Engineered nanomaterials (ENMs) possess unique characteristics affecting their interactions in biological media and biological tissues. Systematic investigation of the effects of particle properties on biological toxicity requires a comprehensive modeling framework which can be used to predict ENM particokinetics in a variety of media. The Agglomeration-diffusion-sedimentation-reaction model (ADSRM) described here is stochastic, using a direct simulation Monte Carlo method to study the evolution of nanoparticles in biological media, as they interact with each other and with the media over time. Nanoparticle diffusion, gravitational settling, agglomeration, and dissolution are treated in a mechanistic manner with focus on silver ENMs (AgNPs). The ADSRM model utilizes particle properties such as size, density, zeta potential, and coating material, along with medium properties like density, viscosity, ionic strength, and pH, to model evolving patterns in a population of ENMs along with their interaction with associated ions and molecules. The model predictions for agglomeration and dissolution are compared with in vitro measurements for various types of ENMs, coating materials, and incubation media, and are found to be overall consistent with measurements. The model has been implemented for an in vitro case in cell culture systems to inform in vitro dosimetry for toxicology studies, and can be directly extended to other biological systems, including in vivo tissue subsystems by suitably modifying system geometry.

Zearalenone (ZEA), a fungal mycotoxin, and its metabolite zeranol (ZAL) are known estrogen agonists in mammals, and are found as contaminants in food. Zeranol, which is more potent than ZEA and comparable in potency to estradiol, is also added as a growth additive in beef in the US and Canada. This article presents the development and application of a Physiologically-Based Toxicokinetic (PBTK) model for ZEA and ZAL and their primary metabolites, zearalenol, zearalanone, and their conjugated glucuronides, for rats and for human subjects. The PBTK modeling study explicitly simulates critical metabolic pathways in the gastrointestinal and hepatic systems. Metabolic events such as dehydrogenation and glucuronidation of the chemicals, which have direct effects on the accumulation and elimination of the toxic compounds, have been quantified. The PBTK model considers urinary and fecal excretion and biliary recirculation and compares the predicted biomarkers of blood, urinary and fecal concentrations with published in vivo measurements in rats and human subjects. Additionally, the toxicokinetic model has been coupled with a novel probabilistic dietary exposure model and applied to the Jersey Girl Study (JGS), which involved measurement of mycoestrogens as urinary biomarkers, in a cohort of young girls in New Jersey, USA. A probabilistic exposure characterization for the study population has been conducted and the predicted urinary concentrations have been compared to measurements considering inter-individual physiological and dietary variability. The in vivo measurements from the JGS fall within the high and low predicted distributions of biomarker values corresponding to dietary exposure estimates calculated by the probabilistic modeling system. The work described here is the first of its kind to present a comprehensive framework developing estimates of potential exposures to mycotoxins and linking them with biologically relevant doses and biomarker measurements, including a systematic characterization of uncertainties in exposure and dose estimation for a vulnerable population.