SAN FRANCISCO--(BUSINESS WIRE)--Unlearn.AI, developer of the first machine-learning (ML) platform that creates Digital Twins to populate Intelligent Control Arms in clinical studies, today presented results generated from the company’s latest Alzheimer’s disease model at the Clinical Trials on Alzheimer’s Disease (CTAD) meeting in San Diego. The results presented during a late-breaking session demonstrated that the company’s platform can provide digital patient records designed to supplement actual control patients in Alzheimer’s disease clinical trials.
“We are excited by the results we presented today as they further validate our platform and its potential to significantly decrease the time spent running clinical trials in an area of significant patient need like Alzheimer’s disease,” said Charles K. Fisher, Ph.D., founder and CEO of Unlearn.AI. “Drug development in Alzheimer’s disease is increasingly expensive and time consuming. We believe that our platform can help alleviate these burdens and accelerate clinical trials to help get new medicines to the patients who need them.”
Unlearn is addressing patient recruitment, one of the biggest challenges associated with Alzheimer’s disease clinical trials, through its machine learning-based model that incorporates Digital Twins into Intelligent Control Arms of clinical studies. A Digital Twin is a comprehensive, longitudinal and computationally generated clinical record that describes what would have happened if a specific patient had received a placebo. The proprietary DiGenesis™ process leverages historical clinical trial datasets from thousands of patients, disease-specific machine learning models and rigorous statistical analysis to create digital records that are perfectly matched to patients in the investigational-treatment arm of studies.
To obtain a large and diverse sample of control data for its Alzheimer’s disease model, Unlearn utilized records through its membership with Critical Path for Alzheimer’s Disease (CPAD) from 5,000 people with early to moderate Alzheimer’s disease from the control arms of 16 historical clinical trials. The model captured the relationship between 50 clinical variables relevant to Alzheimer’s disease, such as components of neurologic exams, over 18 months to track the progression of the disease.
“The current state of clinical trials is challenging, especially for debilitating diseases like Alzheimer’s where patient recruitment is especially difficult,” said Marina Brodsky, Ph.D., former vice president, therapeutic area head, pain and neuroscience, medical affairs at Pfizer. “I am encouraged by these findings and see strong potential in the Unlearn platform to change the way we populate control arms for clinical trials, dramatically reducing time to bring them sooner to patients who desperately need them.''
Details on the effectiveness of Unlearn’s earlier Alzheimer’s disease model have also been published in Nature Scientific Reports in an article titled Machine learning for comprehensive forecasting of Alzheimer’s disease progression.
About the DiGenesis™ process
Unlearn is developing machine learning-based tools to address clinical trial challenges by incorporating Digital Twins into Intelligent Control Arms through its proprietary DiGenesis™ process. By leveraging historical clinical trial datasets, machine learning models specific for each disease state and rigorous statistical analysis, Unlearn creates Digital Twins that are perfectly matched to treatment patients.
Unlearn has developed the first machine learning (ML) platform for creating Intelligent Control Arms with Digital Twins through its proprietary DiGenesis™ process, allowing drug developers to dramatically reduce therapy development time, while lowering the risk of trial failure, thereby increasing confidence in clinical trial results. Unlearn is working closely with biopharmaceutical and medical device companies as well as regulators to ensure its methods meet the highest scientific and regulatory standards. Visit https://www.unlearn.ai or follow @UnlearnAI on Twitter, @unlearn-ai on LinkedIn.