Mathematical modeling of antihypertensive therapy

Area of Science:

• Computational Biology
• Pharmacology
• Cardiovascular Medicine

Background:

• Hypertension is a complex disease with varied patient responses to antihypertensive medications.
• Personalized medicine requires accurate prediction of therapeutic efficacy based on individual patient characteristics.

Purpose of the Study:

• To extend a modular agent-based model of the cardiovascular and renal systems.
• To incorporate simulations of antihypertensive therapies with diverse mechanisms of action.

Main Methods:

• The study integrated pharmacodynamic effects of six antihypertensive drug classes: angiotensin II receptor blockers, calcium channel blockers, ACE inhibitors, direct renin inhibitors, thiazide diuretics, and beta-blockers.
• Model parameters were calibrated using data from clinical trials.
• The model's predictive accuracy was tested by simulating responses to monotherapies and dual combinations in virtual hypertensive patients.

Main Results:

• The extended agent-based model demonstrated reasonable dynamic responses to simulated antihypertensive treatments, aligning with clinical observations.
• The model successfully predicted patient responses to individual drugs and dual combinations.
• The simulation provides a foundation for predicting individual patient responses to antihypertensive therapy.

Conclusions:

• The developed computational model offers a powerful tool for predicting antihypertensive therapy efficacy.
• This approach supports the advancement of personalized medicine by tailoring treatment strategies to individual patient profiles.
• The model is available in BioUML software, facilitating further research and application.