GNS Healthcare (GNS), a leading precision medicine company, announced today the discovery of genetic and molecular markers of faster motor progression of Parkinson’s Disease (PD) patients, the LINGO2 gene together with a second genetic variant, along with demographic factors. The publication describing the discovery, titled “Large-scale identification of clinical and genetic predictors of Parkinson’s disease motor progression in newly-diagnosed patients: a longitudinal cohort study and validation,” appears in the journal The Lancet Neurology. The discovery was powered by patient data from the Parkinson’s Progression Markers Initiative sponsored by the Michael J. Fox Foundation for Parkinson’s Research.
“Being able to use these predictors in the clinical setting will lead to faster and significantly cheaper clinical trials and accelerate the availability of new Parkinson’s Disease drugs for patients in need,” said Colin Hill, Chairman, CEO, and co-founder of GNS Healthcare. “A major hurdle in Parkinson’s research is that rates of progression are extremely varied. Some patients progress very quickly while others do not. With accurate predictors of rates of progression, we will be able to remove uncertainties from drug development and patient response, reduce the number of clinical trial enrollees required by as much as twenty percent, and speed up the development of effective new drugs.”
REFS™, the GNS causal machine learning (ML) and simulation platform was used to transform the longitudinal genetic and clinical patient data from 429 individuals (312 PD patients and 117 controls) into computer models that connect the genetic and molecular variation of patients to motor progression rates. These computer models were used to simulate the future effects of the genetic and prognostic variables on motor outcomes, essentially predicting the motor progression rate for each patient. The models were validated in an independent longitudinal study, and clearly demonstrated the ability to prospectively differentiate between patient progression rates.
“There is still so much to understand about the progression of chronic, debilitating illnesses like Parkinson’s disease,” said Jeanne C. Latourelle, D.Sc., a co-author of the study and Director of Precision Medicine, GNS Healthcare. “The validation of our models in this study underscores the power of our REFS™ technology and its ability to accelerate the development of effective therapies for patients in need.”
This paper was co-authored by Jeanne C. Latourelle, Michael T. Beste, Tiffany C. Hadzi, Robert E. Miller, Jacob N. Oppenheim, Matthew P. Valko, Diane M. Wuest, Iya G. Khalil, Boris Hayete, of GNS Healthcare; and Charles S. Venuto of Center for Health + Technology and the Department of Neurology, University of Rochester, Rochester, NY. This work was supported by grants from the Michael J. Fox Foundation for Parkinson’s Research and the National Institute for Neurological Disorders and Stroke.
About REFS™
REFS™ (Reverse Engineering & Forward Simulation) is GNS Healthcare’s patented causal machine learning platform. Unlike traditional artificial intelligence platforms, REFS analyzes data sets beyond correlation, instead inferring causal mechanisms between variables to answer questions such as: How will the patient respond to this treatment? What if we choose one intervention over another? REFS uses a two-step process, first reverse engineering causative mechanisms from multi-model datasets, then running “what if?” simulations to determine which treatments and therapeutics will produce the best outcomes for every individual in the population. REFS is the only commercially available platform that infers causal mechanisms from patient data at scale from traditional healthcare and emerging data sources to bring the promise of precision medicine within reach.
About GNS Healthcare
GNS Healthcare applies causal machine learning and simulation technology to predict which treatments will work for which patients, improving individual patient outcomes and the health of populations, while reducing the total cost of care. The GNS technology is based on its MeasureBase™ data integration architecture and patented REFS™ (Reverse Engineering and Forward Simulation) causal inference and simulation engine. Health plans, bio-pharmaceutical companies, healthcare providers, foundations, academic medical centers, and self-insured employers use these cloud-based solutions to solve pressing and costly problems including metabolic syndrome, medication adherence, end-of-life care, preterm birth, personalized care pathways in specialty care, oncology, and diabetes, new drug target discovery, patient stratification in clinical trials, and more. GNS solutions focus on reducing adverse events, slowing disease progression, and improving therapeutic effectiveness through precision matching that maximizes impact on individual patient health outcomes while reducing wasteful spending and downstream medical costs.
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Media Contact:
Karen Sharma
MacDougall Biomedical Communications
ksharma@macbiocom.com
