Model-supported patient stratification using multi-objective synergy optimization in combination therapy.

Area of Science:

• Oncology
• Pharmacology
• Computational Biology

Background:

• Patient stratification for combination therapy is complex and clinically vital.
• Previous work established a multi-objective optimization framework for synergistic combination protocols.
• Inter-individual variability in drug sensitivity complicates dose optimization for efficacy and toxicity.

Purpose of the Study:

• To extend existing methodology for quantifying the impact of inter-individual variability on combination therapy dose optimization.
• To develop a voxel-based stratification approach for characterizing patient subgroups based on drug sensitivity.
• To apply and validate the methodology using preclinical data of combined immune checkpoint inhibitors and antiangiogenic agents.

Main Methods:

• Developed a multi-objective optimization framework to balance synergy of efficacy and potency.
• Introduced a voxel-based stratification approach to subgroup patients by drug sensitivity.
• Applied the method to preclinical murine data for immune checkpoint inhibitor and antiangiogenic agent combination therapy.
• Proposed an initiation protocol for parameter identification when biomarkers are unavailable.

Main Results:

• Quantified how optimal combination therapies differ across patient response subgroups.
• Demonstrated the ability to identify subpopulations lacking meaningful combination efficacy.
• Showcased the method's utility in a preclinical combination therapy case study.
• Validated the stratification approach for personalized treatment strategies.

Conclusions:

• The methodology enables personalized combination therapy by identifying optimal doses for specific patient subgroups.
• It facilitates finding the right patient subgroup for existing combination therapies.
• The approach addresses the challenge of determining effective combination treatments in the presence of inter-individual variability.