The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be to develop an online, fully automated platform for designing high-affinity antibodies for use as potential drug candidates. The success of antibody-based drugs has generated interest in faster and more efficient methods to discover and optimize antibodies. The goal of this project will be to develop and implement a computational method for producing protein sequences of humanized antibodies. This will be achieved by providing software that allows scientists to move some of their initial experiments into the cloud, and achieve results much more quickly by using computational methods saving time and cost for new drug development. In addition, it is anticipated that this will improve the features of antibody-based drug candidates, enhance the success rate of clinical studies, and accelerate the commercialization of new drugs.
This SBIR Phase II project aims to develop and implement computational tools for designing antibodies that are more effective, have fewer side effects, and have fewer problems in manufacturing. Current computational design methods rely almost entirely on the expertise of scientists iterating between experimental and bioinformatics approaches. An automated, systematic approach will help researchers design better antibodies with desired features in a shorter amount of time. The final platform will allow researchers to incorporate experimental and structural information to develop better drugs by determining which experiments will be necessary, assessing the viability of a potential candidate, and identifying structural features responsible for the molecule?s stability, immune response, and binding properties. The typical antibody design process takes many months and tens of thousands of dollars. With the aid of a computational process, this time can be cut back to the click of a button.
