Fulfilling the Promise of Breathomics: Considerations for the Discovery and Validation of Exhaled Volatile Biomarkers.

The exhaled breath represents an ideal matrix for non-invasive biomarker discovery, and exhaled metabolomics have the potential to be clinically useful in the era of precision medicine. In this concise translational review we will specifically address volatile organic compounds in the breath, with a view towards fulfilling the promise of these as actionable biomarkers, in particular for lung diseases. We review the literature paying attention to seminal work linked to key milestones in breath research; discuss potential applications for breath biomarkers across disease areas and healthcare systems, including the perspectives of industry; and outline critical aspects of study design that will need to be considered for any pivotal research going forward, if breath analysis is to provide robust validated biomarkers that meet the requirements for future clinical implementation.

Exhaled Volatile Organic Compounds for Asthma Control Classification in Children with Moderate to Severe Asthma: Results from the SysPharmPediA Study.

RATIONALE: 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 a discovery (SysPharmPediA) and validation phase (U-BIOPRED, PANDA). Firstly, exhaled VOCs that discriminated asthma control levels were identified. Subsequently, outcomes were validated in two independent cohorts. Patients were classified as controlled or uncontrolled, based on asthma control test scores and number of severe attacks in the past year. Additionally, potential of VOCs in predicting two or more future severe asthma attacks in SysPharmPediA was evaluated. MEASUREMENTS AND MAIN RESULTS: Complete data were available for 196 children (SysPharmPediA=100, U-BIOPRED=49, PANDA=47). In SysPharmPediA, after randomly splitting the population into training (n=51) and test sets (n=49), three compounds (acetophenone, ethylbenzene, and styrene) distinguished between uncontrolled and controlled asthmatics. The area under the receiver operating characteristic curve (AUROCC) for training and test sets were respectively: 0.83 (95% 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 (UBIOPRED) and 0.68 ±0.05 (PANDA). Attacks prediction tests, resulted in AUROCCs of 0.71 (95% CI 0.51-0.91) and 0.71 (95% CI 0.52-0.90) for training and test sets. CONCLUSIONS: Exhaled metabolites analysis might enable asthma control classification in children. This should stimulate further development of exhaled metabolites-based point-of-care tests in asthma.

Whole blood transcriptome in long-COVID patients reveals association with lung function and immune response.

BACKGROUND: Months after infection with severe acute respiratory syndrome coronavirus 2, at least 10% of patients still experience complaints. Long-COVID (coronavirus disease 2019) is a heterogeneous disease, and clustering efforts revealed multiple phenotypes on a clinical level. However, the molecular pathways underlying long-COVID phenotypes are still poorly understood. OBJECTIVES: We sought to cluster patients according to their blood transcriptomes and uncover the pathways underlying their disease. METHODS: Blood was collected from 77 patients with long-COVID from the Precision Medicine for more Oxygen (P4O2) COVID-19 study. Unsupervised hierarchical clustering was performed on the whole blood transcriptome. These clusters were analyzed for differences in clinical features, pulmonary function tests, and gene ontology term enrichment. RESULTS: Clustering revealed 2 distinct clusters on a transcriptome level. Compared with cluster 2 (n = 65), patients in cluster 1 (n = 12) showed a higher rate of preexisting cardiovascular disease (58% vs 22%), higher prevalence of gastrointestinal symptoms (58% vs 29%), shorter hospital duration during severe acute respiratory syndrome coronavirus 2 infection (median, 3 vs 8 days), lower FEV1/forced vital capacity (72% vs 81%), and lower diffusion capacity of the lung for carbon monoxide (68% vs 85% predicted). Gene ontology term enrichment analysis revealed upregulation of genes involved in the antiviral innate immune response in cluster 1, whereas genes involved with the adaptive immune response were upregulated in cluster 2. CONCLUSIONS: This study provides a start in uncovering the pathophysiological mechanisms underlying long-COVID. Further research is required to unravel why the immune response is different in these clusters, and to identify potential therapeutic targets to create an optimized treatment or monitoring strategy for the individual long-COVID patient.

