Identifying novel inhibitors targeting Exportin-1 for the potential treatment of COVID-19.

The nuclear export protein 1 (XPO1) mediates the nucleocytoplasmic transport of proteins and ribonucleic acids (RNAs) and plays a prominent role in maintaining cellular homeostasis. XPO1 has emerged as a promising therapeutic approach to interfere with the lifecycle of many viruses. In our earlier study, we proved the inhibition of XPO1 as a therapeutic strategy for managing SARS-COV-2 and its variants. In this study, we have utilized pharmacophore-assisted computational methods to identify prominent XPO1 inhibitors. After several layers of screening, a few molecules were shortlisted for further experimental validation on the in vitro SARS-CoV-2 cell infection model. It was observed that these compounds reduced spike positivity, suggesting inhibition of SARS-COV-2 infection. The outcome of this study could be considered further for developing novel antiviral therapeutic strategies against SARS-CoV-2.

Global loss of cellular m6A RNA methylation following infection with different SARS-CoV-2 variants.

Insights into host-virus interactions during SARS-CoV-2 infection are needed to understand COVID-19 pathogenesis and may help to guide the design of novel antiviral therapeutics. N 6-Methyladenosine modification (m6A), one of the most abundant cellular RNA modifications, regulates key processes in RNA metabolism during stress response. Gene expression profiles observed postinfection with different SARS-CoV-2 variants show changes in the expression of genes related to RNA catabolism, including m6A readers and erasers. We found that infection with SARS-CoV-2 variants causes a loss of m6A in cellular RNAs, whereas m6A is detected abundantly in viral RNA. METTL3, the m6A methyltransferase, shows an unusual cytoplasmic localization postinfection. The B.1.351 variant has a less-pronounced effect on METTL3 localization and loss of m6A than did the B.1 and B.1.1.7 variants. We also observed a loss of m6A upon SARS-CoV-2 infection in air/liquid interface cultures of human airway epithelia, confirming that m6A loss is characteristic of SARS-CoV-2-infected cells. Further, transcripts with m6A modification are preferentially down-regulated postinfection. Inhibition of the export protein XPO1 results in the restoration of METTL3 localization, recovery of m6A on cellular RNA, and increased mRNA expression. Stress granule formation, which is compromised by SARS-CoV-2 infection, is restored by XPO1 inhibition and accompanied by a reduced viral infection in vitro. Together, our study elucidates how SARS-CoV-2 inhibits the stress response and perturbs cellular gene expression in an m6A-dependent manner.

Development of machine learning models for the screening of potential HSP90 inhibitors.

Heat shock protein 90 (Hsp90) is a molecular chaperone playing a significant role in the folding of client proteins. This cellular protein is linked to the progression of several cancer types, including breast cancer, lung cancer, and gastrointestinal stromal tumors. Several oncogenic kinases are Hsp90 clients and their activity depends on this molecular chaperone. This makes HSP90 a prominent therapeutic target for cancer treatment. Studies have confirmed the inhibition of HSP90 as a striking therapeutic treatment for cancer management. In this study, we have utilized machine learning and different in silico approaches to screen the KCB database to identify the potential HSP90 inhibitors. Further evaluation of these inhibitors on various cancer cell lines showed favorable inhibitory activity. These inhibitors could serve as a basis for future development of effective HSP90 inhibitors.

Combining structure-based pharmacophore modeling and machine learning for the identification of novel BTK inhibitors.

Bruton's tyrosine kinase (BTK) is a critical enzyme which is involved in multiple signaling pathways that regulate cellular survival, activation, and proliferation, making it a major cancer therapeutic target. We applied the novel integrated structure-based pharmacophore modeling, machine learning, and other in silico studies to screen the Korean chemical database (KCB) to identify the potential BTK inhibitors (BTKi). Further evaluation of these inhibitors on three different human cancer cell lines showed significant cell growth inhibitory activity. Among the 13 compounds shortlisted, four demonstrated consistent cell inhibition activity among breast, gastric, and lung cancer cells (IC50 below 3 µM). The selected compounds also showed significant kinase inhibition activity (IC50 below 5 µM). The current study suggests the potential of these inhibitors for targeting BTK malignant tumors.

