Bioanalytical cross-validation plays a crucial role in ensuring data exchangeability throughout the assay life cycle for data generated between methods or laboratories. The ICH M10 guideline addresses gaps from previous guidelines concerning the conduct and data analysis of cross-validation studies. While the guideline provides high-level direction, it allows flexibility for sponsors to implement their own statistical analysis and acceptance criteria. This flexibility can lead to variability in interpretation and practices across the industry. This manuscript presents a practical framework for implementing ICH M10 in cross-validation studies, with an emphasis on rigorous experimental design and robust statistical analysis. Our approach integrates Incurred Sample Reanalysis (ISR) criteria, Bland–Altman analysis, and Deming regression. A case study illustrates the application of this framework in cross-validating a pharmacodynamic biomarker assay across multiple laboratories. Our study revealed significant inter-laboratory variability in post-dose measurements, driven by the dynamic equilibrium between free and complexed forms of the biomarker. Assay conditions, such as temperature and incubation time, were found to significantly contribute to the observed variability, suggesting that cross-laboratory comparisons of post-dose results are not reliable. In contrast, pre-treatment baseline samples, with no drug on board, exhibited strong alignment across laboratories. Our experimental design captures variability reflective of clinical trial datasets, and the integrated statistical methodology ensures a robust assessment of method variability. This framework supports reliable bioanalytical data integration for Pharmacokinetic/Pharmacodynamic (PK/PD) modeling and regulatory submissions.