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Effect of Food on Balcinrenone/Dapagliflozin Pharmacokinetics and the Pharmacokinetics of Balcinrenone When Dosed with a P-gp Inhibitor.

Balcinrenone (AZD9977) is a novel selective non-steroidal mineralocorticoid receptor antagonist with a distinct mode of action being developed as a fixed-dose combination with the sodium-glucose cotransporter-2 inhibitor dapagliflozin for the treatment of heart failure with impaired kidney function, and chronic kidney disease. In this Phase 1 randomized open-label three-way crossover study we investigated the effect of food on balcinrenone/dapagliflozin pharmacokinetics, and the pharmacokinetics of balcinrenone when dosed with a P-glycoprotein (P-gp) inhibitor. Fourteen healthy participants were administered an oral capsule of balcinrenone/dapagliflozin 40 mg/10 mg in three dosing periods: fasted (reference), fed (high-fat, high-calorie meal) and with a P-gp inhibitor (quinidine 300 mg &#xd7; 2). Balcinrenone exposure was comparable in the fed and fasted states (geometric mean ratios [GMRs] [90% CI]: maximum plasma concentration [Cmax] 1.05 [0.88, 1.25]; area under the plasma concentration-time curve from time 0 to infinity [AUCinf] 1.12 [1.06, 1.19]). In the fed state, dapagliflozin AUCinf was comparable to the fasted state (GMR [90% CI] 1.05 [1.01, 1.09]), whereas Cmax was decreased (GMR [90% CI] 0.59 [0.51, 0.69]), in line with previous dapagliflozin food interaction studies. Co-administration with quinidine increased balcinrenone exposure: GMRs (90% CI) 1.48 (1.24, 1.76) and 1.24 (1.17, 1.31) for Cmax and AUCinf, respectively, but AUC fold increase was <2, the level used for classification of sensitive P-gp substrates. All interventions were well tolerated. In conclusion, this study supports dosing of balcinrenone/dapagliflozin without regard to food. Balcinrenone is not considered a sensitive P-gp substrate. No P-gp based dosing precautions are warranted based on this study.

Adult

Quantitative susceptibility mapping in neurodegenerative diseases: An umbrella review of iron-related biomarkers and mechanisms.

Pathological iron accumulation is a common pathophysiological hallmark across multiple neurodegenerative diseases (NDDs), motivating the need for accurate, non-invasive quantification methods. Quantitative susceptibility mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique that enables in vivo measurement of tissue magnetic susceptibility (&#x3c7;), providing a sensitive proxy for iron content. This umbrella review systematically evaluates the diagnostic accuracy, clinical correlations, and distinct iron distribution patterns of QSM in major NDDs, such as Parkinson's disease (PD), Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), and atypical Parkinsonism. We included 15 (13/15 were rated Low or Critically Low on AMSTAR 2) systematic reviews and meta-analyses (through July 15, 2026); however, the findings should be interpreted cautiously because of heterogeneity and the low methodological quality. A Corrected Covered Area (CCA) analysis demonstrated only slight overlap of primary studies across the included reviews (CCA&#xa0;=&#xa0;5.42%). Collectively, the evidence indicates that QSM provides comparable or higher diagnostic sensitivity and reliability than conventional R2* and SWI techniques, particularly for deep gray matter structures. The findings support significant iron overload in the substantia nigra, particularly in the pars compacta, as a robust biomarker for PD that correlates with motor severity and disease duration. Furthermore, regional iron profiling in the basal ganglia is critical for differential diagnosis; specifically, elevated &#x3c7; in the putamen and globus pallidus effectively distinguishes multiple system atrophy and progressive supranuclear palsy from idiopathic PD. Distinctively, AD and ALS exhibit specific &#x3c7; alterations in the thalamus, motor cortex, and hippocampus, reflecting divergent iron-related pathophysiological mechanisms, which correlate with cognitive impairment and upper motor neuron signs. Overall, QSM shows diagnostic promise and offers mechanistic insights into iron-related neurodegenerative processes.

Humans

Epigenetic drift and LINE-1 activation in aging brain: Implications for neurodegenerative disease.

Brain aging and age-associated neurological diseases, such as Alzheimer's Disease (AD), Parkinson's Disease (PD), and Amyotrophic Lateral Sclerosis (ALS), are largely attributed to epigenetic drift which is characterized by the gradual accumulation of alterations in neural cell methylation patterns over time. These methylation changes are particularly evident in transposable element (TE)-derived sequences such as Long interspersed element-1 (LINE-1) which comprises approximately 17% of the human genome. During aging, LINE-1 elements gradually lose their methylation, as well as the regulatory safeguard mechanisms that usually keep them inactive. This repression loss can lead to LINE-1 reactivation, contributing to harmful effects including genomic instability, neuroinflammation, and more. Together these findings indicate that impaired epigenetic maintenance, especially in repetitive genome regions, plays a key role in biological aging of neurons and glial cells. In this narrative review, we discuss the methylation dynamics and regulatory mechanisms of LINE-1 retrotransposons, their activation processes during aging, and contribution to age-associated neurological diseases. We also highlight the potential of targeting LINE-1 methylation to restore methylation homeostasis, epigenetic stability and delay brain aging.

Humans

GPR3 in neuro-metabolic-immune-reproductive nexus - a potential therapeutic target for Multi-System diseases.

BACKGROUND: GPR3(G-protein-coupled receptor 3), an orphan G-protein-coupled receptor (GPCR) with constitutive Gs activity, is expressed in the brain, liver, ovary, and other tissues, regulating cell proliferation, differentiation, and apoptosis across the nervous, reproductive, immune, and metabolic systems. This review synthesizes evidence on its integrated signaling and physiological functions to address the lack of a comprehensive multisystem pathophysiology overview. METHODS: A systematic literature search was conducted on PubMed and Web of Science, using keywords such as "GPR3", "GPCR", "neurodegeneration", "metabolism", "immune", "reproduction", "agonist", "inhibitor", and "therapeutic target". This search identified GPR3's roles in neurodegenerative diseases, immune inflammation, reproduction, and energy metabolism. The analysis focused on signaling pathways, ligand regulation, and therapeutic potential. RESULTS: The research indicates that GPR3 is involved in neuronal survival, synaptic plasticity, and microglial activity via the cAMP/PKA, PI3K/Akt, and &#x3b2; - arrestin pathways. It promotes amyloid - &#x3b2; formation in Alzheimer's disease (AD), yet provides neuroprotection in Parkinson's disease (PD) models. It may contribute to anxiety/depression - like states, maintain oocyte meiotic arrest in the ovary, and activate thermogenic genes in adipose tissue. GPR3 modulates immune responses. Using oleic acid (OA) and diphenyleneiodonium (DPI) as activators, and AF64394 and cannabidiol (CBD) as antagonists, it shows potential in disease models. CONCLUSION: GPR3 acts as a central molecular hub integrating neural, metabolic, immune, and reproductive signaling, highlighting its potential as a therapeutic target for chronic multisystem disorders. However, its dual roles in certain pathologies and translation challenges necessitate further research.

Humans

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans