Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods.
Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods.
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DOI：
10.1080/19420862.2021.1895540
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发表时间：
2021-01
期刊：
影响因子：
5.3
通讯作者：
Tessier PM
中科院分区：
文献类型：
--
作者：
Makowski EK;Wu L;Gupta P;Tessier PM
人们对开发单克隆抗体作为治疗多种人类疾病的治疗剂有着强烈和广泛的兴趣。在早期阶段的抗体发现中，要识别出数百到数千种主要候选药物，而那些缺乏最佳物理和化学性质的候选药物必须尽早取消选择，以避免在药物开发后期出现问题。对于在发现阶段可用的具有低抗体量、浓度和纯度的大量候选物，表征这些性质，以及预测浓缩的抗体性质（例如，溶解度、粘度），这是有效配制、递送和功效所需。在这里，我们回顾了开发和实施高通量方法用于鉴定具有理想的体外和体内特性的抗体的最新进展，包括有利的抗体稳定性，特异性，溶解度，药代动力学和免疫原性特征，这些特征共同涵盖了整体药物开发能力。特别是，我们强调了令人印象深刻的最新进展，在开发计算方法，以改善合理的抗体设计和预测的药物样行为，持有很大的希望，减少所需的实验量。我们还讨论了未来需要解决的突出挑战，以充分实现使用这种分析来最大限度地缩短开发时间和提高临床候选抗体成功率的巨大潜力。
There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.
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