Establishing short-term occupational exposure limits (STELs) for sensory irritants using predictive and <i>in silico</i> respiratory rate depression (RD₅₀) models

Sensory irritation is a health endpoint that serves as the critical effect basis for many occupational exposure limits (OELs). Schaper 1993 described a significant relationship with high correlation between the measured exposure concentration producing a 50% respiratory rate decrease (RD₅₀) in a standard rodent assay and the American Conference of Governmental Industrial Hygienists (ACGIH®) Threshold Limit Values (TLVs®) as time-weighted averages (TWAs) for airborne chemical irritants. The results demonstrated the potential use of the RD₅₀ values for deriving full-shift TWA OELs protective of irritant responses. However, there remains a need to develop a similar predictive model for deriving workplace short-term exposure limits (STELs) for sensory irritants. The aim of our study was to establish a model capable of correlating the relationship between RD₅₀ values and published STELs to prospectively derive short-term exposure OELs for sensory irritants. A National Toxicology Program (NTP) database that included chemicals with both an RD₅₀ and established STELs was used to fit several linear regression models. A strong correlation between RD₅₀s and STELs was identified, with a predictive equation of ln (STEL) (ppm) = 0.86 * ln (RD₅₀) (ppm) − 2.42 and an R² value of 0.75. This model supports the use of RD₅₀s to derive STELs for chemicals without existing exposure recommendations. Further, for data-poor sensory irritants, predicted RD₅₀ values from <i>in silico</i> quantitative structure activity relationship (QSAR) models can be used to derive STELs. Hence, <i>in silico</i> methods and statistical modeling can present a path forward for establishing reliable OELs and improving worker safety and health.