Case Report: Zolpidem’s paradoxical restorative action: A case report of functional brain imaging

Boisgontier, Jennifer; Saitovitch, Ana; Blauwblomme, Thomas; Guida, Lelio; Fillon, Ludovic; Dufour, Christelle; Grill, Jacques; Lemaître, Hervé; Puget, Stéphanie; Vinçon-Leite, Alice; Dangouloff-Ros, Volodia; Charpy, Sarah; Benichi, Sandro; Lévy, Raphaël; Roux, Charles‐Joris; Grévent, David; Bourgeois, Marie; Saidoun, Lila; Gaillard, Raphaël; Zilbovicius, Mônica; Boddaert, Nathalie

doi:10.3389/fnins.2023.1127542

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…This paradoxical improvement in neuronal activity can lead to enhancements in speech, motor skills, and alertness in individuals with severe brain injuries or neurodevelopmental disorders, as supported by anecdotal evidence and a handful of clinical studies 46,47 . TXGNN 's prediction and explanatory rationale are both aligned with medical evidence about the paradoxical mechanism of action for Zolpidem, despite none of these clinical cases being directly encountered by the model during training.…”

Section: Txgnn Explainermentioning

confidence: 96%

A foundation model for clinician-centered drug repurposing

Huang

Chandak

Wang

et al. 2023

Preprint

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Of the several thousand diseases that affect humans, only about 500 have treatments approved by the U.S. Food and Drug Administration. Even for those with approved treatments, discovering new drugs can offer alternative options that cause fewer side effects and replace drugs that are ineffective for certain patient groups. However, identifying new therapeutic opportunities for diseases with limited treatment options remains a challenge, as existing algorithms often perform poorly. Here, we leverage recent advances in geometric deep learning and human-centered AI to introduce TxGNN, a model for identifying therapeutic opportunities for diseases with limited treatment options and minimal molecular understanding. TxGNN is a graph neural network pre-trained on a comprehensive knowledge graph of 17,080 clinically-recognized diseases and 7,957 therapeutic candidates. The model can process various therapeutic tasks, such as indication and contraindication prediction, in a unified formulation. Once trained, we show that TxGNN can perform zero-shot inference on new diseases without additional parameters or fine-tuning on ground truth labels. Evaluation of TxGNN shows significant improvements over existing methods, with up to 49.2% higher accuracy in indication tasks and 35.1% higher accuracy in contraindication tasks. TxGNN can also predict therapeutic use for new drugs developed since June 2021. To facilitate interpretation and analysis of the model's predictions by clinicians, we develop a human-AI explorer for TxGNN and evaluate its usability with medical experts. Finally, we demonstrate that TxGNN's novel predictions are consistent with off-label prescription decisions made by clinicians in a large healthcare system. These label-efficient and clinician-centered learning systems pave the way for improvements for many therapeutic tasks.

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Section: Txgnn Explainermentioning

confidence: 96%