Project description:Primary brain tumors, both malignant and benign, are diagnosed in adults at an incidence rate of approximately 23 people per 100 thousand. The role of AhR in carcinogenesis has been a subject of debate, given that this protein may act as either an oncogenic protein or a tumor suppressor in different cell types and contexts. Lately, there is growing evidence that aryl hydrocarbon receptor (AhR) plays an important part in the development of brain tumors. The role of AhR in brain tumors is complicated, depending on the type of tumor, on ligands that activate AhR, and other features of the pathological process. In this review, we summarize current knowledge about AhR in relation to brain tumors and provide an overview of AhR's potential as a therapeutic target.

| S-EPMC7215596 | biostudies-literature

Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms.

Project description:Computational modeling has emerged as a time-saving and cost-effective alternative to traditional animal testing for assessing chemicals for their potential hazards. However, few computational modeling studies for immunotoxicity were reported, with few models available for predicting toxicants due to the lack of training data and the complex mechanisms of immunotoxicity. In this study, we employed a data-driven quantitative structure-activity relationship (QSAR) modeling workflow to extensively enlarge the limited training data by revealing multiple targets involved in immunotoxicity. To this end, a probe data set of 6,341 chemicals was obtained from a high-throughput screening (HTS) assay testing for the activation of the aryl hydrocarbon receptor (AhR) signaling pathway, a key event leading to immunotoxicity. Searching this probe data set against PubChem yielded 3,183 assays with testing results for varying proportions of these 6,341 compounds. 100 assays were selected to develop QSAR models based on their correlations to AhR agonism. Twelve individual QSAR models were built for each assay using combinations of four machine-learning algorithms and three molecular fingerprints. 5-fold cross-validation of the resulting models showed good predictivity (average CCR = 0.73). A total of 20 assays were further selected based on QSAR model performance, and their resulting QSAR models showed good predictivity of potential immunotoxicants from external chemicals. This study provides a computational modeling strategy that can utilize large public toxicity data sets for modeling immunotoxicity and other toxicity endpoints, which have limited training data and complicated toxicity mechanisms.

| S-EPMC11264268 | biostudies-literature

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