Fatigue and symptom-based clusters in post COVID-19 patients: a multicentre, prospective, observational cohort study.

BACKGROUND: In the Netherlands, the prevalence of post COVID-19 condition is estimated at 12.7% at 90-150 days after SARS-CoV-2 infection. This study aimed to determine the occurrence of fatigue and other symptoms, to assess how many patients meet the Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) criteria, to identify symptom-based clusters within the P4O2 COVID-19 cohort and to compare these clusters with clusters in a ME/CFS cohort. METHODS: In this multicentre, prospective, observational cohort in the Netherlands, 95 post COVID-19 patients aged 40-65 years were included. Data collection at 3-6 months after infection included demographics, medical history, questionnaires, and a medical examination. Follow-up assessments occurred 9-12 months later, where the same data were collected. Fatigue was determined with the Fatigue Severity Scale (FSS), a score of ≥ 4 means moderate to high fatigue. The frequency and severity of other symptoms and the percentage of patients that meet the ME/CFS criteria were assessed using the DePaul Symptom Questionnaire-2 (DSQ-2). A self-organizing map was used to visualize the clustering of patients based on severity and frequency of 79 symptoms. In a previous study, 337 Dutch ME/CFS patients were clustered based on their symptom scores. The symptom scores of post COVID-19 patients were applied to these clusters to examine whether the same or different clusters were found. RESULTS: According to the FSS, fatigue was reported by 75.9% of the patients at 3-6 months after infection and by 57.1% of the patients 9-12 months later. Post-exertional malaise, sleep disturbances, pain, and neurocognitive symptoms were also frequently reported, according to the DSQ-2. Over half of the patients (52.7%) met the Fukuda criteria for ME/CFS, while fewer patients met other ME/CFS definitions. Clustering revealed specific symptom patterns and showed that post COVID-19 patients occurred in 11 of the clusters that have been observed in the ME/CFS cohort, where 2 clusters had > 10 patients. CONCLUSIONS: This study shows persistent fatigue and diverse symptomatology in post COVID-19 patients, up to 12-18 months after SARS-CoV-2 infection. Clustering showed that post COVID-19 patients occurred in 11 of the clusters that have been observed in the ME/CFS cohort.

International Care programs for Pediatric Post-COVID Condition (Long COVID) and the way forward.

BACKGROUND: Pediatric Post-COVID-Condition (PPCC) clinics treat children despite limited scientific substantiation. By exploring real-life management of children diagnosed with PPCC, the International Post-COVID-Condition in Children Collaboration (IP4C) aimed to provide guidance for future PPCC care. METHODS: We performed a cross-sectional international, multicenter study on used PPCC definitions; the organization of PPCC care programs and patients characteristics. We compared aggregated data from PPCC cohorts and identified priorities to improve PPCC care. RESULTS: Ten PPCC care programs and six COVID-19 follow-up research cohorts participated. Aggregated data from 584 PPCC patients was analyzed. The most common symptoms included fatigue (71%), headache (55%), concentration difficulties (53%), and brain fog (48%). Severe limitations in daily life were reported in 31% of patients. Most PPCC care programs organized in-person visits with multidisciplinary teams. Diagnostic testing for respiratory and cardiac morbidity was most frequently performed and seldom abnormal. Treatment was often limited to physical therapy and psychological support. CONCLUSIONS: We found substantial heterogeneity in both the diagnostics and management of PPCC, possibly explained by scarce scientific evidence and lack of standardized care. We present a list of components which future guidelines should address, and outline priorities concerning PPCC care pathways, research and international collaboration. IMPACT: Pediatric Post-COVID Condition (PPCC) Care programs have been initiated in many countries. Children with PPCC in different countries are affected by similar symptoms, limiting many to participate in daily life. There is substantial heterogeneity in diagnostic testing. Access to specific diagnostic tests is required to identify some long-term COVID-19 sequelae. Treatments provided were limited to physical therapy and psychological support. This study emphasizes the need for evidence-based diagnostics and treatment of PPCC. The International Post-COVID Collaboration for Children (IP4C) provides guidance for guideline development and introduces a framework of priorities for PPCC care and research, to improve PPCC outcomes.

