Artificial neural network identifies nonsteroidal anti-inflammatory drugs exacerbated respiratory disease (N-ERD) cohort

Epimedii Folium and Ligustri Lucidi Fructus synergistically delay renal aging through AMPK/ULK1/Bcl2L13-mediated mitophagy

ETHNOPHARMACOLOGICAL RELEVANCE

The root of aging is attributed to kidney essence insufficiency and gradual loss of kidney function. The combination of Epimedii Folium and Ligustri Lucidi Fructus (ELL) is traditionally recognized to tonify kidney yin and yang and has significant efficacy in delaying aging and aging-related diseases, but little is known about the exact mechanism.

AIM OF THE STUDY

The research focuses on the mechanism of delaying renal aging by which ELL regulates mitophagy through serine/threonine kinase AMP-activated protein kinase (AMPK)/UNC-51- like autophagy activating kinase 1 (ULK1)/B-cell lymphoma-2-like protein 13 (Bcl2L13) in vivo.

MATERIALS AND METHODS

We employed a rat model of natural aging, using rats of different ages as dynamic controls, and a natural aging mouse model to evaluate the effects of ELL on delaying renal aging via AMPK/ULK1/Bcl2L13. The assessment included renal histopathology, oxidative stress, cell senescence, mitochondrial dynamics, mitophagy, and the AMPK/ULK1/Bcl2L13 signaling pathway. In the aging rat model, network pharmacology and proteomics were combined to dissect the renal aging process, and a Multilayer Perceptron (MLP) -artificial neural networks (ANN) model was used to evaluate the effects of ELL comprehensively. In the aging mouse model, the AMPK inhibitor dorsomorphin was applied to assess whether the AMPK signaling pathway was involved in ELL-induced mitophagy.

RESULTS

Compared with the young rats, the kidney exhibited signs of degenerative pathologies and increased oxidative stress in 17-month-old rats. A thorough analysis identified the mitochondrial protein Bcl2L13 as a crucial biomarker associated with renal aging. The AMPK/ULK1/Bcl2L13 pathway significantly regulated mitochondrial function and mitophagy, which were potential mechanisms underlying renal aging. In contrast to aged rats, the renal pathological changes and cell senescence in rats treated with ELL were significantly mitigated, the antioxidant capacity, mitochondrial dynamics, and mitophagy were improved, and the expression of AMPK/ULK1/Bcl2L13 was upregulated. After the application of AMPK inhibitor dorsomorphin, the effects of ELL were reversed. It appears that ELL modulates the AMPK/ULK1/Bcl2L13 signaling pathway, and upregulates mitophagy to potentially decelerate renal aging.

CONCLUSIONS

The findings indicate that aging kidneys display mitochondrial dysfunction, disorganization of mitochondria, and a decrease in mitophagy. Concurrently, ELL significantly regulates mitochondrial dynamics and mitophagy via AMPK/ULK1/Bcl2L13. This regulation helps mitigate mitochondrial dysfunction, suggesting ELL as a promising herbal remedy for delaying renal aging and age-related kidney diseases.

Aspirin hypersensitivity diagnostic index (AHDI): In vitro test for diagnosing of N-ERD based on urinary 15-oxo-ETE and LTE4 excretion

BACKGROUND

15-oxo-eicosatetraenoic acid (15-oxo-ETE), is a product of arachidonic acid (AA) metabolism in the 15-lipoxygenase-1 (15-LOX-1) pathway. 15-oxo-ETE was overproduced in the nasal polyps of patients with nonsteroidal anti-inflammatory drug-exacerbated respiratory disease (N-ERD). In this study we investigated the systemic biosynthesis of 15-oxo-ETE and leukotriene E4 (LTE4) and assessed their diagnostic value to identify patients with N-ERD.

METHODS

The study included 64 patients with N-ERD, 59 asthmatics who tolerated aspirin well (ATA), and 51 healthy controls. A thorough clinical characteristics of asthmatics included computed tomography of paranasal sinuses. Plasma and urinary 15-oxo-ETE levels, and urinary LTE4 excretion were measured using high-performance liquid chromatography and tandem mass spectrometry. Repeatability and precision of the measurements were tested.

