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利用超高通量微流控平台进行多维蛋白质溶解度优化。

Multidimensional Protein Solubility Optimization with an Ultrahigh-Throughput Microfluidic Platform.

机构信息

Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.

Biophysics and Injectable Formulation, Global Research Technology, Novo Nordisk A/S, 2760 Maaloev, Denmark.

出版信息

Anal Chem. 2023 Mar 28;95(12):5362-5368. doi: 10.1021/acs.analchem.2c05495. Epub 2023 Mar 17.

DOI:10.1021/acs.analchem.2c05495
PMID:36930285
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10061369/
Abstract

Protein-based biologics are highly suitable for drug development as they exhibit low toxicity and high specificity for their targets. However, for therapeutic applications, biologics must often be formulated to elevated concentrations, making insufficient solubility a critical bottleneck in the drug development pipeline. Here, we report an ultrahigh-throughput microfluidic platform for protein solubility screening. In comparison with previous methods, this microfluidic platform can make, incubate, and measure samples in a few minutes, uses just 20 μg of protein (>10-fold improvement), and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation excipients, such as sodium chloride, polysorbate, histidine, arginine, and sucrose. Additionally, we can measure how solubility is affected by the combinatorial effect of multiple additives, find a suitable pH for the formulation, and measure the impact of mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multidimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.

摘要

蛋白质类生物制剂因其对靶点的低毒性和高特异性,非常适合药物开发。然而,对于治疗应用,生物制剂通常需要配制成高浓度,因此溶解度不足成为药物开发过程中的一个关键瓶颈。在这里,我们报告了一种用于蛋白质溶解度筛选的超高通量微流控平台。与以前的方法相比,这种微流控平台可以在几分钟内完成制样、孵育和测量,仅需 20μg 蛋白质(提高 10 倍),可获得 10,000 个数据点(提高 1000 倍)。这允许对氯化钠、聚山梨酯、组氨酸、精氨酸和蔗糖等制剂赋形剂进行定量比较。此外,我们还可以测量溶解度如何受到多种添加剂组合效应的影响,找到合适的配方 pH 值,并测量突变对溶解度的影响,从而能够筛选大型文库。通过降低材料和时间成本,这种方法使详细的多维溶解度优化实验成为可能,简化药物开发并加深我们对生物治疗溶解度和赋形剂影响的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43c0/10061369/20ef4dacd0b7/ac2c05495_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43c0/10061369/dc9c05e197d9/ac2c05495_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43c0/10061369/4eaf145394f0/ac2c05495_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43c0/10061369/26040347bccf/ac2c05495_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43c0/10061369/20ef4dacd0b7/ac2c05495_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43c0/10061369/dc9c05e197d9/ac2c05495_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43c0/10061369/4eaf145394f0/ac2c05495_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43c0/10061369/26040347bccf/ac2c05495_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43c0/10061369/20ef4dacd0b7/ac2c05495_0005.jpg

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