Mirror, Mirror makes RNA-targeted discovery smarter
Solving the biggest challenge in RNA-targeted DEL screens with a mirror-image approach
RNA-targeted small molecule drugs are one of the most promising frontiers in drug development, but they’re still surprisingly underexplored. That’s starting to change. A major milestone came in 2020 with the FDA approval of Genentech’s Risdiplam, an RNA splicing modulator that has significantly improved outcomes for people living with spinal muscular atrophy (SMA). Since then, interest in this space has only grown. Companies like Arrakis, Ribometrix, Lilly and others are actively building pipelines for RNA modulators.
That said, finding small molecules that effectively target RNA isn’t easy. Current methods such as affinity selection-mass spectrometry (ASMS) and phenotypic screening have their strengths, but they’re often limited in scale and throughput. That’s where DNA-encoded library (DEL) screening comes in. DEL can accelerate progress by exploring much larger chemical space, giving drug discovery efforts a serious boost right from the start.
However, historically, the perception is that RNA is not amenable to DEL screening. This is because the DNA tags on DEL can hybridize with the RNA target itself, resulting in spurious enrichment of false positives. Earlier studies from WuXi and HitGen have introduced various blocking methods to minimize this kind of interference. But even when hybridization is reduced, it can still produce false hits and bury the enrichment signal of real binders. In response to growing interest in RNA-targeted drug discovery, we’ve established a patent-pending strategy, “mirror image DEL screen,” a major step forward in tackling the challenges of this space.
Inspired by Sczepanski and Joyces’ cross-chiral SELEX method, we perform an affinity-mediated screen with our collection of DEL against an L-RNA target, which is the mirror image of natural D-RNA. This chiral form preserves the sequence and folding conformation of natural D-RNA but does not hybridize with DNA tags, helping us avoid the false positives that have historically muddled with true binding signals. The mirror-image versions of the L-RNA-enriched hits are then deduced to be binders of the natural D-RNA and can be confirmed as genuine RNA target engagers by off-DNA synthesis and orthogonal assays.
How does our new approach affect DEL screening outcome? The difference is striking. When we screened our DEL against mirror-image L-RNA targets, we saw no evidence of nucleotide sequence-based enrichment. This elimination of DNA:RNA hybridization translates to the complete removal of false hits. For comparison, under similar conditions using natural D-RNA, DNA sequence motifs were enriched up to 1000-fold.
In addition, we consistently observed >10-fold enhancement in enrichment signal from real binders (both spiked-in positive controls and de novo discovered hits) toward L-RNA compared to natural D-RNA. For difficult targets like RNA, enrichment enhancements of this magnitude can often uncover new hits and turn failed screens into productive ones.
For further proof of concept, we screened our DEL deck of more than 100 billion compounds against three distinct RNA targets – an expansion repeat, a splice site, and a riboswitch. In each case, we discovered novel chemical matter with favorable physicochemical properties. After synthesizing the mirror-image versions of the DEL hits, we confirmed their binding to their respective natural RNA targets in orthogonal biophysical assays like SPR and/or ASMS. Further details can be found in our scientific poster here.
By eliminating DNA:RNA hybridization, our mirror-image strategy addresses the biggest barrier in RNA-directed DEL screens. RNA is now just as amenable to DEL screens as any other target class. With this technology, our partners can confidently screen RNA targets against more than 100 billion diverse drug-like compounds – 6 orders of magnitude higher than other screening methods.
And we don’t stop at hit discovery. Every confirmed hit comes with rich structure-activity relationship (SAR) data, which our team can help you build pharmacophore models and machine learning tools to guide optimization and accelerate progress toward clinical candidates.
In short, our DEL platform is now fully equipped to realize the potential of RNA-targeted drug discovery. We’re excited to help you make the most of it.
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