By Steve Herne, CEO of Unlearn
‍

When ProJenX and our team presented at the ALS Drug Development Summit last year, we focused on a problem sponsors across early-stage ALS development keep running into: how to generate interpretable evidence from small, often open-label single-arm trials where no concurrent placebo arm is available. Digital twins are designed to help with this directly, by serving as a patient-level comparator for each participant: an individualized prediction of how their control trajectory would unfold without treatment, generated from their baseline data alone.

That broader challenge intersects with three more specific questions sponsors are weighing in early-stage ALS trial design today: how to stratify within the heterogeneous sporadic ALS population, how to extract reliable readouts from trials sized and timed to match patient urgency, and how to corroborate existing and novel late-phase endpoints in trials that are often too small to power them. Each comes back to the same underlying problem: how to read meaningful signal from constrained, often open-label data.

These questions aren't unique to any one program. They're part of how the field is thinking about early-stage ALS development right now.

One of those programs is PRO-101, ProJenX's Phase 1 study of prosetin, a brain-penetrant MAP4 kinase inhibitor in development for the treatment of ALS. ProJenX's team needs findings from this small, open-label Phase 1 that are rigorous enough to inform Phase 2 decisions.

ProJenX's team addressed this using Unlearn's digital twins, generated by a disease-specific ML model trained on more than 13,600 ALS participants from RCTs and observational studies, including NEALS, PRO-ACT, PRO-ACE, and APST Research. The analyses are intended to support the clinical team's interpretation of the data, not to replace the statistical and clinical judgment they bring to it. They run alongside standard methodologies and are consistent with regulatory expectations for external comparators.

What this gives ProJenX's team is a way to answer specific questions they couldn't answer otherwise.

Is there a subgroup of participants who respond differently to prosetin? Because each participant is paired with their own digital twin, the team can compute an individual treatment effect for that participant: how their actual outcomes diverge from the trajectory predicted by their twin. Patterns across those individual effects surface candidate subgroups, even in a small single-arm study.

Which endpoints carry the strongest signal for Phase 2? Digital twins predict multiple endpoints for each participant in the same dataset. Comparing observed outcomes to predicted outcomes across endpoints lets the team see which endpoints show the strongest divergence relative to their noise, indicating which endpoints are most likely to power a Phase 2 study.

How do you read treatment effects in an open-label setting? In an open-label trial, comparing a treated cohort against an external expected outcome leaves the team with treatment effects that are difficult to interpret. Pairing each participant with their own digital twin replaces that with a per-participant comparison: each participant's actual outcome against their own predicted untreated trajectory. 

In an illustrative analysis we presented with ProJenX at the ALS Drug Development Summit, paired digital-twin comparators made treatment effects readable at both the cohort and subject level in a 35-participant open-label dataset, with no concurrent placebo arm and no external matching dataset required.

Erin Fleming, COO of ProJenX, framed the value of this approach in her own words at the Summit:

"Digital twins help us make the most of the data we're getting from here. In this open-label study, digital twins provide built-in placebo controls for each participant. They have a lot of key advantages over propensity score matching or other natural history controls that allow us to have more confidence in the data we're taking out of that person-intraperson comparison."

Our research team has recently outlined a 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 PRO-101 is part of that broader work.

A year or two ago, digital twins as patient-level comparators in single-arm rare-disease trials would have been an exception. They're increasingly part of the toolkit for generating interpretable evidence in early-stage single-arm trials. PRO-101 is one of the trials helping make that real.