Integrative and Emerging Models in Antibody Research: A Comprehensive Review

Antibody research has advanced through the integration of in vivo, in vitro, and in silico models, each offering distinct advantages and limitations. In vivo models, such as traditional animal models and humanized mouse models, provide critical insights into antibody efficacy and pharmacokinetics but face ethical and translational challenges. In vitro techniques, including hybridoma technology, phage display, and B-cell culture, enable efficient screening and optimization but often lack physiological relevance. In silico approaches, powered by computational biology and machine learning, accelerate antibody design and prediction, addressing challenges in cost and scalability. Emerging technologies like CRISPR-based engineering, single-cell sequencing, microfluidics, and organ-on-chip platforms are reshaping antibody discovery and therapeutic development. This review critically evaluates these models, emphasizing their integration to overcome existing challenges such as reproducibility, immunogenicity prediction, and scalability. As innovations continue, a multidisciplinary approach promises to enhance antibody research, driving next-generation therapeutics for cancer, autoimmune diseases, and infectious conditions.