Computational approaches to understanding ARB resistance: a route to personalized ARB therapy

Abstract

The binding of Angiotensin II to the Angiotensin II type 1 receptor (AT1Rs) plays a pivotal role in the regulation of blood pressure and other physiological processes. AT1R blockers (ARBs) are commonly prescribed anti‐hypertensive drugs; however, not all patients respond equally to ARBs and some patients are ARB resistant. One documented reason behind these clinical observations is non‐synonymous polymorphisms within the AT1R that alter ARB affinity. There are 103 known polymorphisms within the crystallized AT1R structure that we utilized for computational approaches to predict which polymorphisms contribute to ARB resistance. To accomplish this, a 150 ns molecular dynamic (MD) simulation was performed to create a stable‐empty AT1R in an 87% POPC and 13% cholesterol membrane. Each polymorphism was made in the MD generated AT1R via MOE and minimized prior to docking performed with autodock4. Each ARB, including the active metabolite of Losartan, was docked using an experimentally derived grid that produced affinities in the wild‐type AT1R comparable to published reports. In total, 93,600 drug‐receptor complexes were analyzed and the data indicates that polymorphisms fall into two general categories: ARB specific resistance and total ARB resistance. Polymorphic AT1Rs displaying ARB specific resistance display wild‐type affinities for a subset of ARBs and in some cases, such as C101Y, G196V, and F313C, not the common ARBs valsartan and losartan; whereas, total ARB resistance, such as W84C, V108I, H132Y, and C289W, indicates that all ARBs are likely ineffective. In conclusion, patients that appear ARB resistant should have their AT1R sequenced and the sequence could be used to determine which ARB may be effective or suggest discontinuing ARB therapy.Support or Funding InformationThis project is funded by internal WesternU funds.