Sage Journals: Discover world-class research

Abstract

The authors describe a structure-based strategy to identify therapeutically beneficial off-target effects by screening a chemical library of Food and Drug Administration (FDA)–approved small-molecule drugs matching pharmacophores defined for specific target proteins. They applied this strategy to angiotensin-converting enzyme 2 (ACE2), an enzyme that generates vasodilatory peptides and promotes protection from hypertension-associated cardiovascular disease. The conformation-based structural selection method by molecular docking using DOCK allowed them to identify a series of FDA-approved drugs that enhance catalytic efficiency of ACE2 in vitro. These data demonstrate that libraries of approved drugs can be rapidly screened to identify potential side effects due to interactions with specific proteins other than the intended targets.

Keywords

computational chemistry compound repositories chemoinformatics general pharmaceutical process cardiac diseases

Introduction

There is a well-established paradigm for translation of small molecules into therapeutic agents.^1–3 This process consists of identification of lead compounds that are elaborated until a sufficient level of efficacy and safety is established for human clinical trials.^4–6 Studies show that clinical trials for treatment of chronic conditions such as hypertension typically require 6 to 7 years to obtain statistically significant data regarding the safety of new drugs.^7–10

We previously used in silico docking to identify the compounds capable of enhancing catalytic activity of Angiotensin-Converting enzyme 2 (ACE2) to lower blood pressure and prevent cardiovascular disease.^11,12 We used DOCK5.1 (University of California, San Francisco [UCSF]) to explore a structural feature in the hinge region of ACE2 (site 1, Fig. 1 ) that is implicated in conformational shuffling between the two isoforms of the enzyme.¹³ A small molecule library of 139,735 compounds (molecular weight less than 500 Da) from National Cancer Institute (NCI) Developmental Therapeutics program plated set collection was docked into the selected site 1 and scored using grid-based scoring system. Compounds were ranked according to their combined energy scores for hydrogen bonding and van der Waals contact interactions. The highest scoring compounds were obtained and assayed in vitro for their abilities to enhance ACE2 catalytic activity. One of the compounds, [8-(2-dimethylaminoethylamino)-5-(hydroxymethyl)-9-oxoxanthen-2-yl]4-methyl-benzenesulfonate (from here on referred to as XNT) was shown to enhance the rate of catalysis by increasing the velocity of the enzyme approximately twofold (13). In vivo administration of XNT in the spontaneous hypertensive rat model induced a significant (71 ± 9 mm Hg) decrease in high blood pressure and resulted in improvements in cardiac function, including reversal of myocardial, perivascular, and renal fibrosis.¹⁴

Fig. 1.

Angiotensin-converting enzyme 2 (ACE2) site 1 used as a structural basis for molecular docking of Food and Drug Administration–approved compounds. Site 1, located in the hinge region separating the two subdomains of the enzyme, is characterized by high solvent accessibility and flexibility. Molecular surface and secondary structure of ACE2 in the open conformation (PDB ID: 1R42) are colored by accessible surface area (ASA): red for the highest and blue the lowest ASA. Yellow spheres represent the site selected for in silico screening; 10 highest scoring small molecules are shown in their predicted binding positions and represented as sticks. Inset shows zoomed-in view of the site selected for molecular docking.

Because the molecular docking selection strategy was useful in identifying compounds that enhance ACE2 catalytic activity and lower blood pressure in vivo, in this study we applied this selection strategy to a chemical library of 1217 Food and Drug Administration (FDA)–approved compounds with extensive information on bioavailability, toxicity, and safety (Distributed Structure-Searchable Toxicity Database Network, www.epa.gov/ncct/dsstox). Here we report identification of specific FDA-approved compounds that enhance the catalysis of ACE2. This rapid and economical molecular docking approach can be applied to other clinically relevant proteins to explore their structural features and identify off-target effects of the small molecule compounds.

