Navigating Critical Reagent Challenges in Cell-Based Potency Assays: Case Studies on Variability and Solutions

Bhoomi Patel, BioAgilytix Critical reagents are the backbone of cell-based potency assays (CBA), as such their variability can create major challenges in assay development, validation, and routine sample testing. Issues such as lot-to-lot inconsistencies, supply chain disruptions, and reagent discontinuation can lead to unexpected changes in assay performance, potentially impacting result consistency over time and regulatory compliance. This poster presents two real-world case studies that highlight these challenges and discuss practical mitigation strategies to ensure long-term assay sustainability and robustness. Case Study 1: One flow cytometry-based potency assay faced multiple changes for its Reference Standard lots as well as the Assay Control lots throughout its lifecycle—from development to validation and through routine sample analysis. Each lot transition introduced variability, particularly affecting assay control recoveries, which impacted the reported potency, that consistently trended high. This made assay qualification difficult and complicated regulatory submissions. The case underscores the importance of proactive lot-change management to maintain consistency. Case Study 2: A flow cytometry potency assay relied on an in-house critical reagent (engineered cells), but as the project scaled up, outsourcing became necessary to meet requirements for volume of reagent. Finding a suitable vendor proved difficult due to differences in reagent quality and sourcing. A custom manufacturing approach was ultimately chosen, but the new lots introduced additional variability, requiring extensive method and reagent requalification efforts. To address these challenges, this poster will discuss key strategies such as: ● Conducting early risk assessments and securing backup suppliers to avoid supply chain disruptions. ● Establishing a strong lot-bridging strategy with predefined acceptance criteria to minimize variability. ● Forming long-t