DEVELOPMENT OF AN INTEGRATED EXPOSURE MODEL COUPLED TO PBBK MODEL FOR MIXTURES OF NEUROTOXICANTS

This PhD thesis addresses the critical need for a more comprehensive approach to neurotoxic risk assessment, moving beyond the limitations of traditional methods that focus on single chemicals in isolation. The research recognizes the pervasive reality of human exposure to complex mixtures of neurotoxicants, a reality often overlooked in current risk assessment practices. This oversight is particularly problematic for vulnerable populations, such as children and pregnant women, whose developing nervous systems are uniquely susceptible to the damaging effects of neurotoxic substances. The long-term consequences of even subtle neurotoxic effects can be profound, impacting cognitive function, motor skills, and overall health and well-being across a population. The core objective of this thesis is to construct a robust and reliable integrated modeling framework that accurately estimates cumulative exposures to neurotoxicants and translates those exposures into internal dose metrics—specifically, tissue concentrations—that are most relevant for assessing neurotoxicity. The framework skillfully combines two distinct but complementary models: a comprehensive exposure model and a physiologically based biokinetic (PBBK) model. The framework's capabilities are then rigorously demonstrated through a series of case studies. A key aspect of the thesis involves a novel approach to validating the modeling framework by using Human Biomonitoring (HBM) data. Existing HBM data from multiple European studies, comprising chemical concentration measurements in biological fluids (blood and urine), are used as real-world constraints to refine the exposure model and assess its predictions. This data-driven validation significantly enhances the model’s credibility and practical applicability. The study also includes specific case studies examining exposure levels in different age groups and diverse environmental settings (agricultural and urban), highlighting the unique risk profiles associated with various exposure scenarios. The insights gleaned from these case studies show clear advantages over traditional methods, as the integrated modeling framework more accurately captures the complexity and variability in real-world neurotoxic exposures. This PhD thesis makes a compelling case for the transformative potential of integrated exposure-PBBK modeling in revolutionizing neurotoxic risk assessment. The innovative framework developed offers a significant advancement, leading to more accurate, equitable, and actionable risk management strategies for safeguarding public health against neurotoxic chemical mixtures. The limitations identified and suggestions for future research further solidify the thesis' contribution to the field.