SHIRLEY, NY, January 15, 2026 /24-7PressRelease/ -- In the development process of antibody drugs, a common scenario is that a candidate molecule performs well in in vitro experiments, with satisfactory affinity and functional data, but reveals immunogenicity risks during further evaluation and even has to return to the design stage for re-optimization. As antibody drugs are widely used in fields such as oncology, autoimmune diseases, and infectious diseases, such "late-stage rework" problems are frequently emerging in the R&D process, forcing R&D teams to seek a new balance between efficiency, safety, and molecular performance.

During the process of humanizing antibodies, researchers often have to repeatedly balance between reducing immune risks and preserving binding activity. To address this issue, AI models are used to conduct multi-dimensional analyses of antibody sequences, systematically evaluating the potential impacts of different framework replacement schemes on immunogenicity, structural stability, and other aspects. While maintaining the original binding characteristics as much as possible, this data-driven design approach helps to avoid high-risk schemes in advance, reducing the time and cost consumption caused by repeated experiments.

For candidate molecules that have undergone initial humanization but still pose immune risks during further evaluation, Creative Biolabs has introduced an AI immunogenicity removal strategy. By predicting potential T-cell epitopes and identifying high-risk regions, researchers can precisely optimize the sequence without interfering with functional areas, thereby enhancing the safety and acceptability of the candidate antibodies in subsequent clinical development stages.

During the affinity maturation stage, AI-driven mutation prediction models are employed to identify key sites that enhance antigen binding and guide the construction of more focused mutation libraries. Combined with high-throughput experimental screening, the R&D team can obtain antibody variants with significantly improved affinity and good development potential within a relatively short period. Project data indicates that with the help of AI prediction strategies, the proportion of ineffective mutations can be effectively reduced, thereby enhancing the overall screening efficiency.

The expert in charge of the antibody engineering platform at Creative Biolabs stated, "AI does not simply replace experiments; rather, it helps us make more rational judgments during the design stage. By continuously iterating and integrating algorithmic predictions with experimental data, we can identify potential risks earlier and provide our clients with more forward-looking optimization solutions."

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Creative Biolabs, by integrating algorithmic capabilities with experimental platforms, offers a more efficient and controllable option for the early optimization of antibody drugs and also provides a new practical path for the industry to explore data-driven R&D models.

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