Sitagliptin: a potential drug for the treatment of COVID-19?

Recently, an outbreak of a fatal coronavirus, SARS-CoV-2, has emerged from China and is rapidly spreading worldwide. Possible interaction of SARS-CoV-2 with DPP4 peptidase may partly contribute to the viral pathogenesis. An integrative bioinformatics approach starting with mining the biomedical literature for high confidence DPP4-protein/gene associations followed by functional analysis using network analysis and pathway enrichment was adopted. The results indicate that the identified DPP4 networks are highly enriched in viral processes required for viral entry and infection, and as a result, we propose DPP4 as an important putative target for the treatment of COVID-19. Additionally, our protein-chemical interaction networks identified important interactions between DPP4 and sitagliptin. We conclude that sitagliptin may be beneficial for the treatment of COVID-19 disease, either as monotherapy or in combination with other therapies, especially for diabetic patients and patients with pre-existing cardiovascular conditions who are already at higher risk of COVID-19 mortality

Analyzing the Systems Biology Effects of COVID-19 mRNA Vaccines to Assess Their Safety and Putative Side Effects

COVID-19 vaccines have been instrumental tools in reducing the impact of SARS-CoV-2 infections around the world by preventing 80% to 90% of hospitalizations and deaths from reinfection, in addition to preventing 40% to 65% of symptomatic illnesses. However, the simultaneous large-scale vaccination of the global population will indubitably unveil heterogeneity in immune responses as well as in the propensity to developing post-vaccine adverse events, especially in vulnerable individuals. Herein, we applied a systems biology workflow, integrating vaccine transcriptional signatures with chemogenomics, to study the pharmacological effects of mRNA vaccines. First, we derived transcriptional signatures and predicted their biological effects using pathway enrichment and network approaches. Second, we queried the Connectivity Map (CMap) to prioritize adverse events hypotheses. Finally, we accepted higher-confidence hypotheses that have been predicted by independent approaches. Our results reveal that the mRNA-based BNT162b2 vaccine affects immune response pathways related to interferon and cytokine signaling, which should lead to vaccine success, but may also result in some adverse events. Our results emphasize the effects of BNT162b2 on calcium homeostasis, which could be contributing to some frequently encountered adverse events related to mRNA vaccines. Notably, cardiac side effects were signaled in the CMap query results. In summary, our approach has identified mechanisms underlying both the expected protective effects of vaccination as well as possible post-vaccine adverse effects. Our study illustrates the power of systems biology approaches in improving our understanding of the comprehensive biological response to vaccination against COVID-19

New derivatives of sulfonylhydrazone as potential antitumor agents: Design, synthesis and cheminformatics evaluation

Phosphoinositide 3-kinase α (PI3Kα) is a propitious target for designing anticancer drugs. A series of new N\u27-(diphenylmethylene)benzenesulfonohydrazide was synthesized and characterized using FT-IR, NMR (1H and 13C), HRMS, and elemental analysis. Target compounds exhibited an antiproliferative effect against the human colon carcinoma (HCT-116) cell line. Our cheminformatics analysis indicated that the para-tailored derivatives [p-NO2 (3) and p-CF3 (7)] have better ionization potentials based on calculated Moran autocorrelations and ionization potentials. Subsequent in vitro cell proliferation assays validated our cheminformatics results by providing experimental evidence that both derivatives 3 and 7 exhibited improved antiproliferative activities against HCT-116. Hence, our results emphasized the importance of electron-withdrawing groups and hydrogen bond-acceptors in the rational design of small-molecule chemical ligands targeting PI3Kα. These results agreed with the induced-fit docking against PI3Kα, highlighting the role of p-substituted aromatic rings in guiding the ligand-PI3Kα complex formation, by targeting a hydrophobic pocket in the ligand-binding site and forming π-stacking interactions with a nearby tryptophan residue

A cross-sectional study confirms temporary post-COVID-19 vaccine menstrual irregularity and the associated physiological changes among vaccinated women in Jordan

BackgroundCOVID-19 vaccines continue to save people’s lives around the world; however, some vaccine adverse events have been a major concern which slowed down vaccination campaigns. Anecdotal evidence pointed to the vaccine effect on menstruation but evidence from the adverse event reporting systems and the biomedical literature was lacking. This study aimed to investigate the physiological changes in women during menstruation amid the COVID-19 vaccination.MethodsA cross-sectional online survey was distributed to COVID-19 vaccinated women from Nov 2021 to Jan 2022. The results were analyzed using the SPSS software.ResultsAmong the 564 vaccinated women, 52% experienced significant menstrual irregularities post-vaccination compared to before regardless of the vaccine type. The kind of menstrual irregularity varied among the vaccinated women, for example, 33% had earlier menstruation, while 35% reported delayed menstruation. About 31% experienced heavier menstruation, whereas 24% had lighter menstrual flow. About 29% had menstruation last longer, but 13% had it shorter than usual. Noteworthy, the menstrual irregularities were more frequent after the second vaccine shot, and they disappeared within 3 months on average. Interestingly, 24% of the vaccinated women reported these irregularities to their gynecologist.ConclusionThe COVID-19 vaccine may cause physiological disturbances during menstruation. Luckily, these irregularities were short-termed and should not be a reason for vaccine hesitancy in women. Further studies are encouraged to unravel the COVID-19 vaccine adverse effect on women’s health

