Uncertainty factors and relative response factors: correcting detection and quantitation bias in extractables and leachables studies.

Abstract

The transfer of chemicals from packaging or medical devices to drug formulations, known as extractables and leachables (E&L) release, can affect drug strength and safety. These released substances must be monitored and assessed through toxicological evaluation. Identifying and quantifying analytes above a specific analytical evaluation threshold (AET) is crucial, but variability in response factors (RFs) complicates accurate detection, leading to potential errors in quantitation. An uncertainty factor (UF) can partially correct this, though it is limited by RF variability, and a multidetector approach improves characterization but does not fully resolve quantitation bias. The RRFlow model proposed in this study offers a solution by determining E&L concentrations without real-time reference standards analysis. It involves identity confirmation, RRF validation, and applies an average corrective factor (RRFi). A numerical simulation benchmark (NSB) is used to compare different scenarios, such as varying UF values, RRFlow application, and fixed rescaling factors. The benchmark assigns concentration values to model compounds with different response factors, iterating the process to evaluate the number of false positive and negative errors. The numerical simulations show that RRFlow reduces detection bias and outperforms UF-based methods, mitigating false positives and negatives.