3D QSAR Pharmacophore Based Virtual Screening for Identification of Potential Inhibitors for CDC25B

Unveiling Arformoterol as a potent LSD1 inhibitor for breast cancer treatment: A comprehensive study integrating 3D-QSAR pharmacophore modeling, molecular docking, molecular dynamics simulations and in vitro assays

Lysine Specific Demethylase 1 (LSD1) has been identified as a chromatin-modifying enzyme implicated in various cancer pathogeneses, highlighting the potential for novel epigenetic cancer treatments through the development of effective inhibitors. We employed 3D-QSAR pharmacophore modeling, molecular docking, and molecular dynamics simulations to identify a promising drug candidate for LSD1 inhibition. RMSD, RMSF, H-bond, and DSSP analysis demonstrated that ZINC02599970 (Arformoterol) and ZINC13453966 exhibited the highest LSD1 inhibitory potential. Experimental validation using MCF-7 and MDA-MB-231 cell lines revealed that Arformoterol displayed potent antiproliferative activity with IC50 values of 12.30 ± 1.48 μM and 19.69 ± 1.15 μM respectively. In contrast, the IC50 values obtained for the control (tranylcypromine) in exposure to MCF-7 and MDA-MB-231 cells were 104.6 ± 1.69 μM and 77 ± 0.67 μM, respectively. Arformoterol demonstrated greater LSD1 inhibitory potency in MCF-7 cells compared to MDA-MB-231 cells. Also, the expression of genes involved in chromatin rearrangement (LSD1), angiogenesis (VEGF1), cell migration (RORα), signal transduction (S100A8), apoptosis, and cell cycle (p53) were investigated. Arformoterol enhanced apoptosis and induced cell cycle arrest at the G2/M phase, both in MCF-7 and MDA-MB-231 cancer cells. Based on our findings, we propose that Arformoterol represents a promising candidate for breast cancer treatment, owing to its potent LSD1 inhibitory activity.

Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.

Lansoprazole as a potent HDAC2 inhibitor for treatment of colorectal cancer: An in-silico analysis and experimental validation

BACKGROUND

Histone deacetylase 2 (HDAC2), belonging to the class I HDAC family, holds significant therapeutic potential as a crucial target for diverse cancer types. As key players in the realm of epigenetic regulatory enzymes, histone deacetylases (HDACs) are intricately involved in the onset and progression of cancer. Consequently, pursuing isoform-specific inhibitors targeting histone deacetylases (HDACs) has garnered substantial interest in both biological and medical circles. The objective of the present investigation was to employ a drug repurposing approach to discover novel and potent HDAC2 inhibitors.

MATERIALS AND METHODS

In this study, our protocol is presented on virtual screening to identify novel potential HDAC2 inhibitors through 3D-QSAR, molecular docking, pharmacophore modeling, and molecular dynamics (MD) simulation. Afterward, In-vitro assays were employed to evaluate the cytotoxicity, apoptosis, and migration of HCT-116 cell lines under treatment of hit compound and valproic acid as a control inhibitor. The expression levels of HDAC2, TP53, BCL2, and BAX were evaluated by QRT-PCR.

RESULTS

RMSD, RMSF, H-bond, and DSSP analysis results confirmed that among bioinformatically selected compounds, lansoprazole exhibited the highest HDAC2 inhibitory potential. Experimental validation revealed that lansoprazole displayed significant antiproliferative activity. The determined IC50 value was 400 ± 2.36 μM. Furthermore, the apoptotic cells ratio concentration-dependently increased under Lansoprazole treatment. Results of the Scratch assay indicated that lansoprazole led to decreasing the migration of CRC cells. Finally, under Lansoprazole treatment the expression level of BCL2 and HDAC2 decreased and BAX and TP53 increased.

CONCLUSION

Taking together the results of the current study indicated that Lansoprazole as a novel HDAC2 inhibitor, could be used as a potential therapeutic agent for the treatment of CRC. Although, further experimental studies should be performed before using this compound in the clinic.

