In a single-arm early-phase ALS study, the sponsor gets one read. There’s no control group to anchor against, and no way to re-run the study if the data come back hard to interpret. Disease heterogeneity and steady functional decline blur the signal. When every participant is declining at a different rate, separating a treatment effect from natural variation is hard. How a sponsor designs the study from the start to handle this uncertainty determines what its data can ultimately say.

That's why SOLA Biosciences chose to structure its collaboration with Unlearn as a staged engagement for SOL-257, its long-term Phase 1/2 study of an investigational gene therapy designed to address a core pathological driver of ALS. The engagement spans three stages: data-driven trial planning, regulatory support across the Pre-IND and IND process, and digital twins as participant-level external comparators through the Phase 1/2 study and long-term follow-up.

In the partnership announcement, Keizo Koya, Founder and CEO of SOLA, framed the value this way:

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“SOL-257 is designed to address a core pathological driver of ALS. In this Phase 1/2 study, our objective is to generate data that meaningfully informs downstream development decisions. Incorporating AI-generated digital twins strengthens the scientific rigor of our study design and supports disciplined, data-driven development decisions as we advance SOL-257.”

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What this buys SOLA is interpretability it cannot add later. Each enrolled participant will be paired with their individual digital twin – an AI-generated prediction of their own control outcomes across multiple endpoints, generated from their baseline data using Unlearn’s ALS Digital Twin Generator. The study compares each trial participant’s actual trajectory against their predicted one (their digital twin), not against a population average. In a heterogeneous, progressive disease, that individual-level anchor is the difference between a result the team can defend and one it can’t

Trial planning. Ahead of protocol finalization, SOLA's clinical team worked through precedent across ALS trials, design assumptions, and how the comparator strategy fits with inclusion criteria, endpoint selection, and the statistical analysis plan. Those decision propagate to every subsequent analysis.

Regulatory support. The digital twin comparator strategy is built into the regulatory plan from the Pre-IND stage onward. The digital twins work within the framework of, and do not replace, established statistical and clinical analyses; they run alongside standard methodologies, consistent with the FDA’s draft guidance on the use of artificial intelligence to support regulatory decision-making for drug and biological products.

Trial execution and long-term follow-up. The ALS Digital Twin Generator is trained on more than 13,600 ALS participants from RCTs and observational studies, including NEALS, PRO-ACT, and PRO-ACE, and is independently validated. Once the Phase 1/2 study is underway, the same paired-comparator approach carries through long-term follow-up, where natural-history data is typically sparse and individual-level comparators are hardest to source and most valuable.

Our research team has recently outlined the statistical framework for using digital twins as synthetic control arms in single-arm trials, with worked examples in ALS and Huntington's disease. The methodology underlying SOL-257 is part of that broader work.

SOL-257 has not enrolled its first patient. But the decisions that will determine what its data can tell us have already been made, in the design.