N-phenyl-6-chloro-4-hydroxy-2-quinolone-3-carboxamides: Molecular Docking, Synthesis, and Biological Investigation as Anticancer Agents

Cancer is a multifactorial disease and the second leading cause of death worldwide. Diverse factors induce carcinogenesis, such as diet, smoking, radiation, and genetic defects. The phosphatidylinositol 3-kinase (PI3Kα) has emerged as an attractive target for anticancer drug design. Eighteen derivatives of N-phenyl-6-chloro-4-hydroxy-2-quinolone-3-carboxamide were synthesized and characterized using FT-IR, NMR (1H and 13C), and high-resolution mass spectra (HRMS). The series exhibited distinct antiproliferative activity (IC50 µM) against human epithelial colorectal adenocarcinoma (Caco-2) and colon carcinoma (HCT-116) cell lines, respectively: compounds 16 (37.4, 8.9 µM), 18 (50.9, 3.3 µM), 19 (17.0, 5.3 µM), and 21 (18.9, 4.9 µM). The induced-fit docking (IFD) studies against PI3Kαs showed that the derivatives occupy the PI3Kα binding site and engage with key binding residues

Sitagliptin: a potential drug for the treatment of COVID-19?

Recently, an outbreak of a fatal coronavirus, SARS-CoV-2, has emerged from China and is rapidly spreading worldwide. Possible interaction of SARS-CoV-2 with DPP4 peptidase may partly contribute to the viral pathogenesis. An integrative bioinformatics approach starting with mining the biomedical literature for high confidence DPP4-protein/gene associations followed by functional analysis using network analysis and pathway enrichment was adopted. The results indicate that the identified DPP4 networks are highly enriched in viral processes required for viral entry and infection, and as a result, we propose DPP4 as an important putative target for the treatment of COVID-19. Additionally, our protein-chemical interaction networks identified important interactions between DPP4 and sitagliptin. We conclude that sitagliptin may be beneficial for the treatment of COVID-19 disease, either as monotherapy or in combination with other therapies, especially for diabetic patients and patients with pre-existing cardiovascular conditions who are already at higher risk of COVID-19 mortality

Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers

The human microbiome encodes more than three million genes, outnumbering human genes by more than 100 times, while microbial cells in the human microbiota outnumber human cells by 10 times. Thus, the human microbiota and related microbiome constitute a vast source for identifying disease biomarkers and therapeutic drug targets. Herein, we review the evidence backing the exploitation of the human microbiome for identifying diagnostic biomarkers for human disease. We describe the importance of the human microbiome in health and disease and detail the use of the human microbiome and microbiota metabolites as potential diagnostic biomarkers for multiple diseases, including cancer, as well as inflammatory, neurological, and metabolic diseases. Thus, the human microbiota has enormous potential to pave the road for a new era in biomarker research for diagnostic and therapeutic purposes. The scientific community needs to collaborate to overcome current challenges in microbiome research concerning the lack of standardization of research methods and the lack of understanding of causal relationships between microbiota and human disease

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types

Identifying a causal link between prolactin signaling pathways and COVID-19 vaccine-induced menstrual changes

Abstract COVID-19 vaccines have been instrumental tools in the fight against SARS-CoV-2 helping to reduce disease severity and mortality. At the same time, just like any other therapeutic, COVID-19 vaccines were associated with adverse events. Women have reported menstrual cycle irregularity after receiving COVID-19 vaccines, and this led to renewed fears concerning COVID-19 vaccines and their effects on fertility. Herein we devised an informatics workflow to explore the causal drivers of menstrual cycle irregularity in response to vaccination with mRNA COVID-19 vaccine BNT162b2. Our methods relied on gene expression analysis in response to vaccination, followed by network biology analysis to derive testable hypotheses regarding the causal links between BNT162b2 and menstrual cycle irregularity. Five high-confidence transcription factors were identified as causal drivers of BNT162b2-induced menstrual irregularity, namely: IRF1, STAT1, RelA (p65 NF-kB subunit), STAT2 and IRF3. Furthermore, some biomarkers of menstrual irregularity, including TNF, IL6R, IL6ST, LIF, BIRC3, FGF2, ARHGDIB, RPS3, RHOU, MIF, were identified as topological genes and predicted as causal drivers of menstrual irregularity. Our network-based mechanism reconstruction results indicated that BNT162b2 exerted biological effects similar to those resulting from prolactin signaling. However, these effects were short-lived and didn’t raise concerns about long-term infertility issues. This approach can be applied to interrogate the functional links between drugs/vaccines and other side effects