3D QSAR pharmacophore based lead identification of G9a lysine methyltransferase towards epigenetic therapeutics

The G9a, Lysine Methyltransferase that methylates the histone 3 lysine 9 (H3K9) of the nucleosome, is an excellent epigenetic target having no clinically passed inhibitor currently owing to adverse in vivo ADMET toxicities. In this work, we have carried out detailed computational investigations to find novel and safer lead against the target using advanced 3 D QSAR pharmacophore screening of databases containing more than 400000 entrees of natural compounds. The screening was conducted at different levels at increasing stringencies by employing pharmacophore mapping, druglikenesses and interaction profiles of the selected to identify potential hit compounds. The potential hits were further screened by advanced flexible docking, ADME and toxicity analysis to eight hit compounds. Based on the comparative analysis of the hits with the reference inhibitor, we identified one lead inhibitor against the G9a, having better binding efficacy and a safer ADMET profile than the reference inhibitor. Finally, the results were further verified using robust molecular dynamics simulation and MM-GBSA binding energy calculation. The natural compounds are generally considered benign due to their long human uses and this is the first attempt of in silico screening of a large natural compound library against G9a to our best knowledge. Therefore, the finding of this study may add value towards the development of epigenetic therapeutics against the G9a.Communicated by Ramaswamy H. Sarma.

Discovery of novel carboxylesterase 2 inhibitors for the treatment of delayed diarrhea and ulcerative colitis

Human carboxylesterase 2 (hCES2) is an enzyme that metabolizes irinotecan to SN-38, a toxic metabolite considered a significant source of side effects (lethal delayed diarrhea). The hCES2 inhibitors could block the hydrolysis of irinotecan in the intestine and thus reduce the exposure of intestinal SN-38, which may alleviate irinotecan-associated diarrhea. However, existing hCES2 inhibitors (except loperamide) are not used in clinical applications due to lack of validity or acceptable safety. Therefore, developing more effective and safer drugs for treating delayed diarrhea is urgently needed. This study identified a lead compound 1 with a novel scaffold by high-throughput screening in our in-house library. After a comprehensive structure-activity relationship study, the optimal compound 24 was discovered as an efficient and highly selective hCES2 inhibitor (hCES2: IC50 = 6.72 μM; hCES1: IC50 > 100 μM). Further enzyme kinetics study indicated that compound 24 is a reversible inhibitor of hCES2 with competitive inhibition mode (Ki = 6.28 μM). The cell experiments showed that compound 24 could reduce the level of hCES2 in living cells (IC50 = 6.54 μM). The modeling study suggested that compound 24 fitted very well with the binding pocket of hCES2 by forming multiple interactions. Notably, compound 24 can effectively treat irinotecan-induced delayed diarrhea and DSS-induced ulcerative colitis, and its safety has also been verified in subtoxic studies. Based on the overall pharmacological and preliminary safety profiles, compound 24 is worthy of further evaluation as a novel agent for irinotecan-induced delayed diarrhea.

Design, synthesis, and biological evaluation studies of novel carboxylesterase 2 inhibitors for the treatment of irinotecan-induced delayed diarrhea

Human carboxylesterase 2 (hCES2A), one of the most important serine hydrolases distributed in the small intestine and colon, plays a crucial role in the hydrolysis of various prodrugs and esters. Accumulating evidence has demonstrated that the inhibition of hCES2A effectively alleviate the side effects induced by some hCES2A-substrate drugs, including delayed diarrhea caused by the anticancer drug irinotecan. Nonetheless, there is a scarcity of selective and effective inhibitors that are suitable for irinotecan-induced delayed diarrhea. Following screening of the in-house library, the lead compound 01 was identified with potent inhibition on hCES2A, which was further optimized to obtain LK-44 with potent inhibitory activity (IC50 = 5.02 ± 0.67 μM) and high selectivity on hCES2A. Molecular docking and molecular dynamics simulations indicated that LK-44 can formed stable hydrogen bonds with amino acids surrounding the active cavity of hCES2A. The results of inhibition kinetics studies unveiled that LK-44 inhibited hCES2A-mediated FD hydrolysis in a mixed inhibition manner, with a Ki value of 5.28 μM. Notably, LK-44 exhibited low toxicity towards HepG2 cells according to the MTT assay. Importantly, in vivo studies showed that LK-44 significantly reduced the side effects of irinotecan-induced diarrhea. These findings suggested that LK-44 is a potent inhibitor of hCES2A with high selectivity against hCES1A, which has potential as a lead compound for the development of more effective hCES2A inhibitors to mitigate irinotecan-induced delayed diarrhea.

