Good news! The AI doctor is in the house! This is only the beginning!
This seems to be a great example where AI can make huge difference in finding a cure for a very rare and complex disorder.
AI identified a FDA-approved drug to treat Rett Syndrome.
"Rett syndrome is a devastating, rare genetic childhood disorder primarily affecting girls. Merely 1 out of 10,000 girls are born with it, and much fewer boys. It is caused by mutations in the MeCP2 gene on the X chromosome, leading to a spectrum of cognitive and physical impairments, including repetitive hand motions, speech difficulties, and seizures.
However, besides severe impairment of neurological functions, which has been the primary focus of researchers, Rett syndrome also upsets the functions of many non-neurological organs, including the digestive, musculoskeletal, and immune systems. This complexity has made the development of an effective cure able to treat the disease across multiple tissues an extreme challenge. ..."
From the abstract and plain language summary:
"Background
Many neurodevelopmental genetic disorders, such as Rett syndrome, are caused by a single gene mutation but trigger changes in expression of numerous genes. This impairs functions of multiple organs beyond the central nervous system (CNS), making it difficult to develop broadly effective treatments based on a single drug target. This is further complicated by the lack of sufficiently broad and biologically relevant drug screens, and the inherent complexity in identifying clinically relevant targets responsible for diverse phenotypes that involve multiple organs.
Methods
Here, we use computational drug prediction that combines artificial intelligence, human gene regulatory network analysis, and in vivo screening in a CRISPR-edited, Xenopus laevis tadpole model of Rett syndrome to carry out target-agnostic drug discovery.
Four-week-old MeCP2-null male mice expressing the Rett phenotype are used to validate the therapeutic efficacy.
Results
This approach identifies the FDA-approved drug, vorinostat, which broadly improves both CNS and non-CNS (e.g., gastrointestinal, respiratory, inflammatory) abnormalities in X. laevis and MeCP2-null mice. To our knowledge, this is the first Rett syndrome treatment to demonstrate pre-clinical efficacy across multiple organ systems when dosed after the onset of symptoms.
Gene network analysis also reveals a putative therapeutic mechanism for the cross-organ normalizing effects of vorinostat based on its impact on acetylation metabolism and post-translational modifications of microtubules.
Conclusions
Although vorinostat is an inhibitor of histone deacetylases (HDAC), it unexpectedly reverses the Rett phenotype by restoring protein acetylation across hypo- and hyperacetylated tissues, suggesting its activity is based on a previously unknown therapeutic mechanism.
Plain language summary
Traditional drug discovery platforms focus on singular targets and take several years to validate treatment efficacy before entering clinical trials.
Here, we describe a discovery platform that leverages artificial intelligence (AI) and gene expression profiles in combination with a genetically engineered tadpole and mouse models of a form of autism, known as Rett syndrome, to identify an existing FDA approved anticancer drug (vorinostat) that may be repurposed as a treatment for this condition. We show that vorinostat improves both the neurological and non-neurological symptoms of Rett syndrome in both models. Analysis of vorinostat’s therapeutic action reveals that internal structural elements in cells, known as microtubules, represent a suitable target for treatment of this disease. This AI-based computational discovery platform demonstrates the possibility of rapidly identifying alternative uses for existing FDA approved drugs for treatments of patients with complex genetic disorders."
Fig. 2: Network-based computational prediction of effective drugs to treat Rett syndrome in tadpole models.
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