Influence of the gut and airway microbiome on asthma development and disease.

There are ample data to suggest that early-life dysbiosis of both the gut and/or airway microbiome can predispose a child to develop along a trajectory toward asthma. Although individual studies show clear associations between dysbiosis and asthma development, it is less clear what (collection of) bacterial species is mechanistically responsible for the observed effects. This is partly due to issues related to the asthma diagnosis and the broad spectrum of anatomical sites, sample techniques, and analysis protocols that are used in different studies. Moreover, there is limited attention for potential differences in the genetics of individuals that would affect the outcome of the interaction between the environment and that individual. Despite these challenges, the first bacterial components were identified that are able to affect the transcriptional state of human cells, ergo the immune system. Such molecules could in the future be the basis for intervention studies that are now (necessarily) restricted to a limited number of bacterial species. For this transition, it might be prudent to develop an ex vivo human model of a local mucosal immune system to better and safer explore the impact of such molecules. With this approach, we might move beyond association toward understanding of causality.

Dried Blood Spot Method Development and Clinical Validation for the Analysis of Elexacaftor, Elexacaftor-M23, Tezacaftor, Tezacaftor-M1, Ivacaftor, Ivacaftor Carboxylate, and Hydroxymethyl Ivacaftor Using LC-MS/MS.

BACKGROUND: The highly effective Cystic Fibrosis Transmembrane conductance Regulator (CFTR) modulator, elexacaftor-tezacaftor-ivacaftor, is now widely being used by people with cystic fibrosis. However, few independent studies have detailed the pharmacokinetics (PK) of CFTR modulators. Blood collection by venipuncture is the gold standard for PK measurements, but it is invasive. The aim of this study was to develop and clinically validate a quantification method for elexacaftor, tezacaftor, ivacaftor, and their main metabolites in dried blood spots (DBSs) using liquid chromatography with tandem mass spectrometry. METHODS: Linearity, accuracy, precision, stability, hematocrit (Hct), spot-to-spot carryover, spot volume, and extraction efficiency were validated in DBS for all analytes. The clinical validation of elexacaftor-tezacaftor-ivacaftor in patients was performed by comparing 21 DBS samples with matched plasma samples. RESULTS: The preset requirements for linearity, within-run and between-run accuracy, precision, Hct, spot volume, and extraction efficiency were met. Puncher carryover was observed and resolved by punching 3 blanks after each sample. The samples remained stable and showed no notable degradation across the tested temperatures and time intervals. Corrected DBS values with the Passing-Bablok regression equation showed good agreement in Bland-Altman plots, and acceptance values were within 20% of the mean for a minimum of 67% of the repeats, according to the EMA guidelines. CONCLUSIONS: A quantification method for the analysis of elexacaftor, tezacaftor, ivacaftor, and their main metabolites was developed and clinically validated in DBS. This method could be valuable in both clinical care and research to address unanswered PK questions regarding CFTR modulators.

Oropharyngeal Microbiota Clusters in Children with Asthma or Wheeze Associate with Allergy, Blood Transcriptomic Immune Pathways, and Exacerbation Risk.