3D QSAR based Virtual Screening of Flavonoids as Acetylcholinesterase Inhibitors

In an attempt to develop therapeutic agents to treat Alzheimer's disease, a series of flavonoid analogues were collected, which already had established acetylcholinesterase (AChE) enzyme inhibition activity. For each molecule we also collected biological activity data (Ki). Then, 3D-QSAR (quantitative structure-activity relationship model) was developed which showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 and q2. This SAR data can explain the key descriptors which can be related to AChE inhibitory activity. Using the QSAR model, pharmacophores were developed based on which, virtual screening was done and a dataset was obtained which loaded as a prediction set to fit the developed QSAR model. Top 10 compounds fitting the QSAR model were subjected to molecular docking. CHEMBL1718051 was found to be the lead compound. This study is offering an example of a computationally-driven tool for prioritisation and discovery of probable AChE inhibitors. Further, in vivo and in vitro testing will show its therapeutic potential.

Applications of the Novel Quantitative Pharmacophore Activity Relationship Method QPhAR in Virtual Screening and Lead-Optimisation

Pharmacophores are an established concept for the modelling of ligand–receptor interactions based on the abstract representations of stereoelectronic molecular features. They became widely popular as filters for the fast virtual screening of large compound libraries. A lot of effort has been put into the development of sophisticated algorithms and strategies to increase the computational efficiency of the screening process. However, hardly any focus has been put on the development of automated procedures that optimise pharmacophores towards higher discriminatory power, which still has to be done manually by a human expert. In the age of machine learning, the researcher has become the decision-maker at the top level, outsourcing analysis tasks and recurrent work to advanced algorithms and automation workflows. Here, we propose an algorithm for the automated selection of features driving pharmacophore model quality using SAR information extracted from validated QPhAR models. By integrating the developed method into an end-to-end workflow, we present a fully automated method that is able to derive best-quality pharmacophores from a given input dataset. Finally, we show how the QPhAR-generated models can be used to guide the researcher with insights regarding (un-)favourable interactions for compounds of interest.

Computational simulation studies on the binding selectivity of Wee1 and Checkpoint kinase 1 by molecular dynamics simulation combined with free energy calculations

Wee1 kinase and Checkpoint kinase 1 (Chk1) kinase, which are well known to be involved in cancer, are promising targets for cancer therapy. Most of developed Wee1 inhibitors can inhibit activity of Chk1 kinase to different degrees as well. The poor selectivity brought side effects and selective inhibitor is needed. However, the selective mechanisms of Wee1 versus Chk1 are not clear. Therefore, the design of selective Wee1 and Chk1 inhibitors would provide a meaningful starting for the development of anticancer drugs with optimal efficacy. In this study, Wee1 inhibitors with different selectivity over Chk1 were chosen to analyze the selectivity mechanism by means of molecular docking, molecular dynamics simulations and binding free energy calculations. Two key residues of Wee1 kinase and two critical residues of Chk1 were mutated to detect their effect on ligand binding into protein. The results indicated that these residues play a pivotal role in the binding interactions of ligands to receptors through hydrogen bond and hydrophobic interaction with inhibitors. This may provide a better understanding of the selective mechanism of Wee1 and Chk1. It would be beneficial to the discovery and optimization of selective Wee1 and Chk1 inhibitors.Communicated by Ramaswamy H. Sarma.

The identification of novel inhibitors of human angiotensin-converting enzyme 2 and main protease of Sars-Cov-2: A combination of in silico methods for treatment of COVID-19

The angiotensin-converting enzyme 2 (ACE2) and main protease (MPro), are the putative drug candidates for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we performed 3D-QSAR pharmacophore modeling and screened 1,264,479 ligands gathered from Pubchem and Zinc databases. Following the calculation of the ADMET properties, molecular docking was carried out. Moreover, the de novo ligand design was performed. MD simulation was then applied to survey the behavior of the ligand-protein complexes. Furthermore, MMPBSA has utilized to re-estimate the binding affinities. Then, a free energy landscape was used to find the most stable conformation of the complexes. Finally, the hybrid QM-MM method was carried out for the precise calculation of the energies. The Hypo1 pharmacophore model was selected as the best model. Our docking results indicate that the compounds ZINC12562757 and 112,260,215 were the best potential inhibitors of the ACE2 and MPro, respectively. Furthermore, the Evo_1 compound enjoys the highest docking energy among the designed de novo ligands. Results of RMSD, RMSF, H-bond, and DSSP analyses have demonstrated that the lead compounds preserve the stability of the complexes' conformation during the MD simulation. MMPBSA data confirmed the molecular docking results. The results of QM-MM showed that Evo_1 has a stronger potential for specific inhibition of MPro, as compared to the 112,260,215 compound.

