With many safety and technical limitations partly mitigated through chemical modifications, antisense oligonucleotides (ASOs) are gaining recognition as therapeutic entities. The increase in potency realized by ‘third generation chemistries’ may, however, simultaneously increase affinity to unintended targets with partial sequence complementarity. However, putative hybridization-dependent off-target effects (OTEs), a risk historically regarded as low, are not being adequately investigated. Here we show an unexpectedly high OTEs confirmation rate during screening of fully phosphorothioated (PS)-LNA gapmer ASOs designed against the BACH1 transcript. We demonstrate in vitro mRNA and protein knockdown of off-targets with a wide range of mismatch (MM) and gap patterns. Furthermore, with RNase H1 activity residing within the nucleus, hybridization predicted against intronic regions of pre-mRNAs was tested and confirmed. This dramatically increased ASO-binding landscape together with relatively high potency of such interactions translates into a considerable safety concern. We show here that with base pairing-driven target recognition it is possible to predict the putative off-targets and address the liability during lead design and optimization phases. Moreover, in silico analysis performed against both primary as well as spliced transcripts will be invaluable in elucidating the mechanism behind the hepatoxicity observed with some LNA-modified gapmers.

RNA interference (RNAi) is a powerful tool for post-transcriptional gene silencing. However, the siRNA guide strand may bind unintended off-target transcripts via partial sequence complementarity by a mechanism closely mirroring micro RNA (miRNA) silencing. To better understand these off-target effects, we investigated the correlation between sequence features within various subsections of siRNA guide strands, and its corresponding target sequences, with off-target activities. Our results confirm previous reports that strength of base-pairing in the siRNA seed region is the primary factor determining the efficiency of off-target silencing. However, the degree of downregulation of off-target transcripts with shared seed sequence is not necessarily similar, suggesting that there are additional auxiliary factors that influence the silencing potential. Here, we demonstrate that both the melting temperature (Tm) in a subsection of siRNA non-seed region, and the GC contents of its corresponding target sequences, are negatively correlated with the efficiency of off-target effect. Analysis of experimentally validated miRNA targets demonstrated a similar trend, indicating a putative conserved mechanistic feature of seed region-dependent targeting mechanism. These observations may prove useful as parameters for off-target prediction algorithms and improve siRNA ‘specificity’ design rules.

Antisense oligonucleotide (ASO) gapmers downregulate gene expression by inducing enzyme-dependent degradation of targeted RNA and represent a promising therapeutic platform for addressing previously undruggable genes. Unfortunately, their therapeutic application, particularly that of the more potent chemistries (e.g., locked-nucleic-acid-containing gapmers), has been hampered by their frequent hepatoxicity, which could be driven by hybridization-mediated interactions. An early de-risking of this liability is a crucial component of developing safe, ASO-based drugs. To rank ASOs based on their effect on the liver, we have developed an acute screen in the mouse that can be applied early in the drug development cycle. A single-dose (3-day) screen with streamlined endpoints (i.e., plasma transaminase levels and liver weights) was observed to be predictive of ASO hepatotoxicity ranking established based on a repeat-dose (15 day) study. Furthermore, to study the underlying mechanisms of liver toxicity, we applied transcriptome profiling and pathway analyses and show that adverse in vivo liver phenotypes correlate with the number of potent, hybridization-mediated off-target effects (OTEs). We propose that a combination of in silico OTE predictions, streamlined in vivo hepatotoxicity screening, and a transcriptome-wide selectivity screen is a valid approach to identifying and progressing safer compounds.