Drug-loaded nanoparticles for cancer therapy: A high-throughput multicellular agent-based modeling study

Area of Science:

• Biomedical Engineering
• Computational Biology
• Nanomedicine

Background:

• Engineered nanomaterials offer promising applications in medicine, particularly for cancer diagnosis and treatment.
• The complex biology of cancer across multiple scales presents challenges for nanotherapy effectiveness.
• Existing models often overlook individual cell-nanoparticle interactions, limiting optimization.

Purpose of the Study:

• To develop a multicellular agent-based model for simulating cancer nanotherapy.
• To explore the impact of nanoparticle (NP) parameters and therapeutic strategies on tumor growth.
• To investigate the role of NP inheritance during cell division in treatment efficacy.

Main Methods:

• Developed a multicellular agent-based model simulating NP internalization, drug release, and cell division.
• Utilized a large-scale parallel computational framework for simulations.
• Investigated the influence of pharmacokinetic parameters (e.g., internalization, decay, release rates) and therapeutic strategies (e.g., dose, frequency).

Main Results:

• NP inheritance at cell division can improve outcomes for cytotoxic chemotherapy.
• Smaller doses of cytostatic chemotherapy may enhance tumor growth inhibition when cell division is not fully suppressed.
• Heritable NPs enable sustained therapeutic responses, suggesting new nanotherapy design dimensions.

Conclusions:

• Simulations highlight the importance of NP inheritance in enhancing cancer nanotherapy.
• Heritable nanoparticles offer a novel approach for sustained and effective drug delivery.
• This model provides insights for optimizing nanotherapy design for improved patient outcomes.