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Therapeutic monitoring in a pediatric clinical setting via breath analysis by high resolution mass spectrometry

Zeng, Jiafa. Therapeutic monitoring in a pediatric clinical setting via breath analysis by high resolution mass spectrometry. 2024, Doctoral Thesis, University of Basel, Faculty of Medicine.

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Abstract

Background: Precision medicine is a hallmark of modern medicine. It aims to achieve the maximum therapeutic efficacy and minimum adverse effects by optimizing the treatment process based on the unique health condition of each patient. Therapeutic monitoring is an indispensable part of precision medicine, especially in pediatric clinical populations, because of the rapid physiological and metabolic changes in pediatric patients, resulting in different response to therapy than adults. Pharmacometabolomics is a novel approach that can support therapeutic monitoring by providing information about metabolites that are altered by pharmaceuticals, or alternatively, that can affect the clinical outcome of pharmaceuticals. Among various types of human specimens that pharmacometabolomics studies can interrogate, exhaled breath is a non-invasive and convenient biofluid that contains a wealth of biological information.
Aim: Pharmacometabolomics via exhaled breath analysis holds promise for therapeutic monitoring. In this thesis, we will evaluate this approach by integrating breath analysis platform in a real-world pediatric hospital setting. We hypothesize that breath analysis can provide a comprehensive layer of metabolic information to better phenotype patient heterogeneity and clinical outcome to therapeutic interventions. In particular, we tested this hypothesis in two clinical populations: i) patients suffering from asthma requiring bronchodilators and ii) patients undergoing total intravenous anesthesia (TIVA).
Methods: i) A well-established ionization method used for real-time breath analysis dubbed secondary electrospray ionization (SESI) was compared against a novel method known as plasma ionization (PI). These two mass spectrometric techniques we evaluated in a head-to-head comparison study by performing breath analysis with healthy subjects with the aim of determining which one would be better suited for subsequent deployment in a clinical setting. ii) Online breath analysis was applied to asthma patients to characterize their response to bronchodilator salbutamol. Breath data of before and after salbutamol inhalation was collected, a sequence of statistical methods was applied to unveil drug-related metabotypes of patients. iii) Offline breath analysis was performed on patients who underwent TIVA with propofol. Propofol dosage during surgery was accomplished using the gold-standard target-controlled infusion (TCI) system. Parallel breath and serum samples were collected at multiple time points during surgery. Linear regression methods were applied to generated models to predict serum propofol concentrations via breath data. Lin’s concordance correlation coefficient (CCC), median performance error (MdPE) and Bland-Altman analysis were used to determine the agreement between calculated TCI concentration and the breath test vs the actual serum concentrations of propofol.
Results: i) A total of 58 breath samples were collected from two healthy subjects. 60 % of all the mass spectral features were detected in both platforms, SESI and PI. Signal-to-noise (S/N) ratios of detected features were higher in SESI as compared to PI: median (interquartile range) of 115 (408) vs 5 (5). In addition, the more complex gas-phase ion chemistry of PI led to differences in the mass spectral profiles for the same compounds, making the inter-comparability of both techniques problematic. ii) We evaluated changes at the metabolic level by interrogating the exhaled breath before and after salbutamol inhalation in n = 38 asthmatic children. Significant metabolic changes associated with salbutamol inhalation were detected in over 200 breath mass spectral features. Enrichment analysis pointed to sphingolipid metabolism and arginine biosynthesis as significantly altered pathways. Finally, 30 metabolites that correlated to these pathways revealed an association of such metabolites with patient heterogeneity and metabotypes poor salbutamol responsiveness. iii) Propofol and three compounds tentatively assigned as propofol metabolites were detected in breath samples from n = 10 pediatric patients who underwent propofol-induced TIVA. These mass spectra features were used as variables in regression models with the aim of predicting serum propofol concentrations. The best predictive model based on breath data provided good agreement metrics with the actual serum concentrations with CCC = 0.926, MdPE = -0.38% (interquartile range, IQR: -10.54% – 12.74%) and bias = 0.01 mg/L. In contrast, the MdPE of TCI was 10.35% (-3.81% – 22.14 %) and bias = 0.18 mg/L. Great inter-individual variability was observed.
Conclusion: This thesis provided substantial contributions towards the integration of online and offline breath analysis techniques into a real-world clinical setting. It showed the feasibility of implementation, both in an outpatient clinic and in an operating room. In a first study comparing two ionization techniques, we concluded that SESI provides more easily interpretable mass spectral information from metabolomics data. Hence it was chosen data interpretation on biological function is key for the further implementation of breath analysis in a clinical context. The use of such breath analysis platform to study the association of metabolic changes associated to inhaled therapy in asthmatic patients, enabled us to put forward the hypothesis that sphingolipid metabolism and arginine biosynthesis may allow for patient stratification in terms of response to bronchodilators. The second application case on patients undergoing surgery under propofol anesthesia revealed that exhaled propofol and its metabolites may serve as surrogate markers to predict systemic drug concentrations during surgery. Overall, we conclude that pharmacometabolomics via breath analysis may provide substantial contributions towards precision medicine.
Advisors:Sinues, Pablo
Committee Members:Frey, Urs Peter and Kohler, Malcolm and Günter, Andreas T.
Faculties and Departments:03 Faculty of Medicine > Departement Biomedical Engineering > Imaging and Computational Modelling > Translational Medicine Breath Research (Sinues)
UniBasel Contributors:Sinues, Pablo and Frey, Urs Peter
Item Type:Thesis
Thesis Subtype:Doctoral Thesis
Thesis no:15605
Thesis status:Complete
Number of Pages:XII, 143
Language:English
Identification Number:
  • urn: urn:nbn:ch:bel-bau-diss156055
edoc DOI:
Last Modified:05 Feb 2025 05:30
Deposited On:04 Feb 2025 10:57

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