Use of Peptide Microarrays for Fast and Informative Profiling of Therapeutic Antibody Formulation Conditions.
Use of Peptide Microarrays for Fast and Informative Profiling of Therapeutic Antibody Formulation Conditions.
复制标题
使用肽微阵列快速、信息丰富地分析治疗性抗体配方条件。
DOI：
10.1021/acs.molpharmaceut.1c00543
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发表时间：
2021
影响因子：
4.9
通讯作者：
Austerberry J
中科院分区：
文献类型：
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作者：
Austerberry J
优化治疗性蛋白质的溶液行为的方法通常很耗时，提供的信息有限，而且经常使用毫克量的材料。在这里，我们提出了一种简单、通用的方法，它提供了有价值的信息来指导原则上任何生物制药药物的配方条件的鉴定和比较。将目标蛋白与设计的合成多肽微阵列孵育；与每个多肽的结合程度取决于溶液条件。洗涤阵列，并使用二次抗体检测目标蛋白的粘附性。我们使用一种具有良好特性的人类单链抗体和一系列人类单抗来举例说明该方法。多肽粘附谱的相关性可以用来建立不同溶液条件之间的定量关系，从而允许分组到树状图中。多维降维方法，如t分布随机邻域嵌入，可以用来比较不同的单克隆在不同的溶液条件下的粘附性的变化。最后，我们使用一系列在特定缓冲条件下可以进行生物物理测量的单抗来筛选多肽结合图谱。我们使用神经网络方法根据聚集温度、Kd、25℃孵化后的回收率和熔化温度来训练数据。结果表明，多肽结合图谱确实可以根据这些蛋白质稳定性和溶液中自结合的指标进行有效的训练。该方法为机器学习方法在治疗性蛋白质配方中的应用开辟了多种可能性。
Methods to optimize the solution behavior of therapeutic proteins are frequently time-consuming, provide limited information, and often use milligram quantities of material. Here, we present a simple, versatile method that provides valuable information to guide the identification and comparison of formulation conditions for, in principle, any biopharmaceutical drug. The subject protein is incubated with a designed synthetic peptide microarray; the extent of binding to each peptide is dependent on the solution conditions. The array is washed, and the adhesion of the subject protein is detected using a secondary antibody. We exemplify the method using a well-characterized human single-chain Fv and a selection of human monoclonal antibodies. Correlations of peptide adhesion profiles can be used to establish quantitative relationships between different solution conditions, allowing subgrouping into dendrograms. Multidimensional reduction methods, such as t-distributed stochastic neighbor embedding, can be applied to compare how different monoclonals vary in their adhesion properties under different solution conditions. Finally, we screened peptide binding profiles using a selection of monoclonal antibodies for which a range of biophysical measurements were available under specified buffer conditions. We used a neural network method to train the data against aggregation temperature,kD, percentage recovery after incubation at 25 °C, and melting temperature. The results demonstrate that peptide binding profiles can indeed be effectively trained on these indicators of protein stability and self-association in solution. The method opens up multiple possibilities for the application of machine learning methods in therapeutic protein formulation.