A mechanistic tumor penetration model to guide antibody drug conjugate design

Area of Science:

• Pharmacology and Pharmaceutical Sciences
• Computational Biology and Bioinformatics
• Oncology

Background:

• Antibody drug conjugates (ADCs) are advanced anti-cancer agents designed for targeted tumor cell killing.
• Challenges in ADC therapy include heterogeneous tumor distribution due to large size and complex pharmacokinetics, leading to resistant regions.
• Understanding tumor properties and ADC kinetics is crucial for effective therapeutic outcomes.

Purpose of the Study:

• To develop a modeling framework simulating ADC distribution, binding, and payload release within tumors.
• To investigate the impact of tumor properties and receptor dynamics on ADC efficacy and tumor shrinkage.
• To explore ADC design optimization strategies to enhance therapeutic outcomes.

Main Methods:

• Developed a computational model incorporating systemic distribution, vascular permeability, interstitial transport, and ADC-target interactions.
• Simulated receptor dynamics, including endocytic trafficking, to assess their influence on tumor regression.
• Analyzed the effects of payload release kinetics and bystander killing mechanisms on treatment response.

Main Results:

• Identified intrinsic tumor properties that negatively affect ADC tissue homogeneity and therapeutic success.
• Demonstrated that ADC design optimization can counteract unfavorable tumor attributes.
• Highlighted the significant role of payload release and bystander effects in achieving tumor shrinkage.

Conclusions:

• The developed modeling platform provides a customizable environment for interpreting experimental ADC data.
• This framework can guide the rational design of novel ADC therapeutics and treatment strategies.
• Optimizing ADC design and understanding tumor-specific factors are key to improving anti-cancer efficacy.