Computational and Pharmacogenomic Insights on Hypertension Treatment: Rational Drug Design and Optimization Strategies

Abstract

Background: Hypertension is a prevalent cardiovascular complication caused by genetic and nongenetic factors. Blood pressure (BP) management is difficult because most patients become resistant to monotherapy soon after treatment initiation. Although many antihypertensive drugs are available, some patients do not respond to multiple drugs. Identification of personalized antihypertensive treatments is a key for better BP management.

Objective: This review aimed to elucidate aspects of rational drug design and other methods to develop better hypertension management.

Results: Among hypertension-related signaling mechanisms, the renin-angiotensin-aldosterone system is the leading genetic target for hypertension treatment. Identifying a single drug that acts on multiple targets is an emerging strategy for hypertension treatment, and could be achieved by discovering new drug targets with less mutated and highly conserved regions. Extending pharmacogenomics research to include patients with hypertension receiving multiple antihypertensive drugs could help identify the genetic markers of hypertension. However, available evidence on the role of pharmacogenomics in hypertension is limited and primarily focused on candidate genes. Studies on hypertension pharmacogenomics aim to identify the genetic causes of response variations to antihypertensive drugs. Genetic association studies have identified single nucleotide polymorphisms affecting drug responses. To understand how genetic traits alter drug responses, computational screening of mutagenesis can be utilized to observe drug response variations at the protein level, which can help identify new inhibitors and drug targets to manage hypertension.

Conclusion: Rational drug design facilitates the discovery and design of potent inhibitors. However, further research and clinical validation are required before novel inhibitors can be clinically used as antihypertensive therapies.

Keywords: Computational mutagenesis; RAAS; SNPs; blood pressure; drug discovery; hypertension; pharmacogenomics..