Nanomedicine for glioblastoma: Progress and future prospects.

Glioblastoma is the most aggressive form of brain tumor, accounting for the highest mortality and morbidity rates. Current treatment for patients with glioblastoma includes maximal safe tumor resection followed by radiation therapy with concomitant temozolomide (TMZ) chemotherapy. The addition of TMZ to the conformal radiation therapy has improved the median survival time only from 12 months to 16 months in patients with glioblastoma. Despite these aggressive treatment strategies, patients' prognosis remains poor. This therapeutic failure is primarily attributed to the blood-brain barrier (BBB) that restricts the transport of TMZ from reaching the tumor site. In recent years, nanomedicine has gained considerable attention among researchers and shown promising developments in clinical applications, including the diagnosis, prognosis, and treatment of glioblastoma tumors. This review sheds light on the morphological and physiological complexity of the BBB. It also explains the development of nanomedicine strategies to enhance the permeability of drug molecules across the BBB.

Investigating the Mechanism of Inhibition of Cyclin-Dependent Kinase 6 Inhibitory Potential by Selonsertib: Newer Insights Into Drug Repurposing.

Cyclin-dependent kinases (CDKs) play significant roles in numerous physiological, and are considered an attractive drug target for cancer, neurodegenerative, and inflammatory diseases. In the present study, we have aimed to investigate the binding affinity and inhibitory potential of selonsertib toward CDK6. Using the drug repurposing approach, we performed molecular docking of selonsertib with CDK6 and observed a significant binding affinity. To ascertain, we further performed essential dynamics analysis and free energy calculation, which suggested the formation of a stable selonsertib-CDK6 complex. The in-silico findings were further experimentally validated. The recombinant CDK6 was expressed, purified, and treated with selonsertib. The binding affinity of selonsertib to CDK6 was estimated by fluorescence binding studies and enzyme inhibition assay. The results indicated an appreciable binding of selonsertib against CDK6, which subsequently inhibits its activity with a commendable IC50 value (9.8 µM). We concluded that targeting CDK6 by selonsertib can be an efficient therapeutic approach to cancer and other CDK6-related diseases. These observations provide a promising opportunity to utilize selonsertib to address CDK6-related human pathologies.

Probing the Interaction of Selonsertib with Human Serum Albumin: In silico and In vitro Approaches.

INTRODUCTION: Selonsertib, the most recently developed selective inhibitor of apoptosis signal-regulating kinase 1. We elucidated the binding characteristics, mechanism of interaction, and dynamic behaviors of selonsertib with human serum albumin (HSA), a major circulatory transport protein. METHODS: Different biophysical approaches (fluorescence quenching and isothermal titration calorimetry (ITC) were combined with various in silico techniques to examine the binding of selonsertib to HSA. Molecular docking results, analysis of molecular dynamics trajectories, and essential dynamics investigations indicated the stable binding of selonsertib to HSA. Further in vitro studies were performed to validate the observed interaction. RESULTS: ITC results confirmed the robust binding and high affinity of selonsertib and HSA. Likewise, the fluorescence quenching results highlighted the binding affinity of selonsertib and HSA. Collectively, our findings offer deeper insight into the binding mechanism of selonsertib and HSA, emphasizing the selonsertib-mediated structural changes within HSA, along with a comprehensive rationale for the biological transport and accumulation of selonsertib in the blood plasma. CONCLUSION: Therefore, considering the bioavailability and effectiveness of selonsertib, assessing the interactions of this inhibitor with carrier proteins is crucial to elucidate its biological processes at the molecular level. This evidence carries the considerable scientific potential for future drug design.

Computational screening of camostat and related compounds against human TMPRSS2: A potential treatment of COVID-19.

