Non-constant mean relative potency for antibody-dependent cellular cytotoxicity assays.

4PL ADCC EC50 bioassay parallelism similarity

Journal

Journal of biopharmaceutical statistics
ISSN: 1520-5711
Titre abrégé: J Biopharm Stat
Pays: England
ID NLM: 9200436

Informations de publication

Date de publication:
22 Sep 2024
Historique:
medline: 22 9 2024
pubmed: 22 9 2024
entrez: 22 9 2024
Statut: aheadofprint

Résumé

Bioassays are regulated, analytical methods used to ensure proper activity (potency) of biological products at release and during long-term storage. Potency is commonly reported on a relative basis by comparing and calibrating a concentration-response curve from the test material to that of a reference standard material. The relative potency approach depends on an assumption that the two concentration-response curves exhibit similar (equivalent) shapes, except for a potency shift. In certain circumstances, however, biological factors preclude the similarity assumption, and the traditional approach becomes unworkable. The antibody-mediated cytotoxicity assay is one example where the similarity assumption does not always hold. Other examples also arise in the fields of toxicology and pharmacology. In this work, we present a non-constant mean relative potency approach which averages the relative potency across a common range of the concentration-response curves. The proposed method captures the changing nature of the relative potency into a summary statistic that can be reported for batch calibration and quality control purposes. We provide inferential methods for this statistic and summarize the results of a simulation comparing these methods across a number of non-constant relative potency scenarios and assay conditions.

Identifiants

pubmed: 39306756
doi: 10.1080/10543406.2024.2403435
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1-12

Auteurs

Paul Faya (P)

Global Statistical Sciences, Eli Lilly and Company, Indianapolis, IN, USA.

Tianhui Zhang (T)

Global Statistical Sciences, Eli Lilly and Company, Indianapolis, IN, USA.

Wendy Walton (W)

Bioproduct Research and Development, Eli Lilly and Company, Indianapolis, IN, USA.

Steven Novick (S)

Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, MA, USA.

Classifications MeSH