A scalable and equitable framework for target and patient prioritisation in rare disease antisense therapeutics
Whittle, E. F.; Montgomery, K.-A.; Camps, C.; Elkhateeb, N.; Ryan, C.; Aguti, S.; de Guimaraes, T. A. C.; Kini, U.; Stewart, H.; Douglas, A. G. L.; Wilson, L.; Leitch, H. G.; Lynch, D. S.; Robinson, R.; Michaelides, M.; Yu, T. W.; Gissen, P.; Lauffer, M. C.; Lench, N.; O'Connor, D.; Tavares, A. L.; Sanders, S. J.; Kurian, M. A.; Titheradge, H.; Clement, E.; van der Spuy, J.; Taylor, J. C.; Rinaldi, C.; Muntoni, F.; Zhou, H.; Davidson, A. E.; Ryten, M.; UPNAT consortium,
Show abstract
BackgroundNucleic acid therapies (NATs) comprise engineered DNA- or RNA-based medicines that act through sequence-specific interactions to modify gene function. Among these, antisense oligonucleotide (ASO) therapies are designed to bind messenger RNA (mRNA) or pre-mRNA to alter splicing, transcript stability, or translation. Many patients with a rare genetic disease stand to benefit from these treatments and, as underlying technologies continue to advance, a critical barrier to care is the equitable selection of targets and patients. Owing to landmark progress in genomic health care, the UK is uniquely positioned to develop a national framework on NAT patient-selection infrastructure. The UK Platform for Nucleic Acid Therapies (UPNAT) has been launched, in part, to meet this goal, with a key output being a structured patient and target selection framework to support NAT development and clinical application, using ASO therapies as a pilot modality. Methodology and ResultsA multidisciplinary panel of UK-based experts established the UPNAT framework to enable systematic assessment of ASO amenability across modular domains encompassing disease understanding, functional models, variant characteristics, and the individual patient, incorporating the recently published N1C VARIANT guidelines. This modular structure supports consistent prioritisation of tractable targets while identifying biological, clinical, technical, or evidentiary gaps currently limiting ASO development. Designed for implementation within the UK healthcare infrastructure and amenable to future automation using open-access resources, the framework was iteratively refined through application to genomic and clinical data from approved ASO therapies and selected real-world patient case studies. ConclusionWe present the first disease-agnostic framework to support structured prioritisation of patients and targets (diseases, genes, or variants) for ASO development and consideration within specialist healthcare services. Designed to accommodate rapid technological advances in NATs, the framework promotes transparent, equitable, and reproducible decision-making within the UK National Health Service (NHS), with principles transferable to other healthcare systems.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- IGenomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes 92%
- The Importance of Automation in Genetic Diagnosis: Lessons from Analyzing an Inherited Retinal Degeneration Cohort with the Mendelian Analysis Toolkit (MATK) 91%
- Evaluation of Bayesian Classification Framework on the Variant Classification of Hereditary Cancer Predisposition Genes 91%
Similar papers in this journal
- Consensus guidelines for eligibility assessment of pathogenic variants to antisense oligonucleotide treatments 95%
- Validating data from Multiplex Assays of Variant Effect (MAVEs): A CanVIG-UK National Survey of NHS Clinical Scientists 92%
- Advanced variant classification framework reduces the false positive rate of predicted loss of function (pLoF) variants in population sequencing data 91%
Similar papers in this journal
- Systematic analysis of genetic and phenotypic characteristics reveals antisense oligonucleotide therapy potential for one-third of neurodevelopmental disorders 94%
- Evaluating Genome Sequencing Strategies: Trio, Singleton, and Standard Testing in Rare Disease Diagnosis 92%
- The Genomic Landscape of Rare Disorders in the Middle East 91%
Similar papers in this journal
- Consistent Performance of GPT-4o in Rare Disease Diagnosis Across Nine Languages and 4967 Cases 90%
- Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology 89%
- A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care data 89%
Similar papers in this journal
- A Standardized Metric to Enhance Clinical Trial Design and Outcome Interpretation in Type 1 Diabetes 90%
- Angiogenic and Immune Predictors of Neoadjuvant Axitinib Response in Renal Cell Carcinoma with Venous Tumour Thrombus 90%
- Clinical Impact of Pharmacogenetic Risk Variants in a Large Chinese Cohort 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.