Background: Asthma and COPD are among the most common respiratory diseases. To improve the early detection of exacerbations and the clinical course of asthma and COPD new biomarkers are needed. The development of noninvasive metabolomics of exhaled air into a point-of-care tool is an appealing option. However, risk factors for obstructive pulmonary diseases can potentially introduce confounding markers due to altered volatile organic compound (VOC) patterns being linked to these risk factors instead of the disease. We conducted a systematic review and presented a comprehensive list of VOCs associated with these risk factors.Methods: A PRISMA-oriented systematic search was conducted across PubMed, Embase and Cochrane Libraries between 2000 and 2022. Full-length studies evaluating VOCs in exhaled breath were included. A narrative synthesis of the data was conducted, and the Newcastle–Ottawa Scale was used to assess the quality of included studies.Results: The search yielded 2209 records and, based on the inclusion/exclusion criteria, 24 articles were included in the qualitative synthesis. In total, 232 individual VOCs associated with risk factors for obstructive pulmonary diseases were found; 58 compounds were reported more than once and 12 were reported as potential markers of asthma and/or COPD in other studies. Critical appraisal found that the identified studies were methodologically heterogeneous and had a variable risk of bias.Conclusion: We identified a series of exhaled VOCs associated with risk factors for asthma and/or COPD. Identification of these VOCs is necessary for the further development of exhaled metabolites-based point-of-care tests in these obstructive pulmonary diseases.

Rationale: The early identification of children with poorly controlled asthma is imperative for optimizing treatment strategies. The analysis of exhaled volatile organic compounds (VOCs) is an emerging approach to identify prognostic and diagnostic biomarkers in pediatric asthma. Objectives: To assess the accuracy of gas chromatography–mass spectrometry–based exhaled metabolite analysis to differentiate between controlled and uncontrolled pediatric asthma. Methods: This study encompassed discovery (SysPharmPediA [Systems Pharmacology Approach to Uncontrolled Paediatric Asthma]) and validation (U-BIOPRED [Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes] and PANDA [Paediatric-Asthma-Non-Invasive-Diagnostic-Approaches]) phases. First, exhaled VOCs that discriminated degrees of asthma control were identified. Subsequently, outcomes were validated in two independent cohorts. Patients were classified as controlled or uncontrolled on the basis of asthma control test scores and the number of severe attacks in the past year. In addition, the potential of VOCs to predict two or more future severe asthma attacks in SysPharmPediA was evaluated. Measurements and Main Results: Complete data were available for 196 children (SysPharmPediA, n = 100; U-BIOPRED, n = 49; PANDA, n = 47). In SysPharmPediA, after randomly splitting the population into training (n = 51) and test (n = 49) sets, three compounds (acetophenone, ethylbenzene, and styrene) distinguished between patients with uncontrolled and controlled asthma. The areas under the receiver operating characteristic curves (AUROCCs) for training and test sets were, respectively, 0.83 (95% confidence interval [CI], 0.65–1.00) and 0.77 (95% CI, 0.58–0.96). Combinations of these VOCs resulted in AUROCCs of 0.74 ± 0.06 (U-BIOPRED) and 0.68 ± 0.05 (PANDA). Attack prediction tests resulted in AUROCCs of 0.71 (95% CI, 0.51–0.91) and 0.71 (95% CI, 0.52–0.90) for the training and test sets. Conclusions: Exhaled metabolite analysis might enable asthma control classification in children. This should stimulate the further development of exhaled metabolite–based point-of-care tests in asthma.

