[{"command":"add_css","data":[{"rel":"stylesheet","media":"all","href":"\/sites\/default\/files\/css\/css_-kTcRwkqPhQLsqEvdSSyU2XZ3CV5VEleXoE3_xZi2cg.css?delta=0\u0026language=sv\u0026theme=hm_theme\u0026include=eJxLzi9K1U8pKi1IzNErSa0oSSxKTdQtSi3OrEoFAJQ3Cqw"}]},{"command":"openDialog","selector":"#drupal-modal","settings":null,"data":"\u003Cdiv id=\u0022republish_modal_form\u0022\u003E\u003Cform class=\u0022modal-form-example-modal-form ecl-form\u0022 data-drupal-selector=\u0022modal-form-example-modal-form\u0022 action=\u0022\/sv\/article\/modal\/14838\u0022 method=\u0022post\u0022 id=\u0022modal-form-example-modal-form\u0022 accept-charset=\u0022UTF-8\u0022\u003E\u003Cp\u003EHorizon articles can be republished for free under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence.\u003C\/p\u003E\n      \u003Cp\u003EYou must give appropriate credit. We ask you to do this by:\u003Cbr \/\u003E\n      1) Using the original journalist\u0027s byline\u003Cbr \/\u003E\n      2) Linking back to our original story\u003Cbr \/\u003E\n      3) Using the following text in the footer: This article was originally published in \u003Ca href=\u0027#\u0027\u003EHorizon, the EU Research and Innovation magazine\u003C\/a\u003E\u003C\/p\u003E\n      \u003Cp\u003ESee our full republication guidelines \u003Ca href=\u0027\/horizon-magazine\/republish-our-stories\u0027\u003Ehere\u003C\/a\u003E\u003C\/p\u003E\n      \u003Cp\u003EHTML for this article, including the attribution and page view counter, is below:\u003C\/p\u003E\u003Cdiv class=\u0022js-form-item form-item js-form-type-textarea form-item-body-content js-form-item-body-content ecl-form-group ecl-form-group--text-area form-no-label ecl-u-mv-m\u0022\u003E\n        \n\u003Cdiv\u003E\n  \u003Ctextarea data-drupal-selector=\u0022edit-body-content\u0022 aria-describedby=\u0022edit-body-content--description\u0022 id=\u0022edit-body-content\u0022 name=\u0022body_content\u0022 rows=\u00225\u0022 cols=\u002260\u0022 class=\u0022form-textarea resize-vertical ecl-text-area\u0022\u003E\u003Ch2\u003EAI joins the quest to find new treatments for rare diseases\u003C\/h2\u003E\u003Cp\u003EBelgian AI company Kantify was doing business as usual until\u0026nbsp;a cancer diagnosis\u0026nbsp;forced the team to rethink their direction. \u201cWe built algorithms for sectors like marketing or transport,\u201d said S\u00e9gol\u00e8ne Martin, Kantify\u2019s co-founder and CEO.\u003C\/p\u003E\u003Cp\u003E\u201cThose were complex projects that had nothing to do with health, but they enabled us to build a deep expertise in AI.\u201d\u003C\/p\u003E\u003Cp\u003EThat changed in 2017, when Nik Subramanian, the company\u2019s CTO, was diagnosed with sarcoma, a rare type of cancer that forms tumours in connective tissues such as bones, muscles and blood vessels.\u003C\/p\u003E\u003Cp\u003EThis experience prompted the company to shift its focus to health and explore the potential of AI to help improve how new drugs for rare diseases are identified and tested.\u003C\/p\u003E\u003Cp\u003E\u201cIt changed our lives,\u201d said Martin. \u201cWe are now completely focused on AI for human health, and have developed a specialised technology for AI-based drug discovery.\u201d\u003C\/p\u003E\u003Cp\u003EThat expertise is now being fully used\u0026nbsp;as part of a five-year EU\u2011funded research initiative called DREAMS. It is focused on improving treatments for a group of five rare neuromuscular disorders that progressively weaken muscle function.\u003C\/p\u003E\u003Ch2\u003EIncreasing the odds for patients\u003C\/h2\u003E\u003Cp\u003E\u003Cblockquote class=\u0022text-center text-blue font-bold text-2xl w-full lg:w-1\/2 border-2 border-blue p-12 my-8 lg:m-12 lg:-ml-16 float-left\u0022\u003E\n  \u003Cspan class=\u0022text-5xl rotate-180\u0022\u003E\u201c\u003C\/span\u003E\n  \u003Cp class=\u0022font-serif italic\u0022\u003EWe are now completely focused on AI for human health, and have developed a specialised technology for AI-based drug discovery.