Mitigating off‐target effects of small RNAs: conventional approaches, network theory and artificial intelligence

计算机科学 人工智能 计算生物学 神经科学 生物
作者
Zoltán Bereczki,Bettina Benczik,Olivér M. Balogh,S. 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
出处
期刊:British Journal of Pharmacology [Wiley]
被引量:15
标识
DOI:10.1111/bph.17302
摘要

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
科研通AI2S应助科研通管家采纳,获得10
刚刚
SciGPT应助科研通管家采纳,获得10
刚刚
共享精神应助科研通管家采纳,获得10
1秒前
LZQ应助科研通管家采纳,获得10
1秒前
Xiaoxiao应助科研通管家采纳,获得50
1秒前
1秒前
Hello应助科研通管家采纳,获得10
1秒前
1秒前
田様应助科研通管家采纳,获得10
1秒前
1秒前
1秒前
无恙发布了新的文献求助10
1秒前
含糊的茹妖完成签到 ,获得积分10
1秒前
2秒前
2秒前
微笑晓丝完成签到,获得积分10
2秒前
JamesPei应助罗大壮采纳,获得10
3秒前
3秒前
lkkkkkk完成签到,获得积分10
4秒前
4秒前
科研通AI2S应助LC采纳,获得10
4秒前
Rondab应助洪焕良采纳,获得10
4秒前
ling完成签到,获得积分20
5秒前
cacaldon发布了新的文献求助10
5秒前
日出发布了新的文献求助10
6秒前
幸运周一完成签到 ,获得积分10
6秒前
6秒前
6秒前
ZZC发布了新的文献求助10
6秒前
7秒前
柚子成精完成签到,获得积分10
7秒前
lkkkkkk发布了新的文献求助10
8秒前
8秒前
manan发布了新的文献求助10
9秒前
搞怪莫茗发布了新的文献求助10
10秒前
桐桐应助kryptonite采纳,获得10
10秒前
11秒前
ssw发布了新的文献求助10
12秒前
美姿发布了新的文献求助10
12秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Effective Learning and Mental Wellbeing 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3975986
求助须知:如何正确求助?哪些是违规求助? 3520289
关于积分的说明 11202025
捐赠科研通 3256778
什么是DOI,文献DOI怎么找? 1798453
邀请新用户注册赠送积分活动 877605
科研通“疑难数据库(出版商)”最低求助积分说明 806482