| Title: | Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics |
| Journal: | Patterns |
| Published: | 10 Jun 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35755863/ |
| DOI: | https://doi.org/10.1016/j.patter.2022.100496 |
| URL: | http://www.cell.com/article/S2666389922000812/pdf |
Publication 7371
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Abstract
Indication extension or repositioning of drugs can, if done well, provide a faster, cheaper, and derisked route to the approval of new therapies, creating new options to address pockets of unmet medical need for patients and offering the potential for significant commercial and clinical benefits. We look at the promises and challenges of different repositioning strategies and the disease insights and scalability that new high-resolution patient stratification methodologies can bring. This is exemplified by a systematic analysis of all development candidates and on-market drugs, which identified 477 indication extension opportunities across 30 chronic disease areas, each supported by patient stratification biomarkers. This illustrates the potential that new artificial intelligence (AI) and combinatorial analytics methods have to enhance the rate and cost of innovation across the drug discovery industry.4 Authors
- Sayoni Das
- Krystyna Taylor
- Simon Beaulah
- Steve Gardner
Enabling scientific discoveries that improve human health