The global coronavirus pandemic has burdened the human population with mass fatalities and disastrous socio-economic consequences. The frequent occurrence of these new variants has fueled the already prevailing challenge. There is still a necessity for highly effective small molecular agents to prevent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Here, we targeted the human transmembrane surface protease TMPRSS2, which is essential for proteolytic activation of SARS-CoV-2. Camostat is a well-known inhibitor of serine proteases and an effective TMPRSS2 inhibitor. A virtual library of camostat-like compounds was computationally screened against the catalytic site of TMPRSS2. Following a sequential in-depth molecular docking and dynamics simulation, we report the compounds that exhibited promising efficacy against TMPRSS2. The molecular docking and MM/PBSA free energy calculation study indicates these compounds carry excellent binding affinity against TMPRSS2 and found them more effective than camostat. The study will open doors for the effective treatment of coronavirus disease 2019.

Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time.

Early detection of bacteremia is important to prevent antibiotic abuse. Therefore, we aimed to develop a clinically applicable bacteremia prediction model using machine learning technology. Data from two tertiary medical centers' electronic medical records during a 12-year-period were extracted. Multi-layer perceptron (MLP), random forest, and gradient boosting algorithms were applied for machine learning analysis. Clinical data within 12 and 24 hours of blood culture were analyzed and compared. Out of 622,771 blood cultures, 38,752 episodes of bacteremia were identified. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUROC) of the prediction performance in 12- and 24-h data models was 0.762 (95% confidence interval (CI); 0.7617-0.7623) and 0.753 (95% CI; 0.7520-0.7529), respectively. AUROC of causative-pathogen subgroup analysis predictive value for Acinetobacter baumannii bacteremia was the highest at 0.839 (95% CI; 0.8388-0.8394). Compared to primary bacteremia, AUROC of sepsis caused by pneumonia was highest. Predictive performance of bacteremia was superior in younger age groups. Bacteremia prediction using machine learning technology appeared possible for acute infectious diseases. This model was more suitable especially to pneumonia caused by Acinetobacter baumannii. From the 24-h blood culture data, bacteremia was predictable by substituting only the continuously variable values.

Discovery of novel acetylcholinesterase inhibitors through integration of machine learning with genetic algorithm based in silico screening approaches.

Introduction: Alzheimer's disease (AD) is the most studied progressive eurodegenerative disorder, affecting 40-50 million of the global population. This progressive neurodegenerative disease is marked by gradual and irreversible declines in cognitive functions. The unavailability of therapeutic drug candidates restricting/reversing the progression of this dementia has severed the existing challenge. The development of acetylcholinesterase (AChE) inhibitors retains a great research focus for the discovery of an anti-Alzheimer drug. Materials and methods: This study focused on finding AChE inhibitors by applying the machine learning (ML) predictive modeling approach, which is an integral part of the current drug discovery process. In this study, we have extensively utilized ML and other in silico approaches to search for an effective lead molecule against AChE. Result and discussion: The output of this study helped us to identify some promising AChE inhibitors. The selected compounds performed well at different levels of analysis and may provide a possible pathway for the future design of potent AChE inhibitors.

Impact of the Double Mutants on Spike Protein of SARS-CoV-2 B.1.617 Lineage on the Human ACE2 Receptor Binding: A Structural Insight.

The recent emergence of novel SARS-CoV-2 variants has threatened the efforts to contain the COVID-19 pandemic. The emergence of these "variants of concern" has increased immune escape and has supplanted the ancestral strains. The novel variants harbored by the B.1.617 lineage (kappa and delta) carry mutations within the receptor-binding domain of spike (S) protein (L452R + E484Q and L452R + T478K), the region binding to the host receptor. The double mutations carried by these novel variants are primarily responsible for an upsurge number of COVID-19 cases in India. In this study, we thoroughly investigated the impact of these double mutations on the binding capability to the human host receptor. We performed several structural analyses and found that the studied double mutations increase the binding affinity of the spike protein to the human host receptor (ACE2). Furthermore, our study showed that these double mutants might be a dominant contributor enhancing the receptor-binding affinity of SARS-CoV-2 and consequently making it more stable. We also investigated the impact of these mutations on the binding affinity of two monoclonal antibodies (Abs) (2-15 and LY-CoV555) and found that the presence of the double mutations also hinders its binding with the studied Abs. The principal component analysis, free energy landscape, intermolecular interaction, and other investigations provided a deeper structural insight to better understand the molecular mechanism responsible for increased viral transmissibility of these variants.

