Molecular insights into human transmembrane protease serine-2 (TMPS2) inhibitors against SARS-CoV2: homology modelling, molecular dynamics, and docking studies

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), which caused novel corona virus disease-2019 (COVID-19) pandemic, necessitated a global demand for studies related to genes and enzymes of SARS-CoV2. SARS-CoV2 infection depends on the host cell Angiotensin-Converting Enzyme-2 (ACE2) and Transmembrane Serine Protease-2 (TMPRSS2), where the virus uses ACE2 for entry and TMPRSS2 for S protein priming. The TMPRSS2 gene encodes a Transmembrane Protease Serine-2 protein (TMPS2) that belongs to the serine protease family. There is no crystal structure available for TMPS2, therefore, a homology model was required to establish a putative 3D structure for the enzyme. A homology model was constructed using SWISS-MODEL and evaluations were performed through Ramachandran plots, Verify 3D and Protein Statistical Analysis (ProSA). Molecular dynamics simulations were employed to investigate the stability of the constructed model. Docking of TMPS2 inhibitors, camostat, nafamostat, gabexate, and sivelestat, using Molecular Operating Environment (MOE) software, into the constructed model was performed and the protein-ligand complexes were subjected to MD simulations and computational binding affinity calculations. These in silico studies determined the tertiary structure of TMPS2 amino acid sequence and predicted how ligands bind to the model, which is important for drug development for the prevention and treatment of COVID-19

Enhancement of Neprilysin Activity by Natural Polyphenolic Compounds and Their Derivatives in Cultured Neuroglioma Cells

The onset of Alzheimer’s disease (AD) is characterized by accumulation of amyloid β peptide (Aβ) in the brain. Neprilysin (NEP) is one of the major Aβ-degrading enzymes. Given findings that NEP expression in the brain declines from the early stage of AD before apparent neuronal losses are observed, enhancement of NEP activity and expression may be a preventive and therapeutic strategy relevant to disease onset. We screened for compounds that could enhance the activity and expression of NEP using a polyphenol library previously constructed by our research group and investigated the structure–activity relationships of the identified polyphenols. We found that amentoflavone, apigenin, kaempferol, and chrysin enhanced the activity and expression of NEP, suggesting that chemical structures involving a double bond between positions 2 and 3 in the C ring of flavones are important for NEP enhancement, while catechol or pyrogallol structures, except for the galloyl group of catechins, abolished these effects. Moreover, natural compounds, such as quercetin, were not effective per se, but were changed to effective compounds by adding a lipophilic moiety. Using our study findings, we propose improvements for dietary habits with experimental evidence, and provide a basis for the development of novel small molecules as disease-modifying drugs for AD

Synthesis, 3D-QSAR, and Molecular Modeling Studies of Triazole Bearing Compounds as a Promising Scaffold for Cyclooxygenase-2 Inhibition

Targeting of cyclooxygenase-2 (COX-2) has emerged as a powerful tool for therapeutic intervention because the overexpression of this enzyme is synonymous with inflammation, cancer, and neurodegenerative diseases. Herein, a new series of 1,2,4-triazole Schiff bases scaffold with aryl and heteroaryl systems 9a–12d were designed, synthesized, structurally elucidated, and biologically evaluated as a potent COX-2 blocker. The rationale beyond the current study is to increase the molecule bulkiness allowing a selective binding to the unique hydrophobic pocket of COX-2. Among the triazole–thiazole hybrids, the one with the para-methoxy moiety linked to a phenyl ring 12d showed the highest In vitro selectivity by COX-2 inhibition assay (IC50 of 0.04 μM) and in situ anti-inflammatory activity when evaluated using the protein denaturation assay (IC50 of 0.88 μM) in comparison with commercially available selective COX-2 inhibitor, Celecoxib (IC50 of 0.05 μM). Towards the COX-2 selectivity, ligand-based three dimensional quantitative structures activity relationship (3D-QSAR) employing atomic-based and field-based approaches were performed and resulted in the necessity of triazole and thiazole/oxazole scaffolds for COX-2 blocking. Furthermore, the molecular modeling study indicated a high selectivity and promising affinity of our prepared compounds to COX-2, especially the hydrophobic pocket and the mouth of the active site holding hydrogen-bonding, hydrophobic, and electrostatic interactions. In Silico absorption, delivery, metabolism, and excretion (ADME) predictions showed that all the pharmacokinetic and physicochemical features are within the appropriate range for human use

Discovery of Potent Dual EGFR/HER2 Inhibitors Based on Thiophene Scaffold Targeting H1299 Lung Cancer Cell Line

Dual targeting of epidermal growth factor receptor (EGFR) and human EGFR-related receptor 2 (HER2) is a proven approach for the treatment of lung cancer. With the aim of discovering effective dual EGFR/HER2 inhibitors targeting non-small cell lung cancer cell line H1299, three series of thieno[2,3-d][1,2,3]triazine and acetamide derivatives were designed, synthesized, and biologically evaluated. The synthesized compounds displayed IC50 values ranging from 12 to 54 nM against H1299, which were superior to that of gefitinib (2) at 40 µM. Of the synthesized compounds, 2-(1H-pyrazolo[3,4-b]pyridin-3-ylamino)-N-(3-cyano4,5,6,7-tetrahydrobenzo[b]thiophen-2-yl)acetamide (21a) achieved the highest in vitro cytotoxic activity against H1299, with an IC50 value of 12.5 nM in situ, and 0.47 and 0.14 nM against EGFR and HER2, respectively, values comparable to the IC50 of the approved drug imatinib (1). Our synthesized compounds were promising, demonstrating high selectivity and affinity for EGFR/HER2, especially the hinge region forming a hydrophobic pocket, which was mediated by hydrogen bonding as well as hydrophobic and electrostatic interactions, as indicated by molecular modeling. Moreover, the designed compounds showed good affinity for T790M EGFR, one of the main mutants resulting in acquired drug resistance. Furthermore, both pharmacokinetic and physicochemical properties of the designed compounds were within the appropriate range for human usage as predicted by the in Silico ADME study. The designed compound (21a) might serve as an encouraging lead compound for the discovery of promising anti-lung cancer agents targeting EGFR/HER2

Accuracy of the Traditional COVID-19 Phone Triaging System and Phone Triage-Driven Deep Learning Model

Objectives: During the COVID-19 pandemic, a quick and reliable phone-triage system is critical for early care and efficient distribution of hospital resources. The study aimed to assess the accuracy of the traditional phone-triage system and phone triage-driven deep learning model in the prediction of positive COVID-19 patients. Setting: This is a retrospective study conducted at the family medicine department, Cairo University. Methods: The study included a dataset of 943 suspected COVID-19 patients from the phone triage during the first wave of the pandemic. The accuracy of the phone triaging system was assessed. PCR-dependent and phone triage-driven deep learning model for automated classifications of natural human responses was conducted. Results: Based on the RT-PCR results, we found that myalgia, fever, and contact with a case with respiratory symptoms had the highest sensitivity among the symptoms/ risk factors that were asked during the phone calls (86.3%, 77.5%, and 75.1%, respectively). While immunodeficiency, smoking, and loss of smell or taste had the highest specificity (96.9%, 83.6%, and 74.0%, respectively). The positive predictive value (PPV) of phone triage was 48.4%. The classification accuracy achieved by the deep learning model was 66%, while the PPV was 70.5%. Conclusion: Phone triage and deep learning models are feasible and convenient tools for screening COVID-19 patients. Using the deep learning models for symptoms screening will help to provide the proper medical care as early as possible for those at a higher risk of developing severe illness paving the way for a more efficient allocation of the scanty health resources