RESULTS

Plasma 15-oxo-ETE levels were the highest in N-ERD (p < .001). A receiver operator characteristic (ROC) revealed that 15-oxo-ETE had certain sensitivity (64.06% in plasma, or 88.24% in urine) for N-ERD discrimination, while the specificity was rather limited. Modeling of variables allowed to construct the Aspirin Hypersensitivity Diagnostic Index (AHDI) based on urinary LTE4-to-15-oxo-ETE excretion corrected for sex and the Lund-Mackay score of chronic rhinosinusitis. AHDI outperformed single measurements in discrimination of N-ERD among asthmatics with an area under ROC curve of 0.889, sensitivity of 81.97%, specificity of 87.23%, and accuracy of 86.87%.

CONCLUSIONS

We confirmed 15-oxo-ETE as a second to cysteinyl leukotrienes biomarker of N-ERD. An index based on these eicosanoids corrected for sex and Lund-Mackay score has a similar diagnostic value as gold standard oral aspirin challenge in the studied group of patients with asthma.

An overview of immunotoxicity in drug discovery and development

The immune system is one of the common targets of drugs' toxicity (Immunotoxicity) and/or efficacy (Immunotherapy). Immunotoxicity leads to adverse effects on human health, which raises serious concerns for the regulatory agencies. Currently, immunotoxicity assessment is conducted using different in vitro and in vivo assays. In silico and in vitro human cell-based immunotoxicity assays should also be explored for screening purposes as these are time and cost effective as well as for ethical reasons. For in vivo studies, tier 1-3 assessments (Tier 1: hematology, serum globulin levels, lymphoid organ's weight and histopathology; Tier 2: immunophenotyping, TDAR and cell mediated immunity; and Tier 3: host resistance) should be used. These non-clinical in vivo assessments are useful to select immunological endpoints for clinical trials as well as for precautionary labeling. As per regulatory guidelines, adverse immunogenicity information of drug should be included in product's labeling to make health care practitioner aware of safety concerns before prescribing medicines and patient management (USFDA, 2022a, 2022b). This review mainly focuses on the importance of immunotoxicity assessment during drug discovery and development.

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

PURPOSE OF REVIEW

Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research.

RECENT FINDINGS

In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.

Non-eosinophilic asthma in nonsteroidal anti-inflammatory drug exacerbated respiratory disease

BACKGROUND

The cellular inflammatory pattern of nonsteroidal anti-inflammatory drug-exacerbated respiratory disease (N-ERD) is heterogeneous. However, data on the heterogeneity of non-eosinophilic asthma (NEA) with aspirin hypersensitivity are scanty. By examination of N-ERD patients based on clinical data and eicosanoid biomarkers we aimed to identify NEA endotypes potentially guiding clinical management.

METHODS

Induced sputum was collected from patients with N-ERD. Sixty six patients (49.6% of 133 N-ERD) with NEA were included in the hierarchical cluster analysis based on clinical and laboratory data. The quality of clustering was evaluated using internal cluster validation with different indices and a practical decision tree was proposed to simplify stratification of patients.

RESULTS

The most frequent NEA pattern was paucigranulocytic (PGA; 75.8%), remaining was neutrophilic asthma (NA; 24.2%). Four clusters were identified. Cluster #3 included the highest number of NEA patients (37.9%) with severe asthma and PGA pattern (96.0%). Cluster #1 (24.2%) included severe only asthma, with a higher prevalence of NA (50%). Cluster #2 (25.8%) comprised well-controlled mild or severe asthma (PGA; 76.5%). Cluster #4 contained only 12.1% patients with well-controlled moderate asthma (PGA; 62.5%). Sputum prostaglandin D2 levels distinguished cluster #1 from the remaining clusters with an area under the curve of 0.94.

CONCLUSIONS

Among identified four NEA subtypes, clusters #3 and #1 represented N-ERD patients with severe asthma but a different inflammatory signatures. All the clusters were discriminated by sputum PGD2 levels, asthma severity, and age of patients. The heterogeneity of non-eosinophilic N-ERD suggests a need for novel targeted interventions.

