Untangling the nets: a roadmap to standardized sampling and analysis of exhaled volatile organic compounds powered by in silico medicine.

Biomarkers based on volatile organic compounds (VOCs) measured in human breath have been investigated in a wide range of diseases. However, the excitement surrounding such biomarkers has not yet translated to the discovery of any that are ready for clinical implementation. A lack of standardization in sampling and analysis has been identified as a key obstacle to the validation of potential biomarkers in multicenter studies. Some progress toward standardization has been made, but further progress is required to optimize sampling protocols and account for the confounding factors identified. This review highlights the important role that in silico (i.e., computational modeling) methods can play in addressing these gaps. Moreover, we discuss their potential for targeting and validating disease biomarkers by mechanistically linking them to the underlying metabolomic processes. We explore pertinent examples of mathematical, computational, and machine learning models that have proven useful in similar contexts, such as the development of fractional exhaled nitric oxide sampling standards. We then propose a roadmap outlining how existing and new modeling approaches can be applied to the problem of standardization in breathomics research.