Approaches for setting occupational exposure limits in the pharmaceutical industry

The use of pharmaceutical drugs has provided a cure for many diseases. However, unintended exposure to drugs in the manufacturing workplace can cause significant health hazards to workers. It is important to protect the workforce from these deleterious effects by limiting exposure to an acceptable level, the occupational exposure limit (OEL). OEL is defined as airborne concentrations (expressed as a time-weighted average for a conventional 8-h workday and a 40-h work week) of a substance to which nearly all workers may be repeatedly exposed (for a working lifetime) without adverse effects. Determination of OELs has become very challenging over time, requiring an overall assessment of the preclinical and clinical data of the drug being manufactured. Previously, to derive OEL values, toxicologists used animal no-observed-adverse-effect level (NOAEL) data, which have been replaced with the overall assessment of animal and human data, placing a higher emphasis on human health-based data. A major advantage of working with human pharmaceuticals is that sufficient clinical data are available for them in most cases. The present manuscript reviews the latest knowledge regarding the derivation of occupational exposure limits as health-based exposure limits (HBELs) for pharmaceuticals. We have provided examples of OEL calculations for various drugs including levofloxacin (CAS No. 100986-85-4), dienogest (CAS no. 65928-58-7), and acetylsalicylic acid (ASA, CAS no. 50-78-2) using human data. This report will benefit professionals in the OEL domain in understanding this highly important, growing, and challenging field.

Automated summary

The use of pharmaceutical drugs has provided treatment for many diseases, but exposure to drugs in the workplace can pose health hazards. Setting occupational exposure limits (OELs) is crucial to protect workers. Determining OELs is challenging and requires comprehensive evaluation of preclinical and clinical data.