减轻小 RNA 的脱靶效应:传统方法、网络理论和人工智能

IF 6.8 2区 医学 Q1 PHARMACOLOGY & PHARMACY
Zoltán Bereczki, Bettina Benczik, Olivér M. Balogh, Szandra Marton, Eszter Puhl, Mátyás Pétervári, Máté Váczy-Földi, Zsolt Tamás Papp, András Makkos, Kimberly Glass, Fabian Locquet, Gerhild Euler, Rainer Schulz, Péter Ferdinandy, Bence Ágg
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引用次数: 0

摘要

与小分子药物相比,三种极具前景的小 RNA 疗法,即小干扰 RNA(siRNA)、microRNA(miRNA)和反义寡核苷酸的 RNA 亚型(ASO)具有优势。这些小 RNA 可以靶向任何基因产物,为治疗各种疾病开辟了有效、安全的新途径。在临床前研究中,合成小 RNA 作为特定基因的沉默子,在生理和病理途径的研究中发挥着重要作用,有助于在不同条件下发现和验证药物靶点。然而,小 RNA 的脱靶效应会使临床前阶段的实验结果难以解释,并可能导致小 RNA 疗法的不良反应。在两大类脱靶效应中,我们主要关注杂交依赖性脱靶效应,尤其是类似 miRNA 的脱靶效应。我们的主要目的是讨论几种方法,包括序列设计、化学修饰和靶点预测,以减少杂交依赖性脱靶效应,即使是在小 RNA 疗法的早期开发阶段也应考虑到这一点。由于目前还没有预测杂交依赖性脱靶效应的标准方法,本综述概述了所有主要的最新计算方法,并提出了一些新方法,如可能将网络理论和人工智能(AI)纳入预测工作流程。此外,还介绍了用于验证硅学预测的案例研究和实验方法的简要调查。这些方法有助于解释实验结果,最大限度地减少脱靶效应,并有望避免小 RNA 疗法的脱靶相关不良事件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mitigating off-target effects of small RNAs: conventional approaches, network theory and artificial intelligence
Three types of highly promising small RNA therapeutics, namely, small interfering RNAs (siRNAs), microRNAs (miRNAs) and the RNA subtype of antisense oligonucleotides (ASOs), offer advantages over small-molecule drugs. These small RNAs can target any gene product, opening up new avenues of effective and safe therapeutic approaches for a wide range of diseases. In preclinical research, synthetic small RNAs play an essential role in the investigation of physiological and pathological pathways as silencers of specific genes, facilitating discovery and validation of drug targets in different conditions. Off-target effects of small RNAs, however, could make it difficult to interpret experimental results in the preclinical phase and may contribute to adverse events of small RNA therapeutics. Out of the two major types of off-target effects we focused on the hybridization-dependent, especially on the miRNA-like off-target effects. Our main aim was to discuss several approaches, including sequence design, chemical modifications and target prediction, to reduce hybridization-dependent off-target effects that should be considered even at the early development phase of small RNA therapy. Because there is no standard way of predicting hybridization-dependent off-target effects, this review provides an overview of all major state-of-the-art computational methods and proposes new approaches, such as the possible inclusion of network theory and artificial intelligence (AI) in the prediction workflows. Case studies and a concise survey of experimental methods for validating in silico predictions are also presented. These methods could contribute to interpret experimental results, to minimize off-target effects and hopefully to avoid off-target-related adverse events of small RNA therapeutics.
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来源期刊
CiteScore
15.40
自引率
12.30%
发文量
270
审稿时长
2.0 months
期刊介绍: The British Journal of Pharmacology (BJP) is a biomedical science journal offering comprehensive international coverage of experimental and translational pharmacology. It publishes original research, authoritative reviews, mini reviews, systematic reviews, meta-analyses, databases, letters to the Editor, and commentaries. Review articles, databases, systematic reviews, and meta-analyses are typically commissioned, but unsolicited contributions are also considered, either as standalone papers or part of themed issues. In addition to basic science research, BJP features translational pharmacology research, including proof-of-concept and early mechanistic studies in humans. While it generally does not publish first-in-man phase I studies or phase IIb, III, or IV studies, exceptions may be made under certain circumstances, particularly if results are combined with preclinical studies.
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