Kinetic reaction modeling for antibody-drug conjugate process development

Area of Science:

• Bioconjugation Chemistry
• Pharmaceutical Process Development
• Computational Modeling

Background:

• Antibody-drug conjugates (ADCs) are a vital class of anti-cancer therapeutics, combining monoclonal antibodies (mAbs) with cytotoxic drugs.
• The conjugation reaction is a critical step in ADC manufacturing, requiring optimization to balance efficacy and minimize toxic drug excess.
• Enhanced process understanding through kinetic modeling can lead to improved ADC production efficiency.

Purpose of the Study:

• To develop and validate a kinetic model for the conjugation reaction of cysteine-engineered mAbs with maleimide-functionalized drugs.
• To utilize the model for improved process understanding and in silico optimization of ADC production.
• To demonstrate the integration of kinetic modeling with Process Analytical Technology (PAT) for reaction monitoring.

Main Methods:

• Development of six differential equation-based kinetic model structures for mAb-drug conjugation.
• Fitting models to experimental data and selecting the best model using cross-validation.
• Validation of the selected model with an external dataset and application to in silico screening.

Main Results:

• The best kinetic model achieved high predictive accuracy (R² = 0.978) on an external validation dataset.
• The model revealed that the binding of the second drug molecule is influenced by the first drug's attachment.
• Increased reaction rates were observed with the addition of specific salts, and in silico optimization demonstrated potential for process efficiency.

Conclusions:

• The developed kinetic model provides significant insights into ADC conjugation mechanisms.
• This modeling approach offers valuable in silico decision support for optimizing ADC manufacturing processes.
• Integration with PAT tools can further enhance real-time monitoring and control of ADC production.