Why the study?
Does in silico modeling of antihypertensive drugs accurately predict blood pressure reduction in virtual patients with hypertension?
Does in silico modeling of antihypertensive drugs accurately predict blood pressure reduction in virtual patients with hypertension?
A modular mathematical model of the human cardiovascular system successfully predicted the blood pressure-lowering effects of five classes of antihypertensive drugs in a virtual patient population.
Provides proof-of-concept for in silico antihypertensive prediction; leaves open prospective human validation before clinical use.
Arterial hypertension is one of the most common diseases of the human cardiovascular system and is characterized by persistent increase in blood pressure. Normalization of blood pressure can be achieved by using antihypertensive drugs with various mechanisms of action. In this work, we investigated a modular mathematical model of the human cardiovascular system created earlier, and complemented it with pharmacodynamic models of five different classes of antihypertensive drugs with such exemplars as aliskiren, losartan, bisoprolol, enalapril and amlodipine. We used clinical trials found in the literature in order to validate the resulting model. Specifically, we generated a population of virtual patients with high blood pressure and modeled their treatment with these antihypertensive drugs. Eventually, the model predicted blood pressure decrease in good agreement with clinical trials. In this way, our model can be further used to optimize the choice of drugs for a particular patient. In silico
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Kiselev et al. (2019) studied this question.
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