Karande, Pankaj / Chen, Wilfred 1403697 / 1403724 Rensselaer Polytechnic Institute / University of Delaware
The proposed project is a collaboration between industry (Bristol-Myers Squibb) and academia (Rensselaer Polytechnic Institute and University of Delaware) aimed at developing the next generation and state-of-art smart biopolymers system for the purification of protein therapeutics in industrial bioprocessing. There is a significant need and interest in the industry for the development of novel techniques for recovery of biological materials that are advantageous in terms of scale, cost, and ease-of-deployment compared to conventional chromatographic processes. The PIs propose to develop affinity precipitation processes that combine the selectivity of affinity chromatography along with the scalability and cost benefits of precipitation. This project will employ new classes of affinity peptides in concert with efficient smart biopolymers which can be induced to come in and out of solution with minor changes in salt and/or temperature.
There is significant interest in the development of non-chromatographic affinity recovery processes for biological products. Affinity precipitation combines the selectivity of affinity chromatography along with the scalability and cost benefits of precipitation. In prior research supported by NSF the PIs have employed Elastin like polypeptides fused to the mAb binding Z domain (ELP-Z) to develop a scalable mAb affinity precipitation method. The proposed work builds upon the previous work to generate entirely new classes of smart biopolymer affinity reagents and evaluate their utility for the industrial purification of mAbs, FCfusions, and non-mAb biologics from inclusion body and pegylation processes.
Their practical utility will be evaluated for large-scale protein purification in partnership with Bristol-Myers Squibb (BMS). This research will result in a flexible platform for large-scale purification of biological products. The first component of the proposed project will be a new approach for affinity precipitation of mAbs and Fc fusion proteins under milder operating conditions using ZELP-E2 nanocages. The second component of the project will develop novel peptide affinity ligands for two classes of non-mAb proteins provided by our industrial collaborator BMS. The affinity peptides will then be employed in affinity precipitation formats to selectively capture the products from challenging feed stocks such as refolded protein pools and post-pegylation reaction mixtures. This work brings together Co-PIs with diverse expertise in protein engineering, peptide affinity design, affinity precipitation and downstream bioprocessing to develop entirely new classes of affinity precipitation reagents (Z-ELP-E2 nanocages, ELPaffinity peptides, and polyvalent affinity capture E2-(xAPy-ELP)) which if successful may result in a new integrated platform that will greatly simplify the recovery and purification of biological products.
The proposed research could have a dramatic impact on the affinity capture of a broad range of therapeutic products represented by monoclonal antibodies, fusion proteins, biologics from inclusion bodies, and pegylated proteins. The combination of these novel technologies could represent entirely new approaches for the downstream processing of biologics with significant long-term industrial impact. Success in this work could not only impact the processing of difficult to recover and synthetically modified biologics but may also establish new paradigms for the bioprocessing of a wide range of many new biologics in development. This collaboration between academia and industry will serve as a proof-of-concept study for the potential industrial implementation of this transformative technology for the purification of biological products.
Mechanisms for impact on education via graduate, undergraduate and K12 inclusion in research opportunities are discussed. This proposed molecular level and macroscopic separations level training is highly sought after by the biotechnology industry.
