Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation.

The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.

PABPN1 promotes clear cell renal cell carcinoma progression by suppressing the alternative polyadenylation of SGPL1 and CREG1.

Alternative polyadenylation (APA) is an important post-transcriptional regulatory mechanism in cancer development and progression. Poly(A) binding protein nuclear 1 (PABPN1) is a gene that encodes abundant nuclear protein, binds with high affinity to nascent poly(A) tails, and is crucial for 3'-UTR (3'-untranslated region) APA. Although PABPN1 has been recently reported as a dominant master APA regulator in clear cell renal cell carcinoma (ccRCC), the underlying functional mechanism remain unclear and the genes subject to PABPN1 regulation that contribute to ccRCC progression have not been identified. Here, we found that PABPN1 is upregulated in ccRCC, and its expression is highly associated with the clinical prognosis of ccRCC patients. PABPN1 promotes ccRCC cell proliferation, migration, invasion, and exerts an influence on sphingolipid metabolism and cell cycle. Moreover, PABPN1 depletion significantly suppressed cancer cell growth via induction of cell cycle arrest and apoptosis. In particular, we characterized PABPN1-regulated 3'-UTR APA of sphingosine-1-phosphate lyase 1 (SGPL1) and cellular repressor of E1A stimulated genes 1 (CREG1), which contribute to ccRCC progression. Collectively, our data revealed that PABPN1 promotes ccRCC progression at least in part, by suppressing SGPL1 and CREG1. Thus, PABPN1 may be a potential therapeutic target in ccRCC.

Comprehensive analysis of alternative polyadenylation regulators of CTLA4 and immune infiltration in clear cell renal cell carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer. Alternative polyadenylation (APA) plays an important role in the progression and immunity of multiple tumors. Although immunotherapy has emerged as an important treatment option for metastatic renal cell carcinoma, whether APA affects the tumor immune microenvironment (TIME) in ccRCC remains unclear. Methods: Patients with ccRCC were classified into two groups by performing a consensus clustering analysis of APA factor expression profiles. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to assess the association between APA regulators and ccRCC prognosis. Through the use of the R package, GSVA, the correlation between SNRNP70 expression and tumor immune features were analyzed. Results: The TCGA data revealed that APA regulators were associated with Cytotoxic T-Lymphocyte Associated Protein 4 (CTLA4) expression. Cluster 1 exhibited a higher grade and histological tumor stage, as well as a worse prognosis compared to Cluster 2. A ssGSEA analysis demonstrated that Cluster 2 possessed an extensively higher level of immune infiltration. Moreover, high SNRNP70 expression was found to be positively correlated with CTLA4 expression and a poor prognosis in ccRCC. Thus, SNRNP70 might represent a novel immune-related prognostic biomarker in ccRCC. A pan-cancer analysis suggested that SNRNP70 may also play a role in other types of cancer by affecting the TIME. Conclusions: The data from this study indicate that APA regulators play a key role in immune infiltration in ccRCC. SNRNP70 is a promising prognostic biomarker and a potential target for ccRCC's immunotherapy.

Unraveling Structural Alerts in Marketed Drugs for Improving Adverse Outcome Pathway Framework of Drug-Induced QT Prolongation.

In pharmaceutical treatment, many non-cardiac drugs carry the risk of prolonging the QT interval, which can lead to fatal cardiac complications such as torsades de points (TdP). Although the unexpected blockade of ion channels has been widely considered to be one of the main reasons for affecting the repolarization phase of the cardiac action potential and leading to QT interval prolongation, the lack of knowledge regarding chemical structures in drugs that may induce the prolongation of the QT interval remains a barrier to further understanding the underlying mechanism and developing an effective prediction strategy. In this study, we thoroughly investigated the differences in chemical structures between QT-prolonging drugs and drugs with no drug-induced QT prolongation (DIQT) concerns, based on the Drug-Induced QT Prolongation Atlas (DIQTA) dataset. Three categories of structural alerts (SAs), namely amines, ethers, and aromatic compounds, appeared in large quantities in QT-prolonging drugs, but rarely in drugs with no DIQT concerns, indicating a close association between SAs and the risk of DIQT. Moreover, using the molecular descriptors associated with these three categories of SAs as features, the structure-activity relationship (SAR) model for predicting the high risk of inducing QT interval prolongation of marketed drugs achieved recall rates of 72.5% and 80.0% for the DIQTA dataset and the FDA Adverse Event Reporting System (FAERS) dataset, respectively. Our findings may promote a better understanding of the mechanism of DIQT and facilitate research on cardiac adverse drug reactions in drug development.