\u003C\/p\u003E\n  \u003Cfooter\u003E\n    \u003Ccite class=\u0022not-italic font-normal text-sm text-black\u0022\u003ES\u00e9gol\u00e8ne Martin, DREAMS\u003C\/cite\u003E\n  \u003C\/footer\u003E\n\u003C\/blockquote\u003E\n\u003C\/p\u003E\u003Cp\u003EDeveloping treatments for rare diseases remains one of the biggest challenges in modern medicine. The process is long, expensive and uncertain, requiring years of research, regulatory approval and clinical trials.\u003C\/p\u003E\u003Cp\u003EFor pharmaceutical companies, the small number of patients often makes such investments difficult to justify. As a result, many conditions remain untreated.\u003C\/p\u003E\u003Cp\u003EToday, only around 5\u20136% of the estimated 7 000 to 10 000 rare diseases have an approved therapy, a gap that organisations such as the World Health Organization continue to highlight as a global priority.\u003C\/p\u003E\u003Cp\u003EAI could help change that. By rapidly analysing vast datasets, AI can narrow down potential drug candidates at an early stage and identify existing medicines that might be repurposed for rare conditions.\u003C\/p\u003E\u003Ch2\u003EA focus on neuromuscular disorders\u003C\/h2\u003E\u003Cp\u003EWithin DREAMS, researchers are focusing on five rare neuromuscular disorders, including Duchenne muscular dystrophy, which primarily affects young boys and causes muscle degeneration, and Emery\u2013Dreifuss muscular dystrophy, which can cause severe heart issues.\u003C\/p\u003E\u003Cp\u003EAlthough these conditions differ in their genetic causes, they appear to share underlying mechanisms of action at the cellular level. By targeting these common pathways, researchers hope to develop treatments that could benefit several diseases at once, rather than tackling each one separately.\u003C\/p\u003E\u003Cp\u003ETo do this, the team reprogrammes patient cells into so-called induced pluripotent stem cells (iPSCs) \u2013 a type of master cell that can be turned into many other cell types \u2013 and then turns them into skeletal muscle tissue. This allows researchers to study the disease in a controlled setting.\u003C\/p\u003E\u003Cp\u003EBy combining these lab models with AI, researchers can identify shared therapeutic targets across multiple conditions.\u003C\/p\u003E\u003Cp\u003E\u201cThis kind of research is crucial for patients and their families,\u201d said Xavier Nissan, project coordinator of DREAMS and research director at French research institute I\u2011Stem that specialises in stem cell therapy and the treatment of monogenic diseases.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cThey are suffering and have to deal with their disease every day. They need technology like this.\u201d\u003C\/p\u003E\u003Ch2\u003EFrom data to potential treatments\u003C\/h2\u003E\u003Cp\u003EAs well as identifying the underlying disease processes, the DREAMS researchers are using AI to analyse billions of new and existing drugs, to try and predict which compounds are most likely to work.\u003C\/p\u003E\u003Cp\u003E\u201cThis is one of the ways in which AI can generate real societal value,\u201d said Martin. \u201cIt can truly accelerate research and create impact in areas where progress has been slow.\u201d\u003C\/p\u003E\u003Cp\u003EIn the lab, the team is testing a library of 2\u0026nbsp;700 EMA\u2011 and FDA\u2011approved drugs to assess whether any of them could\u0026nbsp;improve shared disease-related symptoms.\u003C\/p\u003E\u003Cp\u003E\u201cIn the lab, we test thousands of existing drugs on iPSC-derived muscle cells from patients with these diseases,\u201d said Nissan.\u003C\/p\u003E\u003Cp\u003E\u201cAI is then also used to understand how the drugs might work, identify targets, and even predict additional diseases where the treatment could be useful. By combining these approaches, we can move faster and focus on the most promising candidates.