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.