Is PF-00835231 a Pan-SARS-CoV-2 Mpro Inhibitor? A Comparative Study.

The COVID-19 outbreak continues to spread worldwide at a rapid rate. Currently, the absence of any effective antiviral treatment is the major concern for the global population. The reports of the occurrence of various point mutations within the important therapeutic target protein of SARS-CoV-2 has elevated the problem. The SARS-CoV-2 main protease (Mpro) is a major therapeutic target for new antiviral designs. In this study, the efficacy of PF-00835231 was investigated (a Mpro inhibitor under clinical trials) against the Mpro and their reported mutants. Various in silico approaches were used to investigate and compare the efficacy of PF-00835231 and five drugs previously documented to inhibit the Mpro. Our study shows that PF-00835231 is not only effective against the wild type but demonstrates a high affinity against the studied mutants as well.

Screening of inhibitors against SARS-CoV-2 spike protein and their capability to block the viral entry mechanism: A viroinformatics study.

SARS-CoV-2, previously named 2019 novel coronavirus (2019-nCoV), has been associated with the global pandemic of acute respiratory distress syndrome. First reported in December 2019 in the Wuhan province of China, this new RNA virus has several folds higher transmission among humans than its other family member (SARS-CoV and MERS-CoV). The SARS-CoV-2 spike receptor-binding domain (RBD) is the region mediating the binding of the virus to host cells via Angiotensin-converting enzyme 2 (ACE2), a critical step of viral. Here in this study, we have utilized in silico approach for the virtual screening of antiviral library extracted from the Asinex database against the Receptor binding domain (RBD) of the S1 subunit of the SARS-CoV-2 spike glycoprotein. Further, the molecules were ranked based on their binding affinity against RBD, and the top 15 molecules were selected. The affinity of these selected molecules to interrupt the ACE2-Spike interaction was also studied. It was found that the chosen molecules were demonstrating excellent binding affinity against spike protein, and these molecules were also very effectively interrupting the ACE2-RBD interaction. Furthermore, molecular dynamics (MD) simulation studies were utilized to investigate the top 3 selected molecules' stability in the ACE2-RBD complexes. To the best of our knowledge, this is the first study where molecules' inhibitory potential against the Receptor binding domain (RBD) of the S1 subunit of the SARS-CoV-2 spike glycoprotein and their inhibitory potential against the ACE2-Spike has been studied. We believe that these compounds can be further tested as a potential therapeutic option against COVID-19.

Screening of drug databank against WT and mutant main protease of SARS-CoV-2: Towards finding potential compound for repurposing against COVID-19.

Although several pharmacological agents are under investigation to be repurposed as therapeutic against COVID-19, not much success has been achieved yet. So, the search for an effective and active option for the treatment of COVID-19 is still a big challenge. The Spike protein (S), RNA-dependent RNA polymerase (RdRp), and Main protease (Mpro) are considered to be the primary therapeutic drug target for COVID-19. In this study we have screened the drugbank compound library against the Main Protease. But our search was not limited to just Mpro. Like other viruses, SARS-CoV-2, have also acquired unique mutations. These mutations within the active site of these target proteins may be an important factor hindering effective drug candidate development. In the present study we identified important active site mutations within the SARS-CoV-2 Mpro (Y54C, N142S, T190I and A191V). Further the drugbank database was computationally screened against Mpro and the selected mutants. Finally, we came up with the common molecules effective against the wild type (WT) and all the selected Mpro. The study found Imiglitazar, was found to be the most active compound against the wild type of Mpro. While PF-03715455 (Y54C), Salvianolic acid A (N142S and T190I), and Montelukast (A191V) were found to be most active against the other selected mutants. It was also found that some other compounds such as Acteoside, 4-Amino-N- {4-[2-(2,6-Dimethyl-Phenoxy)-Acetylamino]-3-Hydroxy-1-Isobutyl-5-Phenyl-Pentyl}-Benzamide, PF-00610355, 4-Amino-N-4-[2-(2,6-Dimethyl-Phenoxy)-Acetylamino]-3-Hydroxy-1-Isobutyl-5-Phenyl-Pentyl}-Benzamide and Atorvastatin were showing high efficacy against the WT as well as other selected mutants. We believe that these molecules will provide a better and effective option for the treatment of COVID-19 clinical manifestations.

