Prediction of potential small molecule-miRNA associations based on heterogeneous network representation learning

Small molecules targeting microRNAs: new opportunities and challenges in precision cancer therapy

Noncoding RNAs, especially miRNAs, play a pivotal role in cancer initiation and metastasis, underscoring their susceptibility to precise modulation via small molecule inhibitors. This review examines the innovative strategy of targeting oncogenic miRNAs with small drug-like molecules, an approach that can reshape the cancer treatment landscape. We review the current understanding of the multifaceted roles of miRNAs in oncogenesis, highlighting emerging therapeutic paradigms that have the potential to expand cancer treatment options. As research on small molecule inhibitors of miRNA is still in its early stages, ongoing investigative efforts and the development of new technologies and chemical matter are essential to fulfill the significant potential of this innovative approach to cancer treatment.

Targeting MicroRNAs with Small Molecules

MicroRNAs (miRs) have been implicated in numerous diseases, presenting an attractive target for the development of novel therapeutics. The various regulatory roles of miRs in cellular processes underscore the need for precise strategies. Recent advances in RNA research offer hope by enabling the identification of small molecules capable of selectively targeting specific disease-associated miRs. This understanding paves the way for developing small molecules that can modulate the activity of disease-associated miRs. Herein, we discuss the progress made in the field of drug discovery processes, transforming the landscape of miR-targeted therapeutics by small molecules. By leveraging various approaches, researchers can systematically identify compounds to modulate miR function, providing a more potent intervention either by inhibiting or degrading miRs. The implementation of these multidisciplinary approaches bears the potential to revolutionize treatments for diverse diseases, signifying a significant stride towards the targeting of miRs by precision medicine.

Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning

Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.

Gene expression inhibitors for the treatment of liver fibrosis: drugs under preclinical and early clinical investigation

INTRODUCTION

Liver fibrosis represents an unmet medical condition with growing incidence and only limited therapeutic options. Interfering with dysregulated gene expression was considered a specific treatment approach, and we are here reviewing the current options to modulate transcription and translation with small molecule inhibitors of involved enzymes, transcription factors or by using non-coding RNA molecules (RNA interference) or DNA antisense oligonucleotides. Despite promising results in preclinical models, only limited data are available from studies in humans.

AREAS COVERED

This expert opinion provides a general overview of how to interfere with gene expression (transcription and translation) and highlighting recent achievements in liver fibrosis.

EXPERT OPINION

Many compounds that were explored to modulate gene expression in liver fibrosis (models) were developed as anti-cancer agents. Their use in humans with impaired liver function is often impaired by the lack of specificity to inhibit only fibrosis-related genes in the liver and by associated general toxicity and narrow therapeutic windows. RNAi approaches show a higher degree of specificity and potentially less systemic toxicity. Clinical development in liver fibrosis requires close interaction between pharmaceutical companies and regulatory authorities to address topics like relevant (surrogate) endpoints to achieve meaningful readouts faster.