Rationale: Children with preschool wheezing or school-age asthma are reported to have airway microbial imbalances. Objectives: To identify clusters in children with asthma or wheezing using oropharyngeal microbiota profiles. Methods: Oropharyngeal swabs from the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) pediatric asthma or wheezing cohort were characterized using 16S ribosomal RNA gene sequencing, and unsupervised hierarchical clustering was performed on the Bray-Curtis ß-diversity. Enrichment scores of the Molecular Signatures Database hallmark gene sets were computed from the blood transcriptome using gene set variation analysis. Children with severe asthma or severe wheezing were followed up for 12-18 months, with assessment of the frequency of exacerbations. Measurements and Main Results: Oropharyngeal samples from 241 children (age range, 1-17 years; 40% female) revealed four taxa-driven clusters dominated by Streptococcus, Veillonella, Rothia, and Haemophilus. The clusters showed significant differences in atopic dermatitis, grass pollen sensitization, FEV1% predicted after salbutamol, and annual asthma exacerbation frequency during follow-up. The Veillonella cluster was the most allergic and included the highest percentage of children with two or more exacerbations per year during follow-up. The oropharyngeal clusters were different in the enrichment scores of TGF-ß (transforming growth factor-ß) (highest in the Veillonella cluster) and Wnt/ß-catenin signaling (highest in the Haemophilus cluster) transcriptomic pathways in blood (all q values <0.05). Conclusions: Analysis of the oropharyngeal microbiota of children with asthma or wheezing identified four clusters with distinct clinical characteristics (phenotypes) that associate with risk for exacerbation and transcriptomic pathways involved in airway remodeling. This suggests that further exploration of the oropharyngeal microbiota may lead to novel pathophysiologic insights and potentially new treatment approaches.

Development of Core Outcome Measures sets for paediatric and adult Severe Asthma (COMSA).

BACKGROUND: Effectiveness studies with biological therapies for asthma lack standardised outcome measures. The COMSA (Core Outcome Measures sets for paediatric and adult Severe Asthma) Working Group sought to develop Core Outcome Measures (COM) sets to facilitate better synthesis of data and appraisal of biologics in paediatric and adult asthma clinical studies. METHODS: COMSA utilised a multi-stakeholder consensus process among patients with severe asthma, adult and paediatric clinicians, pharmaceutical representatives, and health regulators from across Europe. Evidence included a systematic review of development, validity and reliability of selected outcome measures plus a narrative review and a pan-European survey to better understand patients' and carers' views about outcome measures. It was discussed using a modified GRADE (Grading of Recommendations Assessment, Development and Evaluation) Evidence to Decision framework. Anonymous voting was conducted using predefined consensus criteria. RESULTS: Both adult and paediatric COM sets include forced expiratory volume in 1 s (FEV1) as z-scores, annual frequency of severe exacerbations and maintenance oral corticosteroid use. Additionally, the paediatric COM set includes the Paediatric Asthma Quality of Life Questionnaire and Asthma Control Test or Childhood Asthma Control Test, while the adult COM set includes the Severe Asthma Questionnaire and Asthma Control Questionnaire-6 (symptoms and rescue medication use reported separately). CONCLUSIONS: This patient-centred collaboration has produced two COM sets for paediatric and adult severe asthma. It is expected that they will inform the methodology of future clinical trials, enhance comparability of efficacy and effectiveness of biological therapies, and help assess their socioeconomic value. COMSA will inform definitions of non-response and response to biological therapy for severe asthma.

Haemophilus influenzae and Moraxella catarrhalis in sputum of severe asthma with inflammasome and neutrophil activation.

BACKGROUND: Because of altered airway microbiome in asthma, we analysed the bacterial species in sputum of patients with severe asthma. METHODS: Whole genome sequencing was performed on induced sputum from non-smoking (SAn) and current or ex-smoker (SAs/ex) severe asthma patients, mild/moderate asthma (MMA) and healthy controls (HC). Data were analysed by asthma severity, inflammatory status and transcriptome-associated clusters (TACs). RESULTS: α-diversity at the species level was lower in SAn and SAs/ex, with an increase in Haemophilus influenzae and Moraxella catarrhalis, and Haemophilus influenzae and Tropheryma whipplei, respectively, compared to HC. In neutrophilic asthma, there was greater abundance of Haemophilus influenzae and Moraxella catarrhalis and in eosinophilic asthma, Tropheryma whipplei was increased. There was a reduction in α-diversity in TAC1 and TAC2 that expressed high levels of Haemophilus influenzae and Tropheryma whipplei, and Haemophilus influenzae and Moraxella catarrhalis, respectively, compared to HC. Sputum neutrophils correlated positively with Moraxella catarrhalis and negatively with Prevotella, Neisseria and Veillonella species and Haemophilus parainfluenzae. Sputum eosinophils correlated positively with Tropheryma whipplei which correlated with pack-years of smoking. α- and ß-diversities were stable at one year. CONCLUSIONS: Haemophilus influenzae and Moraxella catarrhalis were more abundant in severe neutrophilic asthma and TAC2 linked to inflammasome and neutrophil activation, while Haemophilus influenzae and Tropheryma whipplei were highest in SAs/ex and in TAC1 associated with highest expression of IL-13 type 2 and ILC2 signatures with the abundance of Tropheryma whipplei correlating positively with sputum eosinophils. Whether these bacterial species drive the inflammatory response in asthma needs evaluation.

