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.


  • 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



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