inhibitors

This review highlights the potential role of cyclooxygenase-2 enzyme (COX-2) in the pathogenesis of Alzheimer's disease (AD) and the potential therapeutic use of non-steroidal anti-inflammatory drugs (NSAIDs) in the management of AD. In addition to COX-2 enzymes role in inflammation, the formation of amyloid plaques and neurofibrillary tangles in the brain, the review emphasizes that COXs-2 have a crucial role in normal synaptic activity and plasticity, and have a relationship with acetylcholine, tau protein, and beta-amyloid (Aβ) which are the main causes of Alzheimer's disease. Furthermore, the review points out that COX-2 enzymes have a relationship with kinase enzymes, including Cyclin Dependent Kinase 5 (CDK5) and Glycogen Synthase Kinase 3β (GSK3β), which are known to play a role in tau phosphorylation and are strongly associated with Alzheimer's disease. Therefore, the use of drugs like NSAIDs may be a hopeful approach for managing AD. However, results from studies examining the effectiveness of NSAIDs in treating AD have been mixed and further research is needed to fully understand the mechanisms by which COX-2 and NSAIDs may be involved in the development and progression of AD and to identify new therapeutic strategies.

Computer-Aided Designing Peptide Inhibitors of Human Hematopoietic Prostaglandin D2 Synthase Combined Molecular Docking and Molecular Dynamics Simulation

Human hematopoietic prostaglandin D2 synthase (HPGDS) is involved in the production of prostaglandin D2, which participates in various physiological processes, including inflammation, allergic reactions, and sleep regulation. Inhibitors of HPGDS have been investigated as potential anti-inflammatory agents. For the investigation of potent HPGDS inhibitors, we carried out a computational modeling study combining molecular docking and molecular dynamics simulation for selecting and virtual confirming the designed binders. We selected the structure of HPGDS (PDB ID: 2CVD) carrying its native inhibitor compound HQL as our research target. The random 5-mer peptide library was created by building the 3-D structure of random peptides using Rosetta Buildpeptide and performing conformational optimization. Molecular docking was carried out by accommodating the peptides into the location of their native binder and then conducting docking using FlexPepDock. The two peptides RMYYY and VMYMI, which display the lowest binding energy against HPGDS, were selected to perform a comparative study. The interaction of RMYYY and VMYMI against HPGDS was further confirmed using molecular dynamics simulation and aligned with its native binder, HQL. We show the selected binders to have stronger binding energy and more frequent interactions against HPGDS than HQL. In addition, we analyzed the solubility, hydrophobicity, charge, and bioactivity of the generated peptides, and we show that the selected strong binder may be further used as therapeutic drugs.

Anti-Inflammatory Effect of Izalpinin Derived from Chromolaena leivensis: λ-Carrageenan-Induced Paw Edema and In Silico Model

The flavonoid izalpinin was isolated from the aerial parts of Chromolaena leivensis. Its structural determination was carried out using MS and NMR spectroscopic techniques (¹H, ¹³C). This compound was evaluated for its anti-inflammatory effect in a rat model on λ-carrageenan-induced plantar edema. Paw inflammation was measured at one-hour intervals for seven hours following the administration of λ-carrageenan. Serum creatine kinase (CK) levels were evaluated, obtaining statistically significant results with the treatments at doses of 10 mg/kg (* p < 0.01) and 20 mg/kg (** p < 0.005). The anti-inflammatory effect of the compound was evaluated by using plethysmography, and the results showed significant differences at the three concentrations (10 mg/kg, 20 mg/kg, 40 mg/kg) in the first and third hours after treatment. * p < 0.05; ** p < 0.001; **** p < 0.0001 vs. the negative control group treated with vehicle (DMSO). Lastly, molecular docking analyses reveal that izalpinin has a strong binding affinity with five target proteins involved in the inflammatory process. The analysis using molecular dynamics allowed demonstrating that the ligand-protein complexes present acceptable stability, with RMSD values within the allowed range.

LIGHTHOUSE illuminates therapeutics for a variety of diseases including COVID-19

One of the bottlenecks in the application of basic research findings to patients is the enormous cost, time, and effort required for high-throughput screening of potential drugs for given therapeutic targets. Here we have developed LIGHTHOUSE, a graph-based deep learning approach for discovery of the hidden principles underlying the association of small-molecule compounds with target proteins. Without any 3D structural information for proteins or chemicals, LIGHTHOUSE estimates protein-compound scores that incorporate known evolutionary relations and available experimental data. It identified therapeutics for cancer, lifestyle related disease, and bacterial infection. Moreover, LIGHTHOUSE predicted ethoxzolamide as a therapeutic for coronavirus disease 2019 (COVID-19), and this agent was indeed effective against alpha, beta, gamma, and delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are rampant worldwide. We envision that LIGHTHOUSE will help accelerate drug discovery and fill the gap between bench side and bedside.

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LIGHTHOUSE discovers therapeutics solely on the basis of the primary sequence

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The predictions of LIGHTHOUSE against multiple diseases were experimentally correct

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LIGHTHOUSE facilitates optimization of lead compounds as well

Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors via pharmacophore exploration combined with flexible docking-based ligand/receptor contact fingerprints and machine learning

Cdc2-like kinase 4 (CLK4) inhibitors are of potential therapeutic value in many diseases particularly cancer. In this study, we combined extensive ligand-based pharmacophore exploration, ligand–receptor contact fingerprints generated by flexible docking, physicochemical descriptors and machine learning-quantitative structure–activity relationship (ML-QSAR) analysis to investigate the pharmacophoric/binding requirements for potent CLK4 antagonists. Several ML methods were attempted to tie these properties with anti-CLK4 bioactivities including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGBoost), probabilistic neural network (PNN), and support vector regression (SVR). A genetic function algorithm (GFA) was combined with each method for feature selection. Eventually, GFA-SVR was found to produce the best self-consistent and predictive model. The model selected three pharmacophores, three ligand–receptor contacts and two physicochemical descriptors. The GFA-SVR model and associated pharmacophore models were used to screen the National Cancer Institute (NCI) structural database for novel CLK4 antagonists. Three potent hits were identified with the best one showing an anti-CLK4 IC₅₀ value of 57 nM.

Ligand-based pharmacophores, ligand–receptor contact fingerprints, physicochemical descriptors and machine learning were combined to probe binding of potent CLK4 antagonists. GFA-SVR gave the best model. Virtual screening identified 3 nanomolar hits.