It is difficult to develop effective treatments for neurodevelopmental genetic disorders, such as Rett syndrome, which are caused by a single gene mutation but trigger changes in numerous other genes, and thereby also severely impair functions of organs beyond the central nervous system (CNS). This challenge 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. Here, we combined human gene regulatory network-based computational drug prediction with in vivo screening in a population-level diversity, CRISPR-edited, Xenopus laevis tadpole model of Rett syndrome to carry out target-agnostic drug discovery, which rapidly led to the identification of the FDA-approved drug vorinostat as a potential repurposing candidate. Vorinostat broadly improved both CNS and non-CNS (e.g., gastrointestinal, respiratory, inflammatory) abnormalities in a pre-clinical mouse model of Rett syndrome. This is the first Rett syndrome treatment to demonstrate pre-clinical efficacy across multiple organ systems when dosed after the onset of symptoms, and network analysis revealed a putative therapeutic mechanism for its cross-organ normalizing effects based on its impact on acetylation metabolism and post-translational modifications of microtubules.
Background In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. Here we explored the possibility that different statins might differ in their ability to exert protective effects based on computational predictions. Methods A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2, with a total of 2,436 drugs investigated. Top drug predictions included statins, which were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. A database containing over 4,000 COVID-19 patients on statins was also analyzed to determine mortality risk in patients prescribed specific statins versus untreated matched controls. Findings Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins were predicted to be active in > 50% of analyses. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. Interpretation Different statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and validate non-obvious mechanisms and drug repurposing opportunities. Funding DARPA, Wyss Institute, Hess Research Fund, UCSF Program for Breakthrough Biomedical Research, and NIH
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