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Development and validation of an automated assay for anti-drug-antibodies in rat serum.
Terrell, Kristy A; Sempowski, Gregory D; Macintyre, Andrew N.
Afiliação
  • Terrell KA; Duke Human Vaccine Institute and Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.
  • Sempowski GD; Duke Human Vaccine Institute and Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.
  • Macintyre AN; Duke Human Vaccine Institute and Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA. Electronic address: a.n.macintyre@duke.edu.
SLAS Technol ; 28(5): 361-368, 2023 Oct.
Article em En | MEDLINE | ID: mdl-37120133
ABSTRACT
The potential immunogenicity of therapeutic human and humanized monoclonal antibodies (mAb) is a significant concern, and so preclinical testing of therapeutic mAbs routinely includes assessment of anti-drug antibody (ADA) induction. Here, we report the development of automated screening and confirmatory bridging ELISAs for the detection of rat antibodies against DH1042, an engineered human mAb for the SARS-CoV-2 receptor-binding domain. The assays were evaluated for specificity, sensitivity, selectivity, absence of a prozone effect, linearity, intra- and inter- assay precision, and robustness, and found to be suitable for purpose. The assays were then used to evaluate anti-DH1042 antibodies in the sera of rats dosed with lipid-nanoparticle (LNP)-encapsulated mRNA encoding DH1042. Rats received two doses of 0.1, 0.4 or 0.6 mg/kg/dose LNP-mRNA 8 days apart. Twenty-one days after the second dose, 50-100% of rats had developed confirmed anti-DH1042 ADA depending on dose level. No animals in the control group developed anti-DH1042 ADA. These assays reflect new applications for a non-specialized laboratory automation platform, and the methodologies and approaches reported here provide a template that can be adapted for the automated detection and confirmation of ADA in preclinical testing of other biologics.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article