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Related Experiment Video

Updated: Jan 25, 2026

RNA Interference in Ticks

RNA Interference in Ticks

Published on: January 20, 2011

Evaluation of potential biotin interference in immunogenicity testing.

Alexander Pöhler¹, Janine Faigle¹, Roland F Staack¹

¹Roche Pharma Research & Early Development (pRED), Pharmaceutical Sciences, Bioanalytical R&D, Roche Innovation Center Munich, Roche Diagnostics GmbH, Nonnenwald 2, 82377 Penzberg, Germany.

|May 10, 2019

Summary

This summary is machine-generated.

Biotin interference (BI) can affect immunogenicity testing. Adjusting minimum required dilution or using sample pretreatment can reduce or eliminate BI in antidrug antibody assays.

Keywords:

ELISA antidrug antibody biotin immunogenicity interference

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Area of Science:

Biotechnology
Analytical Chemistry
Pharmacology

Background:

One-step bridging assays are standard for immunogenicity testing.
Biotin interference (BI) is a known issue, stemming from supplements and medical therapies.
High biotin levels can compromise assay accuracy.

Purpose of the Study:

To investigate biotin interference (BI) in two distinct one-step antidrug antibody assays.
To evaluate the impact of BI on peptide-based versus antibody-based assay formats.
To identify strategies for mitigating BI in immunogenicity testing.

Main Methods:

Two one-step antidrug antibody assays were utilized.
Assays were challenged with varying biotin levels.
Minimum required dilution (MRD) was adjusted (10% to 1%).
Sample pretreatment involving depletion was tested.

Main Results:

Both assays exhibited biotin-related interference.
The peptide-based assay demonstrated less interference than the antibody-based assay.
Reducing MRD to 1% decreased BI.
Depletion-based sample pretreatment effectively eliminated BI.

Conclusions:

Elevated biotin levels pose a significant risk to immunogenicity testing employing biotin technology.
The degree of BI varies between assay formats, necessitating assay-specific evaluation.
Adjusting MRD and implementing sample pretreatment are viable methods to manage BI.