PubMed2003
The respiratory tract is frequently identified as a site of toxicity for inhaled xenobiotic chemicals. Usually, these observations come from controlled animal studies. For these studies to be of quantitative value to human health risk assessment, species-specific factors governing dosimetry of inhaled substances must be taken into account. Toxicokinetics of vapours in the respiratory tract are defined by absorption, distribution, metabolism, and excretion, as they are in other tissues; however, these concepts take on new dimensions when considering respiratory tract toxicants, especially those that elicit portal of entry effects by directly interacting with the tissue lining the respiratory tract. Species-specific factors related to anatomy, physiology and biochemistry govern inter-species extrapolation of toxicokinetics. This article discusses critical factors of respiratory tract kinetics that should be considered when developing physiological-based toxicokinetic (PBTK) models for inhaled vapours. Important considerations such as impact of regional airflow-delivery, water solubility, reactivity, and rates of local biotransformation on respiratory tract tissue dosimetry are highlighted. These factors can be accounted for only to a limited extent when using default approaches to extrapolate dosimetry of inhaled substances across species. On the other hand, PBTK modeling has the flexibility to accommodate many of the critical determinants of respiratory tract toxicity. PBTK models can also help identify the most critical toxicokinetic data necessary to replace defaults. PBTK approaches have led to more informed estimates of human target tissue dose, and therefore human health risk, especially where these risk assessments have been based on extrapolation of animal dosimetry studies. Experience derived from the development of more intensive case studies have, in turn, enabled simplified approaches to the use of PBTK modeling for respiratory tract toxicants. Whether simplified or highly complex, PBTK modeling approaches are proven to be of great utility to risk assesors interested in applying quantitative information to informed risk assessment evaluations.