Recommendations for Development and Validation of a Fit-For-Purpose Biomarker Assays Using Western Blotting; An-AAPS Sponsored Initiative to Harmonize Industry Practices.

Western blot (WB) assays are routinely used for detection and quantification of biomarkers. Although assay validation to measure biomarkers in complex matrices has become a mainstay process for ligand binding assays (LBA) and mass spectrometry (MS), no guidelines exist yet validate biomarker methods using WB techniques. In this cross-industry white paper, we outlined in detail the key steps for development and for validation of WB assays for protein biomarkers under different contexts of use (COU). In addition, we described how to determine the level of assay validation needed for biomarker assays using Western blotting. For simplicity, we described two paths of WB assay validation. The first path (Path 1) is for biomarkers being analyzed for exploratory research or for internal go- or no/go- decision making. The second path (Path 2) is for clinical decision making such as dose determination or drug response that need to be run in a regulated environment. This work is supported through AAPS Biomarkers and Precision Medicine subteam and represents AAPS members opinion.

Cloud solutions for GxP laboratories: considerations for data storage.

Challenges for data storage during drug development have become increasingly complex as the pharmaceutical industry expands in an environment that requires on-demand availability of data and resources for users across the globe. While the efficiency and relative low cost of cloud services have become increasingly attractive, hesitancy toward the use of cloud services has decreased and there has been a significant shift toward real-world implementation. Within GxP laboratories, the considerations for cloud storage of data include data integrity and security, as well as access control and usage for users around the globe. In this review, challenges and considerations when using cloud storage options for the storage of laboratory-based GxP data are discussed and best practices are defined.