Materials and Methods

Molecular docking

We used the crystal structure of the apo form of human ACE2 in the open conformation (PDB ID: 1R42) to provide the basis for molecular docking.¹⁵ To prepare the site for docking, all water molecules have been removed. Protonation of ACE2 residues was done with SYBYL.¹⁶ Intermolecular assisted model building and energy refinement (AMBER) energy scoring (van der Waals + coulombic), contact scoring, and bump filtering were done in DOCK5.1.0.^17,18 Atomic coordinates for 1,217 FDA-approved compounds were obtained from the ZINC structural database (http://zinc.docking.org). Each of the molecules was positioned into the selected site (Site 1) in 1000 different orientations and scored with rank based on the predicted polar (H-bonding) and nonpolar (van der Waals) interactions. Compounds were selected to include protonation variants at medium pH (5.75–8.25). The scoring grid was set at 5 Å around the spheres selected for molecular docking. Molecular surface of the structure was explored using sets of spheres to describe potential binding pockets. The sites selected for molecular docking were defined using the SPHGEN program and filtered through the CLUSTER program.¹⁸ The SPHGEN program generates sets of overlapping spheres to describe the shape of the selected site. The CLUSTER program groups the selected spheres to define the points that are used by DOCK to match (superimpose) potential ligand atoms with spheres. Intermolecular AMBER energy scoring (van der Waals + columbic), contact scoring, and bump filtering were implemented in DOCK v5.1.0. UCSF Chimera software package²⁰ was used to generate molecular graphic images. The UF High-Performance Computing Center linux cluster was used to run docking jobs by parallel processing. The 38 highest scoring compounds (based on the combined contact and electrostatic score) were obtained from NCI Developmental Therapeutics program for testing in ACE2 catalytic assays.

ACE2 enzyme kinetic assays

Effects of 40 top-scoring compounds selected in virtual screening were tested in fluorescence-based kinetic assays using recombinant human enzyme and fluorogenic peptide substrate. Kinetic parameters for ACE2 in the presence of selected compounds were determined under steady-state conditions in the presence of saturating amounts of the substrate. Enzyme concentration was adjusted to ensure that <15% of the substrate was consumed at the lowest substrate concentration and product formation was linear with time. ACE2 assays for catalytic activity were carried out in a total volume of 100 µL, containing 75 mM Tris-HCl (pH 7.4), 0.1M NaCl, 0.5 µM ZnCl₂, 10 nM ACE2, and 0.01% Triton-X. Small-molecule stocks were prepared by dissolving the compounds in DMSO to a concentration of 50 to 100 mM; a final concentration of 50 µM was used in all screening experiments. Compounds were preincubated in black 96-well plates with 10 nM human recombinant ACE2 (Enzo Life Sciences, Plymouth Meeting, PA) for 15 min at 37 °C. Reactions were initiated by addition of 25 to 250 µM fluorogenic peptide substrate Mca-APK(Dnp)-OH (AnaSpec, Fremont, CA) and monitored continuously for 30 min with Spectra Max Gemini M5 Fluorescence Reader from Molecular Devices (Sunnyvale, CA; λ_excitation = 325 nm, λ_emission = 395nm). Initial velocities of ACE2 in the absence and in presence of 50-µM compounds were determined by measuring an increase in fluorescence upon hydrolysis of the substrate. Nonlinear regression analysis and fitting of the data into Michaelis– $V_{o} = \frac{V_{\max} \cdot [S]}{K_{m} + [S]}$ Menten were done with SigmaPlot software(Systat Software, San Jose, CA). Turnover numbers (k_cat) were calculated from the equation $k_{cat} = \frac{V_{max}}{[E]}$ . EC₅₀ values for diminazene (DMZ) were determined by fitting initial velocity data for ACE2 in the presence of varying concentrations of DMZ and 50 µM Mca-APK(Dnp)-OH into a nonlinear regression model for the four-parameter logistic Hill equation $E_{o} + \frac{E_{max} \cdot C^{α}}{E C_{50}^{α} + C^{α}}$ , where E corresponds to effect (initial velocity) at a given substrate concentration), E_max is the maximal velocity measured, C is the drug concentration, and α is the Hill coefficient of sigmoidicity.

