Using transcriptomic data to detect, understand, and treat injury in the context of drug toxicity and fibrotic disease

In drug discovery, it is crucial to understand how drugs relate to complex phenotypes. This includes understanding how a drug can help to treat a condition, but also how it can result in adverse effects so that safety risks can be mitigated earlier. How effects propagate from the molecular to the systems scale is, however, in many cases not fully clear, in particular for complex phenotypes which cannot be narrowed down to individual causes. In this thesis, transcriptomics data was used as intermediate layer alongside additional data sources to study links between compounds and phenotypes. First, safety biomarker candidates in Drug-Induced Vascular Injury were identified based on changes in tissue expression across adverse and non-adverse treatments. Further characterization of their biological role and predictive performance thereby identified multiple secreted proteins as most promising candidates. Secondly, pathways and transcription factors involved in the pathogenesis of Drug-Induced Liver Injury, another safety-related endpoint, were identified and characterised based on time concordance in repeat-dose studies in rats. In Chapter 3, it is demonstrated how time concordance can be combined with other streams of evidence towards causal hypothesis and mechanistic biomarkers. In order to make time concordance analysis on the Open TG-GATEs liver data also accessible to researchers in an interactive manner, the R/Shiny app “DILI Cascades” is presented in Chapter 4. Instead of drug toxicity, the last chapter then focusses on efficacy and aims to prioritise repurposing candidates, direct targets and downstream effectors which may promote alveolar regeneration in Idiopathic Pulmonary Fibrosis. This demonstrates how single-cell RNA-Seq data can be leveraged for drug repurposing through better characterization of cell transitions followed by signature matching. In summary, data-driven approaches with transcriptomics as key modality were used to derive insights on how drug perturbations are linked to adverse effects and fibrotic disease. Thereby, the presented work did not only aim at better mechanistic understanding but also provides actionable starting points for the discovery of new biomarkers and drug indications.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2022

Author dc:creator

• Liu, Anika

Advisors dc:contributor.advisor

• Bender, Andreas
• Han, Namshik
• Munoz-Muriedas, Jordi

• Transcriptomics
• Computational Toxicology
• Drug-Induced Liver Injury
• Drug-Induced Vascular Injury
• Alveolar regeneration
• Drug repurposing
• Idiopathic Pulmonary Fibrosis
• Adverse Outcome Pathways
• Toxicogenomics

Licence

• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

eng

Author Identifier

0000-0002-8561-4700

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/346042