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Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms

Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms

Published on: April 17, 2017

Evaluation of correlation between bioanalytical methods.

Carol R Gleason¹, Qin C Ji¹, Enaksha R Wickremsinhe²

¹Bristol-Myers Squibb Company, Princeton, NJ 08543, USA.

|April 15, 2020

Summary

This summary is machine-generated.

Comparing bioanalytical methods requires cross-validation. This study introduces graphing techniques to better assess method equivalence and identify trends, improving data comparability in clinical development.

Keywords:

Bland-Altman plots bridging studies concordance correlation coefficient correlative studies cross validations deming regression

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Area of Science:

Analytical Chemistry
Bioanalytical Science
Pharmaceutical Development

Background:

Bioanalytical methods frequently change during drug development.
Comparing data across studies necessitates establishing method equivalency or correlation.
Current cross-validation practices, like incurred sample reanalysis, may not reveal method biases.

Purpose of the Study:

To introduce effective graphing techniques for comparing bioanalytical methods.
To enhance the assessment of equivalence and correlation between different analytical methods.
To raise awareness of advanced graphing approaches for cross-validation studies.

Main Methods:

Review of standard cross-validation and correlative study practices.
Presentation of novel graphing methodologies for data visualization.
Application of graphical analysis to compare two bioanalytical methods.

Main Results:

Standard acceptance criteria may obscure trends and biases between methods.
Specific graphing approaches can visually represent method relationships and differences.
Graphical analysis provides deeper insights than simple statistical measures.

Conclusions:

Graphing techniques offer a superior method for evaluating bioanalytical method equivalence and correlation.
Adopting these techniques can improve the reliability of comparative data in clinical trials.
Awareness and implementation of these methods are crucial for robust bioanalytical science.