基于 scFv 受体的计算建模与优化支持靶向 CD19 的 CAR-T 设计，以提升结合稳健性并降低脱靶倾向

Chimeric Antigen Receptor T-cell (CAR-T) therapy has revolutionized the treatment of B-cell malignancies, with CD19 being a primary target due to its stable expression in lymphomas.

However, current CAR-T therapies face challenges related to antigen escape, treatment resistance, and toxicity. In this study, we employed a computational approach to design and optimize scFv-based receptors to support CAR-T design with reduced predicted off-target interaction propensity.

We utilized in-silico techniques, including PSI-BLAST sequence validation, molecular docking, machine learning-based toxicity prediction, and molecular dynamics simulations, to refine ScFv relevant receptor design.

Our structural modeling and docking studies identified an optimized single-chain variable fragment (scFv) antibody (H8_L1) that demonstrated high binding affinity and stability with both wild-type and mutated CD19 variants. Toxicity assessments confirmed minimal off-target effects.

Additionally, computational mutation docking studies revealed that the optimized scFv-based receptor for CAR-T design maintained stable interactions despite antigenic variations.

These findings provide a robust prioritization study and framework to support the design of CAR-T receptors with enhanced computational efficiency and lower toxicity, paving the way for further experimental validation and clinical applications.

However, this work is limited to computational scFv design and does not evaluate CAR expression, surface localization, or T-cell function.