Articles from Unlearn

Unlearn®, a leader in AI-powered solutions for clinical trials, today announced a collaboration with Trace Neuroscience, a biotechnology company focused on developing genomic therapies for neurodegenerative diseases. The partnership will support Trace Neuroscience as the company plans its upcoming Phase 1/2 clinical trial in amyotrophic lateral sclerosis (ALS), using Unlearn’s Digital Twin Generator for ALS (ALS DTG) and the Unlearn Platform.

In 2024, Unlearn® achieved key milestones in advancing AI to eliminate trial and error in medicine. From securing $50M in Series C funding to launching innovative tools and forming strategic partnerships, Unlearn made significant progress in addressing challenges like slow enrollment, long timelines, and high costs in clinical research.

Unlearn®, a pioneering artificial intelligence company, today announced a groundbreaking partnership with German-based APST Research (APST), a leading platform for ALS clinical research. Founded by renowned ALS Key Opinion Leader (KOL) Prof. Thomas Meyer, APST has played a pivotal role in advancing ALS research, including real-world studies on Biogen’s tofersen in SOD1-associated ALS. Through this collaboration, Unlearn’s Digital Twin Generator (DTG) for ALS will now integrate data from over 8,000 patients in APST’s longitudinal study. This dataset includes clinical data, patient self-assessments, biomarker analyses, and common ALS clinical assessments such as ALSFRS-R, SVC, FVC, and neurofilament light chain (NfL) measurements.

After securing over $130M in funding and achieving multiple regulatory successes, Unlearn today announced changes to its executive leadership to support the commercial expansion of its innovative AI technologies. Effective immediately, CEO and Co‐Founder Charles Fisher will step into the role of Chief Scientist, where he will drive Unlearn’s product and technical innovation. Steve Herne, currently serving as the Chief Commercial Officer, will succeed Fisher as CEO.

Unlearn, an artificial intelligence (AI) company today announced two poster sessions at the Alzheimer's Association International Conference (AAIC) in Philadelphia: “Accelerating randomized clinical trials in Alzheimer’s Disease using generative machine learning model forecasts of progression” (Poster #86486) with Johnson & Johnson Innovative Medicine; and “Assessment of AI-generated digital twin methodology on reduction of treatment effect variance and potential clinical trial sample size saving using a phase 2 trial dataset from patients with Alzheimer’s disease” (Poster #90685) with AbbVie.

Unlearn®, an artificial intelligence research company today announced its peer-reviewed scientific manuscript, entitled "Increasing Acceptance of AI-Generated Digital Twins Through Clinical Trial Applications," was published in the Clinical and Translational Science (CTS) journal.

Unlearn®, a leader in artificial intelligence research in medicine, today announced the appointment of Steve Herne as its first Chief Commercial Officer. Herne will spearhead commercial strategies to propel the company’s growth following their successful Series C funding.

Unlearn®, a company at the forefront of AI in medicine, today announced the newest version of its TwinRCT™ solution, TwinRCT 3.0. TwinRCTs are randomized clinical trials that use digital twins of trial participants—comprehensive forecasts of individual clinical outcomes—to optimize clinical trials. Participants’ digital twins are generated by AI models trained on patient-level data, and they enable trials with higher power or smaller control arms.

Unlearn®, a company at the forefront of AI in the clinical trial space, today announced the launch of TrialPioneer. This web-based application is free for teams in pharmaceutical and biotech companies to collaborate and understand how trial design choices affect the sample size requirements, statistical power, and time to enrollment—initially in seven major disease areas (Alzheimer’s Disease, ALS, Crohn’s Disease, Frontotemporal Dementia, Huntington’s Disease, Parkinson's Disease, and Ischemic Stroke). It also reveals the major advantages that novel AI-powered trial designs offer over traditional approaches.

Unlearn®, an AI company creating digital twins of clinical trial participants that enable smaller, faster studies, today announced a $50M Series C round led by Altimeter Capital, joined by returning investors Radical Ventures, Wittington Ventures, Mubadala Capital, Epic Ventures, and Necessary Venture Capital. This round of funding was secured to propel the company’s mission to advance AI to eliminate trial and error in medicine by investing in its people, data, engineering capabilities, and longer-term R&D initiatives.

Unlearn®, the only artificial intelligence (AI) company developing prognostic digital twins to run smaller, faster clinical trials, today announced that Arman Sabbaghi, Ph.D. has been appointed Head of Biostatistics Research. In this role, he will lead the company’s research and development activities to develop novel clinical trial applications that capitalize on the unique value of prognostic digital twins to reimagine the clinical trial for the 21st century.

Unlearn®, the only artificial intelligence (AI) company developing prognostic digital twins to run smaller, faster clinical trials, today announced that Alex Lang, Ph.D. has been appointed Vice President, Technology. In this role, he will lead the company’s data science, modeling, and platform teams responsible for delivering innovative solutions to Unlearn’s pharma and biotech partners to run more efficient clinical trials that help bring new treatments to patients sooner.

Unlearn®, the only artificial intelligence (AI) company developing prognostic digital twins to run smaller, faster clinical trials, today announced that the European Medicines Agency (EMA) has released its final favorable qualification opinion providing a regulatory framework for the application of the company’s TwinRCT™ solution in Phase 2 and 3 clinical trials. The three-step PROCOVA™ procedure (patent-pending) is the foundation for TwinRCTs and describes how to use patient-specific prognostic scores derived from digital twins to reduce clinical trial sizes while controlling Type-1 error rates. This qualification opinion represents the first time a regulatory body has formally supported a machine learning-based method for reducing sample size in pivotal trials.

Unlearn®, developer of the first machine-learning technology that creates digital twins of patients in clinical trials to enable smaller, faster studies, today announced that the European Medicines Agency (EMA) recently released a draft qualification opinion providing a regulatory framework for the application of the company’s TwinRCT™ solution to Phase 2 and 3 clinical trials. The three-step PROCOVA™ procedure (patent-pending) is the foundation of Unlearn’s TwinRCT™ solution and describes how to use patient-specific prognostic scores derived from participants’ digital twins to run smaller clinical trials without introducing bias.