Computer-aided antibody discovery for emerging contaminants: bridging molecular immunotechnology and food-safety sustainability

Background: Emerging contaminants (ECs) pose growing environmental threats globally, and immunoassays based on antigen-antibody interactions are vital for detecting their residues, offering eco-friendly and low-impact advantages. However, finding suitable antibodies for new ECs remains challenging, limiting the effectiveness of immunoassays. Scope and approach: To overcome this, innovations in antibody discovery are crucial for enhancing EC detection and reducing risks to food safety and public health. This article provides a balanced view of antibody detection advancements for ECs, examining hybridoma methods, antibody display libraries, and single B-cell techniques. It also explores the limitations and new developments in hapten design and immunization, essential for antibody generation, and discusses future research directions in antibody design. Key findings and conclusions: Effective hapten design is crucial for developing antibodies against low-molecular-weight (LMW) ECs, with emerging computational strategies, including artificial intelligence (AI), enabling precise engineering and accelerating the design process. Immunization strategies are also critical, with nano-adjuvants offering unique benefits and combination adjuvants potentially outperforming single-component ones. Hybridoma technology can be improved by optimizing electrofusion and high-throughput screening. Constructing synthetic antibody libraries with display technologies and high-throughput screening can boost recombinant antibody yields. Advancing single B-cell technology requires improved isolation, amplification accuracy, and intelligent screening. Future advancements in machine learning (ML) and computational biology will make antibody discovery more predictive, and integrating multiple fields is key to developing faster, cheaper antibody solutions for food security.