Drug discovery and development is a high-risk enterprise that requires significant investments in capital, time and scientific
expertise. The studies of xenobiotic metabolism remain as one of the main topics in the research and development of drugs, cosmetics
and nutritional supplements.
Antihypertensive drugs are used for the treatment of high blood pressure, which is one the most frequent symptoms of the patients that
undergo cardiovascular diseases such as myocardial infraction and strokes. In current cardiovascular disease pharmacology, four drug
clusters - Angiotensin Converting Enzyme Inhibitors, Beta-Blockers, Calcium Channel Blockers and Diuretics - cover the major therapeutic
characteristics of the most antihypertensive drugs. The pharmacokinetic and specifically the metabolic profile of the antihypertensive
agents are intensively studied because of the broad inter-individual variability on plasma concentrations and the diversity on the efficacy
response especially due to the P450 dependent metabolic status they present.
Several computational methods have been developed with the aim to: (i) model and better understand the human drug metabolism; and
(ii) enhance the experimental investigation of the metabolism of small xenobiotic molecules. The main predictive tools these methods
employ are rule-based approaches, quantitative structure metabolism/activity relationships and docking approaches.
This review paper provides detailed metabolic profiles of the major clusters of antihypertensive agents, including their metabolites and
their metabolizing enzymes, and it also provides specific information concerning the computational approaches that have been used to
predict the metabolic profile of several antihypertensive drugs.