Systemic Medications in Chronic Obstructive Pulmonary Disease: Use and Outcomes

An Electrochemical Sensor of Theophylline on a Boron-Doped Diamond Electrode Modified with Nickel Nanoparticles

Theophylline is a drug with a narrow therapeutic range. Electrochemical sensors are a potentially effective method for detecting theophylline concentration to prevent toxicity. In this work, a simple modification of a boron-doped diamond electrode using nickel nanoparticles was successfully performed for a theophylline electrochemical sensor. The modified electrode was characterized using a scanning electron microscope and X-ray photoelectron spectroscopy. Square wave voltammetry and cyclic voltammetry methods were used to study the electrochemical behavior of theophylline. The modified nickel nanoparticles on the boron-doped diamond electrode exhibited an electrochemically active surface area of 0.0081 cm2, which is larger than the unmodified boron-doped diamond's area of 0.0011 cm2. This modified electrode demonstrated a low limit of detection of 2.79 µM within the linear concentration range from 30 to 100 µM. Moreover, the modified boron-doped diamond electrode also showed selective properties against D-glucose, ammonium sulfate, and urea. In the real sample analysis using artificial urine, the boron-doped diamond electrode with nickel nanoparticle modifications achieved a %recovery of 105.10%, with a good precision of less than 5%. The results of this work indicate that the developed method using nickel nanoparticles on a boron-doped diamond electrode is promising for the determination of theophylline.

Explainable Machine Learning Model for Predicting First-Time Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease

Background: The study developed accurate explainable machine learning (ML) models for predicting first-time acute exacerbation of chronic obstructive pulmonary disease (COPD, AECOPD) at an individual level. Methods: We conducted a retrospective case–control study. A total of 606 patients with COPD were screened for eligibility using registry data from the COPD Pay-for-Performance Program (COPD P4P program) database at Changhua Christian Hospital between January 2017 and December 2019. Recursive feature elimination technology was used to select the optimal subset of features for predicting the occurrence of AECOPD. We developed four ML models to predict first-time AECOPD, and the highest-performing model was applied. Finally, an explainable approach based on ML and the SHapley Additive exPlanations (SHAP) and a local explanation method were used to evaluate the risk of AECOPD and to generate individual explanations of the model’s decisions. Results: The gradient boosting machine (GBM) and support vector machine (SVM) models exhibited superior discrimination ability (area under curve [AUC] = 0.833 [95% confidence interval (CI) 0.745–0.921] and AUC = 0.836 [95% CI 0.757–0.915], respectively). The decision curve analysis indicated that the GBM model exhibited a higher net benefit in distinguishing patients at high risk for AECOPD when the threshold probability was <0.55. The COPD Assessment Test (CAT) and the symptom of wheezing were the two most important features and exhibited the highest SHAP values, followed by monocyte count and white blood cell (WBC) count, coughing, red blood cell (RBC) count, breathing rate, oral long-acting bronchodilator use, chronic pulmonary disease (CPD), systolic blood pressure (SBP), and others. Higher CAT score; monocyte, WBC, and RBC counts; BMI; diastolic blood pressure (DBP); neutrophil-to-lymphocyte ratio; and eosinophil and lymphocyte counts were associated with AECOPD. The presence of symptoms (wheezing, dyspnea, coughing), chronic disease (CPD, congestive heart failure [CHF], sleep disorders, and pneumonia), and use of COPD medications (triple-therapy long-acting bronchodilators, short-acting bronchodilators, oral long-acting bronchodilators, and antibiotics) were also positively associated with AECOPD. A high breathing rate, heart rate, or systolic blood pressure and methylxanthine use were negatively correlated with AECOPD. Conclusions: The ML model was able to accurately assess the risk of AECOPD. The ML model combined with SHAP and the local explanation method were able to provide interpretable and visual explanations of individualized risk predictions, which may assist clinical physicians in understanding the effects of key features in the model and the model’s decision-making process.

Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning

Theophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water. There was a strong synergistic effect of organic solvents mixed with water, and among the experimentally studied binary systems, the one containing DMSO with water in unimolar proportions was found to be the most effective in theophylline dissolution. Likewise, for NADES, the addition of water (0.2 molar fraction) resulted in increased solubility compared to pure eutectics, with the highest solubilisation potential offered by the composition of choline chloride with glycerol. The ensemble of Statistica Automated Neural Networks (SANNs) developed using intermolecular interactions in pure systems has been found to be a very accurate model for solubility computations. This machine learning protocol was also applied as an extensive screening for potential solvents with higher solubility of theophylline. Such solvents were identified in all three subgroups, including neat solvents, binary mixtures and ternary NADES systems. Some methodological considerations of SANNs applications for future modelling were also provided. Although the developed protocol is focused exclusively on theophylline solubility, it also has general importance and can be used for the development of predictive models adequate for solvent screening of other compounds in a variety of systems. Formulation of such a model offers rational guidance for the selection of proper candidates as solubilisers in the designed solvents screening.

[COPD, anxiety-depression and cognitive disorders: Does inflammation play a major role?]

COPD is a chronic respiratory disease, often associated with extrapulmonary manifestations. Co-morbidities, including anxiety, depression and cognitive impairment, worsen its progression and quality of life. The prevalence of these disorders is high, yet they are often poorly understood and inadequately managed. In the development of psychological disorders, there is accumulated evidence highlighting the major role of systemic inflammation, as well as chronic disease, genetics, the consequences of smoking, hypoxaemia, oxidative stress, and the gut microbiome In addition to traditional treatments such as bronchodilatator medications, respiratory rehabilitation and smoking cessation, systemic inflammation is an interesting therapeutic target, with the use of anti-inflammatory drugs, anti-cytokines, and nutritional interventions.