Comparison of permitted daily exposure (PDE) values for active pharmaceutical ingredients (APIs) - Evidence of a robust approach.

Permitted Daily Exposure Limits (PDEs) are set for Active Pharmaceutical Ingredients (APIs) to control cross-contamination when manufacturing medicinal products in shared facilities. With the lack of official PDE lists for pharmaceuticals, PDEs have to be set by each company separately. Although general rules and guidelines for the setting of PDEs exist, inter-company variations in the setting of PDEs occur and are considered acceptable within a certain range. To evaluate the robustness of the PDE approach between different pharmaceutical companies, data on PDE setting of five marketed APIs (amlodipine, hydrochlorothiazide, metformin, morphine, and omeprazole) were collected and compared. Findings show that the variability between PDE values is within acceptable ranges (below 10-fold) for all compounds, with the highest difference for morphine due to different Point of Departures (PODs) and Adjustment Factors (AFs). Factors of PDE variability identified and further discussed are: (1) availability of data, (2) selection of POD, (3) assignment of AFs, (4) route-to-route extrapolation, and (5) expert judgement and differences in company policies. We conclude that the investigated PDE methods and calculations are robust and scientifically defensible. Additionally, we provide further recommendations to harmonize PDE calculation approaches across the pharmaceutical industry.

A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling.

This study assesses whether currently available acute oral toxicity (AOT) in silico models, provided by the widely employed Leadscope software, are fit-for-purpose for categorization and labelling of chemicals. As part of this study, a large data set of proprietary and marketed compounds from multiple companies (pharmaceutical, plant protection products, and other chemical industries) was assembled to assess the models' performance. The absolute percentage of correct or more conservative predictions, based on a comparison of experimental and predicted GHS categories, was approximately 95%, after excluding a small percentage of inconclusive (indeterminate or out of domain) predictions. Since the frequency distribution across the experimental categories is skewed towards low toxicity chemicals, a balanced assessment was also performed. Across all compounds which could be assigned to a well-defined experimental category, the average percentage of correct or more conservative predictions was around 80%. These results indicate the potential for reliable and broad application of these models across different industrial sectors. This manuscript describes the evaluation of these models, highlights the importance of an expert review, and provides guidance on the use of AOT models to fulfill testing requirements, GHS classification/labelling, and transportation needs.