AD model update: AD DTG 4.2 - exploring biomarker outcomes in Alzheimer's disease. March 13, 2026. Unlearn is excited to announce the release of AD DTG 4.2, the latest update to its Alzheimer's disease Digital Twin Generator. This release expands the biomarker data available to its model and enables early exploration of biomarkers as potential clinical outcomes. Why biomarkers matter in AD trials. Biomarkers are measurable biological molecules whose concentration shifts in response to disease processes or treatments. Unlike traditional lab values that monitor general physiology, such as electrolytes or liver enzymes, disease-specific biomarkers target molecular pathways tied to a specific condition. In Alzheimer's, novel biomarkers like phosphorylated tau-217 (p-tau217), amyloid beta 40 (Aβ40), and amyloid beta 42 (Aβ42) can signal neurodegenerative changes years before symptoms appear, making them valuable for detecting disease, tracking progression, and evaluating therapeutic response in clinical trials. Expanded data, expanded possibilities. The updated data asset now includes or enriches several AD-relevant biomarkers, including: Aβ40, Aβ42, total tau (t-tau), phosphorylated tau-181 (p-tau181), p-tau217, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). Incorporating these biomarkers into the data asset creates new opportunities to explore their role in inputs, outputs, or both when generating digital twins of study patients in future DTG releases. An early exploration of ptau-217 as a clinical outcome. AD DTG 4.2 incorporates two biomarkers from the updated data asset, p-tau217 and GFAP. P-tau217 is a sensitive and specific marker of Alzheimer's pathology, correlating with amyloid accumulation, tau burden, brain atrophy, and physical degradation - and notably does not predict such changes in patients with other neurodegenerative disorders, making it highly specific to Alzheimer's disease. This specificity makes it a compelling feature for modeling disease progression. With this release, the model can now predict longitudinal changes in ptau-217 concentration as a clinical outcome, a capability Unlearn is optimistic about as Unlearn continue to grow and validate the underlying dataset. This capability may help support broader biomarker modeling within the AD DTG framework for future model updates. In addition, treating ptau-217 as the sole input to the model can provide prognostic information for other outcomes commonly measured in Alzheimer's trials. What's next. This biomarker work builds on the validated foundation of the AD DTG, which has already demonstrated meaningful impact in Alzheimer's studies. In retrospective analyses, digital twins of study participants have supported sample size reductions of up to 15% and up to 33% in control arms using Unlearn's EMA-qualified and FDA-supported method. As Unlearn continue to grow and enrich its AD dataset, now trained on over 25,000 patient records spanning cognitively normal individuals through moderate Alzheimer's disease, the model will be positioned to support increasingly robust biomarker modeling and stronger digital wins in future DTG releases. Blog.

Unlearn, a San Francisco-based AI company, will support SOLA Biosciences' Phase 1/2 clinical study of SOL-257, an investigational gene therapy for amyotrophic lateral sclerosis. The partnership will use AI-generated digital twins to strengthen the early-phase study. The single-arm trial will use digital twins as external comparators to help interpret clinical outcomes, addressing challenges posed by disease heterogeneity and small sample sizes in early-stage ALS studies. Unlearn's digital twins are created by a machine-learning model trained on extensive patient-level historical ALS data. The collaboration will support trial planning, regulatory engagement and participant-level analyses during the Phase 1/2 study and long-term follow-up. Unlearn has secured EMA qualification and FDA support for its digital twin technology in clinical trials.