Omics-driven Drug Repurposing and Treatment Assessment in Human Viral Diseases

Abstract

In this cumulative thesis, I present my research on transcriptomic-based drug repurposing in human viral infections describing an optimized workflow for drug prediction, <em>in vitro</em> validation and <em>in vivo</em> studies in clinical cohorts based on three publications. In the first publication, I took lead in a larger team effort to introduce a newly designed drug repurposing approach based on whole blood transcriptomics data and drug signatures databases, which was applied to identify potential drug candidates for treatment of patients across COVID-19 severity groups stratified based on clinical parameters and transcriptomic phenotypes (Aschenbrenner et al. 2021). One of the drug candidates identified using this approach was dexamethasone, which was predicted to be effective in the most severe group of COVID-19 patients. <br /> In the second publication, I present my findings on transcriptomic alterations in the monocyte compartment in chronically infected HIV patients using multi-omics technologies, demonstrate that these alterations originate from a certain disease state and identify potential drug candidates for the reversal of the disease signatures in monocytes (Knoll et al. 2023). In this study, I further extend the transcriptomics drug repurposing approach by refining the underlying disease signatures using single-cell omics for drug prediction and I validate promising drug candidates using <em>in vitro</em> stimulation experiments. Reading out direct drug-induced transcriptional alterations from these <em>in vitro</em> studies substantially strengthened the results from the drug repurposing approach. <br /> In the third publication, I describe our framework on how to investigate repurposed drugs in clinical cohorts <em>in vivo</em> using single-cell transcriptomics towards precision medicine, exemplified with dexamethasone treatment in COVID-19 (Knoll et al. 2024). Dexamethasone caused strong transcriptional and immunomodulatory changes with a reversal of dysregulation in severe COVID-19 monocytes compared to treatment-naïve patients. Moreover, a treatment-specific monocyte response state was identified which stratified outcome and enabled prediction of treatment responses, stressing the potential of single-cell transcriptomics for companion diagnostics and mechanistic studies of repurposed drugs. <br /> In conclusion, the research presented in this thesis describes the design and application of a transcriptomics-based drug repurposing pipeline for human viral infections. It highlights the significant potential of drug repurposing in context of data-driven disease severity stratification using optimized cell-state specific disease signatures. Moreover, it underscores the importance of <em>in vitro</em> validation for promising drug candidates to reverse disease signatures. This paves the way for a standardized analytical approach to evaluate drug indications in clinical cohorts <em>in vivo</em>, utilizing single-cell transcriptomics for treatment response stratification, ultimately enabling precision medicine.