The overall goal of this proposal is to develop integrated mathematical algorithms to assess the similarity of analytical characterization data comparing multiple batches of two different complex molecules. First, multiple batches of a biopolymer-drug conjugate (hyaluronic acid-cisplatin) and a pharmaceutically relevant glycoprotein (IgG1-Fc) will be prepared. For the IgG1-Fc molecules, we utilize technology to produce six different, well- defined glycoforms, which can be characterized individually and then mixed together in different ratios resulting in varying levels of binding activity to various Fc receptors for subsequent analytical comparability testing. Second, the biological, chemical and physical properties of both complex molecules will be monitored using a wide range of analytical technologies, including during accelerated stability testing to elucidate correlations between physicochemical changes and biological activity. Third, we will then employ a combination of currently available data visualization tools used our laboratories, along with novel mathematical algorithms to be developed in this proposal, to integrate the structural and biological data to provide an overall assessment of their critical quality attributes (CQAs). Finally, the ability of the mathematical algorithm(s) to assess the overall similarity of multiple lots of each complex biopharmaceutical will be validated using appropriate positive and negative controls, and therefore, provide an overall assessment of analytical comparability.
This proposal will develop, implement and validate integrated mathematical algorithms to assess the similarity of multiple batches of two different complex molecules (a biopolymer mixture and IgG-based glycoproteins) using analytical comparability data sets from characterization (biological, chemical and physical) methods.