ASO Atlas 2.0: Evaluating antisense oligonucleotide prediction across the preclinical pipeline

Predictive models have the potential to transform drug development by increasing success rates across all stages of development pipelines. Evaluating this potential needs to include the whole pipeline, from initial screens of thousands of compounds to the expensive assays, such as toxicity assessment, focused on a few compounds in the later stages. Genetic medicines, such as antisense oligonucleotides (ASOs), could benefit millions affected by individually rare genetic disorders, if economic limitations imposed by the need to screen hundreds of sequences at considerable expense can be overcome. To enable systematic analysis of this problem, we present ASO Atlas 2.0, the largest public multi-endpoint ASO preclinical dataset, spanning 295,007 data points, including both in vitro efficacy and in vivo toxicology, across 430. Using this resource, we introduce a cost-based evaluation framework that translates per-stage classifier enrichment into projected cost savings under a representative preclinical pipeline model. We find that incorporating model predictions across all stages reduces projected pipeline cost by 71%. This saving is largely attributable to modest enrichment at costly late-stage in vivo studies rather than strong enrichment at lower-cost in vitro assays, reversing the model ranking suggested by accuracy alone and highlighting the need for improved toxicology modelling.