Antisense Oligonucleotides for Duchenne Muscular Dystrophy Exon Skipping

The patent applies preliminary action by using computational algorithms to predict and identify effective antisense oligonucleotide sequences before experimental testing. The software analyzes dystrophin pre-mRNA sequences, predicts exon skipping efficacy, and ranks candidate sequences, allowing researchers to select the most promising candidates for in vitro and in vivo testing. This preliminary computational screening reduces the number of sequences that need experimental validation, saving time and resources.

The patent adds another dimension to the screening process by integrating computational in silico prediction with traditional in vitro cell culture screening. The software provides a third dimension of prediction based on sequence analysis and algorithms that estimate in vivo efficacy. This multi-dimensional approach (in silico + in vitro + in vivo) allows researchers to correlate cell culture results with predicted in vivo performance, improving the reliability of predictive accuracy while maintaining the ease of initial cell culture screening.

The patent implements feedback through its computational algorithm that ranks and scores candidate antisense oligonucleotide sequences based on predicted exon skipping efficiency. The software provides feedback by identifying the top-ranked sequences that are most likely to be effective, allowing researchers to focus on a smaller subset of high-priority candidates. This feedback mechanism reduces selection complexity while maintaining comprehensive target coverage, as the system systematically evaluates all candidates and provides prioritized recommendations.