Micropharmacology: An In Silico Approach for Assessing Drug Efficacy Within a Tumor Tissue

Aleksandra Karolak1, Katarzyna A Rejniak2,3

  • 1Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.

Insights

This study introduces a microscale pharmacokinetics/pharmacodynamics (microPKPD) model to enhance anticancer drug delivery. The framework optimizes drug properties by considering tumor tissue characteristics for personalized medicine.

Area of Science:

  • Pharmacology
  • Biomedical Engineering
  • Computational Biology

Background:

  • Systemic chemotherapy faces challenges due to tumor physical attributes like dense extracellular matrix and irregular vasculature, hindering drug transport.
  • Tumor microenvironments change dynamically, further complicating drug delivery and reducing efficacy.
  • Understanding drug behavior at the single-cell level is crucial for improving therapeutic outcomes.

Purpose of the Study:

  • To develop a modular microscale pharmacokinetics/pharmacodynamics (microPKPD) modeling framework to improve anticancer drug delivery.
  • To create a tool for optimizing drug properties by accounting for tumor tissue morphology and microenvironment.
  • To advance personalized medicine approaches, such as neoadjuvant chemotherapy.

Main Methods:

  • Developed a modular microPKPD modeling framework adaptable to specific biological problems.
  • Applied the model to two case studies: hypoxia-activated drugs and targeted therapy.
  • Incorporated tumor tissue morphology, metabolic gradients, and receptor distribution into the modeling.

Main Results:

  • Demonstrated the model's ability to identify optimal drug properties for enhanced delivery.
  • Showcased applications for continuous drug concentrations with diffusive-advective transport and discrete particle modeling for targeted therapies.
  • Validated the model's capacity to integrate patient-specific biopsy data.

Conclusions:

  • The microPKPD framework offers a versatile approach to overcome drug delivery barriers in solid tumors.
  • The methodology can be tailored for personalized medicine, predicting drug efficacy based on individual tumor characteristics.
  • This computational tool has the potential to guide the development of more effective cancer therapies.

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