Presented at the 20th ASGCT Annual Meeting in Washington, DC, May 10-13, 2017.

Objectives: We hypothesized that computer models incorporating both survival and disease progression predictions could serve as tools to develop virtual controls and to stratify patients into slowly, average and rapidly progressing patients.

Conclusions: 

  • The virtual controls and stratification protocols provided accurate representations of disease progression at the level of individual patients.
  • The predicted slowly progressing group had an observed ALSFRS-R slope of -0.44 pts/month and approximately 90% survived one year. In contrast, the predicted rapid progressing group had an observed slope of -1.80 pts/month and a median survival of 5.8 months while the average progressing group closely resembled the starting group of 425 patients.
  • We conclude that virtual controls and patient stratification based on advanced machine learning algorithms can provide useful drug development tools for gene and cell therapies.

Authors: Albert A. Taylor, Samad Jahandideh, Danielle Beaulieu, Mike Keymer, David L. Ennist

 

2017-04-25-ASGCT

 

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