Use of Peptide Microarrays for Fast and Informative Profiling of Therapeutic Antibody Formulation Conditions

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 f...

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Vydané v:Molecular pharmaceutics Ročník 18; číslo 11; s. 4131
Hlavní autori: Austerberry, James, Edwards, John, Eyes, Tim, Derrick, Jeremy P
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.11.2021
Predmet:
Antibodies, Monoclonal - chemistry
Antibodies, Monoclonal - therapeutic use
Biological Products - chemistry
Biological Products - therapeutic use
Chemistry, Pharmaceutical - methods
Humans
Machine Learning
Peptides - chemistry
Protein Array Analysis - methods
Protein Stability
Single-Chain Antibodies - chemistry
Single-Chain Antibodies - therapeutic use
formulation
microarray
peptide
biopharmaceutical
monoclonal antibody
ISSN:1543-8392, 1543-8392
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Shrnutí: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, , 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.
Bibliografia:ObjectType-Article-1
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ISSN:1543-8392
1543-8392
DOI:10.1021/acs.molpharmaceut.1c00543