"Adenosine an old player with new possibilities in kidney diseases": Preclinical evidences and clinical perspectives

Activation ADORA1 protects against sepsis-associated acute kidney injury by inhibiting pyroptosis

BACKGROUND

The ADORA1 is known to provide renoprotection against acute kidney injury. However, the underlying mechanisms remain unclear. The purpose of this study was to investigate whether and how ADORA1 plays a role in renoprotection in sepsis associated acute kidney injury (SA-AKI).

METHODS

Sepsis model was induced by lipopolysaccharide (LPS) in male C57BL/6 mice, 0.9 % NS served as controls. Animals received ADORA1 agonists and antagonist before the LPS. Renal function, histology and pyroptosis markers were assessed, with simultaneous validation by vitro assays.

RESULTS

The animals treated with ADORA1 agonists exhibited higher survival rates and an improved renal functional recovery, attenuated histological lesions and downgraded pyroptosis. Moreover, which down-regulated the expression of cleaved caspase 11 and GSDMD, while the ADORA1 antagonist group exhibit an oppose results.

CONCLUSIONS

ADORA1 protects against SA-AKI, at least in part, through its inhibitory effects on pyroptosis via the noncanonical inflammasome pathway. If our finding may extrapolated to clinical setting, ADORA1 agonist may serve as a clinical strategy to SA-AKI.

Metabolomics Provides Insights into Renoprotective Effects of Semaglutide in Obese Mice

PURPOSE

Semaglutide, a new long-acting glucagon-like peptide-1 analogue, has shown benefits for renal diseases, but its direct role on kidney metabolism under obesity remains unclear. The study aims to elucidate the protective effect and metabolic modulation mechanism of semaglutide on obesity-related kidney injury.

METHODS

Male C57BL/6J mice were divided into control and obesity groups. Mice in the obesity group had a high-fat diet and were treated with or without semaglutide (30nmol/kg/day). The study assayed blood biochemistry and then evaluated renal pathological injury through Periodic Acid-Schiff staining and electron microscopy. Metabolomics was utilized to analyze obesity-related metabolites in kidney samples.

RESULTS

Semaglutide significantly improved glucose homeostasis, insulin resistance, and kidney injury in obese mice. We successfully identified 377 altered metabolites (P<0.05). It was suggested that semaglutide directly improved oxidative stress and inflammation-related metabolites such as nicotinamide adenine dinucleotide (NAD+) and adenosine in the kidney of obese mice, which have not been documented in obesity-related kidney injury. Relevant enriched pathways were included phospholipids and lysophospholipids metabolism, purine metabolism, NAD+ metabolism, and insulin resistance-related metabolism. They could serve as potential targets for intervention of obesity-related kidney injury.

CONCLUSION

Our study revealed the metabolomics-based renoprotective mechanism of semaglutide in obese mice for the first time. The innovation lied in the identified metabolites such as NAD+ and adenosine targeted by semaglutide, which have not been documented in obesity-related kidney injury. Semaglutide may be a promising therapy for obesity-related kidney diseases.

A metabonomics-based renoprotective mechanism analysis of empagliflozin in obese mice

With an increasing prevalence of obesity related kidney disease, exploring the mechanisms of therapeutic method is of critical importance. Empagliflozin is a new antidiabetic agent with broad clinical application prospect in cardiovascular and renal diseases. However, a metabonomics-based renoprotective mechanism of empagliflozin in obesity remains unclear. Our results showed that empagliflozin significantly alleviated the deposition of lipid droplet, glomerular and tubular injury. The innovation lied in detection of empagliflozin-targeted differential metabolites in kidneys. Compared with normal control mice, obese mice showed higher levels of All-trans-heptaprenyl diphosphate, Biliverdin, Galabiose, Galabiosylceramide (d18:1/16:0), Inosine, Methylisocitric acid, Uric acid, Xanthosine, O-glutarylcarnitine, PG(20:3(8Z,11Z,14Z)/0:0), PG(20:4(5Z,8Z,11Z,14Z)/0:0), PE(O-16:0/0:0), PG(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0), and lower level of Adenosine. Empagliflozin regulated these metabolites in the opposite direction. Associated metabolic pathways were Phospholipids metabolism, Purine metabolism, and Biliverdin metabolism. Most of metabolites were associated with inflammatory response and oxidative stress. Empagliflozin improved the oxidative stress and inflammation imbalance. Our study revealed the metabonomics-based renoprotective mechanism of empagliflozin in obese mice for the first time. Empagliflozin may be a promising tool to delay the progression of obesity-related kidney disease.

Pharmacological Tuning of Adenosine Signal Nuances Underlying Heart Failure With Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) roughly represents half of the cardiac failure events in developed countries. The proposed 'systemic microvascular paradigm' has been used to explain HFpHF presentation heterogeneity. The lack of effective treatments with few evidence-based therapeutic recommendations makes HFpEF one of the greatest unmet clinical necessities worldwide. The endogenous levels of the purine nucleoside, adenosine, increase significantly following cardiovascular events. Adenosine exerts cardioprotective, neuromodulatory, and immunosuppressive effects by activating plasma membrane-bound P1 receptors that are widely expressed in the cardiovascular system. Its proven benefits have been demonstrated in preclinical animal tests. Here, we provide a comprehensive and up-to-date critical review about the main therapeutic advantages of tuning adenosine signalling pathways in HFpEF, without discounting their side effects and how these can be seized.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.