Abstract
The following thesis consists of two distinct projects: monitoring antipsychotic treatment from a single fingerprint (Part A) and untargeted metabolomics of Covid-19 patients’ saliva (Part B). Both projects are focusing on non-invasive matrices, fingerprints and saliva, collected from participants recruited in a hospital setting.
Methods for the detection of antipsychotics have been established in the last two decades in many matrices such blood, urine and oral fluid using liquid-chromatography mass spectrometry. Recently, several authors have shown the possibility to detect pharmaceutical drugs and drugs of abuse from a fingerprint. In this study, the possibility to use fingerprints for the detection of antipsychotics was explored. Fingerprints from individuals seeking treatment for psychosis were collected and analysed for a selection of antipsychotics: risperidone, quetiapine, clozapine, olanzapine and their respective metabolites using liquid chromatography – mass spectrometry (LC-MS). For the first time in literature, these four antipsychotics and metabolites were detected in fingerprints. The presence of the analytes of interest in the collected fingerprint samples was also found to be in good agreement with the participant statement and metadata. As this fingerprint test aims to be taken at home in the future, the difference between ingestion and dermal contact was investigated as well as the prevalence of the analytes in a negative control group. The study revealed a significant difference between ingestion and dermal contact using metabolite over parent drug ratio (MPR) for quetiapine and clozapine (risperidone could not be investigated due to lack of participants’ samples). Similar results were obtained for olanzapine, through analysis of the parent drug only as the metabolite could not be detected. Risperidone, quetiapine, clozapine and their respective metabolites were not present in the samples collected from the negative control group. Only olanzapine was detected from this group and hand washing showed it was effective at reducing external contamination. This study shows the possibility and reliability of fingerprint testing for antipsychotics, albeit for a small cohort.
Over the past two years, the advent of the coronavirus epidemic has caused unparalleled global economic and societal damage. Due to the continuous emergence of new variants as well as vaccine escape, it is likely coronavirus disease will be an ongoing global health issue. Although Covid-19 diagnostic is now well established, the discovery of Covid-19 markers is still in progress. Analysis of biofluids has been utilised in the field of metabolomics in the past to categorise and stratify patients as well as identify key biomarkers. This study aimed to use saliva, a less invasive matrix than blood, to identify biomarkers of Covid-19 through a metabolomic workflow. Saliva samples alongside patient’s metadata were collected from hospitalised individuals showing a suspicion of Covid-19. RT-PCR testing was used to validate the Covid-19 diagnosis and clinical observations were used to determine Covid-19 severity, such as C-reactive protein levels, respiratory rate and peripheral oxygen saturation score. LC-MS was used for the analysis of the extracted saliva and the data was analysed using multivariate techniques. Whilst this study was not able to reveal metabolites separating Covid-19 positives and Covid-19 negatives, biomarkers such as amino acids were able to distinguish Covid-19 low severity patients with high severity patients from an inpatient cohort where the severity was ascertained using clinical observations. These results show the possibility of using saliva for a rapid and non-invasive sampling method from patients that could be used in addition to current clinical testing, allowing the identification of individuals who would benefit from fast turnaround treatment.