The Dipeptidyl Peptidase-4 Inhibitor Saxagliptin as a Candidate Treatment for Disorders of Consciousness: A Deep Learning and Retrospective Clinical Analysis

Neurocritical Care, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 4, 2025

Abstract Background Despite advancements in the neuroscience of consciousness, no new medications for disorders consciousness (DOC) have been discovered more than a decade. Repurposing existing US Food and Drug Administration (FDA)—approved drugs DOC is crucial improving clinical management patient outcomes. Methods To identify potential treatments among FDA-approved drugs, we used deep learning–based drug screening model to predict efficacy as awakening agents based on their three-dimensional molecular structure. A retrospective cohort study from March 2012 October 2024 tested model’s predictions, focusing changes Glasgow Coma Scale (GCS) scores 4047 patients coma traumatic, vascular, or anoxic brain injury. Results Our learning screens identified saxagliptin, dipeptidyl peptidase-4 inhibitor, promising both acute prolonged DOC. The analysis showed that saxagliptin was associated with highest recovery rate diabetes medications. After matching by age, sex, initial GCS score, etiology, glycemic status, brain-injured incretin-based therapies, including inhibitors glucagon-like peptide-1 analogues, recovered at significantly higher rates compared non-incretin-based (95% confidence interval 1.8–14.1% rate, P = 0.0331) without 2–21% 0.0272). Post matching, therapies also treated amantadine difference 2.4–25.1.0%, 0.0364). review preclinical studies several pathways through which other may aid chronic DOC: restoring monoaminergic GABAergic neurotransmission, reducing inflammation oxidative damage, clearing hyperphosphorylated tau amyloid-β, normalizing thalamocortical glucose metabolism, increasing neural plasticity, mitigating excitotoxic damage. Conclusions findings suggest general, particular, novel therapeutic Further prospective trials are needed confirm safety

Language: Английский

Pharmacological therapies for early and long-term recovery in disorders of consciousness: current knowledge and promising avenues

Expert Review of Neurotherapeutics, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 21

Published: May 7, 2025

Disorders of consciousness (DoC) are characterized by impaired arousal and/or awareness, ranging from coma to unresponsive wakefulness syndrome, minimally conscious state, and cognitive motor dissociation. Pharmacological treatment options remain limited, complicated the heterogeneity etiologies, such as traumatic brain injury, stroke, infections. The lack rigorous clinical trials has led off-label use treatments, often without clear mechanistic understanding, posing challenges for effective patient care. In this perspective, authors report on key studies concerning effectiveness pharmacological interventions, including dopaminergic GABAergic agents, antidepressants, statins, anticonvulsants, in promoting recovery DoC. Robust longitudinal needed, with priority given early subacute phase intervention. Outcomes should be better defined, considering immediate responses medication while also increasing emphasis long-term quality life. Unified functional frameworks needed guide research foster collaboration. Furthermore, a shift toward personalized medicine would benefit heterogeneous population. Moving forward, assessing efficacy more unconventional or 'paradoxical' plans will essential. expect an increased AI tools identify factors that best predict responses.

Language: Английский

The Algorithmic Agent Perspective and Computational Neuropsychiatry: From Etiology to Advanced Therapy in Major Depressive Disorder

Entropy, Journal Year: 2024, Volume and Issue: 26(11), P. 953 - 953

Published: Nov. 6, 2024

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using Kolmogorov theory (KT) consciousness, we developed foundational model where algorithmic agents interact with world to maximize an Objective Function evaluating affective valence. Depression, defined in context by state persistently low valence, may arise from various factors-including inaccurate models (cognitive biases), dysfunctional (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, map agent brain circuits functional networks, framing etiological routes linking depression biotypes. Finally, explore how stimulation, psychotherapy, plasticity-enhancing compounds such as psychedelics can synergistically repair neural optimize therapies using personalized computational models.

Language: Английский