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Combination therapy design for maximizing sensitivity and minimizing toxicity
Kevin Matlock1, Noah Berlow2, Charles Keller2
1Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA.
Background:
Design of personalized targeted therapies involve modeling of patient sensitivity to various drugs and drug combinations. Majority of studies evaluate the sensitivity of tumor cells to targeted drugs without modeling the effect of the drugs on normal cells. In this article, we consider the individual modeling of drug responses to tumor and normal cells and utilize them to design targeted combination therapies that maximize sensitivity over tumor cells and minimize toxicity over normal cells.
Results:
The problem is formulated as maximizing sensitivity over tumor cell models while maintaining sensitivity below a threshold over normal cell models. We utilize the constrained structure of tumor proliferation models to design an accelerated lexicographic search algorithm for generating the optimal solution. For comparison purposes, we also designed two suboptimal search algorithms based on evolutionary algorithms and hill-climbing based techniques. Results over synthetic models and models generated from Genomics of Drug Sensitivity in Cancer database shows the ability of the proposed algorithms to arrive at optimal or close to optimal solutions in significantly lower number of steps as compared to exhaustive search. We also present the theoretical analysis of the expected number of comparisons required for the proposed Lexicographic search that compare favorably with the observed number of computations.
Conclusions:
The proposed algorithms provide a framework for design of combination therapy that tackles tumor heterogeneity while satisfying toxicity constraints.
Insights
This study introduces a new computational framework for designing personalized combination therapies. It optimizes drug effectiveness against tumor cells while minimizing toxicity to normal cells, addressing tumor heterogeneity.
Area of Science:
- Computational Biology
- Pharmacology
- Bioinformatics
Background:
- Personalized targeted therapies require modeling patient drug sensitivity.
- Current approaches often neglect drug effects on normal cells, focusing solely on tumor cells.
Purpose of the Study:
- To develop a method for designing combination therapies that individually model drug responses in tumor and normal cells.
- To maximize tumor cell sensitivity while minimizing normal cell toxicity.
Main Methods:
- Formulated the problem as optimizing tumor cell sensitivity under a toxicity constraint for normal cells.
- Developed an accelerated lexicographic search algorithm for optimal solution generation.
- Compared performance against evolutionary and hill-climbing algorithms.
Main Results:
- The proposed lexicographic search algorithm efficiently finds optimal or near-optimal drug combinations.
- Achieved significantly fewer computational steps compared to exhaustive search methods.
- Validated performance on synthetic and real-world cancer genomics data.
Conclusions:
- The developed algorithms offer a robust framework for designing combination therapies.
- Effectively addresses tumor heterogeneity and adheres to critical toxicity constraints.