Deciphering the Role of Autophagy in Treatment of Resistance Mechanisms in Glioblastoma.

Autophagy is a process essential for cellular energy consumption, survival, and defense mechanisms. The role of autophagy in several types of human cancers has been explicitly explained; however, the underlying molecular mechanism of autophagy in glioblastoma remains ambiguous. Autophagy is thought to be a "double-edged sword", and its effect on tumorigenesis varies with cell type. On the other hand, autophagy may play a significant role in the resistance mechanisms against various therapies. Therefore, it is of the utmost importance to gain insight into the molecular mechanisms deriving the autophagy-mediated therapeutic resistance and designing improved treatment strategies for glioblastoma. In this review, we discuss autophagy mechanisms, specifically its pro-survival and growth-suppressing mechanisms in glioblastomas. In addition, we try to shed some light on the autophagy-mediated activation of the cellular mechanisms supporting radioresistance and chemoresistance in glioblastoma. This review also highlights autophagy's involvement in glioma stem cell behavior, underlining its role as a potential molecular target for therapeutic interventions.

Use of Natural Compounds as a Potential Therapeutic Agent Against COVID-19.

The current 2019-nCoV outbreak is becoming extremely harmful and has affected the whole world. Its control is challenging because there is no effective vaccine or drug available for coronavirus disease. The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), previously named as 2019 novel coronavirus (2019-nCoV), primarily targets the human respiratory system to lung lesions and lethal pneumonia. Natural products have always shown a crucial role in the process of drug development against various diseases. They may serve as leads for further drug development to combat emergent mutants of the coronavirus. In this review, the current status of natural compounds and their derivatives acting against different species of CoV are discussed.

Silk Cocoon-Derived Protein Bioinspired Gold Nanoparticles as a Formidable Anticancer Agent.

We synthesized bioinspired sericin encapsulated gold nanoparticles (SGNPs) using HAuCl4 as the starting material in a bottom-up approach. Further, two-dimensional (2D) and three-dimensional (3D) conformational changes (folding and unfolding) in sericin were studied using circular dichroism (CD) and fluorescence spectroscopy, respectively, during and after the synthesis of particles. Finally, the synthesized SGNPs were characterized using several physical techniques to ensure their correct synthesis and study the size, stability, and charge over the surface of particles. At the beginning of the reaction, when gold was in the ionic form (Au+³), sericin exhibited maximum electrostatic interaction and underwent unfolding. Au+³ reduced to Au during the reaction, and sericin regained its 3D confirmation due to a decrease in its native electrostatic interactions. However, CD revealed the same patterns of unfolding and folding; a decrease in α helix and an increase inß3 pleated sheets were noticed. Although the 3D structure of sericin was restored after the synthesis of SGNPs, it was substantially altered. In addition, certain changes in the 2D structure were observed; however, these did not alter the activity of sericin. Furthermore, Fourier-transform infrared spectroscopy (FTIR) confirmed these findings. The SGNPs were found to be effective against lung cancer (A549 cells), with an IC50 of 145.49 ßM, without exerting any toxic effects on normal cells (NRK cells). The effectiveness of SGNPs was examined by MTT cytotoxicity and nuclear fragmentation assays. Furthermore, we assessed their ability to produce excessive ROS and release Cyt-c from the mitochondria for caspase-3-mediated apoptosis.