Classifying asthma control using salivary and fecal bacterial microbiome in children with moderate-to-severe asthma.

BACKGROUND: Uncontrolled asthma can lead to severe exacerbations and reduced quality of life. Research has shown that the microbiome may be linked with asthma characteristics; however, its association with asthma control has not been explored. We aimed to investigate whether the gastrointestinal microbiome can be used to discriminate between uncontrolled and controlled asthma in children. METHODS: 143 and 103 feces samples were obtained from 143 children with moderate-to-severe asthma aged 6 to 17 years from the SysPharmPediA study. Patients were classified as controlled or uncontrolled asthmatics, and their microbiome at species level was compared using global (alpha/beta) diversity, conventional differential abundance analysis (DAA, analysis of compositions of microbiomes with bias correction), and machine learning [Recursive Ensemble Feature Selection (REFS)]. RESULTS: Global diversity and DAA did not find significant differences between controlled and uncontrolled pediatric asthmatics. REFS detected a set of taxa, including Haemophilus and Veillonella, differentiating uncontrolled and controlled asthma with an average classification accuracy of 81% (saliva) and 86% (feces). These taxa showed enrichment in taxa previously associated with inflammatory diseases for both sampling compartments, and with COPD for the saliva samples. CONCLUSION: Controlled and uncontrolled children with asthma can be differentiated based on their gastrointestinal microbiome using machine learning, specifically REFS. Our results show an association between asthma control and the gastrointestinal microbiome. This suggests that the gastrointestinal microbiome may be a potential biomarker for treatment responsiveness and thereby help to improve asthma control in children.

Cisplatin-induced nephrotoxicity in childhood cancer: comparison between two countries.

BACKGROUND: Various definitions used to describe cisplatin nephrotoxicity potentially lead to differences in determination of risk factors. This study evaluated incidence of kidney injury according to commonly used and alternative definitions in two cohorts of children who received cisplatin. METHODS: This retrospective cohort study included children from Vancouver, Canada (one center), and Mexico City, Mexico (two centers), treated with cisplatin for a variety of solid tumors. Serum creatinine-based definitions (KDIGO and Pediatric RIFLE (pRIFLE)), electrolyte abnormalities consisted of hypokalemia, hypophosphatemia and hypomagnesemia (based on NCI-CTCAE v5), and an alternative definition (Alt-AKI) were used to describe nephrotoxicity. Incidence with different definitions, definitional overlap, and inter-definition reliability was analyzed. RESULTS: In total, 173 children (100 from Vancouver, 73 from Mexico) were included. In the combined cohort, Alt-AKI criteria detected more patients with cisplatin nephrotoxicity compared to pRIFLE and KDIGO criteria (82.7 vs. 63.6 vs. 44.5%, respectively). Nephrotoxicity and all electrolyte abnormalities were significantly more common in Vancouver cohort than in Mexico City cohort except when using KDIGO definition. The most common electrolyte abnormalities were hypomagnesemia (88.9%, Vancouver) and hypophosphatemia (24.2%, Mexico City). The KDIGO definition provided highest overlap of cases in Vancouver (100%), Mexico (98.6%), and the combined cohort (99.4%). Moderate overall agreement was found among Alt-AKI, KDIGO, and pRIFLE definitions (κ = 0.18, 95% CI 0.1-0.27) in which KDIGO and pRIFLE showed moderate agreement (κ = 0.48, 95% CI 0.36-0.60). CONCLUSIONS: Compared to pRIFLE and KDIGO criteria, Alt-AKI criteria detected more patients with cisplatin nephrotoxicity. pRIFLE is more sensitive to detect not only actual kidney injury but also patients at risk of cisplatin nephrotoxicity, while KDIGO seems more useful to detect clinically significant kidney injury. A higher resolution version of the Graphical abstract is available as Supplementary information.

