Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains

AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine–based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Antibody Therapeutics Oxford University Press

AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains

10 pages

Loading next page...
 
/lp/oxford-university-press/ab-amy-machine-learning-aided-amyloidogenic-risk-prediction-of-ZgSSb0VY0v

References (52)

Publisher
Oxford University Press
Copyright
© The Author(s) 2023. Published by Oxford University Press on behalf of Antibody Therapeutics. All rights reserved. For Permissions, please email: journals.permissions@oup.com
eISSN
2516-4236
DOI
10.1093/abt/tbad007
Publisher site
See Article on Publisher Site

Abstract

Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine–based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/.

Journal

Antibody TherapeuticsOxford University Press

Published: Apr 12, 2023

Keywords: therapeutic antibody; amyloidosis; developability; support vector machine (SVM); prediction

There are no references for this article.