Evaluation of the impact of uncertainty on mass balance applied to stress testing studies.

Forced degradation studies on active pharmaceutical ingredients (APIs) and drug products allow for the assessment of degradation products that may emerge during stability studies, as well as challenge the ability of the stability-indicating method to assess changes in drug content in the presence of degradation products. A key parameter for evaluating the quality of such methods is Mass Balance (MB), which assesses the analytical method's ability to quantify formed impurities by comparing the sum of the contents of the API and the degradation products formed with the initial condition. A perfect MB, that is, a sum equal to 100% relative to the initial value, is often unattainable due to several reasons, including limited trueness and precision of the analytical method. Measurement uncertainty evaluation can be a viable tool for this purpose, as it allows a metrological comparison between the degradation products formed and the initial amount of the drug. This study aims to investigate the use of measurement uncertainty information applied to a forced degradation study of acetylsalicylic acid to salicylic acid, both quantified by UV-Vis spectroscopy. The data was evaluated using the Monte Carlo Method (MCM), implemented in Python 3.13.5, the software was named 'MBSim'. Based on Monte Carlo simulations, considering the content data of salicylic acid and acetylsalicylic acid and their respective measurement uncertainties, a MB interval was obtained that allows the assessment of the similarity between the initial and final distributions. The comparison was performed using by assessing p-value through Two One-Sided Tests (TOST), degree of overlap (from 0.04 to 0.97) and the calculation of Cohen's d (from -4.14-1.84), and these tools proved to be promising for supporting the understanding of MB in forced degradation studies.