Multi-objective optimization of tumor response to drug release from vasculature-bound nanoparticles

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Computational Biology

Background:

• Nanoparticle drug delivery efficacy relies heavily on design, but empirical testing is costly and time-consuming.
• Computational modeling and optimization offer a more efficient approach to designing nanoparticles for tumor targeting.

Purpose of the Study:

• To investigate the impact of tumor size heterogeneity on optimal nanoparticle design for drug delivery.
• To explore the combined effects of nanoparticle characteristics and drug diffusivity on tumor targeting and regression.
• To develop a framework for personalized nanomedicine by quantifying trade-offs in nanoparticle design.

Main Methods:

• Utilized computational modeling of nanoparticle-mediated drug delivery targeting tumor vasculature.
• Employed numerical optimization to determine optimal nanoparticle designs for various tumor sizes.
• Incorporated drug diffusivity into the optimization process and used bi-objective optimization to analyze trade-offs.

Main Results:

• Smaller nanoparticles demonstrated superior tumor targeting and lesion regression in larger tumors.
• Including drug diffusivity in optimization led to a two-fold decrease in tumor size compared to optimizing nanoparticles alone.
• Generated five Pareto-optimal nanoparticle designs illustrating the spectrum of tumor targeting versus antitumor effect.

Conclusions:

• Computational modeling and optimization are effective tools for designing nanoparticles tailored to specific tumor characteristics.
• Tumor size significantly influences optimal nanoparticle design, with smaller particles being more effective for larger lesions.
• The developed approach enables customized nanomedicine strategies by balancing therapeutic goals for improved cancer treatment outcomes.