SPH7854, a gut-limited RORγt antagonist, ameliorates TNBS-induced experimental colitis in rat.

Multiple lines of evidence suggest that Retinoic Acid Related Orphan Nuclear Receptor gamma t (RORγt) is a potent therapeutic target for inflammatory bowel disease (IBD). However, systemic blockade of RORγt easily leads to thymic lymphoma and aberrant liver function. Therefore, the development of gut-limited RORγt antagonists may lead to the development of innovative IBD therapeutics that improve safety and retain effectiveness.

Development of a Multi-Target Strategy for the Treatment of Vitiligo via Machine Learning and Network Analysis Methods.

Vitiligo is a complex disorder characterized by the loss of pigment in the skin. The current therapeutic strategies are limited. The identification of novel drug targets and candidates is highly challenging for vitiligo.

Pathway-Based Drug Repurposing with DPNetinfer: A Method to Predict Drug-Pathway Associations via Network-Based Approaches.

Identification of drug-pathway associations plays an important role in pathway-based drug repurposing. However, it is time-consuming and costly to uncover new drug-pathway associations experimentally. The drug-induced transcriptomics data provide a global view of cellular pathways and tell how these pathways change under different treatments.

In silico prediction of mitochondrial toxicity of chemicals using machine learning methods.

Mitochondria are important organelles in human cells, providing more than 95% of the energy. However, some drugs and environmental chemicals could induce mitochondrial dysfunction, which might cause complex diseases and even worsen the condition of patients with mitochondrial damage. Some drugs have been withdrawn from the market due to their severe mitochondrial toxicity, such as troglitazone.

NetInfer: A Web Server for Prediction of Targets and Therapeutic and Adverse Effects via Network-Based Inference Methods.

In this study, we developed a web server named NetInfer for prediction of targets and therapeutic and adverse effects via network-based inference methods. Compared with our previously developed standalone version of NetInfer, this web server provides a user-friendly interface. With the web server, users can easily predict potential target proteins, microRNAs, Anatomical Therapeutic Chemical (ATC) classification codes, or adverse drug events for small molecules of their interests in a few steps.

Drug repositioning by prediction of drug's anatomical therapeutic chemical code via network-based inference approaches.

Drug discovery and development is a time-consuming and costly process. Therefore, drug repositioning has become an effective approach to address the issues by identifying new therapeutic or pharmacological actions for existing drugs. The drug's anatomical therapeutic chemical (ATC) code is a hierarchical classification system categorized as five levels according to the organs or systems that drugs act and the pharmacology, therapeutic and chemical properties of drugs.

A novel derivative of artemisinin inhibits cell proliferation and metastasis via down-regulation of cathepsin K in breast cancer.

Breast cancer is one of the main diagnosis cancers annually worldwide. It is difficult to thorough cure due to drug resistance and the high possibility of metastasis. SM934 is a novel water-soluble artemisinin analog, and has been reported to have a promising therapeutic effect on multiple autoimmune diseases.

Insights into mechanisms and severity of drug-induced liver injury via computational systems toxicology approach.

Liver is the central place for drug metabolism. Drug-induced liver injury (DILI) is hence inevitable, and has become one of the leading causes for drug failure in development and drug withdrawal from the market. Due to lack of reliable preclinical and in vivo toxicology test conditions, it is time-consuming, laborious and costly to interpret the mechanisms of DILI through bioassays.