Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Developing drugs with anticancer activity and low toxic side-effects at low costs is a challenging issue for cancer chemotherapy. In this work, we propose to use molecular pathways as the therapeutic targets and develop a novel computational approach for drug repositioning for cancer treatment. We analyzed chemically induced gene expression data of 1112 drugs on 66 human cell lines and searched for drugs that inactivate pathways involved in the growth of cancer cells (cell cycle) and activate pathways that contribute to the death of cancer cells (e.g., apoptosis and p53 signaling). Finally, we performed a large-scale prediction of potential anticancer effects for all the drugs and experimentally validated the prediction results via three in vitro cellular assays that evaluate cell viability, cytotoxicity, and apoptosis induction. Using this strategy, we successfully identified several potential anticancer drugs. The proposed pathway-based method has great potential to improve drug repositioning research for cancer treatment.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1021/acs.jmedchem.8b01044 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Integrating population-level and cell-based signatures for drug repositioning.
Bioinformatics
September 2025
The Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Motivation: Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Drugs with human genetic evidence are more likely to advance successfully through clinical trials towards FDA approval. Single gene-based drug repositioning methods have been implemented, but approaches leveraging a broad spectrum of molecular signatures remain underexplored.
View Article and Find Full Text PDFGraph Clustering-guided Multi-view Neighborhood-enhanced Graph Contrastive Learning for Drug-Target Interaction Prediction.
IEEE J Biomed Health Inform
September 2025
Drug-target interaction (DTI) identification is of great significance in drug development in various areas, such as drug repositioning and potential drug side effects. Although a great variety of computational methods have been proposed for DTI prediction, it is still a challenge in the face of sparsely correlated drugs or targets. To address the impact of data sparsity on the model, we propose a multi-view neighborhood-enhanced graph contrastive learning approach (MneGCL), which is based on graph clustering according to the adjacency relationship in various similarity networks between drugs or targets, to fully exploit the information of drugs and targets with few corrections.
View Article and Find Full Text PDFNeural interaction explainable AI predicts drug response across cancers.
NAR Cancer
September 2025
Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany.
Personalized treatment selection is crucial for cancer patients due to the high variability in drug response. While actionable mutations can increasingly inform treatment decisions, most therapies still rely on population-based approaches. Here, we introduce neural interaction explainable AI (NeurixAI), an explainable and highly scalable deep learning framework that models drug-gene interactions and identifies transcriptomic patterns linked with drug response.
View Article and Find Full Text PDFThe structural basis of drugs targeting protein-protein interactions uncovered with the protein-ligand interaction profiler PLIP.
Expert Opin Drug Discov
September 2025
Biotechnological Center, CMCB and scads.ai, Dresden, Germany.
Background: Promiscuity of drugs and targets plays an important role in drug-target prediction, ranging from the explanation of side effects to their exploitation in drug repositioning. A specific form of promiscuity concerns drugs, which interfere with protein-protein interactions. With the rising importance of such drugs in drug discovery and with the large-scale availability of structural data, the question arises on the structural basis of this form of promiscuity and the commonalities of the underlying protein-ligand (PLI) and protein-protein interactions (PPI).
View Article and Find Full Text PDFNovel approaches to clinical trial design in cancer neuroscience.
Neuron
September 2025
Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Cancer Neuroscience Program, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson
The emerging field of cancer neuroscience has revealed profound bidirectional interactions between the nervous system and cancer cells, identifying novel therapeutic vulnerabilities across diverse malignancies. This review examines the unique challenges and strategies for translating these insights into effective therapies. We propose innovative approaches to overcome these barriers through drug repurposing, enhanced biomarker development, and optimized trial designs.
View Article and Find Full Text PDF