CXCR4 expression is elevated in TNBC patient derived samples and Z-guggulsterone abrogates tumor progression by targeting CXCL12/CXCR4 signaling axis in preclinical breast cancer model.

Environmental factors such as exposure to ionizing radiations, certain environmental pollutants, and toxic chemicals are considered as risk factors in the development of breast cancer. Triple-negative breast cancer (TNBC) is a molecular variant of breast cancer that lacks therapeutic targets such as progesterone receptor, estrogen receptor, and human epidermal growth factor receptor-2 which makes the targeted therapy ineffective in TNBC patients. Therefore, identification of new therapeutic targets for the treatment of TNBC and the discovery of new therapeutic agents is the need of the hour. In this study, CXCR4 was found to be highly expressed in majority of breast cancer tissues and metastatic lymph nodes derived from TNBC patients. CXCR4 expression is positively correlated with breast cancer metastasis and poor prognosis of TNBC patients suggesting that suppression of CXCR4 expression could be a good strategy in the treatment of TNBC patients. Therefore, the effect of Z-guggulsterone (ZGA) on the expression of CXCR4 in TNBC cells was examined. ZGA downregulated protein and mRNA expression of CXCR4 in TNBC cells and proteasome inhibition or lysosomal stabilization had no effect on the ZGA-induced CXCR4 reduction. CXCR4 is under the transcriptional control of NF-κB, whereas ZGA was found to downregulate transcriptional activity of NF-κB. Functionally, ZGA downmodulated the CXCL12-driven migration/invasion in TNBC cells. Additionally, the effect of ZGA on growth of tumor was investigated in the orthotopic TNBC mice model. ZGA presented good inhibition of tumor growth and liver/lung metastasis in this model. Western blotting and immunohistochemical analysis indicated a reduction of CXCR4, NF-κB, and Ki67 in tumor tissues. Computational analysis suggested PXR agonism and FXR antagonism as targets of ZGA. In conclusion, CXCR4 was found to be overexpressed in majority of patient-derived TNBC tissues and ZGA abrogated the growth of TNBC tumors by partly targeting the CXCL12/CXCR4 signaling axis.

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data.

Drug-induced liver injury (DILI) has been significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. The existing suite of in vitro proxy-DILI assays is generally effective at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing in silico prediction of DILI because it allows for the evaluation of large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predicts nine proxy-DILI labels and then uses them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILIst dataset and tested on a held-out external test set of 223 compounds from DILIst dataset. The best model, DILIPredictor, attained an AUC-ROC of 0.79. This model enabled the detection of top 25 toxic compounds compared to models using only structural features (2.68 LR+ score). Using feature interpretation from DILIPredictor, we were able to identify the chemical substructures causing DILI as well as differentiate cases DILI is caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as non-toxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity as well as the potential for mechanism evaluation. DILIPredictor is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download and local implementation via https://pypi.org/project/dilipred/.