An Industry Perspective on Forced Degradation Studies of Biopharmaceuticals: Survey Outcome and Recommendations.

The BioPhorum Development Group is an industry-wide consortium enabling networking and sharing of common practices for the development of biopharmaceuticals. Forced degradation studies (FDSs) are often used in biotherapeutic development to assess criticality of quality attributes and in comparability studies to ensure product manufacturing process consistency. To gain an understanding of current industry approaches for FDS, the BioPhorum Development Group-Forced Degradation Point Share group conducted an intercompany collaboration exercise, which included a benchmarking survey and group discussions around FDS of monoclonal antibodies. The results of this industry collaboration provide insights into the practicalities of these characterization studies and how they are being used to support the product lifecycle from innovation to marketed products. The survey requested feedback on the intended purpose, materials, conditions, number and length of time points used, and analytical techniques carried out to give a complete picture of the range of common industry practices. This article discusses the results of this global benchmarking survey across 12 companies and presents these as a guide to a common approach to FDS across the industry which can be used to guide the design of FDS based on chemistry and manufacturing control product life-cycle and biomolecule needs.

A robust biomarker discovery pipeline for high-performance mass spectrometry data.

A high-throughput software pipeline for analyzing high-performance mass spectral data sets has been developed to facilitate rapid and accurate biomarker determination. The software exploits the mass precision and resolution of high-performance instrumentation, bypasses peak-finding steps, and instead uses discrete m/z data points to identify putative biomarkers. The technique is insensitive to peak shape, and works on overlapping and non-Gaussian peaks which can confound peak-finding algorithms. Methods are presented to assess data set quality and the suitability of groups of m/z values that map to peaks as potential biomarkers. The algorithm is demonstrated with serum mass spectra from patients with and without ovarian cancer. Biomarker candidates are identified and ranked by their ability to discriminate between cancer and noncancer conditions. Their discriminating power is tested by classifying unknowns using a simple distance calculation, and a sensitivity of 95.6% and a specificity of 97.1% are obtained. In contrast, the sensitivity of the ovarian cancer blood marker CA125 is approximately 50% for stage I/II and approximately 80% for stage III/IV cancers. While the generalizability of these markers is currently unknown, we have demonstrated the ability of our analytical package to extract biomarker candidates from high-performance mass spectral data.