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20.04.2026 11:00

Rare Leigh Syndrome: Artificial intelligence supports the search for new therapies

Anne Wansing Stabsstelle Presse und Kommunikation
Heinrich-Heine-Universität Düsseldorf

The need for medical treatments for rare diseases such as Leigh Syndrome is high. However, low patient numbers make research into treatments difficult. Together with a team from the University of Luxembourg, researchers from Heinrich Heine University Düsseldorf (HHU) and University Hospital Düsseldorf (UKD) have succeeded in utilising artificial intelligence (AI) to establish a model that enables a better understanding of Leigh Syndrome. In the course of this work, they succeeded in identifying new drug candidates for the treatment of the disease. The researchers from Düsseldorf and Luxembourg have now published the findings in the renowned scientific journal Nature Communications.

With one case in 36,000 live births, Leigh Syndrome is classified as a “rare disease”. According to the European definition, a rare disease affects fewer than five in 10,000 people. The progressive brain disease is a so-called mitochondrial disease, i.e. a disease that affects the energy metabolism. It usually manifests in childhood and causes brain damage and necrosis, which can lead to severe symptoms such as neurodevelopmental delay, epileptic seizures, muscular weakness and respiratory failure. Those affected have an extremely limited life expectancy and most die within a few years of diagnosis. No therapy has yet been approved.

The low number of cases makes research into Leigh Syndrome and the identification of potential therapy approaches a challenge. The situation is further complicated by the fact that there are very few cellular or animal models, which can faithfully recapitulate the course of the disease in human beings.

Together with Professor Dr Antonio Del Sol (Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, CICbioGUNE Bizkaia, Spain) and his research group, the team headed by Professor Dr Alessandro Prigione (Department of General Paediatrics, Neonatology and Paediatric Cardiology) set out to develop models to advance research into Leigh Syndrome. The researchers in Düsseldorf began by using cells from patients to develop so-called pluripotent stem cells, which have the biological ability to differentiate into all types of cells in the body. The researchers were then able to use these cells to develop brain organoids in the laboratory. These organoids, which can be understood as 3D models of the brain, imitate the structure of and have a similar tissue organisation to the human brain. Professor Prigione and his team succeeded in imitating the gene variation, which triggers Leigh Syndrome in the human brain, in the brain organoids, making it possible to map and research Leigh Syndrome and the use of various medications in the laboratory.

Using the brain organoids, the researchers conducted a screening process to identify potential drug candidates for the treatment of Leigh Syndrome. They examined the effect of various existing medications, some of which have been approved for other indications, on the organoids. This approach of expanding the use of existing medications to treat other conditions is referred to as repurposing. The researchers’ aim was to utilise AI to optimise this process and so the Luxembourg team headed by Professor Del Sol developed an algorithm based on deep learning to help identify drug candidates.

Using this algorithm, the researchers together managed to identify two potentially suitable drug candidates for the treatment of Leigh Syndrome – talarozole and sertaconazole. Talarozole was originally developed to treat acne, while sertaconazole is already approved for the topical treatment of fungal skin infections such as athlete’s foot. The research group of Professor Prigione was able to demonstrate in brain organoids that treatment with both drugs sustained brain cell development, improved growth and reduced lactate release. These results suggest that the drugs could also have a positive effect on disease progression in patients and their ongoing development.

“The development of the brain organoids represents a great success for research into rare diseases,” says Professor Prigione. “The fact that we were able to identify two potential drug candidates using the organoids is highly promising. Further studies are now needed to establish exactly how effective talarozole and sertaconazole are in patients, but we are optimistic.” Professor Del Sol adds: “Screening existing drugs for repurposing is an important approach in the search for therapeutic strategies for rare diseases. We have demonstrated that the use of AI can optimise this process. In the future, we will be able to use the algorithm we have developed not only for Leigh syndrome but also for other diseases.”

In addition to HHU, the UKD and the University of Luxembourg, other organisations involved in the research included the University of Pittsburgh, the Fraunhofer Institute for Translational Medicine and Pharmacology ITMP in Hamburg, the Biogipuzkoa Health Institute San Sebastian, the Max Delbrück Center for Molecular Medicine in Berlin, the Charité hospital Berlin, the Polish Academy of Sciences in Warsaw and the Universidad Autònoma de Barcelona.

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Wissenschaftliche Ansprechpartner:

Prof. Dr. Alessandro Prigione
Prof. Dr. Antonio Del Sol

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Originalpublikation:

Accelerating Leigh syndrome drug discovery through deep learning screening in brain organoids
C. Menacho, S. Okawa, I. Álvarez-Merz, A. Wittich, M. Muñoz-Oreja, P. Lisowski,M. López Martín, T. M. Pentimalli,8,9,13 S. Zakin,14 M. Thevandavakkam, C. Jerred, S. Lickfett, L. Petersilie, A. Rybak-Wolf, A. Seibt, D. Herebian, G. Inak, S. Brodesser, A. Zaliani, B. Mlody, J. Donnelly, K. Woleben, F. X. Soriano, J. Fernandez-Checa, N. Ventura, S. Cambridge, E. Mayatepek, A. Spinazzola, M. Schuelke, N. Rajewsky, A. Rossi, A. Peralvarez-Marin, F. Distelmaier, E. Perlstein, I. J. Holt, E. Puighermanal, O. Pless, C. R. Rose, A. Del Sol, and A. Prigione. Nature Communications 2026.
DOI: 10.1038/s41467-026-71391-2.

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Weitere Informationen:

https://www.nature.com/articles/s41467-026-71391-2

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