Updates in biologic therapy for chronic rhinosinusitis with nasal polyps and NSAID-exacerbated respiratory disease

Chronic rhinosinusitis with nasal polyps (CRSwNP) associated with type 2 inflammation and non-steroidal anti-inflammatory drug (NSAID)-exacerbated respiratory disease (N-ERD) can be difficult to control with standard medical therapy and sinus surgery. In this group, biologicals are potentially promising treatment options. The phase III clinical trials for omalizumab, dupilumab, mepolizumab and benralizumab in CRSwNP have demonstrated favourable outcomes. Moving forward, direct comparisons among biologicals, refining patient selection criteria for specific biologicals, determining optimal treatment duration and monitoring long-term outcomes are areas of emerging interest. This review summarizes the clinical evidence from the recent 2 years on the role of biologicals in severe CRSwNP and N-ERD, and proposes an approach towards decision-making in their use.

Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic

Background

During the coronavirus disease 2019 (COVID-19) pandemic, it has become a pressing need to be able to diagnose aspirin hypersensitivity in patients with asthma without the need to use oral aspirin challenge (OAC) testing. OAC is time consuming and is associated with the risk of severe hypersensitive reactions. In this study, we sought to investigate whether machine learning (ML) based on some clinical and laboratory procedures performed during the pandemic might be used for discriminating between patients with aspirin hypersensitivity and those with aspirin-tolerant asthma.

Methods

We used a prospective database of 135 patients with non-steroidal anti-inflammatory drug (NSAID)-exacerbated respiratory disease (NERD) and 81 NSAID-tolerant (NTA) patients with asthma who underwent OAC. Clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant and urine were extracted for the purpose of applying ML techniques.

Results

The overall best ML model, neural network (NN), trained on a set of best features, achieved a sensitivity of 95% and a specificity of 76% for diagnosing NERD. The 3 promising models (i.e., multiple logistic regression, support vector machine, and NN) trained on a set of easy-to-obtain features including only clinical characteristics and laboratory data achieved a sensitivity of 97% and a specificity of 67%.

Conclusions

ML techniques are becoming a promising tool for discriminating between patients with NERD and NTA. The models are easy to use, safe, and achieve very good results, which is particularly important during the COVID-19 pandemic.

Local and Systemic Production of Pro-Inflammatory Eicosanoids Is Inversely Related to Sensitization to Aeroallergens in Patients with Aspirin-Exacerbated Respiratory Disease

Aspirin-exacerbated respiratory disease (AERD) is characterized by overproduction of the pro-inflammatory eicosanoids. Although immunoglobulin E-mediated sensitization to aeroallergens is common among AERD patients, it does not belong to the defining disease characteristics. In this study of 133 AERD patients, we sought to find a relationship between sensitization to aeroallergens and local (leukotriene E4, prostaglandin E2 and prostaglandin D2) and/or systemic (leukotriene E4) production of arachidonic acid metabolites. Interestingly, a negative association between pro-inflammatory eicosanoid levels in induced sputum supernatant or urine and sensitization to aeroallergens was observed. This inverse relationship might suggest the presence of a protective effect of atopic sensitization to aeroallergens against stronger local airway inflammation and higher systemic AERD-related inflammatory activity.

Artificial neural network identifies nonsteroidal anti-inflammatory drugs exacerbated respiratory disease (N-ERD) cohort

Background

To date, there has been no reliable in vitro test to either diagnose or differentiate nonsteroidal anti‐inflammatory drug (NSAID)–exacerbated respiratory disease (N‐ERD). The aim of the present study was to develop and validate an artificial neural network (ANN) for the prediction of N‐ERD in patients with asthma.

Methods

This study used a prospective database of patients with N‐ERD (n = 121) and aspirin‐tolerant (n = 82) who underwent aspirin challenge from May 2014 to May 2018. Eighteen parameters, including clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant (ISS) and urine were extracted for the ANN.

Results

The validation sensitivity of ANN was 94.12% (80.32%‐99.28%), specificity was 73.08% (52.21%‐88.43%), and accuracy was 85.00% (77.43%‐92.90%) for the prediction of N‐ERD. The area under the receiver operating curve was 0.83 (0.71‐0.90).

Conclusions

The designed ANN model seems to have powerful prediction capabilities to provide diagnosis of N‐ERD. Although it cannot replace the gold‐standard aspirin challenge test, the implementation of the ANN might provide an added value for identification of patients with N‐ERD. External validation in a large cohort is needed to confirm our results.