QSAR Implementation for HIC Retention Time Prediction of mAbs Using Fab Structure: A Comparison between Structural...

Area of Science:

• Biopharmaceutical Development
• Computational Chemistry
• Protein Engineering

Background:

• Monoclonal antibodies (mAbs) are vital biopharmaceuticals, but high failure rates in clinical trials and development pose significant challenges.
• Early identification of problematic mAb candidates is essential to mitigate risks and improve success rates.
• Current in silico tools for mAb risk assessment require enhancement for better predictive accuracy.

Purpose of the Study:

• To design and validate a quantitative structure-activity relationship (QSAR) modeling workflow for predicting mAb hydrophobic interaction chromatography (HIC) retention times.
• To evaluate the impact of different structural resolutions (primary sequence, homology modeling, molecular dynamics) on HIC retention time prediction accuracy.
• To establish a robust in silico method for early-stage risk assessment of mAb candidates.

Main Methods:

• Development of three novel descriptor sets based on primary sequence, homology modeling, and atomistic molecular dynamics (MD) simulations.
• Application of QSAR modeling to correlate structural descriptors with experimental HIC retention times of mAbs.
• Comparative analysis of descriptor sets to determine the optimal level of structural detail for accurate HIC prediction.

Main Results:

• Descriptors derived from 3D structures obtained after MD simulations demonstrated the highest predictive power for HIC retention times (R² = 0.63 in an external test set).
• Homology modeling resulted in biased 3D structures, highlighting the necessity of MD simulations for structural relaxation and natural conformation.
• The proposed QSAR workflow effectively captured the relationship between mAb structure and HIC behavior.

Conclusions:

• Molecular dynamics simulations provide essential structural information for accurate HIC retention time prediction in mAbs.
• The developed QSAR workflow offers a valuable tool for early-stage risk assessment of mAb candidates in biopharmaceutical development.
• This approach can help reduce late-stage failures by identifying potentially problematic mAbs earlier in the development pipeline.