Project description
Simplifying and accelerating the monoclonal antibody discovery process
The immune system relies heavily on antibodies, immune cells that seek out specific markers called antigens on cells that are considered ‘threats’. Monoclonal antibodies (mAbs) are manmade proteins that act like human antibodies. The more than 100 commercially approved mAbs have become essential tools for clinicians. Nevertheless, the Ab discovery process is currently very inefficient, requiring considerable time, money, effort, and use of mammals. Focusing on cancer, the EIC-funded ALADDIN project will dramatically streamline the process. To do so, it will integrate several tools: bacterial cells with a universal library of single-domain Abs called nanobodies; AI tools; microfluidics; a zebrafish model of cancer; and mathematical models.
Objective
Since the approval of the first monoclonal antibody (mAb) 30 years ago, therapeutic mAbs and Ab-derived molecules have come to dominate the biologics market. Currently, +100 Abs are in clinical use for different diseases, out of which +30 mAb target cancer. Despite the huge market potential, multiple and complex steps make the therapeutic Ab discovery process long, expensive, laborious, and inefficient. These steps include target identification, animal immunization, Ab selection and engineering, humanization and preclinical validation. All these are not integrated, costly, require large equipments and facilities, and are highly dependent on experimental animals (mostly mammals, including genetically modified mice), both for immunization and for preclinical validation of the therapeutic Ab candidates.
ALADDIN project emerges to bring to the market a novel AcceLerAteD DIscovery Nanobody platform that will increase the efficiency of therapeutic Ab discovery and preclinical validation for human cancer by: 1) integrating selection and in vivo affinity maturation of Abs in bacterial cells holding an universal library of single domain Abs (nanobodies, Nbs) that fully eliminates animal inmunization; 2) using in silico Artificial Intelligent (AI) tools for structure-based epitope mapping, AI-guided affinity maturation, and Nb humanization; 3) developing cost-effective miniaturized microfluidic-based devices for in vitro Ab selection from bacterial cultures; 4) accelerating Ab validation with a fast non-mammalian in vivo model for preclinical testing based on patient-derived tumor xenografts in zebrafish larvae; 5) impacting target and Ab validation with dynamic mathematical models to extract clinical and efficacy data of the Ab candidates. These ambitious goals will be possible through the multidisciplinary ALADDIN consortium, formed by eight partners with complementary skills.
Fields of science
Keywords
Programme(s)
- HORIZON.3.1 - The European Innovation Council (EIC) Main Programme
Funding Scheme
HORIZON-EIC - HORIZON EIC GrantsCoordinator
28006 Madrid
Spain