C(2)Maps: a network pharmacology database with comprehensive disease-gene-drug connectivity relationships

BACKGROUND: Network pharmacology has emerged as a new topic of study in recent years. It aims to study the myriad relationships among proteins, drugs, and disease phenotypes. The concept of molecular connectivity maps has been proposed to establish comprehensive knowledge links between molecules of interest in a given biological context. Molecular connectivity maps between drugs and genes/proteins in specific disease contexts can be particularly valuable, since the functional approach with these maps helps researchers gain global perspectives on both the therapeutic profiles and toxicological profiles of candidate drugs. METHODS: To assess drug pharmacological effect, we assume that "ideal" drugs for a patient can treat or prevent the disease by modulating gene expression profiles of this patient to the similar level with those in healthy people. Starting from this hypothesis, we build comprehensive disease-gene-drug connectivity relationships with drug-protein directionality (inhibit/activate) information based on a computational connectivity maps (C(2)Maps) platform. An interactive interface for directionality annotation of drug-protein pairs with literature evidences from PubMed has been added to the new version of C(2)Maps. We also upload the curated directionality information of drug-protein pairs specific for three complex diseases - breast cancer, colorectal cancer and Alzheimer disease. RESULTS: For relevant drug-protein pairs with directionality information, we use breast cancer as a case study to demonstrate the functionality of disease-specific searching. Based on the results obtained from searching, we perform pharmacological effect evaluation for two important breast cancer drugs on treating patients diagnosed with different breast cancer subtypes. The evaluation is performed on a well-studied breast cancer gene expression microarray dataset to portray how useful the updated C(2)Maps is in assessing drug efficacy and toxicity information. CONCLUSIONS: The C(2)Maps platform is an online bioinformatics resource that provides biologists with directional relationships between drugs and genes/proteins in specific disease contexts based on network mining, literature mining, and drug effect annotating. A new insight to assess overall drug efficacy and toxicity can be provided by using the C(2)Maps platform to identify disease relevant proteins and drugs. The case study on breast cancer correlates very well with the existing pharmacology of the two breast cancer drugs and highlights the significance of C(2)Maps database.