Objective: Package inserts (PIs) provide information for the safe and effective use of medication. There is no study on the evaluation of PIs in Iran. The purpose of this study was to evaluate the completeness of PIs supplied with the 100 top-selling medications in Iran. Methods: This cross-sectional observational study was conducted during 3 weeks in January 2017. One hundred medications were chosen from a list supplied by the Iran Food and Drug Administration (IFDA). The PIs were assessed for the presentation and completeness of quality criteria, which was consisted of two parts. The first part was the criteria required by the IFDA, mentioned in Chapter 16 of the Pharmaceutical Regulations and Instructions provided by the IFDA. The second part of the criteria was defined according to the critical comments of clinical and industrial pharmacists. Findings: Thirty-seven out of 100 medications included no PIs. None of the PIs met all the criteria required by the IFDA. The highest score for completeness was 18 out of 21 (85.7%). Medication name, description, and adverse reaction were mentioned in all PIs. Other items such as patient counseling information (98%), warnings (95.2%), precautions (95.2%), pregnancy/lactation (95%), and storage condition (90.5%) have been mentioned in a high percentage of PIs. Conclusion: PIs have improved in recent years in Iran, but there is an absolute need for more accurate and up-to-date information. The IFDA should supervise pharmaceutical companies more strictly in this regard and should revise its regulations requiring PIs to conform to the FDA regulations.

Rationale: Chronic obstructive pulmonary disease (COPD) exacerbations significantly contribute to disease progression, hospitalizations, and decreased quality of life. Early detection of exacerbations through non-invasive methods, such as exhaled volatile organic compounds (VOCs), could enable timely interventions. This study aimed to identify and validate candidate VOC biomarkers that are associated with exacerbations and stable phases of COPD, and could contribute to the development of a breath-based monitoring device. Methods: A systematic review was conducted to identify VOCs associated with COPD and exacerbations. VOCs were selected as candidate biomarkers if they were reported in at least two studies by different research groups. These VOCs were then validated using longitudinal exhaled breath data from the TEXACOLD study, where exhaled breath samples were collected at baseline, during exacerbation, and at follow-up in 14 COPD patients. Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) was applied to differentiate between samples collected during exacerbation and those at stable phases. Diagnostic accuracy was assessed using Receiver Operating Characteristic (ROC) curves. Results: The systematic review identified nine candidate VOCs. Three were excluded from validation because their dataset overlapped with one used in one of the included review studies. Validation confirmed the discriminatory power of a composite model of these six VOCs, achieving an area under the ROC curve of 0.98, a diagnostic accuracy of 94.3% and a sensitivity of 0.97 and a specificity of 0.93. Conclusion: This study demonstrates that exhaled VOCs can differentiate between exacerbations and stable phases in COPD patients. The validated biomarkers hold promise for future clinical applications, particularly in the development of a non-invasive, breath-based monitoring device for early detection and management of COPD exacerbations, potentially reducing hospitalizations and improving patient outcomes.

Background: Early identification of children at risk of asthma attacks is important for optimizing treatment strategies. We aimed to integrate salivary microbiome and serum inflammatory mediator profiles with asthma attacks history to develop a comprehensive predictive model for future attacks. Methods: This study contained a discovery (SysPharmPediA) and a replication phase (U‐BIOPRED). School‐aged children with asthma were classified into at risk and no‐risk groups, based on the presence or absence of one or more severe attacks during one‐year follow‐up. Prediction models were developed using random forest on the training set (70%) with data on past asthma attacks, microbiome composition, serum inflammatory mediator levels, and their combinations and then tested on the rest of the population (30%). Outcomes were replicated in a subset of children with severe asthma from U‐BIOPRED. Results: Complete data were available for 154 children (SysPharmPediA = 121, U‐BIOPRED = 33). In discovery, the model based on past attacks resulted in an area under the receiving characteristic curve (AUROCC) ~ 0.7. Models including six salivary bacteria or six inflammatory mediators achieved similar results. The combined model incorporating seven features, past asthma attacks, Capnocytophaga , Corynebacterium , and Cardiobacterium , TIMP‐4, VEGF, and MIP‐3β achieved the highest accuracy with AUROCC ~0.87. The combined model in the U‐BIOPRED limited to available inflammatory mediators (VEGF), and incorporating past asthma attacks, Capnocytophaga, Corynebacterium, and Cardiobacterium , resulted in an AUROCC of 0.84. Conclusion: Serum inflammatory mediators and salivary microbiome complement asthma attacks history for predicting future attacks. These results highlight the imperative for continued investigation into oral microbiota and its interaction with the immune system.