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Rapid Estimation of Size-Based Heterogeneity in Monoclonal Antibodies by Machine Learning-Enhanced Dynamic Light
Anuj Shrivastava1, Shyamapada Mandal1, Sudip K Pattanayek1
1Department of Chemical Engineering, IIT Delhi, Hauz Khas, New Delhi 110016, India.
Analytical Chemistry
|May 18, 2023
Summary
A new method uses dynamic light scattering and machine learning to quickly quantify monoclonal antibody (mAb) aggregates. This approach accurately measures therapeutic protein multimers, improving drug safety and efficacy assessments.
Area of Science:
- Biopharmaceutical analysis
- Protein aggregation studies
- Analytical chemistry
Background:
- Monoclonal antibody (mAb) aggregation impacts therapeutic safety and efficacy.
- Current methods for aggregate assessment are time-consuming.
- Rapid, accurate quantification of mAb multimers is needed.
Purpose of the Study:
- To develop a novel dynamic light scattering (DLS) based approach for quantifying mAb aggregates.
- To utilize machine learning (ML) for predicting the percentage of monomer, dimer, trimer, and tetramer species.
- To establish a rapid and user-friendly method for aggregate assessment.
Main Methods:
- A novel DLS-based approach combined with a machine learning (ML) algorithm and regression.
- Modeling the system to predict the amount of relevant species (monomer, dimer, trimer, tetramer) in the 10-100 nm size range.
- Utilizing time-dependent fluctuations in scattered light intensity due to Brownian motion.
Main Results:
- The DLS-ML technique accurately quantifies the relative percentage of mAb multimers.
- Achieved rapid aggregate prediction in under 2 minutes.
- Demonstrated favorable method attributes including low cost, minimal sample requirement (<3 μg), and user-friendliness.
Conclusions:
- The proposed DLS-ML technique offers a rapid and accurate method for quantifying mAb aggregates.
- This approach can serve as an orthogonal tool to size exclusion chromatography.
- The method enhances the assessment of therapeutic protein safety and efficacy.
