Mediated Electrochemical Probing and Machine Learning for Cysteine and Reduced Monoclonal Antibody Quantification

Area of Science:

• Bioprocess Engineering
• Analytical Chemistry
• Biotechnology

Background:

• Biopharmaceutical manufacturing demands precise analytics for high-quality yields.
• Rapid data acquisition from complex biological systems is crucial.
• Existing methods may lack speed and simplicity for real-time bioprocess monitoring.

Purpose of the Study:

• To develop a rapid, simple, and reliable method for interrogating biopharmaceutical production processes.
• To leverage electronic detection of biological redox signatures for at-line monitoring.
• To quantify critical quality attributes like free cysteine and reduced antibody fragments.

Main Methods:

• Utilized mediated electrochemical probing (MEP) to detect biological redox signatures.
• Optimized electrical measurements to decrease sample-to-answer time.
• Implemented a machine learning pipeline for data analysis and quantification.

Main Results:

• Successfully quantified free cysteine concentrations and reduced antibody fragment levels.
• Demonstrated a decrease in sample-to-answer time and increased detection reliability.
• Developed a transferable platform for electrochemical dataset generation and model optimization.

Conclusions:

• Mediated electrochemical probing (MEP) offers an innovative approach for bioprocess analytics.
• The developed machine learning pipeline enhances the reliability and speed of monitoring.
• This method provides opportunities for digital integration in biopharmaceutical manufacturing for real-time quality control.