\u201d\u003C\/p\u003E\u003Cp\u003EThe team has spent three years generating the data to feed the AI and enable these predictions for drug repurposing.\u003C\/p\u003E\u003Cp\u003ECentral to this drug search is Sapian, Kantify\u2019s AI platform. It is trained on a large amount of data, ranging from information on molecules and proteins to patents and characteristics of all kinds of drugs. Based on this information, the platform can predict which drugs might be good candidates for certain kinds of diseases.\u003C\/p\u003E\u003Cp\u003E\u201cIt\u2019s not a crystal ball,\u201d said Martin. \u201cIt will generate hypotheses that will need to be tested in the real world. However, from different projects we have learned that the hypotheses are extremely good.\u201d\u003C\/p\u003E\u003Ch2\u003EIn the same basket\u003C\/h2\u003E\u003Cp\u003EIt is still unclear when we can expect this approach to yield new treatments for people with rare diseases. Nissan is cautious, pointing to a lot of factors at play and not wanting to give false hope to patients living with these conditions today.\u003C\/p\u003E\u003Cp\u003E\u201cThe project concludes at the end of 2028. Beyond that, there is a big question mark. We are doing our best, but in the world of drugs, things can take a long time.\u201d\u003C\/p\u003E\u003Cp\u003EMuch will depend on what happens when these approaches are tested on patients. New drugs sometimes take decades to complete clinical trials, and even existing drugs that have already passed through these processes will still need to go through some trials.\u003C\/p\u003E\u003Cp\u003E\u003Cblockquote class=\u0022text-center text-blue font-bold text-2xl w-full lg:w-1\/2 border-2 border-blue p-12 my-8 lg:m-12 lg:-ml-16 float-left\u0022\u003E\n  \u003Cspan class=\u0022text-5xl rotate-180\u0022\u003E\u201c\u003C\/span\u003E\n  \u003Cp class=\u0022font-serif italic\u0022\u003EWe have a chance to make a big difference in the lives of a lot of people. That\u2019s a beautiful thing to work on.\u003C\/p\u003E\n  \u003Cfooter\u003E\n    \u003Ccite class=\u0022not-italic font-normal text-sm text-black\u0022\u003EXavier Nissan, DREAMS\u003C\/cite\u003E\n  \u003C\/footer\u003E\n\u003C\/blockquote\u003E\n\u003C\/p\u003E\u003Cp\u003EThe DREAMS team is working to make some of these trials more feasible for rare diseases. One problem they encounter is that there are not enough people living with these diseases to do large enough clinical trials.\u003C\/p\u003E\u003Cp\u003EThe researchers are therefore suggesting an alternative approach. \u201cSome rare diseases have wildly different symptoms, but share biological causes,\u201d said Nissan.\u003C\/p\u003E\u003Cp\u003E\u201cThis is why we want to do so-called basket trials, where one drug is tested on people with different symptoms and diseases that share a similar cause,\u201d he said.\u003C\/p\u003E\u003Cp\u003EThis approach, led by patient association AFM\u2011T\u00e9l\u00e9thon and the patients themselves, could help speed up drug development for people with rare diseases.\u003C\/p\u003E\u003Cp\u003ERegulatory agencies are now exploring the broader adoption of basket clinical trial approaches for rare diseases, Nissan said.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EFor Nissan and Martin, this work is more than research. It is personal.\u003C\/p\u003E\u003Cp\u003E\u201cThe patients need us,\u201d said Nissan. \u201cThat\u2019s a huge responsibility. I feel I need to succeed. But that also means we have a chance to make a big difference in the lives of a lot of people. That\u2019s a beautiful thing to work on.\u201d\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003EResearch in this article was funded by the EU\u2019s Horizon Programme. The views of the interviewees don\u2019t necessarily reflect those of the European Commission. 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