In silico prediction of ARB resistance: A first step in creating personalized ARB therapy

Abstract

Angiotensin II type 1 receptor (AT1R) blockers (ARBs) are among the most prescribed drugs. However, ARB effectiveness varies widely, and some patients do not respond to ARB therapy. One reason for the variability between patients is non-synonymous single nucleotide polymorphisms (nsSNPs) within agtr1, the AT1R gene. There are over 100 nsSNPs in the AT1R; therefore, this study embarked on determining which nsSNPs may abrogate the binding of selective ARBs. The crystal structure of olmesartan-bound human AT1R (PDB:4ZUD) served as a template to create an inactive empty AT1R via molecular dynamics simulation (n = 3). All simulations resulted in a smaller ligand-binding pocket than 4ZUD due to the absence of olmesartan in the simulation yet remained inactive with little movement in the receptor core. A single frame representing the average stable AT1R was used as a template to thrice (n = 3) dock each ARB via AutoDock to obtain a predicted affinity from the weighted average of 100 docking simulations. The results were far from known values; thus, an optimization protocol was initiated, resulting in the predicted binding affinities within experimentally determined ranges (n = 6). The empty model AT1R was altered and minimized in Molecular Operating Environment software to represent 103 of the known human AT1R polymorphisms. Each of the eight ARBs was then docked, using the optimized parameters, to each polymorphic AT1R (n = 6). Although each nsSNP has little effect on global AT1R structure, most nsSNPs drastically alter a sub-set of ARBs affinity to the AT1R. Comparisons to previous binding studies suggest that the results have a 60% chance of predicting ARB resistance. Although more biochemical studies and refinement of the model are required to increase the accuracy of the prediction of ARB resistance, personalized ARB therapy based on agtr1 sequence could increase overall ARB effectiveness. Author SummaryThe term "personalized medicine" was coined at the turn of the century, but most medicines are currently prescribed based on disease categories and occasionally racial demographics, but not personalized attributes. In cardiovascular medicine, the personalization of medication is minimal; however, it is accepted that not all patients respond equally to common cardiovascular medications. Here we chose one prominent cardiovascular drug target, the angiotensin receptor, and, using computer modeling, created preliminary models of over 100 known alterations to the angiotensin receptor to determine if the alterations changed the ability of clinically used drugs to interact with the angiotensin receptor. The strength of interaction was compared to the unaltered angiotensin receptor, generating a map predicting which alteration affected each drug. It is expected that in the future, a patients receptors can be sequenced, and maps, such as the one presented here, can be used to select the optimum medication based on the patients genetics. Such a process would allow for the personalization of current medication therapy.