Precision Medicine in Asthma Therapy.

Asthma is a complex, heterogeneous disease that necessitates a proper patient evaluation to decide the correct treatment and optimize disease control. The recent introduction of new target therapies for the most severe form of the disease has heralded a new era of treatment options, intending to treat and control specific molecular pathways in asthma pathophysiology. Precision medicine, using omics sciences, investigates biological and molecular mechanisms to find novel biomarkers that can be used to guide treatment selection and predict response. The identification of reliable biomarkers indicative of the pathological mechanisms in asthma is essential to unravel new potential treatment targets. In this chapter, we provide a general description of the currently available -omics techniques, focusing on their implications in asthma therapy.

Mapping atopic dermatitis and anti-IL-22 response signatures to type 2-low severe neutrophilic asthma.

BACKGROUND: Transcriptomic changes in patients who respond clinically to biological therapies may identify responses in other tissues or diseases. OBJECTIVE: We sought to determine whether a disease signature identified in atopic dermatitis (AD) is seen in adults with severe asthma and whether a transcriptomic signature for patients with AD who respond clinically to anti-IL-22 (fezakinumab [FZ]) is enriched in severe asthma. METHODS: An AD disease signature was obtained from analysis of differentially expressed genes between AD lesional and nonlesional skin biopsies. Differentially expressed genes from lesional skin from therapeutic superresponders before and after 12 weeks of FZ treatment defined the FZ-response signature. Gene set variation analysis was used to produce enrichment scores of AD and FZ-response signatures in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes asthma cohort. RESULTS: The AD disease signature (112 upregulated genes) encompassing inflammatory, T-cell, TH2, and TH17/TH22 pathways was enriched in the blood and sputum of patients with asthma with increasing severity. Patients with asthma with sputum neutrophilia and mixed granulocyte phenotypes were the most enriched (P < .05). The FZ-response signature (296 downregulated genes) was enriched in asthmatic blood (P < .05) and particularly in neutrophilic and mixed granulocytic sputum (P < .05). These data were confirmed in sputum of the Airway Disease Endotyping for Personalized Therapeutics cohort. IL-22 mRNA across tissues did not correlate with FZ-response enrichment scores, but this response signature correlated with TH22/IL-22 pathways. CONCLUSIONS: The FZ-response signature in AD identifies severe neutrophilic asthmatic patients as potential responders to FZ therapy. This approach will help identify patients for future asthma clinical trials of drugs used successfully in other chronic diseases.

Primary ciliary dyskinesia in Volendam: Diagnostic and phenotypic features in patients with a CCDC114 mutation.

Primary ciliary dyskinesia (PCD) is a heterogeneous disease, with impaired mucociliary clearance causing respiratory tract infections. A founding CCDC114 mutation has led to a relatively homogeneous and large Dutch PCD population in Volendam. Our aim was to describe their phenotype. Therefore, all Volendam PCD patients seen at the Amsterdam UMC were included in this study. Data were collected on lung function, microbiology, radiology, and ear-nose-throat (ENT) symptoms. A mixed effects model estimated lung function decline in %point per year (95% confidence interval [CI]). Thirty-three (60%) out of approximately 56 Volendam PCD patients were treated at our center and included in this study. Only 30% of patients had situs inversus. FEV1 declined in children (-1.43%/year, CI: -1.80/-1.05), but not in adults (0.01%/year, CI: -0.36/0.38). Pseudomonas aeruginosa was cultured in 21% of children and 60% of adults, respectively. Patients who have been infected at some point with P. aeruginosa had a steeper decline in FEV1 as compared to patients that have never been infected. Neonatal symptoms (79%) and ENT problems (94%) were common; fertility issues however, were not (11%) common. Compared to other PCD cohorts, the Volendam/CCDC114 patients have a moderately severe phenotype with lung function decline predominantly occurring in childhood.

