<i>In silico</i> occupational exposure banding framework for data poor compounds in biotechnology

Occupational exposure limits (OELs) and occupational exposure bands (OEBs) provide quantitative benchmarks for worker health protection. If empirical toxicology data are insufficient to derive an OEL, an OEB is often assigned using partial toxicology data along with other relevant hazard information. There is no consensus methodology to assign OEBs for chemicals lacking any empirical toxicology data. Thus, this study developed an <i>in</i> <i>silico</i> framework for OEB assignment of data poor compounds. It relies upon computational tools to evaluate standard toxicological end points and to assign reliability ratings, which are then used to assign Global Harmonization System (GHS) hazard categories. Subsequently, the hazard categories are entered into the National Institute for Occupational Safety and Health (NIOSH) occupational exposure banding tool to assign bands for individual end points as well as an overall OEB. As a proof-of-concept, five compounds with established OELs (i.e., "knowns") were evaluated. The knowns were assigned to overall OEBs C, D, or E, four of which were equal to or lower than the OEBs based on actual harmonized GHS categories as well as established OELs, indicating that the OEBs assigned using this framework are likely to be protective. Subsequently, five compounds with little to no experimental data and no established OELs from any U.S. agency or consensus OEL-setting organizations were evaluated (i.e., "unknowns"). The unknowns were assigned to overall OEBs D or E. It was concluded that the proposed framework can be used to assign protective OEBs to compounds with little to no toxicology testing data. As additional data become available, the compound may be de-risked, and a precautionary OEB (or an OEL) can be assigned. The proposed framework provides an example of a practical methodology to evaluate data poor compounds and shows that the output of this framework is expected to be protective of worker health.