Framework for risk assessment of PFAS utilizing experimental studies and in-silico models.

Perfluoroalkyl substances (PFAS), especially PFOS and PFOA, are two widely used synthetic chemicals that can impact human health based on evidence from animal and epidemiologic studies. In this paper, we have reviewed and summarized the influence of PFAS exposure on health, pointing the quality of evidence, and applied translational techniques to integrate evidence for PFAS policy making. This is the first review where highly referred articles on PFAS used for policymaking by several regulatory agencies were collected and evaluated based on the review guidelines developed by the US National Toxicology Program's Office of Health Assessment and Translation (OHAT) review guidelines. Several limitations were observed, including co-exposure to multiple chemicals and limited measurement of primary and secondary outcomes related to specific toxicity. However, data from all the studies provided a moderate to strong level of confidence for link between PFAS exposure and different adverse outcomes. Secondly, for translating the risk to humans, an in-silico model and scaling approach was utilized. Physiologically based pharmacokinetic model (PBPK) was used to calculate the human equivalent dose (HED) from two widely accepted studies and compared with tolerable daily intakes (TDIs) established by various regulatory agencies. Inter-species dose extrapolation was done to compare with human the relevance of dosing scenarios used in animals. Overall, a framework for translation of risk was proposed based on the conclusions of this review with the goal of improving policymaking. The current paper can improve the methodological protocols for PFAS experimental studies and encourage the utilization of in-silico models for translating risk.

Microphysiological sensing platform for an in-situ detection of tissue-secreted cytokines.

Understanding the protein-secretion dynamics from single, specific tissues is critical toward the advancement of disease detection and treatments. However, such secretion dynamics remain difficult to measure in vivo due to the uncontrolled contributions from other tissue populations. Here, we describe an integrated platform designed for the reliable, near real-time measurements of cytokines secreted from an in vitro single-tissue model. In our setup, we grow 3D biomimetic tissues to discretize cytokine source, and we separate them from a magnetic microbead-based biosensing system using a Transwell insert. This design integrates physiochemically controlled biological activity, high-sensitivity protein detection (LOD < 20 pg mL-1), and rapid protein diffusion to enable non-invasive, near real-time measurements. To showcase the specificity and sensitivity of the system, we use our setup to probe the inflammatory process related to the protein Interleukine 6 (IL-6) and to the Tumor Necrosis Factor (TNF-α). We show that our setup can monitor the time-dependence profile of IL-6 and TNF-α secretion that results from the electrical and chemical stimulation of 3D skeletal muscle tissues. We demonstrate a novel and affordable methodology for discretizing the secretion kinetics of specific tissues for advancing metabolic-disorder studies and drug-screening applications.