Search

Sulfonylureas (SUs)—a class of drugs primarily used to treat type 2 diabetes—have recently attracted interest for their potential anticancer properties. While some studies have explored the chemical modification or design of new SU derivatives, our work instead centers on biological evaluations of all commercially available SUs in combination with doxorubicin (DOXO). These antidiabetic agents act by stimulating insulin secretion via KATP channel inhibition, and because KATP channels share structural features with ATP-binding cassette (ABC) transporters involved in multidrug resistance (e.g., P-glycoprotein, MRP1, and MRP2), SUs may also reduce cancer cell drug efflux. In this study, we systematically examined each commercially available SU for potential synergy with DOXO in a panel of human cancer cell lines. Notably, combining DOXO with glimepiride (GLIM), the newest SU, results in a 4.4-fold increase in cytotoxicity against MCF-7 breast cancer cells relative to DOXO alone. Mechanistic studies suggest that the observed synergy may arise from increased intracellular accumulation of DOXO. Preliminary in vivo experiments support these findings, showing that DOXO (5 mg/kg, i.v.) plus GLIM (4 mg/kg, i.p.) is more effective at inhibiting 4T1 tumor growth in mice than DOXO alone. Additionally, we show that adding a small amount of the surfactant Tween-80 to culture media affects SU binding to bovine serum albumin (BSA), potentially unmasking anticancer effects of SUs that strongly bind to proteins. Overall, these results underscore the potential of repurposing existing SUs to enhance standard chemotherapy regimens. [ABSTRACT FROM AUTHOR]

Machine learning models for chemistry require large datasets, often compiled by combining data from multiple assays. However, combining data without careful curation can introduce significant noise. While absolute values from different assays are rarely comparable, trends or differences between compounds are often assumed to be consistent. This study evaluates that assumption by analyzing potency differences between matched compound pairs across assays and assessing the impact of assay metadata curation on error reduction. We find that potency differences between matched pairs exhibit less variability than individual compound measurements, suggesting systematic assay differences may partially cancel out in paired data. Metadata curation further improves inter-assay agreement, albeit at the cost of dataset size. For minimally curated compound pairs, agreement within 0.3 pChEMBL units was found to be 44–46% for Ki and IC50 values respectively, which improved to 66–79% after curation. Similarly, the percentage of pairs with differences exceeding 1 pChEMBL unit dropped from 12 to 15% to 6–8% with extensive curation. These results establish a benchmark for expected noise in matched molecular pair data from the ChEMBL database, offering practical metrics for data quality assessment. [ABSTRACT FROM AUTHOR]

Pathogenic mutations that cause rhodopsin misfolding lead to a spectrum of currently untreatable blinding diseases collectively termed retinitis pigmentosa. Small molecules to correct rhodopsin misfolding are therefore urgently needed. In this study, we utilized virtual screening to search for drug-like molecules that bind to the orthosteric site of rod opsin and improve its folding and trafficking. We identified and validated the biological effects of 2 non-retinoid compounds with favorable pharmacological properties that cross the blood–retina barrier. These compounds reversibly bind to unliganded rod opsin, each with a Kd comparable to 9-cis-retinal and improve opsin stability. By improving the internal protein structure network (PSN), these rod opsin ligands also enhanced the plasma membrane expression of total 36 of 123 tested clinical RP variants, including the most prevalent P23H variant. Importantly, these compounds protected retinas against light-induced degeneration in mice vulnerable to bright light injury and prolonged survival of photoreceptors in a retinitis pigmentosa mouse model for rod opsin misfolding. Retinitis pigmentosa is a blinding disease that is caused by pathogenic misfolding mutations in the rhodopsin protein. This study identifies two compounds with favorable pharmacological properties that stabilize misfolded, ligand-free rod opsin and slow down photoreceptor degeneration in a mouse model of retinitis pigmentosa. [ABSTRACT FROM AUTHOR]

