Development of an integrated physiologically based toxicokinetic-toxicodynamic model for quantitative prediction of polyhexamethylene guanidine-induced human toxicity.

Polyhexamethylene guanidine (PHMG) is a hazardous environmental toxicant with proven lethality and severe societal consequences following the humidifier disinfectant disaster in South Korea. Despite its recognized pulmonary toxicity via inhalation, no quantitative prediction system has been established to assess its in vivo kinetics and exposure-response relationship in humans. This study aimed to develop an integrated prediction system by constructing a physiologically based toxicokinetic (PBTK) model and a toxicodynamic (TD) model to simulate PHMG’s pulmonary toxicity in humans. The PBTK model was developed using in vivo data to describe PHMG distribution across major organs (lungs, gastrointestinal tract, etc.) and validated both qualitatively and quantitatively. The TD model was constructed based on PHMG exposure-cytotoxicity response data from human normal lung cells, capturing time- and dose-dependent toxicity patterns. A hybrid TD model combining a damage accumulation mechanism at lower concentrations and an Emax sigmoid response at higher concentrations was applied. Both models were co-linked to establish an integrated PBTK-TD model capable of predicting PHMG-induced lung toxicity under both single and repeated exposure scenarios. The PBTK model showed strong predictive performance across various exposure routes (intravenous, intratracheal, oral), and was successfully extrapolated to humans. The integrated model quantitatively simulated PHMG concentrations in lung tissue and their associated cytotoxic effects. Furthermore, the model enabled reverse dosimetry to estimate tissue-specific reference doses, population-based external exposure levels, and margins of safety. These features highlight the potential of the model for human health risk assessment and regulatory applications. In conclusion, this is the first system-based approach to integratively predict PHMG’s kinetic behavior and toxicity. It offers a novel and robust tool for quantitative toxicology, supporting future risk-based decision-making for PHMG and similar environmental toxicants.