Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Novel insights into non-coding RNAs and their role in hydrocephalus

A large body of evidence has highlighted the role of non-coding RNAs in neurodevelopment and neuroinflammation. This evidence has led to increasing speculation that non-coding RNAs may be involved in the pathophysiological mechanisms underlying hydrocephalus, one of the most common neurological conditions worldwide. In this review, we first outline the basic concepts and incidence of hydrocephalus along with the limitations of existing treatments for this condition. Then, we outline the definition, classification, and biological role of non-coding RNAs. Subsequently, we analyze the roles of non-coding RNAs in the formation of hydrocephalus in detail. Specifically, we have focused on the potential significance of non-coding RNAs in the pathophysiology of hydrocephalus, including glymphatic pathways, neuroinflammatory processes, and neurological dysplasia, on the basis of the existing evidence. Lastly, we review the potential of non-coding RNAs as biomarkers of hydrocephalus and for the creation of innovative treatments.

Specific Interaction between a Fluoroquinolone Derivative, KG022, and RNAs with a Single Bulge

Small compounds targeting RNAs are recognized as a promising modality in drug discovery. We have found that a fluoroquinolone derivative, KG022, binds to RNAs with single-bulged residues. It has been demonstrated by 1H NMR that KG022 binds to RNAs with a bulged G or C and a GC or AU base pair at the 3' adjacent to the bulged residues. In the present study, the effects of the base pairs at the 5' adjacent to the bulged residues on the interaction of KG022 were analyzed mainly by 1H NMR. It was found that KG022 prefers UA and CG base pairs at the 5' adjacent to the bulged residues, indicating that a stable complex is formed by the stacking interaction among the fluoroquinolone ring and the purine bases of the 5' and 3' sides. In addition, this was confirmed by analysis of the 19F-NMR spectra. Analysis of temperature dependences of NMR spectra revealed that KG022 forms a more stable complex with RNAs having CG base pairs at the 5' adjacent position than those with UA base pairs. This work presented useful information for the development of small molecules having higher affinity to target RNAs.

Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review

MicroRNAs (miRNAs) are evolutionarily conserved small regulatory elements that are ubiquitous in cells and are found to be abnormally expressed during the onset and progression of several human diseases. miRNAs are increasingly recognized as potential diagnostic and therapeutic targets that could be inhibited by small molecules (SMs). The knowledge of SM-miRNA associations (SMAs) is sparse, mainly because of the dynamic and less predictable 3D structures of miRNAs that restrict the high-throughput screening of SMs. Toward augmenting the costly and laborious experiments determining the SM-miRNA interactions, machine learning (ML) has emerged as a cost-effective and efficient platform. In this article, various aspects associated with the ML-guided predictions of SMAs are thoroughly reviewed. Firstly, a detailed account of the SMA data resources useful for algorithms training is provided, followed by an elaboration of various feature extraction methods and similarity measures utilized on SMs and miRNAs. Subsequent to a summary of the ML algorithms basics and a brief description of the performance measures, an exhaustive census of all the 32 ML-based SMA prediction methods developed so far is outlined. Distinctive features of these methods have been described by classifying them into six broad categories, namely, classical ML, deep learning, matrix factorization, network propagation, graph learning, and ensemble learning methods. Trend analyses are performed to investigate the patterns in ML algorithms usage and performance achievement in SMA prediction. Outlining key principles behind the up-to-date methodologies and comparing their accomplishments, this review offers valuable insights into critical areas for future research in ML-based SMA prediction.

microRNA-Mediated Regulation of Oxidative Stress in Cardiovascular Diseases

BACKGROUND

Cardiovascular diseases (CVDs) are the leading cause of mortality globally, often linked to oxidative stress. MicroRNAs (miRNAs) have emerged as significant regulators of oxidative stress within the cardiovascular system.

OBJECTIVE

This review examines the complex relationship between miRNAs and oxidative stress, clarifying their effects on gene expression pathways related to ROS production and detoxification in CVDs.

