Jeremy Smith, Director of Business Analytics at Audentes Therapeutics and Dan Fisher, Principal Consultant at IPM.ai 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.
Learn how IPM.ai transforms real world data into real world insights that uncover the ideal patient and their healthcare ecosystem so pharmaceutical companies can accelerate the successful research, development and commercialization of life-savings therapies.