Deep Learning Applications for the accurate identification of low-transcriptional activity drugs and their mechanism of actions

Tropisetron attenuates high-glucose-induced vascular endothelial dysfunction via inhibition of calcineurin/NFAT signalling

Vascular endothelial dysfunction (VED) is considered an important initiating factor in pathogenesis of diabetic vascular disease. In this process, oxidative insult, cellular hypertrophy, and activation of the calcineurin/nuclear factor of activated T-cell (NFAT) pathway play key roles. Herein, we investigated the effects of tropisetron (TRS), a calcineurin inhibitor, on high glucose (HG)-induced hypertrophy and apoptosis in human umbilical vein endothelial cells (HUVECs). To this end, HUVECs and chorioallantoic membranes (CAMs) were exposed to HG with or without TRS or cyclosporine A (CsA), and the effects of the treatments were evaluated on oxidative stress generation, cell number (proliferation and apoptosis), cell size (hypertrophy), and vessel formation. We also explored the possible role of calcineurin-NFAT signalling in the potential protective effects of TRS on hypertrophy and apoptosis associated with HG. The average size and protein content of the cells exposed to HG for 48h were significantly increased compared with normal glucose (NG). HG significantly increased apoptosis, altered the cell cycle, and elevated oxidative and nitrosative stress in HUVECs. Further, exposing cells to HG resulted in elevated calcineurin activity and NFATc1 translocation to the nuclei. HG also caused a significant decrease in the formation of new blood vessels in CAMs. Inhibition of calcineurin/NFAT pathway by TRS or CsA protected against these pathological changes. Our data demonstrated that inhibition of calcineurin/NFAT signalling by TRS, as a safe calcineurin inhibitor, may ameliorate HG-induced VED. Further in vivo and clinical studies are required to fully determine the protective effects of TRS against VED in diabetes.

Interactions between oxidative stress and senescence in cancer: Mechanisms, therapeutic implications, and future perspectives

BACKGROUND

Recently, numerous studies have reported the interaction between senescence and oxidative stress in cancer. However, there is a lack of a comprehensive understanding of the precise mechanisms involved.

AIM

Therefore, our review aims to summarize the current findings and elucidate by presenting specific mechanisms that encompass functional pathways, target genes, and related aspects.

METHODS

Pubmed and Web of Science databases were retrieved to search studies about the interaction between senescence and oxidative stress in cancer. Relevant publications in the reference list of enrolled studies were also checked.

RESULTS

In carcinogenesis, oxidative stress-induced cellular senescence acts as a barrier against the transformation of stimulated cells into cancer cells. However, the senescence-associated secretory phenotype (SASP) is positively linked to tumorigenesis. In the cancer progression stage, targeting specific genes or pathways that promote oxidative stress-induced cellular senescence can suppress cancer progression. In terms of treatment, many current clinical therapies combine with novel drugs to overcome resistance and reduce side effects by attenuating oxidative stress-induced senescence. Notably, emerging drugs control cancer development by enhancing oxidative stress-induced senescence. These studies highlight the complacted effects of the interplay between oxidative stress and senescence at different cancer stages and among distinct cell populations. Future research should focus on characterizing the roles of distinct senescent cell types in various tumor stages and identifying the specific components of SASP.

CONCLUDSION

We've summarized the mechanisms of senescence and oxidative stress in cancer and provided illustrative figures to guide future research in this area.

Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease

Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targeting Aβ, this study uses deep learning and a pharmacological approach to elucidate the potential of pyrroloquinoline quinone (PQQ) as a neuroprotective agent for AD. Using deep learning for a comprehensive molecular dataset, blood-brain barrier (BBB) permeability is predicted and the anti-inflammatory and antioxidative properties of compounds are evaluated. PQQ, identified in the Mediterranean-DASH intervention for a diet that delays neurodegeneration, shows notable BBB permeability and low toxicity. In vivo tests conducted on an Aβ₁₋₄₂-induced AD mouse model verify the effectiveness of PQQ in reducing cognitive deficits. PQQ modulates genes vital for synapse and anti-neuronal death, reduces reactive oxygen species production, and influences the SIRT1 and CREB pathways, suggesting key molecular mechanisms underlying its neuroprotective effects. This study can serve as a basis for future studies on integrating deep learning with pharmacological research and drug discovery.

Drug Repurposing to Circumvent Immune Checkpoint Inhibitor Resistance in Cancer Immunotherapy

Immune checkpoint inhibitors (ICI) have achieved unprecedented clinical success in cancer treatment. However, drug resistance to ICI therapy is a major hurdle that prevents cancer patients from responding to the treatment or having durable disease control. Drug repurposing refers to the application of clinically approved drugs, with characterized pharmacological properties and known adverse effect profiles, to new indications. It has also emerged as a promising strategy to overcome drug resistance. In this review, we summarized the latest research about drug repurposing to overcome ICI resistance. Repurposed drugs work by either exerting immunostimulatory activities or abolishing the immunosuppressive tumor microenvironment (TME). Compared to the de novo drug design strategy, they provide novel and affordable treatment options to enhance cancer immunotherapy that can be readily evaluated in the clinic. Biomarkers are exploited to identify the right patient population to benefit from the repurposed drugs and drug combinations. Phenotypic screening of chemical libraries has been conducted to search for T-cell-modifying drugs. Genomics and integrated bioinformatics analysis, artificial intelligence, machine and deep learning approaches are employed to identify novel modulators of the immunosuppressive TME.