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  • Review Article
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Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases

Abstract

The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by ‘borrowing’ treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different ‘waves’ of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.

Key points

  • Repurposing drugs for and among rheumatic autoimmune inflammatory diseases (RAIDs) is difficult because of limitations in knowledge surrounding the pathogenesis of these diseases.

  • Clinical and academic-driven drug repurposing was critical for the early identification of treatments for RAIDs.

  • High costs and increased regulation shifted drug repurposing from primarily academia to pharmaceutical companies.

  • Computational repurposing has the ability to elucidate previously unknown drug mechanisms of action and off-target effects.

  • Genomic and genetic data can identify new pathways and targets for drug repurposing.

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Fig. 1: Historical periods of drug class-based repurposing in RAIDs.
Fig. 2: Successful drug repurposing among RAIDs.
Fig. 3: Computational and data-driven advances have changed the landscape of drug repurposing.

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Glossary

In silico

A type of biological thought experiment performed on a computer or using specific computer simulation.

Machine learning

An algorithm by which a computer makes predictions based upon learned patterns in the data.

Pharmacophore

The molecular features that are necessary for interactions between a molecule or protein and its ligand.

Phenome-wide association studies

PheWASs. Analysis of the association between disease manifestations, or phenotypes, and a genetic variant.

Genome-wide association studies

GWASs. Analysis of the association between a disease and genetic variants.

Perturbagens

Small molecules, peptides, or genetic modifications that can induce changes in cell types, including cellular phenotypes, protein make-up and gene expression patterns.

Autoencoder

A type of artificial neural network that is used to learn to efficiently classify data in an unsupervised manner.

Perturbational studies

Studies designed to examine the response of cells to various interventions, including drugs and genetic alterations, for the purpose of understanding mechanism of action, changes in phenotype, gene expression or toxicity.

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Kingsmore, K.M., Grammer, A.C. & Lipsky, P.E. Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases. Nat Rev Rheumatol 16, 32–52 (2020). https://doi.org/10.1038/s41584-019-0337-0

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