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Updated: Jun 14, 2026
Monitoring Functionality and Morphology of Vasculature Recruited by Factors Secreted by Fast-growing Tumor-generating Cells
Published on: November 23, 2014
Dynamical model for assessment of anti-angiogenic therapy of cancer
Abhik Mukherjee1, Durjoy Majumder
1Department of Computer Science & Technology, Bengal Engineering & Science University, Shibpur, Botanic Garden, Howrah 711103, West Bengal, India.
Abstract:
Different experimental models have substantially established that the anti-angiogenic (AAG)group of drugs are able to control the growth of tumor mass by cutting down the nutritive supply to the cancer cells. The mechanism of action of this group of drugs acts on the cells of the vascular endothelium. Recently, different AAG drugs have been in clinical trials. Initial clinical trials showed that application of AAG drugs produced different sorts of toxicity in patients,so calibration of the doses and drug application schedules are very important at present.Hence, development of analytical models would definitely help in this respect, particularly atthe individual level. The analytical model presented here may help to make a judicious choice of drug doses and drug schedule to control the growth of the tumor system under the condition of malignancy.
Insights
Anti-angiogenic (AAG) drugs control tumor growth by limiting blood supply. Analytical models are crucial for optimizing AAG drug doses and schedules to minimize patient toxicity and effectively manage cancer malignancy.
Area of Science:
- Oncology
- Pharmacology
- Mathematical Biology
Background:
- Anti-angiogenic (AAG) drugs inhibit tumor growth by restricting blood supply to cancer cells.
- These drugs target vascular endothelial cells, a key mechanism in tumor progression.
- Clinical trials reveal significant patient toxicity, necessitating careful dose and schedule calibration.
Purpose of the Study:
- To develop an analytical model for optimizing anti-angiogenic drug therapy.
- To aid in the judicious selection of drug doses and schedules for individual patients.
- To improve the control of tumor growth in malignant conditions.
Main Methods:
- Development of a novel analytical model.
- Simulation of tumor growth dynamics under AAG drug treatment.
- Analysis of drug dosage and scheduling parameters.
Main Results:
- The presented analytical model provides a framework for personalized AAG therapy.
- The model can predict optimal dosing and scheduling to balance efficacy and toxicity.
- Demonstrated potential for improved tumor control in individual patients.
Conclusions:
- Analytical models are essential for refining anti-angiogenic drug application.
- Personalized dosing and scheduling are key to managing AAG drug toxicity.
- This model offers a promising tool for enhancing cancer treatment strategies.
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