Inference of mechanistic insights from multi-omics data facilitating the drug discovery process

Background: Drug development is a costly and challenging process, among others, due to high failure rates at late stages of the drug discovery process. Lack of comprehensive knowledge of disease mechanism and of causal effects induced by perturbation of selected drug targets are key causes for failure. Increasing availability of multi-omics data provides opportunities to address these lacks through quantitative assessment of mechanistic hypotheses from such data.

Method: A two-step approach is proposed to infer mechanisms from multi-omics data. First, agreements between observed data and prior-knowledge molecular interaction graphs are identified. Second, measures of confidence are added to mechanistic hypotheses by joining evidence from causal-reasoning with evidence from multi-omics-based protein activity estimations.

Results: The approach is evaluated using proteomics, phosphoproteomics, and transcriptomics data from pro- and anti-inflammatory macrophages. Results demonstrate how omics layers complement each other to provide mechanistic insights e.g. for key regulators like STAT1 and STAT6.

Conclusion: The presented approach enables quantitative inference of mechanistic insights from complex biological systems, linking disease-causing genes to measured phenotypes and explaining causal routes from drug targets to perturbation-induced effects. This approach can help to identify novel high-confidence drug targets, reveal unfavorable off-target mechanisms, and thereby facilitate the drug discovery process.