Drug design and molecular docking simulations of Polo-like kinase 1 inhibitors based on QSAR study

Computationally exploring novel potential Polo-like kinase 1 inhibitors using a systematic modeling study.

Identification of the potential dual inhibitor of protein tyrosine phosphatase sigma and leukocyte common antigen-related phosphatase by virtual screen, molecular dynamic simulations and post-analysis

Owing to their inhibitory role in regulating oligodendrocyte differentiation and apoptosis, protein tyrosine phosphatase sigma (PTPσ) and leukocyte common antigen-related phosphatase (LAR) play a crucial potential role in treating spinal cord injury (SCI) disease. In this research, the computer aided drug design (CADD) methods were applied to discover the potential dual-target drug involving virtual screen, molecular docking and molecular dynamic simulation. Initially, the top 20 compounds with higher docking score than the positive controls (ZINC13749892, ZINC14516161) were virtually screened out from NCI and ZINC databases, and then were submitted in ADMET to predict their drug properties. Among these potential compounds, ZINC72417086 showed a higher docking score and satisfied Lipinski's rule of five. In addition, the post-analysis demonstrated that when ZINC72417086 bound to PTPσ and LAR, it could stable proteins conformations and destroy the residues interactions between P-loop and other loop regions in active pocket. Meanwhile, residue ARG1595 and ARG1528 could play a crucial role in in the inhibition of PTPσ and LAR, respectively. This research offered a novel approach for rapid discovery of dual-target leads compounds to treat SCI.Communicated by Ramaswamy H. Sarma.

Identification of potential leukocyte antigen-related protein (PTP-LAR) inhibitors through 3D QSAR pharmacophore-based virtual screening and molecular dynamics simulation

Owing to its negative regulatory role in insulin signaling, protein tyrosine phosphatase of leukocyte antigen-related protein (PTP-LAR) was widely thought as a potential drug target for diabetes. Now, it was urgent to search for potential LAR inhibitors targeting diabetes. Initially, the pharmacophore models of LAR inhibitors were established with the application of the HypoGen module. The cost analysis, test set validation, as well as Fischer's test was used to verify the efficiency of pharmacophore model. Then, the best pharmacophore model (Hypo-1-LAR) was applied for the virtual screening of the ZINC database. And 30 compounds met the Lipinski's rule of five. Among them, 10 compounds with better binding affinity than the known LAR inhibitor (BDBM50296375) were discovered by docking studies. Finally, molecular dynamics simulations and post-analysis experiments (RMSD, RMSF, PCA, DCCM and RIN) were conducted to explore the effect of ligands (ZINC97018474 and Compound 1) on LAR and preliminary understand why ZINC97018474 had better inhibitory activity than Compound 1 (BDBM50296375). Communicated by Ramaswamy H. Sarma.

Combiphore (Structure and Ligand Based Pharmacophore) - Approach for the Design of GPR40 Modulators in the Management of Diabetes

BACKGROUND

Pharmacophore mapping and molecular docking can be synergistically integrated to improve the drug design and discovery process. A rational strategy, combiphore approach, derived from the combined study of Structure and Ligand based pharmacophore has been described to identify novel GPR40 modulators.

METHODS

DISCOtech module from Discovery studio was used for the generation of the Structure and Ligand based pharmacophore models which gave hydrophobic aromatic, ring aromatic and negative ionizable as essential pharmacophoric features. The generated models were validated by screening active and inactive datasets, GH scoring and ROC curve analysis. The best model was exposed as a 3D query to screen the hits from databases like GLASS (GPCR-Ligand Association), GPCR SARfari and Mini-Maybridge. Various filters were applied to retrieve the hit molecules having good drug-like properties. A known protein structure of hGPR40 (pdb: 4PHU) having TAK-875 as ligand complex was used to perform the molecular docking studies; using SYBYL-X 1.2 software.

RESULTS AND CONCLUSION

Clustering both the models gave RMSD of 0.89. Therefore, the present approach explored the maximum features by combining both ligand and structure based pharmacophore models. A common structural motif as identified in combiphore for GPR40 modulation consists of the para-substituted phenyl propionic acid scaffold. Therefore, the combiphore approach, whereby maximum structural information (from both ligand and biological protein) is explored, gives maximum insights into the plausible protein-ligand interactions and provides potential lead candidates as exemplified in this study.