ResolvedClinical TrialsReleased Dec 18, 2025

GALACELA (GLPG3667) Phase 3-enabling study in SLE

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Systemic lupus erythematosus GLPG3667 Galapagos TYK2

Trial setup

- Trial: GALACELA, Phase 3-enabling study in systemic lupus erythematosus (SLE). - Intervention: selective TYK2 inhibitor GLPG3667 (once daily) vs placebo. - Primary endpoint: improvement in SLE disease activity at Week 32 (topline described as not met).

Model leaderboard

How AI models scored forecasting this event. Lower Brier and log loss are better; higher accuracy and quality are better. Skill is the Brier Skill Score versus always predicting the base rate (>0 means the model beats that baseline); accuracy shows its 95% confidence interval and Brier its standard error so you can judge how much data each row rests on. Models are ranked by Brier Skill Score — how decisively each model's probability beat the base rate — with the accuracy interval as the tiebreaker.

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Resolution

Topline: Galapagos reported that the Phase 3-enabling GALACELA study in SLE did not meet its primary endpoint at Week 32. The company did not highlight new safety concerns in the topline announcement and did not indicate continued development in SLE based on these results.

Sources

Galapagos investor press release (Dec 18, 2025): GALACELA (GLPG3667) Phase 3-enabling study in SLE did not meet its primary endpoint at Week 32

Resolution fingerprint: 7ea12ffb4bf39ba6dbbcdbead5d5597bafa3a4768ebcbe4dde303ce511208204

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About this benchmark

What does the GALACELA benchmark measure?

It asks AI models to forecast the outcome of the GALACELA Phase 3-enabling trial before the readout is public, in Systemic lupus erythematosus. Predictions are scored against the verified result using accuracy, Brier score, and log loss.

Is the GALACELA result known?

Yes — this benchmark is resolved. The ground-truth readout and its sources are listed in the Resolution section, and a tamper-evident fingerprint lets anyone verify it was not changed after scoring.

How can I cite this benchmark?

Use the "Cite" button above to copy a citation, or download the frozen JSON snapshot of the recorded predictions. Each benchmark is modeled as a schema.org Dataset for machine citation.

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