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

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.

Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics

“…Increase in the availability of monoclonal antibody (mAb) experimental characterizations, access to accurate structural information, and homology modeling (HM) capabilities have fostered the development of computational approaches to address several developability aspects of biotherapeutics . In silico evaluations do not require any material and can be performed either in parallel to or ahead of the experimental evaluations to support drug discovery, as shown by several works describing the prediction of experimental properties. − However, these tools often tackle individual aspects of developability and do not take into consideration that the optimization of one antibody feature may potentially have detrimental effects on other properties.…”

“…The use of a single static-energy-minimized antibody conformation is therefore a simplification of a complex conformational landscape that responds to the changes in the environmental conditions of the antibody molecule as it passes through various stages of formulation, manufacturing, and in vivo administration. Moreover, homology models generated using crystal structure templates may keep imprints from the template, suffer from crystal packing effects, and not accurately represent the dominant conformation of the antibody in solution. , For these reasons, it is desirable that in silico methods include, wherever feasible, conformational ensembles accessible to the antibody candidates. ,− …”

Bioprocess monitoring and control: challenges in cell and gene therapy

“…A range of established multivariate data analysis (MVDA) techniques can be valuable for this purpose, as proposed in [18]. Advances in molecular dynamics (MD) modelling also offer an exciting opportunity to increase the understanding of the viral particle behaviour during manufacturing, akin to the benefits such an approach offers in monoclonal antibody (mAb) early process development and manufacturability assessment [51]. Karlberg et al [51] demonstrated that MD can be used effectively for in silico early-stage screening of potentially problematic mAb candidates.…”

“…Advances in molecular dynamics (MD) modelling also offer an exciting opportunity to increase the understanding of the viral particle behaviour during manufacturing, akin to the benefits such an approach offers in monoclonal antibody (mAb) early process development and manufacturability assessment [51]. Karlberg et al [51] demonstrated that MD can be used effectively for in silico early-stage screening of potentially problematic mAb candidates. An approach similar to their proposed quantitative structure-activity relationship (QSAR) modelling workflow for the prediction of hydrophobic interaction chromatography (HIC) retention times of mAbs can be envisaged for in silico exploration of viral particle behaviour during processing or transduction.…”

A multiscale modeling method for therapeutic antibodies in ion exchange chromatography

“…Quantitative structure-property relationships (QSPR) leverage machine learning algorithms and existing data to predict a target property based on the protein structure. When process understanding and experimental data is limited, QSPR models give initial insights into the developability of mAb candidates during early-stage development (Karlberg et al, 2020(Karlberg et al, , 2018. Kizhedath et al (2019) developed a QSPR model for the prediction of retention times in cross-interaction chromatography that revealed the relevance of local protein descriptors on protein-protein interactions.…”