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Article

Modelling of Breath and Various Blood Volatilomic Profiles—Implications for Breath Volatile Analysis

by
Paweł Mochalski
1,2,†,
Julian King
1,†,
Chris A. Mayhew
1,3,† and
Karl Unterkofler
1,4,*,†
1
Institute for Breath Research, Leopold-Franzens-Universität, Innrain 66, A-6020 Innsbruck, Austria
2
Institute of Chemistry, Jan Kochanowski University, 25-369 Kielce, Poland
3
Tiroler Krebsforschungsinstitut (TKFI), Innrain 66, A-6020 Innsbruck, Austria
4
Research Center BI, University of Applied Sciences Vorarlberg, Hochschulstraße 1, A-6850 Dornbirn, Austria
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Molecules 2022, 27(8), 2381; https://doi.org/10.3390/molecules27082381
Submission received: 2 March 2022 / Revised: 31 March 2022 / Accepted: 31 March 2022 / Published: 7 April 2022
(This article belongs to the Special Issue State of the Art in Breath Analysis for Disease Diagnosis and Exposure Assessment: Challenges and Solutions)
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Abstract

Researchers looking for biomarkers from different sources, such as breath, urine, or blood, frequently search for specific patterns of volatile organic compounds (VOCs), often using pattern recognition or machine learning techniques. However, they are not generally aware that these patterns change depending on the source they use. Therefore, we have created a simple model to demonstrate that the distribution patterns of VOCs in fat, mixed venous blood, alveolar air, and end-tidal breath are different. Our approach follows well-established models for the description of dynamic real-time breath concentration profiles. We start with a uniform distribution of end-tidal concentrations of selected VOCs and calculate the corresponding target concentrations. For this, we only need partition coefficients, mass balance, and the assumption of an equilibrium state, which avoids the need to know the volatiles’ metabolic rates and production rates within the different compartments.
Keywords: volatilome; VOCs; breath; end-tidal air; blood; fat; partition coefficients; modelling volatilome; VOCs; breath; end-tidal air; blood; fat; partition coefficients; modelling

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MDPI and ACS Style

Mochalski, P.; King, J.; Mayhew, C.A.; Unterkofler, K. Modelling of Breath and Various Blood Volatilomic Profiles—Implications for Breath Volatile Analysis. Molecules 2022, 27, 2381. https://doi.org/10.3390/molecules27082381

AMA Style

Mochalski P, King J, Mayhew CA, Unterkofler K. Modelling of Breath and Various Blood Volatilomic Profiles—Implications for Breath Volatile Analysis. Molecules. 2022; 27(8):2381. https://doi.org/10.3390/molecules27082381

Chicago/Turabian Style

Mochalski, Paweł, Julian King, Chris A. Mayhew, and Karl Unterkofler. 2022. "Modelling of Breath and Various Blood Volatilomic Profiles—Implications for Breath Volatile Analysis" Molecules 27, no. 8: 2381. https://doi.org/10.3390/molecules27082381

APA Style

Mochalski, P., King, J., Mayhew, C. A., & Unterkofler, K. (2022). Modelling of Breath and Various Blood Volatilomic Profiles—Implications for Breath Volatile Analysis. Molecules, 27(8), 2381. https://doi.org/10.3390/molecules27082381

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