Press Release: Origent Data Sciences Announces Appointment of Dr. David L. Ennist As President And CEO
Origent Data Sciences, Inc. (Origent) today announced that its Board of Directors has appointed Dr. David L. Ennist as President and Chief Executive Officer.
Origent Receives NIH SBIR Award to Develop Methods for Prediction-Based Enrichment of Human Clinical Trials
Origent Data Sciences has been awarded a Phase 1 Small Business Innovation Research (SBIR) award from the NIH National Institute of Mental Health.
Press Release: Cytokinetics announces New Data Presented at the International Symposium on ALS/MND
A poster at the conference presented results from analyses conducted by Origent Data Sciences on the validation of machine learning models to predict ALS disease progression using data from VITALITY-ALS.
Press Release: Mitsubishi Tanabe Pharma America Announces Collaborative Study To Identify And Measure Biomarkers In People With ALS
Origent will collaborate with Mitsubishi Tanabe Pharma America, Inc. on a study to identify and measure specific biomarkers in people with amyotrophic lateral sclerosis (ALS).
Origent’s Dr. David Ennist Joins Panel at ALSA Community Workshop
Origent’s Chief Science Officer Dr. David Ennist joins several other ALS community leaders as a panelist at the ALS Community Workshop with the theme Developing Drugs for Treatment; Guidance for Industry.
Origent Receives NIH SBIR Award to Commercialize Predictive Analytics Software to Increase the Efficiency of Human Clinical Trials
Origent Data Sciences has been awarded a Phase 2 Small Business Innovation Research (SBIR) award from the NIH National Center for Advancing Translational Sciences.
Origent Receives NIH SBIR Award to Study Prediction-Based Randomization for Human Clinical Trials
Origent Data Sciences has been awarded a Phase 1 Small Business Innovation Research (SBIR) award from the NIH National Center for Advancing Translational Sciences.
DCFemTech Recognizes Origent’s Danielle Beaulieu as an Outstanding Data Scientist
DCFemTech, the coalition of women leaders aimed at lowering the barriers to entry for women in tech by amplifying the efforts of women in tech organizations, has recognized Danielle Beaulieu as a recipient of their annual DCFemTech Awards.
Press Release: Origent Data Sciences and Cytokinetics Advance Collaboration Intended to Validate Predictive Analytics Model in ALS
Origent and Cytokinetics announce the advancement of their research collaboration to prospectively validate Origent’s computer model to predict the course of ALS disease progression using data from VITALITY-ALS, Cytokinetics’ ongoing Phase 3 clinical trial of tirasemtiv.
Press Release: Origent and Cytokinetics Present Analyses Demonstrating Baseline Data Predict Measures of Vital Capacity in Patients with ALS
Origent and Cytokinetics announced results from the first step of a research collaboration leveraging an Origent computer model to predict the course of ALS disease progression leveraging data from Cytokinetics’ clinical trials of tirasemtiv.
Origent Presents at 2016 ALS-MND International Symposium
Please join Origent at the 27th International Symposium on Amyotrophic Lateral Sclerosis and Motor Neurone Disease in Dublin Ireland, December 7th-9th, 2016. Click here to read more about our posters and presentations.
Origent Receives NIH SBIR Award to Study Ventilated Patients in ICU Setting
Origent Data Sciences has been awarded a Small Business Innovation Research (SBIR) award from the NIH National Institute of Nursing Research.
ALS Association Awards Origent Data Sciences a Grant to Enable a Research Partnership
On March 31, 2016, the ALS Association announced the awarding of an ALSA-initiated grant to Origent, to enable a research partnership with Cytokinetics Inc. to improve clinical trial design.
Press Release: Origent and Cytokinetics Announce Research Collaboration
Origent and Cytokinetics today announced a research collaboration to refine and prospectively validate an Origent computer model to predict the course of ALS disease progression leveraging data from Cytokinetics’ clinical trials of tirasemtiv.
Origent’s CSO Dr. David Ennist Presents Webinar to ALS Association
Origent’s Chief Science Officer Dr. David Ennist will conduct a webinar discussion to the ALS Association, titled “How Can We Use Predictions of Individual ALS Patient Disease Progressions to Improve ALS Clinical Trials?”
