کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
2485323 | 1114352 | 2008 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Reversible Self-Association of a Concentrated Monoclonal Antibody Solution Mediated by Fab-Fab Interaction That Impacts Solution Viscosity
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
علوم پزشکی و سلامت
داروسازی، سم شناسی و علوم دارویی
اکتشاف دارویی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Reversible self-association of a monoclonal antibody (MAb) in a high concentration formulation results in a solution with a high viscosity. The nature of the self-association of full-length as well as antibody fragments has been studied by rheometry. Chaotropic anions reduced solution viscosity more than kosmotropic anions, a result that can be explained by the Hofmeister series and the net charge of the MAb. The effect of strong chaotropes, such as urea and guanidine HCl at concentration below 300 mM on solution viscosity was also investigated. While the secondary and tertiary structure of the MAb was not altered, as determined by circular dichroism measurements, guanidine HCl reduced viscosity much more effectively than urea. Since urea is uncharged and guanidine HCl is monovalent, this study indicated that a charge effect may be a more important factor than the chaotropic nature of excipients in reducing solution viscosity by breaking network self-association of a MAb. To further understand which part of a MAb participates in this network self-association, a series of titration studies using the full-length MAb, F(abâ²)2, and Fab fragments was conducted. From this study, the Fab was found to be the primary site of the network self-association.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Pharmaceutical Sciences - Volume 97, Issue 10, October 2008, Pages 4219-4227
Journal: Journal of Pharmaceutical Sciences - Volume 97, Issue 10, October 2008, Pages 4219-4227
نویسندگان
Sonoko Kanai, Jun Liu, Thomas W. Patapoff, Steven J. Shire,