SBIR Phase II: Novel, Accurate and Reproducible Platform for the Developability Assessment of Protein Therapeutics

Protein Dynamic Solutions, Inc., Wakefield MA

Investigators

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will address ALL of the factors attributing to protein aggregation by determining the: size, identity, extent, mechanism of aggregation and stability, thus addressing Biopharma industry needs. This information is critical to the development of drug pipeline contributing to a $190 BN biologic's market where $87BN in first generation biologics face patent expiration before 2020. A successful technical approach for its implementation will provide essential information for decision making towards which candidates will enter the market, thus increasing the Biopharma valuation and ensuring supply of drugs to patients. In the end, improving the quality of life of patients with chronic diseases. The proposed project will address the need for a multivariate high-throughput technology to address the risk of protein aggregation, that when adopted in R&D, will increase pipeline approvals, reduce late stage withdrawals and total costs of drug development. Average R&D development costs for the mere 1% of candidates reaching FDA approval have risen to $2.6 BN per product. Protein therapeutic development needs to be guided by a full understanding of protein stability and aggregation. Research objectives are to: develop our innovative First-in-Class high throughput platform for screening protein therapeutics; develop original software capable of deciphering protein aggregation mechanism, size, identity and extent of aggregated protein and product stability; commercialize the innovative technology platform. Fully automated evaluation of protein candidates during early R&D phase will be conducted. Best-in-class image acquisition technology will be employed towards this end, using a label free chemical mapping technology, dedicated software using auto recognition algorithms, and correlations to decipher protein aggregation. We through the use of its breakthrough technology will determine: the aggregate free candidate under various stressor conditions, optimum formulation conditions for the protein therapeutic, the most stable candidate, and electronic data reporting that establishes accuracy, reproducibility, critical quality attributes of the protein product.