Hit identification of novel small molecules interfering with MALAT1 triplex by a structure-based virtual screening

A systematic review of non-coding RNA therapeutics in early clinical trials: a new perspective against cancer

Targeting non-coding RNAs (ncRNAs), including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), has recently emerged as a promising strategy for treating malignancies and other diseases. In recent years, the development of ncRNA-based therapeutics for targeting protein-coding and non-coding genes has also gained momentum. This review systematically examines ongoing and completed clinical trials to provide a comprehensive overview of the emerging landscape of ncRNA-based therapeutics. Significant efforts have been made to advance ncRNA therapeutics to early clinical studies. The most advanced trials have been conducted with small interfering RNAs (siRNAs), miRNA replacement using nanovector-entrapped miRNA mimics, or miRNA silencing by antisense oligonucleotides. While siRNA-based therapeutics have already received FDA approval, miRNA mimics, inhibitors, and lncRNA-based therapeutics are still under evaluation in preclinical and early clinical studies. We critically discuss the rationale and methodologies of ncRNA targeting strategies to illustrate this rapidly evolving field.

Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery

INTRODUCTION

Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted in large amounts of data on disease associated RNAs, as well as on small molecules that bind to such RNAs. Artificial intelligence (AI) approaches, including machine learning and deep learning, present an opportunity to speed up the discovery of RNA-targeted small molecules by improving decision-making efficiency and quality.

AREAS COVERED

The topics described in this review include the recent applications of AI in the identification of RNA targets, RNA structure determination, screening of chemical compound libraries, and hit-to-lead optimization. The impact and limitations of the recent AI applications are discussed, along with an outlook on the possible applications of next-generation AI tools for the discovery of novel RNA-targeted small molecule drugs.

EXPERT OPINION

Key areas for improvement include developing AI tools for understanding RNA dynamics and RNA - small molecule interactions. High-quality and comprehensive data still need to be generated especially on the biological activity of small molecules that target RNAs.

Locked Nucleic Acid Oligonucleotides Facilitate RNA•LNA-RNA Triple-Helix Formation and Reduce MALAT1 Levels

Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and multiple endocrine neoplasia-β (MENβ) are two long noncoding RNAs upregulated in multiple cancers, marking these RNAs as therapeutic targets. While traditional small-molecule and antisense-based approaches are effective, we report a locked nucleic acid (LNA)-based approach that targets the MALAT1 and MENβ triple helices, structures comprised of a U-rich internal stem-loop and an A-rich tract. Two LNA oligonucleotides resembling the A-rich tract (i.e., A9GCA4) were examined: an LNA (L15) and a phosphorothioate LNA (PS-L15). L15 binds tighter than PS-L15 to the MALAT1 and MENβ stem loops, although both L15 and PS-L15 enable RNA•LNA-RNA triple-helix formation. Based on UV thermal denaturation assays, both LNAs selectively stabilize the Hoogsteen interface by 5-13 °C more than the Watson-Crick interface. Furthermore, we show that L15 and PS-L15 displace the A-rich tract from the MALAT1 and MENβ stem loop and methyltransferase-like protein 16 (METTL16) from the METTL16-MALAT1 triple-helix complex. Human colorectal carcinoma (HCT116) cells transfected with LNAs have 2-fold less MALAT1 and MENβ. This LNA-based approach represents a potential therapeutic strategy for the dual targeting of MALAT1 and MENβ.

Targeting non-coding RNAs: Perspectives and challenges of in-silico approaches

The growing information currently available on the central role of non-coding RNAs (ncRNAs) including microRNAs (miRNAS) and long non-coding RNAs (lncRNAs) for chronic and degenerative human diseases makes them attractive therapeutic targets. RNAs carry out different functional roles in human biology and are deeply deregulated in several diseases. So far, different attempts to therapeutically target the 3D RNA structures with small molecules have been reported. In this scenario, the development of computational tools suitable for describing RNA structures and their potential interactions with small molecules is gaining more and more interest. Here, we describe the most suitable strategies to study ncRNAs through computational tools. We focus on methods capable of predicting 2D and 3D ncRNA structures. Furthermore, we describe computational tools to identify, design and optimize small molecule ncRNA binders. This review aims to outline the state of the art and perspectives of computational methods for ncRNAs over the past decade.