| dc.description.abstract | Personalized medicine is a branch of medicine that focuses on how a prescribed therapeutic treatment affects a specific individual as opposed to its general effects for the broader population. The goal of personalized medicine is to improve patient care by enabling concentrations of a therapeutic drug to be monitored in various biological compartments, while also measuring their effects in relation to the administered dose via therapeutic drug monitoring (TDM). Metabolomics—the study of all small endogenous and exogenous molecules within a cell, tissue, or organism—has recently been proposed as a method for developing patient-based metabolic profiles, which could enable clinicians to more effectively predetermine suitable courses of treatment for a variety of patients. The probability of success or failure for a given treatment is determined in large part by metabolic phenotyping, which considers several patient-based influential factors, such as age, diet, environment, and medical history. This approach allows treatment to be tailored to the needs of each individual patient, thereby avoiding under- or over-dosing or wasting time with unnecessary treatment options, which often occurs as a result of the current “trial and error” approach to personalized therapy. In this thesis, solid phase microextraction (SPME) coupled with liquid chromatography-mass spectrometry (LC-MS) is proposed as an alternative sample preparation tool for use in the field of personalized medicine. To this end, the work in this thesis presents the development of various SPME-based methods for TDM, and it explores SPME-based clinical metabolomics and proof-of-concept pharmacometabolomics for a range of biological matrices typically encountered in clinical practice, such as whole blood, serum, plasma, urine, and lung tissue. Furthermore, SPME is proposed as a practical tool for rapid diagnostics, as it can be directly coupled to sensitive detection methods like MS. While a number of preliminary steps are required before important diagnostic markers can be monitored—including the validation of these potential respective candidate biomarkers, which is already a major inherent challenge in metabolomics—the use of SPME for real-time TDM and point-of-care analysis of important metabolic markers remains feasible. This thesis consists of a brief introduction and 6 experimental chapters, with each successive chapter exploring increasingly complex samples of interest and discussing the challenges and limitations associated with their analysis. Moreover, each subsequent chapter also addresses the difficulties associated with performing solely TDM or metabolomics separately and how, particularly in vivo SPME, can overcome these challenges and be used to achieve both goals (TDM and metabolomics) simultaneously under even more complicated and dynamic circumstances. Specifically, Chapter 2 focuses on the therapeutic drug monitoring of TXA in plasma and urine samples from patients with chronic renal dysfunction who are undergoing cardiac surgery, while Chapter 3 presents a metabolomics study entailing the profiling of serum samples from various psoriatic patients. Chapters 4, 5, and 6 illustrate how SPME can be used to enable simultaneous TDM and metabolomics under more complicated and dynamic circumstances by using in vivo SPME for specifically tissue analysis. Chapter 4 explores lung tissue and perfusate metabolomics using a pre-clinical porcine model undergoing normothermic ex vivo lung perfusion (NEVLP). In contrast, Chapters 5 and 6 assess the use of in vivo SPME for the TDM of chemotherapy drugs administered via in vivo lung perfusion (IVLP) in pre-clinical porcine model (Chapter 5) and clinical human trial settings (Chapter 6), followed by proof-of-concept pharmacometabolomics. Finally, the potential use of SPME as a rapid diagnostic tool is showcased in Chapter 7—which shows the rapid analysis of TXA from plasma—concluding the thesis by further demonstrating that the dual goals of TDM and point-of-care testing for metabolic markers can be achieved with rapid analysis via the direct coupling of SPME to MS. | en |
