Quantitative approaches to drug development

As a progressive neurodegenerative disease with no current treatments, Huntington’s disease presents a significant challenge to drug developers. Tominersen, an antisense oligonucleotide, aims to slow the progression by lowering levels of huntingtin gene products, including mutant huntingtin protein. This work demonstrates that Model-Informed Drug Development is an essential tool for decision-making in complex neurological landscapes. Using quantitative frameworks to interpret complex signals, this collaboration successfully identified the optimal dose range for the ongoing GENERATION HD2 study.

A critical challenge in pharmacometrics is how to adequately and confidently analyze exposure-response (relationships for time-to-event data, especially in complex studies involving adaptive dosing, such as down-titration for improved tolerability. By using simulations to compare time-static vs. time-varying metrics across scenarios (adaptive vs. fixed dosing, drug accumulation, event type, and onset, the authors demonstrate a significant pitfall: no time-static metric produces consistently reliable results across all conditions. The authors provide guidance for confident assessment, recommending the use of time-varying exposure for adequate statistical power and to control type-I error – a methodology demonstrating robust performance across all scenarios.