The ubiquitin E2 variant domain of TSG101 (TSG101‐UEV) plays a pivotal role in protein sorting and virus budding by recognizing PTAP motifs within ubiquitinated proteins. Disruption of TSG101‐UEV/PTAP interactions has emerged as a promising strategy for the development of host‐oriented broad‐spectrum antivirals with low susceptibility to resistance. TSG101 is a challenging target characterized by an extended and flat binding interface, low affinity for PTAP ligands, and complex binding energetics. Here, we assess the druggability of the TSG101‐UEV/PTAP binding interface by searching for drug‐like inhibitors and evaluating their ability to block PTAP recognition, impair budding, and inhibit viral proliferation. A discovery workflow was established by combining in vitro miniaturized HTS assays and a set of cell‐based activity assays including high‐content bimolecular complementation, virus‐like particle release measurement, and antiviral testing in live virus infection. This approach has allowed us to identify a set of chemically diverse molecules that block TSG101‐UEV/PTAP binding with IC50s in the low μM range and are able to disrupt the interaction between full‐length TSG101 and viral proteins in human cells and inhibit viral replication. State‐of‐the‐art molecular docking studies reveal that the active compounds exploit binding hotspots at the PTAP binding site, unlocking the full binding potential of the TSG101‐UEV binding pockets. These inhibitors represent promising hits for the development of novel broad‐spectrum antivirals through targeted optimization and are also valuable tools for investigating the involvement of ESCRT in the proliferation of different virus families and study the secondary effects induced by the disruption of ESCRT/virus interactions. [ABSTRACT FROM AUTHOR]

As machine learning (ML) transforms industries, the need for efficient model development tools using high-performance computing (HPC) and ensuring interpretability is crucial. This paper presents SIBILA, an AutoML approach designed for HPC environments, focusing on the interpretation of ML models. SIBILA simplifies model development by allowing users to set objectives and preferences before automating the search for optimal ML pipelines. Unlike traditional AutoML frameworks, SIBILA is specifically designed to exploit the computational capabilities of HPC platforms, thereby accelerating the model search and evaluation phases. The emphasis on interpretability is particularly crucial when model transparency is mandated by regulations or desired for stakeholder understanding. SIBILA has been validated in different tasks with public datasets. The results demonstrate that SIBILA consistently produces models with competitive accuracy while significantly reducing computational overhead. This makes it an ideal choice for practitioners seeking efficient and transparent ML solutions on HPC infrastructures. SIBILA is a major advancement in AutoML, addressing the rising demand for explainable ML models on HPC platforms. Its integration of interpretability constraints alongside automated model development processes marks a substantial step forward in bridging the gap between computational efficiency and model transparency in ML applications. The tool is available as a web service at no charge. [ABSTRACT FROM AUTHOR]

Thistle (Onopordum acanthium) has been traditionally employed for liver protection. However, we recently identified silibinin, the main bioactive compound of thistle extract, as an in vitro pancreatic lipase inhibitor, which suggested a potential role as an anti-obesity agent. This study aimed to assess, in vivo, the efficacy, safety, and effects of silibinin on human lipase. As a secondary objective, we evaluated potential changes in gut microbiota after silibinin treatment. A randomized trial comparing 150 mg/silibinin, 300 mg/silibinin, and a thistle extract (equivalent to 150 mg/silibinin) with placebo and orlistat/120 mg was conducted. Fecal fat excretion, clinical parameters, and microbiota changes were analyzed. Orlistat showed the highest fecal fat excretion, although thistle extract had similar results (p = 0.582). The 150 mg/silibinin group reported the fewest adverse effects. Both silibinin and orlistat reduced plasma triglycerides (p = 0.016) and waist circumference (p = 0.001). Specific microbiota changes, such as increases in Mycobacteriaceae and Veillonellaceae, were associated with higher fat excretion. Although the present work was conducted in the short term and in people of normal weight, our results suggest that silibinin may be safe and effective for obesity, with minimal adverse effects and no significant changes in microbiota diversity. Further studies are needed to explore its microbiota-related benefits. [ABSTRACT FROM AUTHOR]