Inner filter effect

Inherent small-molecule fluorescence may alter apparent initial velocities, k_cat and K_m, as a result of inner filter effect (IFE). IFE is a reduction in fluorescence signal due to absorption of the exciting light and/or emitted radiation in the presence of a second fluorophore. As a result, only a fraction of the photons reach the fluorophores and get picked up by the detection system. For this reason, we corrected observed fluorescence (F_obs) for IFE using the following equation: $F_{observed} = \frac{F_{corrected}}{IFE},$ , where $FE = f_{ex} \cdot f_{em} = 10^{(A_{ex} + A_{em}) /}$ , as described in Palmier and Van Doren.²¹

High-performance liquid chromatography analysis of Ang II cleavage

ACE2 activity assays were carried out in a total volume of 100 µL, containing 100 mM Tris-HCl (pH 7.4), 0.1 M NaCl, and 0.5 µM ZnCl₂; 10 nM ACE2 was preincubated for 5 min with 50 µM DMZ, and the reaction was initiated by addition of 50 µM substrate. Reactions were monitored for the first 15 min at λ_ex = 328 λ_em = 392 and stopped after 2 h at 37 °C by adding 10 µL of 0.5 M EDTA, followed by heating to 100 °C for 2 min. Samples were centrifuged at 11,600 x g for 10 min, supernatants were collected and concentrated under vacuum. The resulting residue was resuspended in 50 µL of the mobile phase B and applied to a C18 reverse-phase high-performance liquid chromatography (HPLC) column (column: Bondclone 10 µm, 150 × 2.1 mm [Phenomenex, Torrance, CA]; HPLC system: Agilent 1100 series [Agilent Technologies, Santa Clara, CA]) with a UV detector set at 214 nm. A linear solvent gradient of 11% to 100% B over 15 min with 10 min at final conditions and 10 min reequilibration was used. Mobile phase A consisted of 0.08% (v/v) phosphoric acid, and mobile phase B consisted of 40% (v/v) acetonitrile in 0.08% (v/v) phosphoric acid. The peaks corresponding to full-length peptides and peptide hydrolysis products were compared with the standards, and peak areas were integrated to calculate the extent of hydrolysis. Peak identities were determined by matrix-assisted laser desorption/ionization–time of flight (MALDI-TOF) spectrometry using Applied Biosystems Voyager System 6031 (Applied Biosystems, Foster City, CA). Ang (1–7) resolved with a characteristic peak at m/z = 899.65, Ang II at m/z = 1046.52.²²

Statistical significance

Data are expressed as mean ± SD. Unpaired Student’s t test and one-way analysis of variance (ANOVA) were performed for statistical analysis. Differences were considered significant at p < 0.01 or p < 0.001, as indicated. All statistical analysis and curve fitting were performed with SigmaPlot 11.0 from Systat Software.

Results

Molecular docking of FDA approved drugs

In the previous study, we used molecular docking to target three structural features on the solvent-accessible surface of ACE2 and used in vitro assays to evaluate the effect of selected small molecules on enhancing catalytic activity.¹³ Small molecules targeted at the ACE2 hinge region, (Site 1, Figure 1 ) produced the strongest effect on enzyme activity, increasing initial velocities by ~1.8- to 2.2-fold of control levels. In this study, we used the atomic coordinates and a scoring grid of Site 1 to dock a library of 1217 FDA-approved compounds.²³ The coordinates of the 1217 compounds were docked into the hinge Site 1 (DOCK5.1, UCSF), and ranked by grid-based energy scores (van der Waals + electrostatic). Figure 1 illustrates the binding pocket selected for high-throughput molecular docking and the positions of high-scoring small-molecule compounds. The functional effects of the top-scoring compounds were evaluated by measuring their effects on ACE2 maximal velocity and K_m.

Effects of FDA-approved drugs on ACE2 catalytic activity

The highest scoring 38 compounds were tested under saturating substrate conditions for their ability to modulate ACE2 enzyme activity in vitro. Figure 2 demonstrates docking scores and initial velocities determined in the screening of 27 top-scoring compounds in the presence of 50 µM substrate. Ten compounds producing the highest initial velocities for ACE2 were selected for further evaluation. Their docking scores and kinetic parameters are reported in Table 1 ; chemical structures are shown in Table 2 (see also Table 3 ). The previously described compound XNT that increases ACE2 activity was used as a reference. Figure 3 demonstrates kinetic curves for ACE2 in the presence of selected compounds and 25 to 125 µM substrate.

Fig. 2.

Docking scores and measured initial velocities for the highest scoring 27 compounds. Angiotensin-converting enzyme 2 (ACE2) experimental initial velocities were determined in the presence of 38 top-scoring Food and Drug Administration–approved compounds (50 µM) and compared to those in the absence of the drugs. Compounds are shown ranked by combined energy score (gray bars). Dark blue bars demonstrate change in initial velocity for the first 27 compounds, $% V_{o} = \frac{V_{o} drug}{V_{o} no drug} \cdot 100 %$ where and 100% represents no change. Nine compounds with the highest initial velocities identified in this screening were selected for further testing.

Fig. 3.

Food and Drug Administration–approved drugs enhance angiotensin-converting enzyme 2 (ACE2) catalytic activity. ACE2 kinetic parameters were determined for 10 compounds that demonstrated highest initial velocities. Kinetic constants for ACE2 in the presence of the 50-µM compounds and 25 to 125 µM substrate were determined by nonlinear regression fit of experimental data into Michaelis-Menten equation $V_{o} = \frac{V_{max} \cdot [S]}{K_{m} + [S]}$

Table 1.

Kinetic Parameters of ACE2 in the Presence of Select Compounds

Name	Rank (Library)	Score ΔG° (kcal/ mol)	K_m (µM)	V_max (RFU·s⁻¹)	V_max/K_m (RFU M⁻¹ s⁻¹·10⁴)
ACE2 at 0.1 M NaCl pH 7.4	—	—	40 ± 1.5	10 ± 1.3	25 ± 1.3
NSC 354677 (XNT)	#3 (NCI)	−55.38	92 ± 14.5	36 ± 3.1	39 ± 3.2
NSC 290956 (ESP)	#4 (FDA)	−47.10	36 ± 2.2	6 ± 1.2	16 ± 1.2
NSC 357775 (DMZ)	#7 (FDA)	−46.53	82 ± 10.3	41 ± 2.7	50 ± 2.8
NSC 169188 (HXZ)	#10 (FDA)	−45.43	11 ± 1.0	4 ± 0.6	36 ± 0.6
NSC 169899 (CTX)	#18 (FDA)	−43.51	13 ± 0.8	6 ± 0.7	46 ± 0.7
NSC 134434 (HCT)	#20 (FDA)	−43.20	20 ± 1.1	6 ± 0.8	30 ± 0.8
NSC 293901 (FMB)	#21 (FDA)	−43.10	33 ± 1.3	9 ± 1.1	27 ± 1.1
NSC 289337 (TIA)	#30 (FDA)	−42.04	23 ± 1.1	7 ± 0.9	30 ± 0.9
NSC 284614 (APR)	#32 (FDA)	−41.96	19 ± 1.5	4 ± 0.8	21 ± 0.8
NSC 290312 (LAB)	#35 (FDA)	−40.52	60 ± 12	21 ± 1.9	35 ± 2.0

ACE2, angiotensin-converting enzyme 2; FDA, Food and Drug Administration; NCI, National Cancer Institute; RFU, relative fluorescent unit.

Table 2.

Chemical Structures of Selected Food and Drug Administration–Approved Compounds

Table 3.

Effect of Diminazene (DMZ) on Enzyme Efficiency

	V_max	K_m	V_max/K_m
10 µM DMZ	32.75	1.00E-04	3.28·10⁵
50 µM DMZ	41.00	8.23E-05	4.98·10⁵
100 µM DMZ	56.62	9.76E-05	5.80·10⁵
ACE2	9.58	3.96E-05	2.42·10⁵

ACE2, angiotensin-converting enzyme 2.

Two of the FDA-approved compounds (labetalol [LAB] and diminazene [DMZ]) were shown to increase maximal velocity at least twofold (21 ± 1.92 and 41 ± 2.69 RFU·s⁻¹, respectively) compared to ACE2 alone (10 ± 1.30 RFU·s⁻¹), whereas incubation with aprindine, minithixen, and hydroxyzine (APR, CTX, and HXZ) resulted in 2.1- to 3.5-fold reduction in K_m. Despite the fact that many of the compounds demonstrated enhancing effects on either V_max or K_m, only 3 of the selected 10 compounds had a statistically significant effect on overall enzyme efficiency (V_max/K_m)—HXZ, CTX, and DMZ.

Interestingly, some of the known antihypertensive agents were identified in this screen. For example, labetalol (2-hydroxy-5-(1-hydroxy-2-((1-methyl-3-phenylpropyl) amino) ethyl) benzamide) is an antihypertensive drug marketed as Normodyne.²⁴ None of the compounds that significantly affect ACE2 enzyme efficiency were previously used for the treatment of hypertension or cardiovascular disease. Hydroxyzine is an antihistamine used in the treatment of allergies and hyperalgesia, minithixen is a dopamine receptor antagonist used as antipsychotic, and diminazene is an antiprotozoan chemotherapeutic used in veterinary medicine. More information on the clinical uses of selected compounds can be found in the Supplemental Figures S1 and S2.

The magnitude of the effect of these compounds has particular significance when compared with clinical effects of ACE inhibitors used for the treatment of hypertension, which typically inhibit enzyme activity to levels below 10%.^25,26 The FDA-approved drug diminazene (benzenecarboximidamide,4,4′-(1-triazene-1,3-diyl)bis-, dihydrochloride) identified in this study was shown to be the most effective in this group of compounds ( Table 1 ), and we selected it for further evaluation.

EC₅₀ and Lineweaver-Burk analysis

A Lineweaver-Burk (LB) plot was generated for five compounds that had the strongest effect on ACE2 (Suppl. Fig. S1). The plot suggests that both DMZ and XNT bind outside the active site and increase V_max ~4-fold and K_m ~2-fold (Km_ACE2 = 40 ± 1.46 µM, Km_XNT = 92 ± 14.55 µM, and Km_DMZ = 82 ± 10.34 µM), but the exact mechanism of enhancement by these compounds might be different. The LB plot demonstrating effects of DMZ on initial velocity of ACE2 in the presence of varying concentrations of the drug is shown in Figure 4 .

Fig. 4.

Lineweaver-Burk double-reciprocal plot for angiotensin-converting enzyme 2 (ACE2) in the presence of 0 (○), 10 µM (■), 50 µM (▲), and 100 µM (◊) diminazene (DMZ). Despite the fact that even low concentrations of diminazene are sufficient to produce a boost in maximal velocity, overall enzyme efficiency responds more slowly to increasing concentrations of the compound and starts to approach 2.5-fold of the control levels only in presence of 100 µM drug.

Titration of ACE2 with diminazene (0.01–1000 µM) results in a biphasic dose–response curve illustrated in Figure 5 . At low concentrations, the enzyme is activated with an EC₅₀ of 8.04 µM, whereas at high concentrations, it is partially inhibited with an IC₅₀ of 200.01 µM. Remarkably, even after the partial inhibition, ACE2 initial velocity remains significantly higher than in the absence of the drug. Previously discovered XNT and resorcinolnaphthalein (EC₅₀ = 20 ± 0.8 and 20 ± 0.4 µM)¹³ produced only about 70% of the maximal increase in initial velocity of diminazene.

Fig. 5.

Angiotensin-converting enzyme 2 (ACE2) dose–response curve for activation with diminazene (DMZ). In total, 10 nM ACE2 was incubated with 10 nM to 1 mM diminazene for 5 min at 37 °C. The reaction was initiated by addition of fluorogenic peptide substrate Mca-APK-(Dnp)-OH and monitored for 15 min. Diminazene enhances ACE2 activity in a dose-dependent manner with an EC₅₀ of 8.04 µM (concentration to achieve 50% increase in initial velocity). Titration results in a biphasic dose–response curve: at low concentrations of DMZ, the enzyme is activated, whereas at high concentrations, it is partially inhibited with an IC₅₀ of 200 µM.

HPLC analysis of Ang II cleavage

It has been determined that vasoconstrictor peptide angiotensin II (Ang II) is a preferred natural substrate for ACE2.²⁷ Angiotensin 1–7 (Ang 1–7), generated by enzymatic cleavage of Ang II, is one of the main effectors responsible for beneficial effects of ACE2 on the cardiovascular system.²⁸ Classical photo- and fluororimetric methods could not provide accurate detection and quantification of Ang 1–7 formation, and therefore we used a liquid chromatography/mass spectrometry (LC-MS)–based assay system to directly analyze ACE2-catalyzed hydrolysis of Ang II and validate our kinetic assay data.

A 2-h incubation of 50 µM Ang II with 10 nM ACE2 in the presence of 50 µM DMZ resulted in a 1.5-fold increase in Ang (1–7) formation compared to that in the absence of the drug. MALDI-TOF spectrometry was used to determine peak identities and confirmed the generation of Ang (1–7) (899 m/z), which provides direct evidence for Ang II cleavage by ACE2. Figure 6 shows HPLC chromatograms for ACE2 in the presence and absence of DMZ.

Fig. 6.

Diminazene (DMZ) enhances cleavage of natural substrate angiotensin II (Ang II). High-performance liquid chromatography (HPLC) analysis of angiotensin II cleavage was performed by incubating 10 nM angiotensin-converting enzyme 2 (ACE2) with 50 µM Ang II in the presence of 50 µM DMZ and in the absence of the drug. Peptide products were separated by HPLC, and their identities were assigned by matrix-assisted laser desorption/ionization (MALDI). Mass spectrometry confirmed the peak for Ang (1–7) at 899 m/z. Two-hour incubation with DMZ resulted in a 1.5-fold increase in Ang (1–7) formation compared to that in the absence of the drug (as determined by integrating area under the curve [AUC]).

Discussion

We designed a study that focuses on a newly discovered enzyme that is a key regulator of hypertension because high blood pressure is a frequently observed and documented off-target effect. We targeted a specific structural pocket in the hinge-bending region of ACE2 (using the coordinates of the crystal structure of ACE2 in the open conformation) and found three FDA-approved compounds that significantly modulated ACE2 activity: hydroxyzine (HXZ), minithixen (CTX), and diminazene (DMZ). Hydroxyzine and minithixen act by increasing specificity for the synthetic substrate, which is reflected by reduction in K_m (Km_HXZ = 11 ± 1.0 µM, Km_CTX = 14 ± 0.8 µM vs Km_ACE2 _alone = 40 ± 1.5 µM). In contrast, diminazene and labetalol produce a large increase in V_max, which comes at a cost of loss in substrate specificity (Km_DMZ = 82 ± 10.3 µM; Table 1 ). These data are consistent with the hypothesis that ACE2 conformational changes associated with substrate binding and/or product release may be rate limiting. This becomes particularly important in proteolytic cleavage of longer substrates, where fast turnover might be more difficult to achieve.

The effectiveness of FDA-approved diminazene as an ACE2 activator was confirmed by analysis of cleavage of the octapeptide angiotensin II, which is considered the main effector of the renin-angiotensin system (RAS) and the most physiologically significant natural substrate for ACE2. ACE2 EC₅₀ values for diminazene fall in the low micromolar range (8.0 µM) and demonstrate stronger response to this FDA-approved compound than to XNT, a non-FDA-approved drug-like small molecule shown to reduce high blood pressure in the in vivo model systems.^13,14

Repurposing or repositioning is a strategy that takes advantage of the information available on drugs that have been approved for clinical use to enable their use in achieving therapeutic goals not intended originally. Because new relevant drug targets are constantly emerging, strategies to validate their targeting in humans are urgently needed.

This study has implications on the specificity of some known drugs. For example, Normodyne, a beta-blocker used to treat hypertension, enhances ACE2 activity in vitro. Effects observed in vivo may be due to the interaction between this drug and a combination of molecular targets, possibly including ACE2. Drugs that are useful in treating psychosis, such as minithixen, enhance ACE2 activity, suggesting that low blood pressure may be considered a possible side effect of this treatment due to the promiscuity of these agents. However, because minithixen is considered safe in humans, experiments may be designed to test its utility in treating hypertension. Similarly, the antibacterial and antifungal drugs diminazene and hydroxyzine, respectively, enhance ACE2 activity, suggesting that these compounds, or related analogs, may be useful in controlling blood pressure. These findings suggest that identification of off-target proteins that FDA-approved drugs modulate can be achieved by molecular docking. This strategy is both an informative way to illuminate possible undesired side effects and a rapid way to screen approved compounds for new clinical purposes.

Footnotes

Supplementary material for this article is available on the Journal of Biomolecular Screening Web site at .

References

Kuntz

I. D.

Structure-Based Strategies for Drug Design and Discovery. Science 1992, 257, 1078–1082.

Lindsley

C. W.

The Akt/PKB Family of Protein Kinases: A Review of Small Molecule Inhibitors and Progress towards Target Validation: A 2009 Update. Curr. Top. Med. Chem. 2010, 10, 458–477.

