Strategies for targeting RNA with small molecule drugs

ABSTRACT

Introduction

Historically, therapeutic treatment of disease has been restricted to targeting proteins. Of the approximately 20,000 translated human proteins, approximately 1600 are associated with diseases. Strikingly, less than 15% of disease-associated proteins are predicted or known to be ‘druggable.’ While the concept and narrative of protein druggability continue to evolve with the development of novel technological and pharmacological advances, most of the human proteome remains undrugged. Recent genomic studies indicate that less than 2% of the human genome encodes for proteins, and while as much as 75% of the genome is transcribed, RNA has largely been ignored as a druggable target for therapeutic interventions.

Areas covered

This review delineates the theory and techniques involved in the development of small molecule inhibitors of RNAs from brute force, high-throughput screening technologies to de novo molecular design using computational machine and deep learning. We will also highlight the potential pitfalls and limitations of targeting RNA with small molecules.

Expert opinion

Although significant advances have recently been made in developing systems to identify small molecule inhibitors of RNAs, many challenges remain. Focusing on RNA structure and ligand binding sites may help bring drugging RNA in line with traditional protein drug targeting.

KEYWORDS:

• RNA
• small molecule
• drug discovery
• RNA structure

Article highlights

• Considerable advances have been made in targeting RNAs with small molecules in the past few years.

• Targeting RNA with small molecules has the potential to greatly increase the ability to inhibit so-called ‘undruggable’ protein targets.

• The key to identifying small molecules binding to RNA is determining either the secondary or tertiary structure of the RNA.

• While several techniques exist to target RNAs, the best approach may be to combine multiple methods to ensure binding of a small molecule to an RNA elicits a functional response that minimizes off-target effects.

• Computational and machine learning approaches to discover RNA ligands are in their infancy but are key to developing virtual screening platforms on par with protein-ligand drug discovery.

This box summarizes key points contained in the article.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This work was supported by the Department of Defense Breast Cancer Expansion Award W81XWH-22-1-0795.