We envision a world where drug discovery is not left to chance but guided by artificial intelligence (AI) and machine learning to make therapeutic development smarter, more precise, and ultimately faster. We imagine a day when a new generation of therapeutics against hard-to-drug targets will treat the untreatable and potentially cure the incurable.
We’re using iBio’s proprietary Discovery Platform to develop the next generation of antibodies for hard-to-drug targets and modes of action, engineered with high developability and enhanced safety. We have a pre-clinical pipeline of immuno-oncology targets for the potential treatment of solid tumors, glioblastoma, head and neck cancers, and through our strategic partnership with AstralBio we are pursuing potential first-in-class and best-in-class molecules for obesity, diabetes, and heart disease. We envision using the vast capabilities of our platform for therapeutics in immunology, neurology, and even vaccines.
An essential challenge to developing antibody drugs is that traditional discovery technologies employ a high degree of randomness. Creating an antibody with a set of desired qualities requires repeated attempts until the optimal result is achieved, often by chance. While this approach has yielded valuable therapeutics in the past, that outcome is rare, with fewer than 1 in 1,000 targets ever reaching the clinic. Traditional antibody development methods are time consuming, costly, and we believe ultimately produce fewer potentially life-saving drugs than possible.
Another critical challenge to antibody therapeutic development is reaching hard-to-drug, complex targets. With 40 percent of approved antibodies binding to just 10 targets, vast areas of the human surface remain untapped. More than 100 monoclonal antibody therapeutics have been approved since 1986, yet they target just 91 of the potentially quintillion antibodies our bodies produce.1,2
