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, +160 mAbs are in clinical use for different diseases, out of which nearly 100 target cancer. Global market of mAbs was +220 USD billion in 2023 and it is expected to reach 500 USD by 2030. 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 are possible through the multidisciplinary ALADDIN consortium, formed by eight partners with complementary skills, including microbiology, synthetic biology, nanobody engineering, artificial intelligence, protein folding and dynamics, physics, microfluidics, proteomic and transcriptomic analysis, preclinical validation, mouse and zebrafish tumor xenograft models, oncology, mathematics, communication and exploitation.
