Accelerating target deconvolution for therapeutic antibody candidates using highly parallelized genome editing.

Area of Science:

• Immunology and Drug Discovery
• Genetic Engineering and Molecular Biology

Background:

• Therapeutic antibodies are revolutionizing cancer and autoimmune disease treatments.
• Identifying novel antibody targets is a significant challenge in drug discovery.
• Phenotypic discovery enables target-agnostic antibody identification but requires target deconvolution.

Purpose of the Study:

• To investigate and validate a pooled CRISPR/Cas9 approach for efficient antibody target deconvolution.
• To assess the efficacy and scalability of this deconvolution method in real-world discovery programs.

Main Methods:

• Application of pooled CRISPR/Cas9 screening for target identification.
• Testing the approach across three distinct phenotypic antibody discovery programs.
• Quantification of target deconvolution success rate and comparison with existing methods.

Main Results:

• Achieved a 97% success rate in deconvoluting targets for 38 out of 39 test antibodies.
• Demonstrated superior efficiency, reduced workload, and scalability compared to current approaches.
• Successfully identified antibodies targeting the major histocompatibility complex (MHC).

Conclusions:

• Pooled CRISPR/Cas9 is a highly efficient and robust method for antibody target deconvolution.
• This approach significantly overcomes a key bottleneck in phenotypic antibody discovery.
• The findings have immediate implications for accelerating the development of novel antibody-based therapeutics.