Unlearn
Overview of Unlearn
Unlearn: AI-Powered Digital Twins for Streamlined Clinical Trials
What is Unlearn? Unlearn is a company that leverages artificial intelligence to create digital twins of patients participating in clinical trials. These digital twins are generated using disease-specific machine learning models trained on extensive historical clinical data. By using only baseline data from each trial participant, Unlearn can forecast clinical outcomes at every future time point with unparalleled precision. This innovative approach helps pharmaceutical companies accelerate clinical development, make faster decisions, and optimize trial designs.
How does Unlearn work?
Unlearn's core technology revolves around creating digital twins that mimic the behavior and predicted outcomes of real patients in a clinical trial. Here's a breakdown of how it works:
- Data Acquisition: Unlearn utilizes vast amounts of historical clinical data relevant to specific diseases.
- Model Training: Disease-specific machine learning models are trained on this historical data.
- Digital Twin Generation: For each new clinical trial participant, a digital twin is created using only their baseline data.
- Outcome Prediction: These digital twins forecast clinical outcomes at every future time point, allowing researchers to explore predicted outcomes for any subgroup of participants.
Key Features and Benefits
- Accelerated Trial Timelines: Design and run smaller randomized controlled trials (RCTs) that maintain or boost power without additional participants. This approach is qualified by the EMA and aligns with current FDA guidance.
- Faster Decision-Making: Explore predicted outcomes for any subgroup of participants for all measured clinical outcomes at any point in time. This capability supports interim analyses and other critical decisions throughout your study.
- Trial Design Optimization: Refine inclusion/exclusion criteria and clinical endpoints using digital twins, enabling more efficient trials with a higher chance of success.
Real-World Results
Unlearn's technology has demonstrated significant potential in accelerating clinical development. Approximate results include:
- Control Arm Size Reduction
- Enrollment Time Saved
Unlearn is working in areas such as neuroscience, immunology, and metabolic disease, and has partnerships with AbbVie, remynd, and Trace Neuroscience.
Who is Unlearn for?
Unlearn is primarily designed for:
- Pharmaceutical Companies: To accelerate drug development and reduce costs.
- Biotech Companies: To optimize clinical trial designs and improve the chances of success.
- Researchers: To gain deeper insights into disease progression and treatment effects.
Why Choose Unlearn?
- AI-Driven Precision: Unlearn's digital twins offer unparalleled precision in predicting clinical outcomes.
- Regulatory Alignment: Unlearn's approach is qualified by the EMA and aligns with current FDA guidance.
- Strategic Partnerships: Unlearn partners with leading biopharma companies to apply its technology in real-world clinical trials.
By leveraging AI-generated digital twins, Unlearn is transforming clinical trials, enabling more informed go/no-go decisions, saving resources, and fast-tracking the most promising therapies.
Scientific Evidence
Unlearn actively participates in scientific conferences and publishes research findings. For example, they have presented at AAIC 2025 on AI-Generated Digital Twins and at AD/PD Vienna 2025 with AbbVie on Machine Learning Models for Accelerated Enrollment in Parkinson’s Disease Clinical Trials.
How to use Unlearn?
To explore how Unlearn can support your study, simply contact them through their website. Their team can help you assess the potential impact of digital twins on your clinical trial design and decision-making processes.
Unlearn represents a significant advancement in clinical trial methodology, offering a data-driven approach to accelerate drug development and improve patient outcomes. Its AI-powered digital twins provide a powerful tool for researchers and pharmaceutical companies looking to optimize their clinical trial processes.
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