Antibody Lead Optimization
Antibody optimization is a critical step in the drug development process as it ensures that the finalized antibody candidates are manufacturable, stable and retain critical binding and specificity properties. Usually the optimization process involves humanization, aggregation optimization, improved stability, etc and each of these optimizations require in vitro experiments which are time consuming and resource intensive. In silico tools provide a cost effective and quick alternative to this process.
In silico pipelines and solutions can alter the amino acid sequences in the CDR, Framework and the Fc regions to optimize the antibody for the desired property. Generative AI based models provide increased precision of the alteration to the property of interest. For example, while humanizing an antibody we would examine both sequence and the structure of an antibody and modify the amino acid sequence. Structural changes that go along with each amino acid change are taken into consideration such that there is least possible impact of binding. Additionally whenever possible the CDR regions are left untouched to keep the structural integrity intact so that binding affinity to the target is not affected.
Below is an example of part of an antibody sequence that is humanized and the relevant humanization score.
Similarly in case of other properties such as aggregation, hydrophobic patches are noticed on the antibody and the regions which are not important to binding are preferentially optimized to reduce the aggregation propensity while maintaining the binding affinity and other developmental parameters of the antibody.
Aganitha with its expertise in generative AI and computational biology provides antibody characterization and optimization services.