Protein-based therapies have become one of the most effective clinical methods to treat a wide spectrum of diseases. The market holds tremendous potential for future growth and it is estimated to reach 217 Billion US$ by 2023, as new products, especially monoclonal antibodies in the advanced clinical phase, may enter the sector. Formation of anti-drug antibodies (ADAs), is a unique risk factor for protein therapeutics and undesired immunogenicity can alter pharmacokinetics, compromise drug efficacy and in some cases even threaten patient safety. This is of special concern in patients suffering from genetic defects or auto-immune diseases that require chronic administration of protein drugs. While most antibodies are fully humanized or at least chimeric, few of them will still induce ADA in patients. Technologies for the assessment of immunogenic hotspots in therapeutic proteins and their deimmunization– i.e. modifications to render the protein less immunogenic still lack a comprehensive solution for the challenges.
We developed a platform technology that combines computational and experimental approaches for the general assessment and de-immunization of therapeutic proteins. The development of ADAs depends on the induction of CD4+ T-cells, which activate plasma B-cells to produce antibodies. Our platform enables the rapid identification of CD4+ T cells epitope in therapeutic proteins, which then followed by a directed evolution approach for the elimination of these epitopes. Our De-Immunization Process include five steps:
1. Epitopes Prediction: Computational prediction of CD4+ epitope hotspots in the target sequence. The predictions are based on: (i) The probability of each 9-mer peptide in the sequence to be presented by HLA class II molecule and (ii) the ‘similarity-to-self’ of each 9-mer peptide in the target sequence.
2. Experimental Validation: The predicted epitopes are verified using functional T-cell assays, e. ELISpot and ICS.
3. Epitopes Elimination: Computational design tools were developed and applied to obtain variants displaying reduced immunogenicity while maintaining protein function and stability.
4. Rational Designed Libraries: Utilizing a yeast display for positive selection of variants with comparable or higher activity of the original drug.
5. Experimental Assessment: The reduced immunogenicity of the selected variants is tested using ELISpot.
- Unique combination of capabilities in in-silico based computational immunology and experimental directed evolution as a mean for protein engineering.
- Technology is being evaluated on Adalimumab (Humira) as a text case and we are looking for additional industrial collaborations with relevant proteins.
Prof. Amir Aharoni, NIBN and the Department of Life Sciences and Dr. Tomer Hertz, NIBN and the Shraga Segal Department of Microbiology, Immunology and Genetics, Ben-Gurion University of the Negev, Israel