Identification of High-Affinity Inhibitors of Cyclin-Dependent Kinase 2 Towards Anticancer Therapy.

Cyclin-dependent kinase 2 (CDK2) is an essential protein kinase involved in the cell cycle regulation. The abnormal activity of CDK2 is associated with cancer progression and metastasis. Here, we have performed structure-based virtual screening of the PubChem database to identify potent CDK2 inhibitors. First, we retrieved all compounds from the PubChem database having at least 90% structural similarity with the known CDK2 inhibitors. The selected compounds were subjected to structure-based molecular docking studies to investigate their pattern of interaction and estimate their binding affinities with CDK2. Selected compounds were further filtered out based on their physicochemical and ADMET properties. Detailed interaction analysis revealed that selected compounds interact with the functionally important residues of the active site pocket of CDK2. All-atom molecular dynamics simulation was performed to evaluate conformational changes, stability and the interaction mechanism of CDK2 in-complex with the selected compound. We found that binding of 6-N,6-N-dimethyl-9-(2-phenylethyl)purine-2,6-diamine stabilizes the structure of CDK2 and causes minimal conformational change. Finally, we suggest that the compound (PubChem ID 101874157) would be a promising scaffold to be further exploited as a potential inhibitor of CDK2 for therapeutic management of cancer after required validation.

Early Detection of Bacteraemia Using Ten Clinical Variables with an Artificial Neural Network Approach.

An adequate model for predicting bacteraemia has not yet been developed. This study aimed to evaluate the performance of an artificial neural network (ANN)-based prediction model in comparison with previous statistical models. The performance of multi-layer perceptron (MLP), a representative ANN model, was verified via comparison with a non-neural network model. A total of 1260 bacteraemia episodes were identified in 13,402 patients. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUC) of the prediction performance was 0.729 (95% confidence interval [CI]; 0.712-0.728), while in MLP with 256 hidden layer nodes, it was 0.727 (95% CI; 0.713-0.727). In a conventional Bayesian statistical method, the AUC was 0.7. The aforementioned two MLP models exhibited the highest sensitivity (0.810). The ranking of clinical variables was used to describe the influential power of the prediction. Serum alkaline phosphatase was one of the most influential clinical variables, and one-out search was the best ranking method for measuring the influence of the clinical variables. Furthermore, adding variables beyond the 10 top-ranking ones did not significantly affect the prediction of bacteraemia. The ANN model is not inferior to conventional statistical approaches. Bacteraemia can be predicted using only the top 10 clinical variables determined by a ranking method, and the model can be used in clinical practice by applying real-time monitoring.

ASK1 and its role in cardiovascular and other disorders: available treatments and future prospects.

Introduction: Apoptosis signal-regulating kinase 1 (ASK1), also known as MAP3K5, is a member of mitogen-activated protein kinase kinase kinase (MAP3K) family and is well reported as crucial in the regulation of the JNK and P38 pathways. ASK1 is activated in response to a diverse array of stresses such as endoplasmic reticulum stress, lipopolysaccharides, tumor necrosis factor alpha, and reactive oxygen species. The activation of ASK1 induces various stress responses. Areas covered: Considering ASK1 as an important therapeutic drug target, here we have discussed the role of ASK1 in the progression of various diseases. We have also provided an overview of the available inhibitors for ASK1. The success of computational-based approaches toward ASK1 inhibitor design has also been discussed. Expert opinion: A number of reports have outlined the prominent role of ASK1 in the pathogenesis of several diseases. The discovery of novel ASK1 inhibitors would have a wide range of applications in medical science. In-silico techniques have been successfully used in the design of some novel ASK1 inhibitors. The use of machine learning-based approaches in combination with structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS) will be helpful toward the development of potent ASK1 inhibitors.