Kavli Affiliate: Xiang Zhang
| First 5 Authors: Tianyu Pan, Xiang Zhang, Weining Shen, Ting Ye,
| Summary:
Gene therapies aim to address the root causes of diseases, particularly those
stemming from rare genetic defects that can be life-threatening or severely
debilitating. While there has been notable progress in the development of gene
therapies in recent years, understanding their long-term effectiveness remains
challenging due to a lack of data on long-term outcomes, especially during the
early stages of their introduction to the market. To address the critical
question of estimating long-term efficacy without waiting for the completion of
lengthy clinical trials, we propose a novel Bayesian framework. This framework
selects pertinent data from external sources, often early-phase clinical trials
with more comprehensive longitudinal efficacy data that could lead to an
improved inference of the long-term efficacy outcome. We apply this methodology
to predict the long-term factor IX (FIX) levels of HEMGENIX (etranacogene
dezaparvovec), the first FDA-approved gene therapy to treat adults with severe
Hemophilia B, in a phase 3 study. Our application showcases the capability of
the framework to estimate the 5-year FIX levels following HEMGENIX therapy,
demonstrating sustained FIX levels induced by HEMGENIX infusion. Additionally,
we provide theoretical insights into the methodology by establishing its
posterior convergence properties.
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