QSAR Implementation for HIC Retention Time Prediction of mAbs Using Fab Structure: A Comparison between Structural Representations.
Int J Mol Sci
; 21(21)2020 Oct 28.
Article
in En
| MEDLINE
| ID: mdl-33126648
ABSTRACT
Monoclonal antibodies (mAbs) constitute a rapidly growing biopharmaceutical sector. However, their growth is impeded by high failure rates originating from failed clinical trials and developability issues in process development. There is, therefore, a growing need for better in silico tools to aid in risk assessment of mAb candidates to promote early-stage screening of potentially problematic mAb candidates. In this study, a quantitative structure-activity relationship (QSAR) modelling workflow was designed for the prediction of hydrophobic interaction chromatography (HIC) retention times of mAbs. Three novel descriptor sets derived from primary sequence, homology modelling, and atomistic molecular dynamics (MD) simulations were developed and assessed to determine the necessary level of structural resolution needed to accurately capture the relationship between mAb structures and HIC retention times. The results showed that descriptors derived from 3D structures obtained after MD simulations were the most suitable for HIC retention time prediction with a R2 = 0.63 in an external test set. It was found that when using homology modelling, the resulting 3D structures became biased towards the used structural template. Performing an MD simulation therefore proved to be a necessary post-processing step for the mAb structures in order to relax the structures and allow them to attain a more natural conformation. Based on the results, the proposed workflow in this paper could therefore potentially contribute to aid in risk assessment of mAb candidates in early development.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Immunoglobulin Fab Fragments
/
Molecular Dynamics Simulation
/
Antibodies, Monoclonal
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Int J Mol Sci
Year:
2020
Type:
Article
Affiliation country:
United kingdom