In antibody drug development, a persistent challenge emerges when candidate molecules show promising results in laboratory tests but reveal immunogenicity risks during advanced evaluations, often forcing researchers back to the design phase. This "late-stage rework" problem, common in fields like oncology and autoimmune diseases, creates significant delays and cost overruns. Creative Biolabs is addressing this issue by strengthening its AI-driven antibody engineering approaches to better balance affinity and safety from the earliest stages.
During humanization processes, where researchers modify antibodies to reduce immune reactions in patients, maintaining binding activity while minimizing immunogenicity requires careful balancing. Creative Biolabs employs AI models to analyze antibody sequences multidimensionally, systematically evaluating how different framework replacements affect immunogenicity and structural stability. This data-driven design helps avoid high-risk schemes early, reducing the time and cost of repeated experiments. For molecules that still pose immune risks after initial humanization, the company has introduced an AI immunogenicity removal strategy, which predicts potential T-cell epitopes and identifies high-risk regions for precise sequence optimization without disrupting functional areas.
At the affinity maturation stage, where antibodies are engineered for stronger target binding, Creative Biolabs uses AI-driven mutation prediction models to identify key sites that enhance antigen binding. These models guide the construction of focused mutation libraries, which, combined with high-throughput experimental screening, enable the development of antibody variants with significantly improved affinity and good development potential in shorter timeframes. Project data indicates that AI prediction strategies effectively reduce the proportion of ineffective mutations, thereby enhancing overall screening efficiency.
The importance of this advancement lies in its potential to transform antibody drug development, which is critical for treating conditions ranging from cancer to infectious diseases. By integrating algorithmic predictions with experimental data, Creative Biolabs aims to identify risks earlier and provide more forward-looking optimization solutions. This approach not only offers a more efficient and controllable option for early antibody optimization but also presents a practical path for the industry to adopt data-driven research and development models, potentially accelerating the delivery of safer, more effective therapies to patients.
