Streamlined High-Throughput Data Analysis Workflow for Antibody-Drug Conjugate Biotransformation Characterization.

Area of Science:

• Bioconjugate Chemistry
• Analytical Chemistry
• Drug Development

Background:

• Antibody-drug conjugates (ADCs) are critical in modern therapeutics, with in vivo biotransformation analysis essential for drug development.
• Current manual analysis of ADC biotransformation is time-consuming, delaying lead selection and drug discovery.
• Streamlining this process is vital for efficient therapeutic development.

Purpose of the Study:

• To develop and validate a streamlined, automated data analysis workflow for antibody-drug conjugate (ADC) biotransformation.
• To significantly improve the efficiency of identifying biotransformed ADC species in vivo.
• To accelerate the drug development cycle through faster analytical insights.

Main Methods:

• Created a linker-payload biotransformation library for new molecules.
• Integrated antibody sequence information for automated peak matching.
• Applied the workflow to ADCs with different payloads and linkers using various mass spectrometers.

Main Results:

• Rapidly identified major biotransformation species, including linker-payload loss and hydrolysis.
• Achieved highly comparable quantification results to manual methods.
• Demonstrated workflow effectiveness across different ADC types and mass spectrometry platforms.

Conclusions:

• The automated workflow significantly enhances the speed and efficiency of ADC biotransformation identification.
• This advancement enables faster design-test-analyze cycles, crucial for early drug discovery.
• Facilitates improved collaboration between analytical chemists and bioconjugate engineers in biotherapeutic development.