Audentes Webinar

Shortening the Diagnostic Journey of a Life-Threatening Rare Disease with Evolutionary Computation

Using AI and RWD to Uncover Rare Disease Insights and Improve Patient Outcomes

Jeremy Smith, Director of Business Analytics at Audentes Therapeutics and Dan Fisher, Principal Consultant at discussed how non-traditional real-world data combined with the power of machine learning and artificial intelligence deepened disease understanding and shortened the diagnostic journey of X-Linked Myotubular Myopathy (XLMTM).

Audentes Therapeutics is developing AT132 for the treatment of XLMTM, a rare, life-threatening neuromuscular disease characterized by extreme muscle weakness, respiratory failure and high mortality. The condition, affecting about 1 in 40-50,000 newborn males, is caused by a gene mutation that leads to a lack or dysfunction of myotubularin, a protein that is needed for normal development, maturation and function of skeletal muscle cells. 

The session covered how we leveraged a KOL’s home-grown patient registry in a privacy-safe fashion and unified it with a robust real-world data universe to uncover the XLMTM patient journey, discovered symptomatology that was not previously understood and identified undiagnosed and misdiagnosed patients to ultimately shorten the diagnostic timeline.

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Jeremy Smith
Director of Business Analytics




Principal Consultant



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