2. Academic Validation
  3. AI-enabled drug prediction and gene network analysis reveal therapeutic use of vorinostat for Rett Syndrome in preclinical models

AI-enabled drug prediction and gene network analysis reveal therapeutic use of vorinostat for Rett Syndrome in preclinical models

  • Commun Med (Lond). 2025 Jul 1;5(1):249. doi: 10.1038/s43856-025-00975-8.
Richard Novak # 1 2,  Tiffany Lin # 1,  Shruti Kaushal 1,  Megan Sperry 1,  Frederic Vigneault 1 2,  Erica Gardner 1 2,  Sahil Loomba 1,  Kostyantyn Shcherbina 1,  Vishal Keshari 1,  Alexandre Dinis 1,  Anish Vasan 1,  Vasanth Chandrasekhar 1,  Takako Takeda 1,  Rahul Nihalani 2,  Sevgi Umur 2,  Jerrold R Turner 3,  Michael Levin 1 4,  Donald E Ingber 5 6 7
Affiliations

Affiliations

  • 1 Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
  • 2 Unravel Biosciences, Inc., Boston, MA, USA.
  • 3 Laboratory of Mucosal Barrier Pathobiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • 4 Allen Discovery Center at Tufts University, Medford, MA, USA.
  • 5 Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA. don.ingber@wyss.harvard.edu.
  • 6 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA. don.ingber@wyss.harvard.edu.
  • 7 Vascular Biology Program and Department of Surgery, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA. don.ingber@wyss.harvard.edu.
  • # Contributed equally.
PMID: 40595330 DOI: 10.1038/s43856-025-00975-8
Abstract

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.

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