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A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
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.
Abstract:
Systemic chemotherapy is one of the main anticancer treatments used for most kinds of clinically diagnosed tumors. However, the efficacy of these drugs can be hampered by the physical attributes of the tumor tissue, such as tortuous vasculature, dense and fibrous extracellular matrix, irregular cellular architecture, tumor metabolic gradients, and non-uniform expression of the cell membrane receptors. This can impede the transport of therapeutic agents to tumor cells in sufficient quantities. In addition, tumor microenvironments undergo dynamic spatio-temporal changes during tumor progression and treatment, which can also obstruct drug efficacy. To examine ways to improve drug delivery on a cell-to-tissue scale (single-cell pharmacology), we developed the microscale pharmacokinetics/pharmacodynamics (microPKPD) modeling framework. Our model is modular and can be adjusted to include only the mathematical equations that are crucial for a biological problem under consideration. This modularity makes the model applicable to a broad range of pharmacological cases. As an illustration, we present two specific applications of the microPKPD methodology that help to identify optimal drug properties. The hypoxia-activated drugs example uses continuous drug concentrations, diffusive-advective transport through the tumor interstitium, and passive transmembrane drug uptake. The targeted therapy example represents drug molecules as discrete particles that move by diffusion and actively bind to cell receptors. The proposed modeling approach takes into account the explicit tumor tissue morphology, its metabolic landscape and/or specific receptor distribution. All these tumor attributes can be assessed from patients' diagnostic biopsies; thus, the proposed methodology can be developed into a tool suitable for personalized medicine, such as neoadjuvant chemotherapy.
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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