Aggregates of the microtubule-associated protein tau are a common marker of neurodegenerative diseases collectively termed as tauopathies, such as Alzheimer's disease (AD) and frontotemporal dementia. Therapeutic strategies based on tau have failed in late stage clinical trials, suggesting that tauopathy may be the consequence of upstream causal mechanisms. As increasing levels of reactive oxygen species (ROS) may trigger protein aggregation or modulate protein degradation and, we had previously shown that the ROS producing enzyme NADPH oxidase 4 (NOX4) is a major contributor to cellular autotoxicity, this study was designed to evaluate if NOX4 is implicated in tauopathy. Our results show that NOX4 is upregulated in patients with frontotemporal lobar degeneration and AD patients and, in a humanized mouse model of tauopathy induced by AVV-Tau^P301L brain delivery. Both, global knockout and neuronal knockdown of the Nox4 gene in mice, diminished the accumulation of pathological tau and positively modified established tauopathy by a mechanism that implicates modulation of the autophagy-lysosomal pathway (ALP) and, consequently, improving the macroautophagy flux. Moreover, neuronal-targeted NOX4 knockdown was sufficient to reduce neurotoxicity and prevent cognitive decline, even after induction of tauopathy, suggesting a direct and causal role for neuronal NOX4 in tauopathy. Thus, NOX4 is a previously unrecognized causative, mechanism-based target in tauopathies and blood-brain barrier permeable specific NOX4 inhibitors could have therapeutic potential even in established disease.

In this review we summarized the actual clinical data for a cardioprotective therapeutic role of melatonin, listed melatonin and its agonists in different stages of development, and evaluated the melatonin cardiovascular target tractability and prediction using machine learning on ChEMBL. To date, most clinical trials investigating a cardioprotective therapeutic role of melatonin are in phase 2a. Selective melatonin receptor agonists Tasimelteon, Ramelteon, and combined melatonergic-serotonin Agomelatine, and other agonists with registered structures in CHEMBL were not yet investigated as cardioprotective or cardiovascular drugs. As drug-able for these therapeutic targets, melatonin receptor agonists have the benefit over melatonin of well-characterized pharmacologic profiles and extensive safety data. Recent reports of the X-ray crystal structures of MT1 and MT2 receptors shall lead to the development of highly selective melatonin receptor agonists. Predictive models using machine learning could help to identify cardiovascular targets for melatonin. Selecting ChEMBL scores > 4.5 in cardiovascular assays, and melatonin scores > 4, we obtained 284 records from 162 cardiovascular assays carried out with 80 molecules with predicted or measured melatonin activity. Melatonin activities (agonistic or antagonistic) found in these experimental cardiovascular assays and models include arrhythmias, coronary and large vessel contractility, and hypertension. Preclinical proof-of-concept and early clinical studies (phase 2a) suggest a cardioprotective benefit from melatonin in various heart diseases. However, larger phase 3 randomized interventional studies are necessary to establish melatonin and its agonists’ actions as cardioprotective therapeutic agents.

Several psychosocial, sleep/circadian, and cardiometabolic disorders have intricately interconnected pathologies involving melatonin disruption. Therefore, we hypothesize that melatonin could be a therapeutic target for treating potential comorbid diseases associated with this triad of psychosocial-sleep/circadian-cardiometabolic disorders. We investigated melatonin’s target prediction and tractability for this triad of disorders. The melatonin’s target prediction for the proposed psychosocial-sleep/circadian-cardiometabolic disorder triad was investigated using databases from Europe PMC, ChEMBL, Open Targets Genetics, Phenodigm, and PheWAS. The association scores for melatonin receptors MT1 and MT2 with this disorder triad were explored for evidence of target–disease predictions. The potential of melatonin as a tractable target in managing the disorder triad was investigated using supervised machine learning to identify melatonin activities in cardiovascular, neuronal, and metabolic assays at the cell, tissue, and organism levels in a curated ChEMBL database. Target–disease visualization was done by graphs created using “igraph” library-based scripts and displayed using the Gephi ForceAtlas algorithm. The combined Europe PMC (data type: text mining), ChEMBL (data type: drugs), Open Targets Genetics Portal (data type: genetic associations), PhenoDigm (data type: animal models), and PheWAS (data type: genetic associations) databases yielded types and varying levels of evidence for melatonin-disease triad correlations. Of the investigated databases, 235 association scores of melatonin receptors with the targeted diseases were greater than 0.2; to classify the evidence per disease class: 37% listed psychosocial disorders, 9% sleep/circadian disorders, and 54% cardiometabolic disorders. Using supervised machine learning, 546 cardiovascular, neuronal, or metabolic experimental assays with predicted or measured melatonin activity scores were identified in the ChEMBL curated database. Of 248 registered trials, 144 phase I to IV trials for melatonin or agonists have been completed, of which 33.3% were for psychosocial disorders, 59.7% were for sleep/circadian disorders, and 6.9% were for cardiometabolic disorders. Melatonin’s druggability was evidenced by evaluating target prediction and tractability for the triad of psychosocial-sleep/circadian-cardiometabolic disorders. While melatonin research and development in sleep/circadian and psychosocial disorders is more advanced, as evidenced by melatonin association scores, substantial evidence on melatonin discovery in cardiovascular and metabolic disorders supports continued R&D in cardiometabolic disorders, as evidenced by melatonin activity scores. A multiplatform analysis provided an integrative assessment of the target–disease investigations that may justify further translational research.

Background: Flavonoids and phenolic acids are recognized for their diverse therapeutic potential, yet their translation into clinical applications remains limited by varying bioavailability and fragmented characterization across databases. A systematic integrative approach is needed to comprehensively evaluate these compounds’ drug-likeness properties based on computational metrics, molecular interactions, and dietary sources within a unified framework. Methods: We analyzed 954 compounds (715 flavonoids, 239 phenolic acids) by integrating data from PhytoHub, Phenol-Explorer, ChEMBL, and FoodDB databases. Drug-likeness was assessed using established metrics, including QED (Quantitative Estimate of Drug-likeness) and DataWarrior drug-likeness scores. Molecular interaction patterns were characterized through ChEMBL activity data, and food source distributions were systematically mapped across major food groups. Results: Drug-likeness assessment revealed complementary evaluation patterns between QED (mean = 0.48 ± 0.24) and DataWarrior scores (mean = −2.46 ± 4.38), with moderate inter-correlation (r = 0.41), indicating that each metric captures distinct aspects of molecular properties. Isoflavones demonstrated the most favorable drug-likeness profiles (mean QED: 0.62 ± 0.18). Molecular interaction analysis demonstrated significantly higher binding affinities for flavonoids (mean ChEMBL activity score: 7.26 ± 1.09) compared to phenolic acids (6.98 ± 0.94, p = 0.014), with flavonoids targeting a broader range of proteins (67 unique targets vs. 33 for phenolic acids). Food source mapping identified herbs and spices as the richest sources (up to 14,500 mg/kg), followed by fruits (40,490 mg/kg total) and teas (37,101 mg/kg total), with distinct compound distribution patterns across food groups. Conclusions: This integrative cross-database approach provides a comprehensive characterization framework for flavonoids and phenolic acids, combining established drug-likeness metrics, molecular interaction analysis, and dietary source mapping. The methodology establishes a systematic foundation for compound evaluation in drug development and nutritional research.