H. F.

Chen

Rao

S. S.

Chen

X. M.

Liu

H. C.

Qin

J. H.

Tang

W. F.

Yue

Zhou

Recent Advances in the Research and Development of B-Raf Inhibitors. Curr. Med. Chem. 2010, 17, 1618–1634.

Gao

Yang

Zhu

Pharmacophore Based Drug Design Approach as a Practical Process in Drug Discovery. Curr. Comput. Aided Drug. Des. 2010, 6, 37–49.

Villoutreix

B. O.

Eudes

Miteva

M. A.

Structure-Based Virtual Ligand Screening: Recent Success Stories. Comb. Chem. High Throughput Screen. 2009, 12, 1000–1016.

Talele

T. T.

Khedkar

S. A.

Rigby

A. C.

Successful Applications of Computer Aided Drug Discovery: Moving Drugs from Concept to the Clinic. Curr. Top. Med. Chem. 2010, 10, 127–141.

Annane

Improving Clinical Trials in the Critically Ill: Unique Challenge—Sepsis. Crit. Care Med. 2009, 37, S117–S128.

Staessen

J. A.

Richart

Wang

Thijs

Implications of Recently Published Trials of Blood Pressure–Lowering Drugs in Hypertensive or High-Risk Patients. Hypertension 2010, 55, 819–831.

Verdecchia

Gentile

Angeli

Mazzotta

Mancia

Reboldi

Influence of Blood Pressure Reduction on Composite Cardiovascular Endpoints in Clinical Trials. J. Hypertens. 2010, 28, 1356–1365.

10.

Bonomi

P. D.

Implications of Key Trials in Advanced Nonsmall Cell Lung Cancer. Cancer 2010, 116, 1155–1164.

11.

Donoghue

Hsieh

Baronas

Godbout

Gosselin

Stagliano

Donovan

Woolf

Robison

Jeyaseelan

. A Novel Angiotensin-Converting Enzyme-Related Carboxypeptidase (ACE2) Converts Angiotensin I to Angiotensin 1–9. Circ. Res. 2000, 87, E1–E9.

12.

Tipnis

S. R.

Hooper

N. M.

Hyde

Karran

Christie

Turner

A. J.

A Human Homolog of Angiotensin-Converting Enzyme: Cloning and Functional Expression as a Captopril-Insensitive Carboxypeptidase. J. Biol. Chem. 2000, 275, 33238–33243.

13.

Hernandez Prada

J. A.

Ferreira

A. J.

Katovich

M. J.

Shenoy

Santos

R. A.

Castellano

R. K.

Lampkins

A. J.

Gubala

Ostrov

D. A.

. Structure-Based Identification of Small-Molecule Angiotensin-Converting Enzyme 2 Activators as Novel Antihypertensive Agents. Hypertension 2008, 51, 1312–1317.

14.

Ferreira

A. J.

Shenoy

Yamazato

Sriramula

Francis

Yuan

Castellano

R. K.

Ostrov

D. A.

S. P.

Katovich

M. J.

Raizada

M. K.

Evidence for Angiotensin-Converting Enzyme 2 as a Therapeutic Target for the Prevention of Pulmonary Hypertension. Am. J. Respir. Crit. Care Med. 2009, 179, 1048–1054.

15.

Towler

Staker

Prasad

S. G.

Menon

Tang

Parsons

Ryan

Fisher

Williams

Dales

N. A.

. ACE2 X-Ray Structures Reveal a Large Hinge-Bending Motion Important for Inhibitor Binding and Catalysis. J. Biol. Chem. 2004, 279, 17996–18007.

16.

Ferrara

Gohlke

Price

D. J.

Klebe

Brooks

C. L.

III

Assessing Scoring Functions for Protein-Ligand Interactions. J. Med. Chem. 2004, 47, 3032–3047.

17.

Gschwend

D. A.

Good

A. C.

Kuntz

I. D.

Molecular Docking towards Drug Discovery. J. Mol. Recognit. 1996, 9, 175–186.

18.

Ewing

T. J.

Makino

Skillman

A. G.

Kuntz

I. D.

DOCK 4.0: Search Strategies for Automated Molecular Docking of Flexible Molecule Databases. J Comput. Aided Mol. Des. 2001, 15, 411–428.