ALSA Announces Grant Award to Origent
The ALS Association has announced the award of a grant made to Dr. David Ennist, Chief Science Officer at Origent Data Sciences, Inc.
Origent Helps Organize Prize4Life’s ALS Stratification Challenge
Origent’s Special Technical Advisor Dr. Liuxia Wang is one of the organizers of the latest ALS prediction challenge sponsored by Prize4Life.
Origent Named as a Finalist for Health & Life Sciences Innovator of the Year
The Fairfax County Chamber of Commerce has named Origent Data Sciences, Inc. as a finalist for Health & Life Sciences Innovator of the Year for the 2015 Greater Washington Innovation Awards.
Neurotherapeutics Features ALS Work Co-Authored by Origent’s Scientists
Performance of Origent’s ALS disease predictive model is highlighted in the article and shows a close agreement between the observed ALSFRS-R scores and the ALSFRS-R scores predicted by Origent’s algorithms for each individual patient.
Nature Biotechnology Features Origent’s ALS Technologies
Scientists evaluated an algorithm developed by Dr. Liuxia Wang and Guang “Eric” Li for predicting the personalized disease progression of patients living with ALS.
Origent Takes the ALS Ice Bucket Challenge
Origent joined with our affiliate company Sentrana to participate in the ALS Ice Bucket Challenge.
Origent and Sentrana – Joining the Battle to Cure ALS
Origent CEO Mike Keymer and Sentrana CEO Syeed Mansur discuss how a precision sales and marketing company came to join in the battle against ALS.
Prize4Life Announces $50,000 ALS Prediction Prize Winners
Sentrana data scientists Dr. Liuxia Wang and Guang Li were announced as first place winners of the ALS Prediction Prize challenge sponsored by Prize4Life.
Combination of ciprofloxacin/celecoxib as a novel therapeutic strategy for ALS, Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration
Salomon-Zimri S, Pushett A, Russek-Blum N, Van Eijk RPA, Birman N, Abramovich B, Eitan E, Elgrart K, Beaulieu D, Ennist DL, Berry JD, Paganoni S, Shefner JM, Drory VE
Detectable Effect Cluster Analysis: A Novel Machine-Learning Subgroup Analysis Method for Drug Rescue
The poster will be presented at the MDA Clinical & Scientific Conference on March 13-16 2022.
Use of Machine Learning Predictions as Covariates to Optimize Clinical Trials of Neurologic Diseases
The poster was presented at the MDA Clinical & Scientific Conference on March 13-16 2022.
Development and Validation of a Machine-Learning ALS Survival Model Lacking Vital Capacity (VC-Free) for use in Clinical Trials during the COVID-19 Pandemic
Danielle Beaulieu,James D. Berry,Sabrina Paganoni,Jonathan D. Glass,Christina Fournier,Jonavelle Cuerdo,Mark Schactman & David L. Ennist.
Published in Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, on August 10, 2021.
Evidence for Generalizability of Edaravone Efficacy using a Novel Machine Learning Risk-based Subgroup Analysis Tool
Benjamin Rix Brooks, Erik P. Pioro, Danielle Beaulieu, Albert A. Taylor, Mark Schactman, Mike Keymer, Wendy Agnese, Johnna Perdrizet, Stephen Apple & David L. Ennist
Published in Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, on July 10, 2021.
Book: Neurotherapeutics in the Era of Translational Medicine
The Origent Team has authored a chapter in the new book Neurotherapeutics in the Era of Translational Medicine.
Poster: A Machine Learning ALS Survival Model Lacking Vital Capacity for use in Clinical Trials during the COVID-19 Pandemic
Presented at the 31st International Symposium on ALS/MND, December 9, 2020.
Poster: Detectable Effect Cluster Analysis: A Novel Machine-Learning Subgroup Analysis Method for Drug Rescue
Presented at the 31st International Symposium on ALS/MND, December 9, 2020.
Poster: Detectable Effect Cluster Analysis: A Novel Machine-Learning Based Clinical Trial Subgroup Analysis Tool
Presented at the 30th International Symposium on ALS/MND in Perth, Australia on December 4, 2019.
Poster: Estimate of an Acthar® Gel Treatment Effect in ALS Patients using Virtual Controls
Presented at the 30th International Symposium on ALS/MND in Perth, Australia on December 4, 2019.
Poster: Detectable Effect Cluster Analysis: A Novel Machine Learning Based Clinical Trial Subgroup Analysis Tool
Presented at the 18th Annual NEALS Meeting in Clearwater, Florida, October 3rd, 2019.
Design and Analysis of a Clinical Trial Using Previous Trials as Historical Control
David A. Schoenfeld, Dianne M. Finkelstein, Eric Macklin, Neta Zach, David L. Ennist, Albert A. Taylor, Nazem Atassi, The Pooled Resource Open-Access ALS Clinical Trials Consortium.
Published in Clinical Trials., on July 1, 2019.
Poster: Rapid Deployment of a Machine Learning-based Derived Biomarker using Publicly Available Data Sources for Covariate Adjusted Descriptive Modeling
Presented at the 2019 ASA Symposium on Data Science and Statistics in Bellevue, Washington on May 31, 2019.
Poster: Increasing ALS Clinical Trial Efficiency using Machine Learning Models
Presented at the 2019 MDA Clinical and Scientific Conference in Orlando, Florida on April 15, 2019. Re-presented at the 71st Annual Meeting of the American Academy of Neurology in Philadelphia, Pennsylvania on May 7, 2019, and at the ENCALS Meeting 2019 in Tours, France on May 16, 2019.
Poster: Validation of a Suite of Machine Learning Models using the Longitudinal VITALITY-ALS Data Set
Presented at the 29th International Symposium on ALS/MND in Glasgow, Scotland on December 8, 2018.
Pilot trial of inosine to elevate urate levels in amyotrophic lateral sclerosis
Katharine Nicholson, James Chan, Eric A. Macklin, Mark Levine‐Weinberg, Christopher Breen, Rachit Bakshi, Daniela L. Grasso, Anne‐Marie Wills, Samad Jahandideh, Albert A. Taylor, Danielle Beaulieu, David L. Ennist, Ovidiu Andronesi, Eva‐Maria Ratai, Michael A. Schwarzschild, Merit Cudkowicz, Sabrina Paganoni
Published online in Annals of Clinical and Translational Neurology on October 22, 2018
Poster: Machine Learning Applications for Increasing the Efficiency of ALS Clinical Trials
Presented at the 17th Annual NEALS Meeting in Clearwater, Florida, October 3rd, 2018.
Poster: Increasing Study Power using a Machine Learning Approach
Presented at the Joint Statistical Meeting (JSM) of the American Statistical Association (ASA) on July 30, 2018.
Poster: Machine Learning Models for the Assessment of Potential ALS Biomarkers
Presented at the 2018 Meeting of the European Network to Cure ALS (ENCALS), on June 21, 2018.
Improved Stratification of ALS Clinical Trials Using Predicted Survival
James D. Berry, Albert A. Taylor, Danielle Beaulieu, Lisa Meng, Amy Bian, Jinsy Andrews, Mike Keymer, David L. Ennist, Bernard Ravina
Published online in Annals of Clinical and Translational Neurology on March 8, 2018
Longitudinal Modeling to Predict Vital Capacity in Amyotrophic Lateral Sclerosis
Samad Jahandideh, Albert A. Taylor, Danielle Beaulieu, Mike Keymer, Lisa Meng, Amy Bian, Nazem Atassi, Jinsy Andrews & David L. Ennist
Published in Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, on December 20, 2017.
Poster: Machine Learning Models for the Assessment of Potential ALS Biomarkers
Presented at the 28th International Symposium on ALS/MND in Boston, Massachusetts, on December 9, 2017
Poster: Validation of Predictive ALS Machine Learning Models with a Contemporary, External Dataset and Application to Trial Simulations
Presented at the 16th Annual NEALS Meeting in Clearwater, Florida, October 4, 2017, and also at the 28th International Symposium on ALS/MND in Boston, Massachusetts, on December 9, 2017
Poster 162: Machine Learning Models for the Clinical Development of Gene and Cell Therapies
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...
