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

• Published in: Molecular pharmaceutics Vol. 18; no. 11; pp. 4131 - 4139
• Main Authors: Austerberry, James, Edwards, John, Eyes, Tim, Derrick, Jeremy P
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 01.11.2021
• Subjects: 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; peptide; microarray; biopharmaceutical; monoclonal antibody
• ISSN: 1543-8384; 1543-8392; 1543-8392
• DOI: 10.1021/acs.molpharmaceut.1c00543

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, k D, 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.