Introduction: The hyperbaric oxygen treatment table 6 (TT6) is widely used to manage dysbaric illnesses in divers and iatrogenic gas emboli in patients after surgery and other interventional procedures. These treatment tables can have adverse effects, such as pulmonary oxygen toxicity (POT). It is caused by reactive oxygen species’ damaging effect in lung tissue and is often experienced after multiple days of therapy. The subclinical pulmonary effects have not been determined. The primary aim of this study was to measure volatile organic compounds (VOCs) in breath, indicative of subclinical POT after a TT6. Since the exposure would be limited, the secondary aim of this study was to determine whether these VOCs decreased to baseline levels within a few hours.Methods: Fourteen healthy, non-smoking volunteers from the Royal Netherlands Navy underwent a TT6 at the Amsterdam University Medical Center—location AMC. Breath samples for GC-MS analysis were collected before the TT6 and 30 min, 2 and 4 h after finishing. The concentrations of ions before and after exposure were compared by Wilcoxon signed-rank tests. The VOCs were identified by comparing the chromatograms with the NIST library. Compound intensities over time were tested using Friedman tests, with Wilcoxon signed-rank tests and Bonferroni corrections used for post hoc analyses.Results: Univariate analyses identified 11 compounds. Five compounds, isoprene, decane, nonane, nonanal and dodecane, showed significant changes after the Friedman test. Isoprene demonstrated a significant increase at 30 min after exposure and a subsequent decrease at 2 h. Other compounds remained constant, but declined significantly 4 h after exposure.Discussion and Conclusion: The identified VOCs consisted mainly of (methyl) alkanes, which may be generated by peroxidation of cell membranes. Other compounds may be linked to inflammatory processes, oxidative stress responses or cellular metabolism. The hypothesis, that exhaled VOCs would increase after hyperbaric exposure as an indicator of subclinical POT, was not fulfilled, except for isoprene. Hence, no evident signs of POT or subclinical pulmonary damage were detected after a TT6. Further studies on individuals recently exposed to pulmonary irritants, such as divers and individuals exposed to other hyperbaric treatment regimens, are needed.

Pharmacogenomics strives to explain the interindividual variability in response to drugs due to genetic variation. Although technological advances have provided us with relatively easy and cheap methods for genotyping, promises about personalised medicine have not yet met our high expectations. Successful results that have been achieved within the field of pharmacogenomics so far are, to name a few, HLA-B*5701 screening to avoid hypersensitivity to the antiretroviral abacavir, thiopurine S-methyltransferase (TPMT) genotyping to avoid thiopurine toxicity, and CYP2C9 and VKORC1 genotyping for better dosing of the anticoagulant warfarin. However, few pharmacogenetic examples have made it into clinical practice in the treatment of complex diseases. Unfortunately, lack of reproducibility of results from observational studies involving many genes and diseases seems to be a common pattern in pharmacogenomic studies. In this article we address some of the methodological and statistical issues within study design, gene and single nucleotide polymorphism (SNP) selection and data analysis that should be considered in future pharmacogenomic research. First, we discuss some of the issues related to the design of epidemiological studies, specific to pharmacogenomic research. Second, we describe some of the pros and cons of a candidate gene approach (including gene and SNP selection) and a genome-wide scan approach. Finally, conventional as well as several innovative approaches to the analysis of large pharmacogenomic datasets are proposed that deal with the issues of multiple testing and systems biology in different ways.

Response to pharmacotherapy can be highly variable amongst individuals. Pharmacogenomics may explain the interindividual variability in drug response due to genetic variation. However, besides genetics, many other factors can play a role in the response to pharmacotherapy, including disease severity, co-morbidity, environmental factors, therapy adherence and co-medication use. Better understanding of these factors and inter-relationships should bring about a much more effective approach to disease management. Systems biology that studies organisms as integrated and interacting networks of genes, proteins and biochemical reactions can contribute to this. Organisms are no longer studied part by part, but in a more integral manner. Integration of the genetic data with intermediate and end point phenotypic characterization may prove essential to define the inherent nature of drug effects. Therefore, in the future, a multidisciplinary systems-based approach will be necessary to deal with the bulk of the biological data that is available and, ultimately, to reach the goal of personalized prescribing.

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