Top-down evaluation of measurement uncertainty from validation data including sampling

Background Measurement uncertainty is essential for reliable pharmaceutical quality control and conformity assessment. However, conventional top-down approaches generally estimate uncertainty from validation data considering analytical variability, while uncertainty arising from sampling is frequently neglected. Objective This study aimed to develop a practical top-down approach for evaluating measurement uncertainty from validation data that explicitly incorporates sampling uncertainty according to the principles of the Eurachem Validation of Measurement Procedures that Include Sampling (VaMPIS) guide. Methods Precision was evaluated using a crossed–nested analysis of variance design involving target samples, independent replicate samples, intermediate precision conditions, and analytical replicates, enabling estimation of variance components associated with sampling, intermediate precision, and analytical repeatability. The trueness contribution to uncertainty was estimated from recovery studies. An MS-Excel workbook with automated routines was developed to support validation protocols and uncertainty calculations. The approach was applied to the assay of furosemide tablets by UV spectrophotometry and ibuprofen oral suspension by high-performance liquid chromatography. Results Combined standard uncertainties of 3.4% and 3.1% were obtained for the furosemide and ibuprofen methods, respectively. The relative contributions to the uncertainty budget differed between the two procedures, while analytical repeatability contributed only marginally in both cases. The breakdown of precision uncertainty identified the principal sources of variability and supported targeted optimization strategies. Comparison with a target standard uncertainty of 3.2%, derived from the assay specification according to WHO recommendations, demonstrated the practical impact of including sampling in the uncertainty evaluation. Conclusion The proposed approach provides a practical framework for integrating sampling uncertainty into pharmaceutical method validation, supporting Analytical Quality by Design principles and improving the reliability of conformity assessment and regulatory decision-making.