Maximizing on Minimal Data using Pharmacometric Modeling to Estimate the Prob. of Technical Success

Hosted by the SxP SIG and Biopharmaceutical Section of the ASA.
sxpsig.github.io/
community.amst...
Speakers: John Prybylski and Min Zhang, Pfizer
Dermatomyositis is a rare disease with manifestations in skin and muscle tissues and is considered a type I interferonopathy largely driven by interferon β (IFNβ). While there are several clinical scores used in dermatomyositis, Total Improvement Score (TIS), which is a holistic measure of skin, muscle and functional endpoints, is preferred by regulators for primary endpoints. In a Phase 2 study of an investigational IFNβ monoclonal antibody, dazukibart, sufficient data were collected in skin-predominant patients with skin-relevant outcomes, but there was only a small arm of muscle-predominant patients and only in this arm was TIS measured. TIS was planned to be used as the primary endpoint in Phase 3, but using the small observed sample for average power-based prediction of technical success was severely limited. In this presentation, we will discuss how exposure-response modeling was essential to overcome some of the data issues and to arrive at a probability of technical success supportive of further development.
Presented on Wednesday, 6th March 10 AM - 11 AM EST (4:00 PM - 5:00 PM CET).