预测抗体可开发性的分子表面描述符:对参数、结构模型和构象取样的敏感性。

IF 5.6 2区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
mAbs Pub Date : 2024-01-01 Epub Date: 2024-06-10 DOI:10.1080/19420862.2024.2362788
Eliott Park, Saeed Izadi
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引用次数: 0

摘要

在早期候选先导药物的筛选和优化过程中,对抗体可开发性的硅学评估至关重要,它提供了一种快速且无需材料的筛选方法。然而,此类方法的预测能力和可重复性在很大程度上取决于分子描述符的选择、模型参数、预测结构模型的准确性以及构象取样技术。在此,我们介绍了一套专门用于预测抗体可开发性的分子表面描述符。我们评估了这些描述符的性能,将其与一系列实验确定的生物物理特性(包括粘度、聚集性、疏水相互作用色谱、人体药代动力学清除率、肝素保留时间和多特异性)进行了基准对比。此外,我们还研究了这些表面描述因子对方法学细微差别的敏感性,如内部介电常数的选择、疏水性尺度、结构预测方法以及构象取样的影响。值得注意的是,我们观察到表面描述符的分布随所使用的结构预测方法的不同而发生系统性变化,从而导致不同结构模型的表面描述符之间存在微弱的相关性。对分子动力学构象分布的描述符值进行平均,可以减轻系统性偏移,提高不同结构预测方法之间的一致性,尽管与生物物理数据的相关性改善不一致。根据我们的基准分析,我们提出了六个硅学可开发性风险标志,并评估了它们在预测一组案例研究分子的潜在可开发性问题方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling.

In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.

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来源期刊
mAbs
mAbs 工程技术-仪器仪表
CiteScore
10.70
自引率
11.30%
发文量
77
审稿时长
6-12 weeks
期刊介绍: mAbs is a multi-disciplinary journal dedicated to the art and science of antibody research and development. The journal has a strong scientific and medical focus, but also strives to serve a broader readership. The articles are thus of interest to scientists, clinical researchers, and physicians, as well as the wider mAb community, including our readers involved in technology transfer, legal issues, investment, strategic planning and the regulation of therapeutics.
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