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Authors
Makarov, Nikita
Bordukova, Maria
Quengdaeng, Papichaya
Garger, Daniel
Rodriguez-Esteban, Raul
Schmich, Fabian
Menden, Michael P.
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Funding
Funding for this research was provided by:
F. Hoffmann-La Roche AG
European Union's Horizon 2020 Research and Innovation Programme (950293 - COMBAT-RES, 950293 - COMBAT-RES)
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Article History
Received: 5 May 2025
Accepted: 13 September 2025
First Online: 1 October 2025
Competing interests
: N.M., M.B., R.R.E. and F.S. are all employees of F. Hoffmann-La Roche. M.P.M. collaborates and is financially supported by GSK, F. Hoffmann-La Roche, and AstraZeneca. M.P.M. is supported by the European Union’s Horizon 2020 Research and Innovation Programme (Grant agreement No. 950293—COMBAT-RES). N.M., M.B., R.R.E., F.S. and M.P.M. are authors of an in-force patent entitled “Forecasting of subject-related attributes using generative machine-learning model” (patent publication number 2025/021719, patent application number EP2024070632) owned by F. Hoffmann-La Roche and Helmholtz Zentrum Munich. The patent covers application of large language models such as DT-GPT for forecasting of clinical trajectories of patients during a clinical trial. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
