American Marconi Wireless News - Unlearn to Apply AI-Generated Digital Twins to the VectorY Therapeutics PIONEER-ALS Study

SAN FRANCISCO, CA / ACCESS Newswire / February 12, 2026 / Unlearn, a leader in AI solutions for clinical trials, today announced it is partnering with VectorY Therapeutics, a biotechnology company developing vectorized antibody therapies, to support VectorY's PIONEER-ALS Phase 1/2 clinical study by using digital twins as patient-level external comparators for exploratory data analysis.

Amyotrophic lateral sclerosis (ALS) is a universally fatal neurodegenerative disease with no cure and only limited symptomatic treatment options. In the United States, more than 5,000 new cases of ALS are diagnosed each year, and more than 30,000 people are currently living with the disease. Median survival is 2-3 years after a diagnosis, and someone is either diagnosed with or dies from ALS every 90 minutes. The disease has an incidence comparable to major indications such as multiple sclerosis, yet far lower prevalence, reflecting a persistent disease burden and urgent unmet medical need.

In rapidly progressing, fatal conditions such as ALS, ethical considerations around placebo use and prolonged enrollment timelines can make placebo-controlled randomized trials difficult to execute, especially in the early phases of clinical development. As a result, sponsors often rely on single-arm study designs in which all participants receive the investigational therapy.

"Single-arm studies play a critical role in ALS drug development as they allow the companies to be nimble in assessing safety and identifying early biomarker efficacy signals. By integrating AI-generated digital twins as participant-level external comparators, VectorY can extract stronger evidence from every enrolled patient-supporting rigorous interpretation of trial outcomes and more confident development decisions in a disease where every data point matters," said Steve Herne, chief executive officer of Unlearn.

VectorY is leveraging Unlearn's ALS Digital Twin Generator (ALS-DTG), an advanced machine learning model trained on extensive, patient-level historical ALS clinical data. For each participant enrolled in the PIONEER-ALS study, the ALS-DTG generates an individualized forecast of their expected outcome under standard care. These digital twins serve as participant-level external comparators, enabling more precise interpretation of observed outcomes in the absence of placebo.

By incorporating digital twins into the study's prespecified analysis, VectorY can reduce uncertainty in counterfactual outcome estimates relative to traditional external control approaches, supporting more informed interim and end-of-study decisions while minimizing patient burden.

"ALS is a devastating disease with rapid progression and significant unmet need, making innovative, rigorous and efficient early-stage development approaches essential," said Olga Uspenskaya-Cadoz M.D., Ph.D. and chief medical officer of VectorY. "In the PIONEER-ALS study, we are focused on generating high-quality safety and biomarker signal data for VTx-002, our first-in-class vectorized antibody targeting TDP-43 pathology. Integrating Unlearn's patient-level digital twin technology into our prespecified exploratory analyses will help strengthen evidence generation from a single-arm design, with the aim to support more confident development decisions, disease progression modelling, and reduce timelines and patient burden."

About Unlearn

Unlearn exists to transform clinical development by making every trial smarter. Partnering with pharmaceutical and biotechnology companies, Unlearn harnesses data, AI, and digital twins to enable faster, more robust studies and clearer decision-making across clinical development. With a science-first approach and deep regulatory engagement-including EMA qualification and FDA support-Unlearn brings unmatched scientific credibility to applying AI in clinical trials.

For more information, visit www.unlearn.ai or follow us on LinkedIn and X/Twitter.

Contact:

Heather D'Angelo
[email protected]

SOURCE: Unlearn AI

View the original press release on ACCESS Newswire

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