Using biological and chemical information to improve understanding of drug mechanism of action on the systems-level

The understanding of a therapeutic compound's mechanism of action (MoA) can provide great benefits to the drug discovery process. Biological and chemical information e.g., transcriptomics data is increasingly becoming easier to measure by high throughput methods, and large amounts of this type of data are available in the public domain. Such information can be used with methods such as machine learning, causal reasoning and pathway enrichment to gain insights on compound MoA across different levels of biology. This presents an opportunity to investigate approaches for integrating information sources and computational methods for the elucidation of a systems-level view of compound MoA, which is the topic of this thesis. In the first chapter, causal reasoning algorithms were benchmarked for their ability to retrieve compound targets and mechanistic signalling pathways from prior knowledge networks. We found that these algorithms generally perform poorly for recovering targets; but are able to recover mechanistic pathways better than traditional pathway enrichment using gene expression data. We also found that algorithms perform differently with different networks, and that the connectivity of mechanistic proteins on the network or their biological function can greatly impact the ability of the algorithms to retrieve them. In the second chapter, we integrated biological and chemical information to generate MoA hypotheses for the tau aggregation inhibitor Anle138b. By using both chemical structure and transcriptomics information, along with causal reasoning, pathway enrichment and machine learning-based target prediction, we were able to generate hypotheses across multiple levels of biology. We found biologically plausible hypotheses which were highlighted across all three methodologies, e.g., cholesterol metabolism modulation, which is a known mediator of tau aggregation. In the final chapter, we developed an R/Shiny app to allow users to carry out integrated chemical structure-based target prediction with gene expression-based causal reasoning and visualisation in a GUI environment. We also provide a case study demonstrating its capabilities.