METHODS

From August to October 2024, we conducted a comprehensive search of PubMed, Scopus, Web of Science, and Google Scholar for studies published between 2014 and 2024 investigating the role of miRNAs in oxidative stress and cardiovascular diseases.

RESULTS

Specific miRNAs have been identified as critical regulators in the pathophysiology of CVDs, with distinct expression patterns correlated with conditions such as hypertension, coronary artery disease, and heart failure. For instance, miR-21 exacerbates oxidative stress by targeting genes essential for redox homeostasis, while miR-210 promotes endothelial cell survival under hypoxic conditions by mitigating ROS levels.

CONCLUSION

The reciprocal relationship between miRNAs and oxidative stress highlights the potential for therapeutic interventions targeting miRNA expression and activity in managing CVDs. Understanding these molecular mechanisms is vital for developing innovative strategies to address oxidative damage in cardiac tissues and improve cardiovascular health outcomes.

Small-molecule RNA ligands: a patent review (2018-2024)

INTRODUCTION

Targeting three-dimensional RNA structures with traditional drug-like small molecules is gaining wide attention in both the academia and the pharmaceutical industries, due to their good oral bioavailability, cheap production cost, and the possibility of fine-tuning ADMET properties, which represent a powerful alternative to the current RNA-targeted therapies, including ASO and siRNA. As RNAs are involved in nearly all the physiological and pathological processes, small molecules RNA ligands can have a plethora of different therapeutic applications, spanning from cancer to infectious and neurological diseases.

AREAS COVERED

This review describes patents concerning small molecules RNA ligands published within January 2018 and October 2024, searched through Espacenet, Patentscope, and Google Patents databases.

EXPERT OPINION

The number of patents that has been released in the last few years demonstrates the relevance of targeting RNA structures for the development of next generation chemotherapeutic agents and antiviral/antibacterial drugs, even though this field is still in its infancy and many issues still need to be resolved, in particular related to selectivity. An emerging approach to considerably limiting side effects is presented by RIBOTAC derivatives, as promoting a selective RNase-L mediated RNA degradation allows to significantly reduce the dose of the compound.

PROTAC and Molecular Glue Degraders of the Oncogenic RNA Binding Protein Lin28

The interaction between proteins and RNA is crucial for regulating gene expression, with dysregulation often linked to diseases such as cancer. The RNA-binding protein (RBP) Lin28 inhibits the tumor suppressor microRNA (miRNA) let-7, making it a significant oncogenic factor in tumor progression and metastasis. In this study, a small molecule is used that binds Lin28 and blocks its inhibition of let-7. To enhance its efficay, the inhibitor is transformed into degraders via two degradation approaches: Proteolysis Targeting Chimera (PROTAC) and molecular glue. A series of PROTAC bifunctional molecules and molecular glues capable of degrading Lin28 in cells.is developed Both strategies significantly reduce overexpressed Lin28 and alleviate cancer cellular phenotypes. Notably, the molecular glue approach demonstrates exceptional potency, surpassing PROTAC in several aspects. This outcome underscores the superior efficiency of the molecular glue approach for targeted Lin28 degradation and highlights its potential for addressing associated diseases with small molecules. Innovative small molecule strategies such as molecular glue and PROTAC technology for targeted RBP degradation, hold promise for opening new avenues in RNA modulation and addressing related diseases.

Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting

MicroRNAs (miRNAs), a class of small non-coding RNAs, play a vital role in regulating gene expression at the post-transcriptional level. Their discovery has profoundly impacted therapeutic strategies, particularly in cancer treatment, where RNA therapeutics, including miRNA-based targeted therapies, have gained prominence. Advances in RNA sequencing technologies have facilitated a comprehensive exploration of miRNAs-from fundamental research to their diagnostic and prognostic potential in various diseases, notably cancers. However, the manual handling and interpretation of vast RNA datasets pose significant challenges. The advent of artificial intelligence (AI) has revolutionized biological research by efficiently extracting insights from complex data. Machine learning algorithms, particularly deep learning techniques are effective for identifying critical miRNAs across different cancers and developing prognostic models. Moreover, the integration of AI has led to the creation of comprehensive miRNA databases for identifying mRNA and gene targets, thus facilitating deeper understanding and application in cancer research. This review comprehensively examines current developments in the application of machine learning techniques in miRNA research across diverse cancers. We discuss their roles in identifying biomarkers, elucidating miRNA targets, establishing disease associations, predicting prognostic outcomes, and exploring broader AI applications in cancer research. This review aims to guide researchers in leveraging AI techniques effectively within the miRNA field, thereby accelerating advancements in cancer diagnostics and therapeutics.

