Filip, Cuyckens, Wenying, Li, Adam M., Auclair, Kenneth C., Cassidy, Cathy, Cantalloube, Gary A., Collins, James, Alexander David, Chris, Gibson, Glaenzel, Ulrike, Huth, Felix, Ping, Kang, Patricia, Moliner, Mette L., Pedersen, Isabel, Piel, Pietro, Brunetti, Hanno, Schieferstein, David, Wagner, Gregory S., Walker, Shuai, Wang, Qin, Yue, Jason, Boer and Cyrus, Khojasteh (2025) Guided Sample Pooling in Human Mass Balance Studies: A Recommended Strategic Decision Framework. Clinical pharmacology and therapeutics. ISSN 1532-6535

Abstract

Radiolabeled human mass balance studies are crucial for identifying circulating metabolites and understanding drug absorption, excretion, and clearance pathways. Metabolite profiling involves quantifying all drug-related entities, including parent drug and metabolites in plasma and excreta, using extended liquid chromatography methods coupled with detection through scintillation counting, accelerator mass spectrometry, or non-radiolabeled approaches. Given the labor-intensive nature of sample extraction and analysis, we propose a new paradigm that maximizes gathering information through sample pooling strategies. Our proposal introduces sample pooling strategies by integrating both individual and pooled sample schemes, simplifying decisions, and consolidating existing knowledge into a cohesive document. This aligns with the low statistical power typically associated with mass balance studies that dose six to eight subjects. In metabolite profiling, it is common practice to pool samples either from the limited number of subjects participating in a human mass balance study or from different time points of sample collection. This approach improves efficiency while preserving data integrity. Pooling reduces resource constraints and enables the concentration of samples with relatively low radioactivity levels, resulting in higher quality metabolite profiles. Nevertheless, there are situations when analyzing samples from individual subjects or time points may be preferred. This proposal presents guidance and decision trees designed to facilitate informed decisions about sample pooling to maximize data quality of metabolite profiling in human mass balance studies while efficiently managing resources. These recommendations stem from discussions within the mass balance working group of the IQ Consortium.

Item Type:	Article
Date Deposited:	17 Jan 2026 00:46
Last Modified:	17 Jan 2026 00:46
URI:	https://oak.novartis.com/id/eprint/56686