Abstract
High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. Examining compounds with beneficial effects in models of Huntington’s Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. The approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these effects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. The use of multiple omics was essential, as some MoAs were virtually undetectable in specific assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases.
Highlights
Unknown modes of action of drug candidates can lead to unpredicted consequences on effectiveness and safety
In the context of late-onset neurodegenerative disorders like Huntington’s Disease (HD), screening efforts focused on protein aggregation, neuronal death, and caspase activation phenotypes have found many compounds that have disease-altering potential, but none have been successful in clinical trials[11]
A study using a small molecule sphingolipid enzyme inhibitor, for example, found a novel MoA related to histone acetylation through the analysis of gene expression and epigenetic profiles in the murine STHdhQ111 HD cell model[15]
Summary
Unknown modes of action of drug candidates can lead to unpredicted consequences on effectiveness and safety Computational methods, such as the analysis of gene signatures, and high-throughput experimental methods have accelerated the discovery of lead compounds that affect a specific target or phenotype[1,2,3]. Gene expression profiles of human lymphoma cells treated with anti-cancer drugs were compared using the gene regulatory network-based DeMAND algorithm to predict novel targets and unexpected similarities between the drugs[10]. All of these methods require prior context-specific knowledge, such as data from reference compounds with known MoAs, sensitivity data, or gene-regulatory interactions. As all small molecule therapeutics have so far failed to modify HD in clinical trials, understanding the disease-relevant MoAs is critical to guide future therapeutic approaches that could target these pathways with new molecules
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