Machine Learning-native technology platform focuses on modeling disease biology to better match patients to the most effective therapies
BASEL, Switzerland & BOSTON–(BUSINESS WIRE)–#AACR—Genialis, a computational precision medicine company unraveling complex biology to find new ways to address disease, today announced the launch of ResponderID™, a new AI/ML-enabled platform specifically for use in clinical biomarker discovery. ResponderID may be implemented to support Pharma drug pipelines and diagnostic test product portfolios from conception to market.
As detailed in a whitepaper released today by Genialis, biomarkers measurably improve patient response rates and have been shown to enable clinical trials to achieve endpoints more quickly. In a retrospective modeling study of five major cancer indications, Parker and colleagues (2021) found clinical trials that used a biomarker for patient stratification — in other words, predict which patients are more likely to respond to therapy — were 5 to 12 times more likely to progress to the next phase. ResponderID has grown out of solutions to real-world challenges as discovered through work with innovative biotechs like InhibRx and OncXerna Therapeutics. OncXerna recently announced its lead biomarker, the XernaTM TME Panel, has been licensed by Qiagen for development as a companion diagnostic (CDx) and RUO kit.
“Most biomarkers never reach the point of use for patient reporting. A search of PubMed today will return just over one million entries for papers related to biomarkers, showing an exponential increase in the database since 1980; yet, the FDA lists only forty-six cleared or approved CDx devices and only 135 human nucleic acid-based tests. This means the vast majority of research into biomarkers remains just that, research,” said Rafael Rosengarten, Ph.D., CEO of Genialis. “Genialis built ResponderID to improve drug development timelines and help deliver more effective therapies to the right patient populations.”
ResponderID defines, models and validates new biomarkers for drug development and discovery programs. The platform is a technology suite for clinical and translational research, built from years of experience working with partners across the industry and advanced internal R&D. ResponderID incorporates technologies and proprietary tools for feature selection, data harmonization, and machine learning modeling. While ResponderID can complement virtually any data analysis environment, it is typically powered by data from Genialis Expressions, the FAIR-inspired data management and bioinformatics processing software suite.
“Discovering biomarkers is challenging for many reasons, including identifying and accessing appropriate data, determining best practices for use, and translation from the lab bench to the patient bedside,” said Robyn Schlicher, Ph.D., Director of Business Development & Strategy at Genialis. “With ResponderID, Genialis helps its partners navigate these obstacles and solve myriad technical challenges to develop biomarker signatures for clinical discovery and eventual patient reporting. Genialis has proved the clinical potential of its technology through the support of diagnostic regulatory submissions and participation in companion diagnostic development.”
Genialis will present more information about ResponderID at the BioData World Congress in Basel, Switzerland, on Wednesday, November 3 at 11:40 a.m. Central European Time/+2 GMT. Additionally, Dr. Rosengarten will chair two of the AI/ML tracks during the week on Tuesday, November 2 at 11:40 a.m. CET and 2:15 p.m. CET. To register, please visit https://biodata.snoball.events/p/genialis
Genialis is a computational precision medicine company unraveling complex biology to find new ways to address disease. ResponderID(™), Genialis’ clinical biomarker discovery platform, defines, models, and validates actionable biomarkers and optimally positions novel drugs to accelerate translational research and clinical development. Genialis is trusted by biopharma and big pharma alike, and together, we bring precision to medicine.
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