کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8417845 | 1545706 | 2014 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Identification and elimination of target-related matrix interference in a neutralizing anti-drug antibody assay
ترجمه فارسی عنوان
شناسایی و حذف تداخل ماتریس مربوط به هدف در یک آنتی بادی ضد عفونی خنثی
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
بیوشیمی، ژنتیک و زیست شناسی مولکولی
بیوتکنولوژی یا زیستفناوری
چکیده انگلیسی
Biopharmaceuticals administered to the human body have the potential to trigger the production of anti-drug (also called anti-therapeutic) antibodies (ADA) that can neutralize the therapeutic activity. For antibody therapeutics, cell-based neutralizing ADA assays are frequently used to evaluate ADA in clinical studies. We developed a method to detect neutralizing antibodies against MEDI-575, a fully human IgG2κ antagonistic antibody against PDGFR-α. We evaluated three assay formats, two of which measured late responses, cell proliferation and apoptosis, whereas the third assay detected an early signaling event, phosphorylation of PDGFR-α. Measuring phosphorylation provided a superior assay window and therefore was developed as a neutralizing ADA (NAb) assay. Matrix interference, however, was significant, and could be identified to be caused by PDGF-AA and PDGF-AB, apparently the two most abundant ligands of PDGFR-α present in human serum samples. A simple pre-treatment step, addition of an inhibitory antibody to PDGF-A, a subunit present in PDGF-AA and PDGF-AB, was found to eliminate matrix interference, increasing assay reliability and sensitivity. We integrated the pre-treatment step into assay development and qualified a robust NAb assay.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Immunological Methods - Volume 403, Issues 1â2, 31 January 2014, Pages 52-61
Journal: Journal of Immunological Methods - Volume 403, Issues 1â2, 31 January 2014, Pages 52-61
نویسندگان
Martin Schwickart, Freshta Mehrzai, Jennifer Pearson, Nabila Shaghasi, Carlos Chavez, Amy Schneider, Spencer Wu, Lorin Roskos, Meina Liang,