Poster: The Proper Use of Historical Controls in ALS Trials
Presented at the 27th International Symposium on Amyotrophic Lateral Sclerosis and Motor Neurone Disease, Dublin Ireland, December 2016. Objectives: We asked, for what types of ALS human clinical trials can concurrent controls, historical controls, and virtual...
Poster: ALS Resistance is Regional and Not Explained by Demographics, Medications or Labs
Presented at the 27th International Symposium on Amyotrophic Lateral Sclerosis and Motor Neurone Disease, Dublin Ireland, December 2016. Objectives: To develop a meaningful operational definition of “ALS resistance” which captures patients with unexpectedly long...
Poster: Predicting Disease Progression for ALS Clinic Patients
Presented at the 27th International Symposium on Amyotrophic Lateral Sclerosis and Motor Neurone Disease, Dublin Ireland, December 2016.
Poster: In silico Stratification of ALS Patients using Machine Learning Algorithms
Presented at the 27th International Symposium on Amyotrophic Lateral Sclerosis and Motor Neurone Disease, Dublin Ireland, December 2016. Objectives: We hypothesized that computer models incorporating predictions for both survival and disease progression as measured...
Poster: Machine Learning Model for the Prediction of Slow Vital Capacity
Presented at the 27th International Symposium on Amyotrophic Lateral Sclerosis and Motor Neurone Disease, Dublin Ireland, December 2016. Objectives: To develop a model that predicts SVC using the PRO-ACT database. Conclusions: We hypothesized the possibility of...
Predicting Disease Progression in Amyotrophic Lateral Sclerosis.
Taylor AA, Fournier C, Polak M, Wang L, Zach N, Keymer M, Glass JD, Ennist DL.
Published online in Annals of Clinical and Translational Neurology on September 7, 2016
Poster: Analysis of Function and Survival in ALS Patients with Diaphragm Pacing using Virtual Controls
Taylor A, Miller R, Onders R and Ennist D. Analysis of function and survival in ALS patients with diaphragm pacing using virtual controls [v1; not peer reviewed]. F1000Research 2016, 5:120 (poster)
Being PRO-ACTive: What can a Clinical Trial Database Reveal About ALS?
Neta Zach, David L. Ennist, Albert A. Taylor, Hagit Alon, Alexander Sherman, Robert Kueffner, Jason Walker, Ervin Sinani, Igor Katsovskiy, Merit Cudkowicz, Melanie L. Leitner
Published online in Neurotherapeutics on January 23, 2015
Crowdsourced Analysis of Clinical Trial Data to Predict Amyotrophic Lateral Sclerosis Progression
Co-authors of this publication include Dr. Liuxia Wang and Guang Li of Origent. Published online in Nature Biotechnology on November 2, 2014.
Sentrana Presents Prize Winning Research at RECOMB Conference
Guang “Eric” Li describes the algorithm that was declared a winner of the DREAM Phil Bowen ALS Prediction Prize Challenge.
Origent Featured on the “Connecting ALS” Podcast Hosted by Jeremy Holden and Produced by the ALS Association
On January 20, 2022, Dr. Dave Ennist, CEO of Origent Data sciences, was featured on an episode of the Connecting ALS podcast.
Origent Awarded Patent for its Drug Rescue DEC Analysis
On October 5, 2021, Origent Data Sciences, Inc. was awarded by the USPTO patent number 11,139,051, titled Systems and Methods for Designing Clinical Trials.
Origent, Biogen and Cytokinetics Present at AAN
Origent Chief Science Officer Dr. Dave Ennist joined representatives from Cytokinetics and Biogen on a panel at the annual conference of the American Academy of Neurology in Vancouver, BC.
Beneficial Stats – The Scientist Magazine
Origent and Sentrana scientist Dr. Liuxia Wang discusses her original work predicting disease progression for individual ALS patients.
Paper featuring Origent’s ALS Work is Designated Editor’s Choice by Science Translational Medicine
SCIENCE Translational Medicine features work by Origent’s scientists.
Crowdsourcing Project Predicts Progression of Neurodegenerative Disease
This article featuring Origent was published in SCIENCE magazine on November 4, 2014.