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Improving monoclonal antibody selection and engineering using measurements of colloidal protein interactions.

ABSTRACT: A limitation of using mAbs as therapeutic molecules is their propensity to associate with themselves and/or with other molecules via nonaffinity (colloidal) interactions. This can lead to a variety of problems ranging from low solubility and high viscosity to off-target binding and fast antibody clearance. Measuring such colloidal interactions is challenging given that they are weak and potentially involve diverse target molecules. Nevertheless, assessing these weak interactions-especially during early antibody discovery and lead candidate optimization-is critical to preventing problems that can arise later in the development process. Here we review advances in developing and implementing sensitive methods for measuring antibody colloidal interactions as well as using these measurements for guiding antibody selection and engineering. These systematic efforts to minimize nonaffinity interactions are expected to yield more effective and stable mAbs for diverse therapeutic applications. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 103:3356-3363, 2014.

SUBMITTER: Geng SB

PROVIDER: S-EPMC4206634 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Improving monoclonal antibody selection and engineering using measurements of colloidal protein interactions.

Geng Steven B SB Cheung Jason K JK Narasimhan Chakravarthy C Shameem Mohammed M Tessier Peter M PM

Journal of pharmaceutical sciences 20140910 11

A limitation of using mAbs as therapeutic molecules is their propensity to associate with themselves and/or with other molecules via nonaffinity (colloidal) interactions. This can lead to a variety of problems ranging from low solubility and high viscosity to off-target binding and fast antibody clearance. Measuring such colloidal interactions is challenging given that they are weak and potentially involve diverse target molecules. Nevertheless, assessing these weak interactions-especially durin ...[more]

PMID: 25209466

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Project description:Patents of some therapeutic monoclonal antibodies (mAb) used for cancer treatment are ending soon, allowing for the entry of similar analogs (biosimilars) onto the market. To have a biosimilar approved by health agencies, the manufacturer must typically demonstrate functional and physicochemical similarity between the biosimilar and the approved reference product. Functional similarity is often validated with cell-based assays, but the information gained is limited. In contrast, RNA-seq methods enable a sensitive transcriptomic analysis, providing detailed information on pathways and cellular responses. Trastuzumab inhibits signaling of the cell-surface receptor HER2, which is overexpressed in approximately 30% of all breast cancer patients. Here, we compare the functional effect of the mAb Trastuzumab and a corresponding biosimilar. The BT-474 breast cancer cell line was treated with the reference product, Trastuzumab (Herceptin®), or a proposed biosimilar (ApoTras), and the cellular transcriptomes were analyzed by RNA-seq. Functional similarity was assessed using two statistical contrasts. One contrast evaluated the mechanism of action by comparing treated samples (Her and ApoTras, n = 19) vs. untreated controls (n = 10), which identified 2623 differentially expressed genes (DEG) at a minimum fold change of 1.25. The other contrast directly compared ApoTras (n = 9) vs. Her (n = 10) to determine differences in expression between treatments and identified 24 DEG using a 1.25 fold- change threshold. This low DEG number reveals a very high similarity of effect of both mAb treatments on the cancer cells. A gene set overrepresentation analysis of DEG for mAb treatments revealed mechanisms of action, which were consistent with known trastuzumab effects. Supporting the small number of changes between both treatments, a post-hoc power analysis for the between-treatment comparison estimated power of 0.88 to detect a gene expression fold-changes of 2.0. To summarize these data, we introduce an overall similarity index (SI) to quantify treatment similarity based on changes in gene expression. Using statistical contrasts with RNA-seq analysis provides a powerful tool for the comparison of biological effects of biosimilar and originator compounds and can be broadly used for functional comparisons of drug treatments.

Dataset Information

Improving monoclonal antibody selection and engineering using measurements of colloidal protein interactions.

Publications

Improving monoclonal antibody selection and engineering using measurements of colloidal protein interactions.

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