A physiologically based toxicokinetic model for microplastics and nanoplastics in mice after oral exposure and its implications for human dietary exposure assessment.

Evidence of microplastics (MPs) and nanoplastics (NPs) in foods and daily-use products, along with their frequent detection in the human body, has raised concerns regarding their potential impact on human health through dietary ingestion. However, there is a lack of quantitative tools to simulate their bioaccumulation and tissue distribution following environmental exposure. To address this gap, we developed the first physiologically based toxicokinetic (PBTK) model for predicting the biodistribution of MPs and NPs in mice following oral exposure under various exposure scenarios.

Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models.

Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tumor DE can help improve the design of nanomedicines. Multiple machine learning and artificial intelligence models, including linear regression, support vector machine, random forest, gradient boosting, and deep neural networks (DNN), were trained and validated to predict tissue distribution and tumor delivery based on NPs' physicochemical properties and tumor therapeutic strategies with the dataset from Nano-Tumor Database.