Adimab LLC, Palo Alto, CA, USA.
Computational Biology, Adimab LLC, Lebanon, NH, USA.
MAbs. 2023 Jan-Dec;15(1):2200540. doi: 10.1080/19420862.2023.2200540.
With the growing significance of antibodies as a therapeutic class, identifying developability risks early during development is of paramount importance. Several high-throughput in vitro assays and in silico approaches have been proposed to de-risk antibodies during early stages of the discovery process. In this review, we have compiled and collectively analyzed published experimental assessments and computational metrics for clinical antibodies. We show that flags assigned based on in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than their in silico counterparts. Additionally, we assessed the performance of published models for developability predictions on molecules not used during model training. We find that generalization to data outside of those used for training remains a challenge for models. Finally, we highlight the challenges of reproducibility in computed metrics arising from differences in homology modeling, in vitro assessments relying on complex reagents, as well as curation of experimental data often used to assess the utility of high-throughput approaches. We end with a recommendation to enable assay reproducibility by inclusion of controls with disclosed sequences, as well as sharing of structural models to enable the critical assessment and improvement of in silico predictions.
随着抗体作为一种治疗类别变得越来越重要，在开发早期识别开发风险至关重要。已经提出了几种高通量的体外检测和计算方法，以在发现过程的早期降低抗体的风险。在这篇综述中，我们收集并综合分析了已发表的用于临床抗体的实验评估和计算指标。我们表明，基于多特异性和疏水性的体外测量分配的标记比其计算标记更能预测临床进展。此外，我们评估了已发表的可开发性预测模型在未用于模型训练的分子上的性能。我们发现，对于模型来说，推广到训练数据以外的数据仍然是一个挑战。最后，我们强调了计算指标中由于同源建模、依赖复杂试剂的体外评估以及经常用于评估高通量方法实用性的实验数据的整理方面的差异所导致的可重复性的挑战。我们建议通过包含具有公开序列的对照以及共享结构模型来实现检测的可重复性，从而能够对计算预测进行关键评估和改进。
