In the latest issue of “Nature Biotechnology”, scientists evaluated an algorithm developed by Dr. Liuxia Wang and Guang “Eric” Li for predicting the disease progression of patients living with amyotrophic lateral sclerosis (ALS), also known as motor neuron disease (MND).

Wang and Li of Sentrana, Inc. submitted one of the two winning algorithms for the DREAM Phil Bowen ALS Prize4Life Prediction Challenge in 2012.  Sentrana later created Origent Data Sciences, Inc., for the specific purpose of continuing this work developing patient-level predictive models and technologies.  Wang and Li remain actively involved in the continued work.

In the Nature Biotechnology publication (which can be viewed here), the reviewing scientists concluded the following:

  • The winning prediction algorithms provide robust and reliable predictions about the progression of symptoms for ALS patients.
  • When a panel of 12 clinicians from top ALS clinics around the world were asked to predict the future symptoms of patients, the prediction algorithms substantially outperformed the clinicians’ abilities to predict patient progression rates.
  • The use of these prediction algorithms could be used to reduce clinical trial populations by at least 20%, through changes in trial design.  For a 1,000 person ALS Phase 3 clinical trial, this represents a savings of USD $6 million.

Origent is proud to support the ALS community in developing new technologies to accelerate the development of new treatments.  We have continued this work since the challenge has completed, with the following accomplishments:

  • We have expanded the data set used to develop these prediction algorithms, and now incorporate the full PRO-ACT database of over 8,000 patients
  • We have increased the accuracy of our prediction algorithms by a substantial margin
  • We have introduced algorithms to predict the progression of individual ALS symptoms, including bulbar function, upper and lower limb function, and respiratory function

Origent remains committed to our continued development of knowledge and applications that will help accelerate the creation of treatments and cures for ALS.

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