MiRNA-based therapeutic potential in multiple sclerosis

This review will briefly introduce microRNAs (miRNAs) and dissect their contribution to multiple sclerosis (MS) and its clinical outcomes. For this purpose, we provide a concise overview of the present knowledge of MS pathophysiology, biomarkers and treatment options, delving into the role of selectively expressed miRNAs in clinical forms of this disease, as measured in several biofluids such as serum, plasma or cerebrospinal fluid (CSF). Additionally, up-to-date information on current strategies applied to miRNA-based therapeutics will be provided, including miRNA restoration therapy (lentivirus expressing a specific type of miRNA and miRNA mimic) and miRNA inhibition therapy such as antisense oligonucleotides, small molecules inhibitors, locked nucleic acids (LNAs), anti-miRNAs, and antagomirs. Finally, it will highlight future directions and potential limitations associated with their application in MS therapy, emphasizing the need for improved delivery methods and validation of therapeutic efficacy.

Quinazoline sulfonamide derivatives targeting MicroRNA-34a/MDM4/p53 apoptotic axis with radiosensitizing activity

Aim: New quinazoline benzenesulfonamide hybrids 4a-n were synthesized to determine their cytotoxicity and effect on the miR-34a/MDM4/p53 apoptotic pathway. Materials & methods: Cytotoxicity against hepatic, breast, lung and colon cancer cell lines was estimated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Results: Compound 4d was the most potent against HepG2 and MCF-7 cancer cells, with potential apoptotic activity verified by a significant upregulation of miR-34a and p53 gene expressions. The apoptotic effect of 4d was further investigated and showed downregulation of miR-21, VEGF, STAT3 and MDM4 gene expression. Conclusion: The anticancer and apoptotic activities of 4d were enhanced post irradiation by a single dose of 8 Gy γ-radiation. Docking analysis demonstrated a valuable affinity of 4d toward VEGFR2 and MDM4 active sites.

DeepdlncUD: Predicting regulation types of small molecule inhibitors on modulating lncRNA expression by deep learning

Targeting lncRNAs by small molecules (SM-lncR) to alter their expression levels has emerged as an important therapeutic modality for disease treatment. To date, no computational tools have been dedicated to predicting small molecule-mediated upregulation or downregulation of lncRNA expression. Here, we introduce DeepdlncUD, which integrates predictions of nine deep learning algorithms together, to infer the regulation types of small molecules on modulating lncRNA expression. Through systematic optimization on a training set of 771 upregulation and 739 downregulation SM-lncR pairs, each encoding 1369 sequence, representational, and physiochemical features, this method outperforms a recently released program, DeepsmirUD, by achieving 0.674 in AUC (area under the receiver operating characteristic curve), 0.722 in AUCPR (area under the precision-recall curve), 0.681 in F1-score, and 0.516 in Jaccard Index on a test set of 222 SM-lncR pairs. By extracting 125 upregulation and 46 downregulation SM-lncR pairs that involve disease-associated lncRNAs, DeepdlncUD is shown to gain an accuracy of 0.700 in the pathological context. Using connectivity scores, around half of the small molecules are correctly estimated as drugs to treat lncRNA-regulated diseases. This tool can be run at a fast speed to assist the discovery of potential small molecule drugs of lncRNA targets on a large scale. DeepdlncUD is publicly available at https://github.com/2003100127/deepdlncud.