Monoamine oxidase A (MAO-A) is the main enzyme that deaminates serotonin. Correct serotonin concentration regulates appetite, improves well-being, reduces symptoms of depression, but also improves memory and heart function. In this study, the use of new compounds chemo-enzymatically synthesized from propenylbenzene derivatives as MAO-A inhibitors was proposed. Isothermal titration calorimetry analysis and molecular modeling were used to determine the inhibitory effect of MAO-A. The main compounds such as propenylbenzenes were observed to have no inhibitory effect. However, in the case of diols and hydroxy ketones, the compounds showed MAO-A inhibitory effects. In particular, 1-(4-hydroxy-3-methoxyphenyl)propane-1,2-diol, both in isothermal titration calorimetry (ITC) and molecular modeling, showed high affinity for the MAO-A enzyme, thus protecting serotonin from deamination. The results suggest that propenylbenzene derivatives, such as diols and hydroxy ketones, bind to MAO-A at the active site. This suggests their potential to be used as drugs or food supplements to prevent depression. These studies may constitute a new tool for further in vivo and in vitro studies. [ABSTRACT FROM AUTHOR]

Nowadays, most of the health expenditure is due to chronic patients who are readmitted several times for their pathologies. Personalized prevention strategies could be developed to improve the management of these patients. The aim of the present work was to develop local predictive models using interpretable machine learning techniques to early identify individual unscheduled hospital readmissions. To do this, a retrospective, case-control study, based on information regarding patient readmission in 2018–2019, was conducted. After curation of the initial dataset (n = 76,210), the final number of participants was n = 29,026. A machine learning analysis was performed following several algorithms using unscheduled hospital readmissions as dependent variable. Local model-agnostic interpretability methods were also performed. We observed a 13% rate of unscheduled hospital readmissions cases. There were statistically significant differences regarding age and days of stay (p < 0.001 in both cases). A logistic regression model revealed chronic therapy (odds ratio: 3.75), diabetes mellitus history (odds ratio: 1.14), and days of stay (odds ratio: 1.02) as relevant factors. Machine learning algorithms yielded better results regarding sensitivity and other metrics. Following, this procedure, days of stay and age were the most important factors to predict unscheduled hospital readmissions. Interestingly, other variables like allergies and adverse drug reaction antecedents were relevant. Individualized prediction models also revealed a high sensitivity. In conclusion, our study identified significant factors influencing unscheduled hospital readmissions, emphasizing the impact of age and length of stay. We introduced a personalized risk model for predicting hospital readmissions with notable accuracy. Future research should include more clinical variables to refine this model further. [ABSTRACT FROM AUTHOR]

Serine protease inhibitors (serpins) include thousands of structurally conserved proteins playing key roles in many organisms. Mutations affecting serpins may disturb their conformation, leading to inactive forms. Unfortunately, conformational consequences of serpin mutations are difficult to predict. In this study, we integrate experimental data of patients with mutations affecting one serpin with the predictions obtained by AlphaFold and molecular dynamics. Five SERPINC1 mutations causing antithrombin deficiency, the strongest congenital thrombophilia were selected from a cohort of 350 unrelated patients based on functional, biochemical, and crystallographic evidence supporting a folding defect. AlphaFold gave an accurate prediction for the wild-type structure. However, it also produced native structures for all variants, regardless of complexity or conformational consequences in vivo. Similarly, molecular dynamics of up to 1000 ns at temperatures causing conformational transitions did not show significant changes in the native structure of wild-type and variants. In conclusion, AlphaFold and molecular dynamics force predictions into the native conformation at conditions with experimental evidence supporting a conformational change to other structures. It is necessary to improve predictive strategies for serpins that consider the conformational sensitivity of these molecules. [ABSTRACT FROM AUTHOR]