{"id":14030,"date":"2026-04-20T11:04:53","date_gmt":"2026-04-20T09:04:53","guid":{"rendered":"https:\/\/www.uni.lu\/lcsb-en\/?post_type=news&p=14030"},"modified":"2026-04-20T11:04:54","modified_gmt":"2026-04-20T09:04:54","slug":"accelerating-drug-discovery-for-leigh-syndrome","status":"publish","type":"news","link":"https:\/\/www.uni.lu\/lcsb-en\/news\/accelerating-drug-discovery-for-leigh-syndrome\/","title":{"rendered":"Accelerating drug discovery for Leigh syndrome"},"content":{"rendered":"\n
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Two recent studies shine a light on a rare neurodegenerative disease and showcase how the integration of artificial intelligence and experimental validation can accelerate the discovery of new therapeutic options. An international research team, combining clinical expertise from the University Hospital D\u00fcsseldorf and several partners, with the computational know-how of teams from the University of Luxembourg and Spain, identified promising drugs for the treatment of Leigh Syndrome.<\/p>\n\n\n\n

\nA devastating rare disease and a lack of models for research<\/h2>\n\n\n\n

Leigh Syndrome is a progressive disease that affects the brain. It usually manifests in childhood, with severe symptoms leading to motor impairment, intellectual disability and early death. Belonging to a group of rare genetic conditions called mitochondrial diseases, Leigh Syndrome concerns 1 in 36,000 people.<\/p>\n\n\n\n

It is a devastating disease for which therapeutic options are currently extremely limited. As often with rare diseases, the small number of patients makes research more complex. In the case of Leigh Syndrome, the issue is exacerbated by a lack of cellular or animal models that can faithfully mimic the disease in the lab. To address this challenge, a large group of international collaborators has worked on developing innovative strategies to accelerate the drug discovery process.<\/p>\n\n\n\n

\nCombined approaches to improve drug discovery: in vitro, in vivo & in silico<\/h2>\n\n\n\n

The two studies, recently published in Cell<\/em><\/a> and Nature Communications<\/a><\/em>, demonstrate the potential of combining approaches for therapeutic discovery. \u201cThere is a growing interest in developing computational tools to fast-track the drug screening process, which is costly and time-consuming,\u201d underlines Prof. Antonio Del Sol<\/a>, head of Computational Biology groups at the Luxembourg Centre for Systems Biomedicine<\/a> (LCSB) and at CIC bioGUNE<\/a> in Bilbao. \u201cWith our artificial intelligence expertise, we can build innovative pipelines that help reduce the number of potential drugs to be assessed in the lab and identify novel compounds of interest.\u201d<\/p>\n\n\n\n

Collaborations with experimental teams and clinicians are then key to validate computational predictions in relevant disease models. There, cell cultures and brain organoids \u2013 complex 3D cellular systems \u2013 derived from skin cells donated by patients are instrumental to uncover pathological mechanisms and to test the effects and mode of action of promising compounds.<\/p>\n\n\n\n

The results obtained for Leigh Syndrome by the interdisciplinary consortium showcase how the integration of computational drug discovery, what is called in silico<\/em> screens, and experimental validation in vitro<\/em> and in vivo<\/em> can accelerate translational research. It led to the identification of several existing drugs that could be repositioned for treating patients affected.<\/p>\n\n\n\n

\nIdentifying promising repurposable compounds<\/h2>\n\n\n\n

In the first study, the researchers tested a library of over 5500 drugs already approved for other conditions or for which extensive safety and efficacy data are available. Among these, Sildenafil, currently approved for the treatment of erectile dysfunction in adults and for the treatment of pulmonary hypertension in infants, was identified as a potential therapeutic candidate for Leigh Syndrome. By combining multi-omics analyses, an approach that integrates data about entire sets of molecules, from RNA to lipids, with computational modelling, the team uncovered the mechanism of action of Sildenafil in Leigh syndrome organoids. \u201cFollowing the positive effects on metabolism, cell function and lifespan observed in cellular and animal models, Sildenafil was used as a compassionate treatment in six patients,\u201d reports Prof. Alessandro Prigione<\/a>, head of the Stem Cell Metabolism group at University Hospital D\u00fcsseldorf<\/a>. \u201cThey showed preliminary improvements in clinical condition and motor function. We are now planning larger clinical trials to confirm its safety and efficacy.\u201d<\/p>\n\n\n\n

Using a deep learning algorithm to accelerate the process, the researchers performed another drug repurposing screening in a second study. They identified an additional compound \u2013 talarozole, initially developed for acne and psoriasis \u2013 as other promising candidates for Leigh Syndrome and filed a patent application for its use in mitochondrial diseases.<\/p>\n\n\n\n

In addition to representing significant progress toward the treatment of a rare disease, these findings highlight the powerful contribution of computational methods when it comes to drug discovery. \u201cFrom facilitating the prioritisation of the most promising compounds and pinpointing novel molecules of interest, to analysing the mode of action of drugs and guiding the design of more effective therapies, in silico<\/em> approaches are becoming a key ingredient to accelerate translation to patients,\u201d concludes Prof. Del Sol.<\/p>\n\n\n\n

The methodological pipeline developed by this international & interdisciplinary group of researchers could represent a blueprint for uncovering new therapeutic opportunities in other rare neurological disorders.

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References:<\/strong><\/p>\n\n\n\n