Bioanalytical strategies for the quantification of xenobiotics in biological fluids and tissues

Abstract

The human body is exposed to a variety of environmental pollutants, of these compounds most are known to be severe endocrine disruptors as well as carcinogens. The chemicals which are not naturally occurring or present at much higher than their usual levels in an organism are called xenobiotics. Their accumulation in human tissues and fluids has been reported to cause irreversible damage to vital tissues and organs which may eventually lead to severe illness like ovarian cancer. For better understanding of the role of xenobiotics in the progression of ovarian cancer, it is necessary to monitor the levels of these xenobiotics and their metabolites in biological fluids. Accurate estimation of these chemicals poses a real challenge because of the complexity of biological matrices. Hence, there is a need for the development of simple and rapid analytical techniques for the analysis of complex biological matrices. This work described in this thesis focused on development of microextraction techniques to analyze bio-fluids such as ovarian tumour cyst fluid, blood plasma and serum. Using these techniques, we explored ovarian tumor cyst fluids, one of the less explored in terms of composition of xenobiotics and their impact on severity of cancer. A wide range of potential carcinogens including estrogens, organochlorine pesticides, polychlorinated biphenyls, heterocyclic amines, aromatic amines, organic acids, polybrominated diphenyl ethers, nitrosamines and inorganic trace metals were quantified in both malignant and benign ovarian tumor cyst fluids, using simple and cost effective microextraction techniques such as micro-solid phase extraction and hollow fiber protected liquid-phase microextraction. Furthermore, apart from impact of xenobiotic on human tissues, we investigated the effect of most common steroid xenobiotics (estrogens) on zebrafish which is known to be a very good model for the human genome. In addition, we developed a novel analytical procedure for early prognosis of ovarian cancer based on the existence of natural cancer biomarker protein, endorepellin. Conventional proteomics coupled with atomic force microscopic imaging was used to distinguish cancer samples from non-cancer samples. Besides, advanced statistics (principle component analysis), imaging methods, computational modelling and database mining techniques for data analysis were applied in unravelling significant conclusions. Overall, microextraction based bioanalytical methodologies were developed for the quantification of xenobiotics present in complex biological matrices. The findings are expected to be significant in understanding the impact of various chemicals on progression of ovarian cancer and its early prognosis.