QUANTITATIVE SYSTEMS TOXICOLOGY MODELING FOR NEURONAL ADVERSE OUTCOME
Humans are exposed to multiple environmental chemicals with some being toxic, and others with unknown or poorly characterized health effects. Chemical risk assessment relies on the knowledge of hazard, the dose–response relationship, and exposure to characterize potential risks to human health. Understanding the human health risk, especially neurotoxicity posed by these chemicals is a significant challenge due to diverse chemical space and limited toxicological information. Moreover, the existing computational models are either focused on exposure or chemical response being unable to capture the dynamic human physiology, mechanistic signaling and hence targeted toxicity. The objective of this thesis is to develop quantitative systems toxicology (QST) models to predict neurotoxicity by integrating chemical exposure, physiology, chemical kinetics, molecular dynamics and cellular response (brain). QST approach was utilized for multiple chemicals like bisphenol A (BPA), flame retardants (FRs), PFOS and PFOA. Risk assessment was improved by including mechanistic kinetics for FRs and sex-specific physiological and biochemical differences for BPA. Dynamic PBPK model was developed to evaluate the toxicokinetic in pediatric, adult, and geriatric for PFOS. In-vitro in-vivo (IVIVE) Brain PBPK Model was developed for predicting the estimated brain tissue dose which was coupled with perturbations in endogenous ROS using systems biology model. PBPK for FRs showed prolonged distribution in brain and the sex-specific BPA model displayed higher daily intake for girls than boys. PFOS exposure increased with age which might be due to the chemical disposition inside bone marrow or adipose tissue. Brain PBPK model implicated that the chemical kinetic varies in brain sub-organs and hence variation in the risk. The integrated approach enabled us to predict the downstream targets alteration like antioxidant regulated genes, mitochondrial damage, and consequently cellular toxicity. QST model along with in-vitro data provided an integrative framework and the mechanistic view of risk assessment for the neurotoxicity prediction.
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