How paediatric drug development and use could benefit from OMICs: A c4c expert group white paper.

The safety and efficacy of pharmacotherapy in children, particularly preterms, neonates and infants, is limited by a paucity of good-quality data from prospective clinical drug trials. A specific challenge is the establishment of valid biomarkers. OMICs technologies may support these efforts by complementary information about targeted and nontargeted molecules through systematic characterization and quantitation of biological samples. OMICs technologies comprise at least genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics in addition to the patient's phenotype. OMICs technologies are in part hypothesis-generating, allowing an in depth understanding of disease pathophysiology and pharmacological mechanisms. Application of OMICs technologies in paediatrics faces major challenges before routine adoption. First, developmental processes need to be considered, including a subdivision into specific age groups as developmental changes clearly impact OMICs data. Second, compared to the adult population, the number of patients is limited as are the type and amount of necessary biomaterial, especially in neonates and preterms. Thus, advanced trial designs and biostatistical methods, noninvasive biomarkers, innovative biobanking concepts including data and samples from healthy children, as well as analytical approaches (eg liquid biopsies) should be addressed to overcome these obstacles. The ultimate goal is to link OMICs technologies with innovative analysis tools, such as artificial intelligence at an early stage. The use of OMICs data based on a feasible approach will contribute to the identification complex phenotypes and subpopulations of patients to improve the development of medicines for children with potential economic advantages.

Pharmacogenomic testing in paediatrics: Clinical implementation strategies.

Pharmacogenomics (PGx) relates to the study of genetic factors determining variability in drug response. Implementing PGx testing in paediatric patients can enhance drug safety, helping to improve drug efficacy or reduce the risk of toxicity. Despite its clinical relevance, the implementation of PGx testing in paediatric practice to date has been variable and limited. As with most paediatric pharmacological studies, there are well-recognised barriers to obtaining high-quality PGx evidence, particularly when patient numbers may be small, and off-label or unlicensed prescribing remains widespread. Furthermore, trials enrolling small numbers of children can rarely, in isolation, provide sufficient PGx evidence to change clinical practice, so extrapolation from larger PGx studies in adult patients, where scientifically sound, is essential. This review paper discusses the relevance of PGx to paediatrics and considers implementation strategies from a child health perspective. Examples are provided from Canada, the Netherlands and the UK, with consideration of the different healthcare systems and their distinct approaches to implementation, followed by future recommendations based on these cumulative experiences. Improving the evidence base demonstrating the clinical utility and cost-effectiveness of paediatric PGx testing will be critical to drive implementation forwards. International, interdisciplinary collaborations will enhance paediatric data collation, interpretation and evidence curation, while also supporting dedicated paediatric PGx educational initiatives. PGx consortia and paediatric clinical research networks will continue to play a central role in the streamlined development of effective PGx implementation strategies to help optimise paediatric pharmacotherapy.

Sputum microbiome profiles identify severe asthma phenotypes of relative stability at 12 to 18 months.

