Optimizing Oligonucleotide Therapeutics: A Model‐Informed Drug Development Perspective

Oligonucleotide therapies have emerged as a powerful therapeutic class, providing novel solutions for diverse diseases through antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), and aptamers that specifically target and modulate gene expression or protein function. However, oligonucleotide development faces distinct challenges, particularly the significant disconnect between systemic exposure and tissue activity, as these therapeutics are cleared from circulation relatively rapidly yet persist intracellularly with sustained pharmacological effects. This temporal disconnection, combined with limited clinical trial data due to the predominance of rare disease indications, complicates dose optimization and efficacy prediction using traditional development approaches. Model-informed drug development (MIDD) has emerged as a critical solution to address these unique oligonucleotide challenges by leveraging quantitative models to bridge the PK-PD disconnect, optimize dosing strategies despite limited data, and enhance decision-making throughout development. This manuscript comprehensively reviews how MIDD has been successfully applied across FDA-approved oligonucleotide therapies to inform endpoint selection for accelerated approvals, guide dosing strategies for general and subpopulations, and support efficient clinical development across multiple therapeutic areas. By elucidating MIDD's specific contributions to overcoming oligonucleotide development challenges, this review highlights the essential role of quantitative modeling strategies in advancing oligonucleotide therapeutics and enhancing their clinical and regulatory success.