19.

Driscoll

J. S.

The Preclinical New Drug Research Program of the National Cancer Institute. Cancer Treat. Rep. 1984, 68, 63–76.

20.

Pettersen

E. F.

Goddard

T. D.

Huang

C. C.

Couch

G. S.

Greenblatt

D. M.

Meng

E. C.

Ferrin

T. E.

UCSF Chimera—A Visualization System for Exploratory Research and Analysis. J. Comput. Chem. 2004, 25, 1605–1612.

21.

Palmier

M. O.

Van Doren

Rapid Determination of Enzyme Kinetics from Fluorescence: Overcoming the Inner Filter Effect. Anal. Biochem. 2007, 371, 43–51.

22.

Elased

K. M.

Cunha

T. S.

Gurley

S. B.

Coffman

T. M.

Morris

New Mass Spectrometric Assay for Angiotensin-Converting Enzyme 2 Activity. Hypertension 2006, 47, 1010–1017.

23.

Williams-DeVane

C. R.

Wolf

M. A.

Richard

A. M.

DSSTox Chemical-Index Files for Exposure-Related Experiments in ArrayExpress and Gene Expression Omnibus: Enabling Toxico-Chemogenomics Data Linkages. Bioinformatics 2009, 25, 692–694.

24.

Ahuja

Charap

M. H.

Management of Perioperative Hypertensive Urgencies with Parenteral Medications. J. Hosp. Med. 2010, 5, E11–E16.

25.

Jorde

U. P.

Ennezat

P. V.

Lisker

Suryadevara

Infeld

Cukon

Hammer

Sonnenblick

E. H.

Le Jemtel

Maximally Recommended Doses of Angiotensin-Converting Enzyme (ACE) Inhibitors Do Not Completely Prevent ACE-Mediated Formation of Angiotensin II in Chronic Heart Failure. Circulation 2000, 101, 844–846.

26.

Hanon

Vijayaraman

Sonnenblick

E. H.

Le Jemtel

Persistent Formation of Angiotensin II Despite Treatment with Maximally Recommended Doses of Angiotensin Converting Enzyme Inhibitors in Patients with Chronic Heart Failure. J. Renin Angiotensin Aldosterone Syst. 2000, 1, 147–150.

27.

Vickers

Hales

Kaushik

Dick

Gavin

Tang

Godbout

Parsons

Baronas

Hsieh

F.;

. Hydrolysis of Biological Peptides by Human Angiotensin-Converting Enzyme-Related Carboxypeptidase. J. Biol. Chem. 2002, 277, 14838–14843.

28.

Schindler

Bramlage

Kirch

Ferrario

C. M.

Role of the Vasodilator Peptide Angiotensin–(1-7) in Cardiovascular Drug Therapy. Vasc. Health Risk. Manag. 2007, 3, 125–137.

29.

Valerio

L. G.

Yang

Arvidson

K. B.

Kruhlak

N. L.

A Structural Feature-Based Computational Approach for Toxicology Predictions. Expert Opin. Drug Metab. Toxicol. 2010, 6, 505–518.

30.

Natesh

Schwager

S. L.

Sturrock

E. D.

Acharya

K. R.

Crystal Structure of the Human Angiotensin-Converting Enzyme-Lisinopril Complex. Nature 2003, 421, 551–554.

31.

Matziari

Beau

Cuniasse

Yiotakis

Dive

Development of Potent and Selective Phosphinic Peptide Inhibitors of Angiotensin-Converting Enzyme 2. J. Med. Chem. 2008, 51,2216–2226.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.18 MB

Prediction of Off-Target Effects on Angiotensin-Converting Enzyme 2

Abstract

Keywords

Introduction

Materials and Methods

Molecular docking

ACE2 enzyme kinetic assays

Inner filter effect

High-performance liquid chromatography analysis of Ang II cleavage

Statistical significance

Results

Molecular docking of FDA approved drugs

Effects of FDA-approved drugs on ACE2 catalytic activity

EC50 and Lineweaver-Burk analysis

HPLC analysis of Ang II cleavage

Discussion

Footnotes

References

Supplementary Material

EC₅₀ and Lineweaver-Burk analysis