BACKGROUND: Asthma is a heterogeneous disease characterized by distinct phenotypes with associated microbial dysbiosis. OBJECTIVES: Our aim was to identify severe asthma phenotypes based on sputum microbiome profiles and assess their stability after 12 to 18 months. A further aim was to evaluate clusters' robustness after inclusion of an independent cohort of patients with mild-to-moderate asthma. METHODS: In this longitudinal multicenter cohort study, sputum samples were collected for microbiome profiling from a subset of the Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adult patient cohort at baseline and after 12 to 18 months of follow-up. Unsupervised hierarchical clustering was performed by using the Bray-Curtis ß-diversity measure of microbial profiles. For internal validation, partitioning around medoids, consensus cluster distribution, bootstrapping, and topological data analysis were applied. Follow-up samples were studied to evaluate within-patient clustering stability in patients with severe asthma. Cluster robustness was evaluated by using an independent cohort of patients with mild-to-moderate asthma. RESULTS: Data were available for 100 subjects with severe asthma (median age 55 years; 42% males). Two microbiome-driven clusters were identified; they were characterized by differences in asthma onset, smoking status, residential locations, percentage of blood and/or sputum neutrophils and macrophages, lung spirometry results, and concurrent asthma medications (all P values < .05). The cluster 2 patients displayed a commensal-deficient bacterial profile that was associated with worse asthma outcomes than those of the cluster 1 patients. Longitudinal clusters revealed high relative stability after 12 to 18 months in those with severe asthma. Further inclusion of an independent cohort of 24 patients with mild-to-moderate asthma was consistent with the clustering assignments. CONCLUSION: Unbiased microbiome-driven clustering revealed 2 distinct robust phenotypes of severe asthma that exhibited relative overtime stability. This suggests that the sputum microbiome may serve as a biomarker for better characterizing asthma phenotypes.

Genome-wide association study of asthma exacerbations despite inhaled corticosteroid use.

RATIONALE: Substantial variability in response to asthma treatment with inhaled corticosteroids (ICS) has been described among individuals and populations, suggesting the contribution of genetic factors. Nonetheless, only a few genes have been identified to date. We aimed to identify genetic variants associated with asthma exacerbations despite ICS use in European children and young adults and to validate the findings in non-Europeans. Moreover, we explored whether a gene-set enrichment analysis could suggest potential novel asthma therapies. METHODS: A genome-wide association study (GWAS) of asthma exacerbations was tested in 2681 children of European descent treated with ICS from eight studies. Suggestive association signals were followed up for replication in 538 European asthma patients. Further evaluation was performed in 1773 non-Europeans. Variants revealed by published GWAS were assessed for replication. Additionally, gene-set enrichment analysis focused on drugs was performed. RESULTS: 10 independent variants were associated with asthma exacerbations despite ICS treatment in the discovery phase (p≤5×10-6). Of those, one variant at the CACNA2D3-WNT5A locus was nominally replicated in Europeans (rs67026078; p=0.010), but this was not validated in non-European populations. Five other genes associated with ICS response in previous studies were replicated. Additionally, an enrichment of associations in genes regulated by trichostatin A treatment was found. CONCLUSIONS: The intergenic region of CACNA2D3 and WNT5A was revealed as a novel locus for asthma exacerbations despite ICS treatment in European populations. Genes associated were related to trichostatin A, suggesting that this drug could regulate the molecular mechanisms involved in treatment response.

Treating severe asthma: Targeting the IL-5 pathway.

Severe asthma is a heterogeneous disease with different phenotypes based on clinical, functional or inflammatory parameters. In particular, the eosinophilic phenotype is associated with type 2 inflammation and increased levels of interleukin (IL)-4, IL-5 and IL-13). Monoclonal antibodies that target the eosinophilic inflammatory pathways (IL-5R and IL-5), namely mepolizumab, reslizumab, and benralizumab, are effective and safe for severe eosinophilic asthma. Eosinophils threshold represents the most indicative biomarker for response to treatment with all three monoclonal antibodies. Improvement in asthma symptoms scores, lung function, the number of exacerbations, history of late-onset asthma, chronic rhinosinusitis with nasal polyposis, low oral corticosteroids use and low body mass index represent predictive clinical markers of response. Novel Omics studies are emerging with proteomics data and exhaled breath analyses. These may prove useful as biomarkers of response and non-response biologics. Moreover, future biomarker studies need to be undertaken in paediatric patients affected by severe asthma. The choice of appropriate biologic therapy for severe asthma remains challenging. The importance of finding biomarkers that can predict response continuous an open issue that needs to be further explored. This review describes the clinical effects of targeting the IL-5 pathway in severe asthma in adult and paediatric patients, focusing on predictors of response and non-response.