Active Components of Essential Oils as Anti-Obesity Potential Drugs

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Letter

Active Components of Essential Oils as Anti-Obesity Potential Drugs investigated by in silico techniques Giosuè Costa, Maria Concetta Gidaro, Daniela Vullo, Claudiu T Supuran, and Stefano Alcaro J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b02004 • Publication Date (Web): 08 Jun 2016 Downloaded from http://pubs.acs.org on June 9, 2016

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Journal of Agricultural and Food Chemistry

Active Components of Essential Oils as Anti-Obesity Potential Drugs investigated by in silico techniques Giosuè Costa†,, Maria Concetta Gidaro†*, Daniela Vullo‡, Claudiu T. Supuran‡, Stefano Alcaro† †

Dipartimento di Scienze della Salute, Università “Magna Græcia” di Catanzaro, Campus

Universitario “S. Venuta”, Viale Europa, Loc. Germaneto, 88100 Catanzaro, Italy. ‡

Laboratorio di Chimica Bioinorganica, Polo Scientifico, Università degli Studi di Firenze, Via

della Lastruccia 3, 50019 Sesto Fiorentino, Florence, Italy.

Corresponding Author *

Dr Maria Concetta Gidaro, Dipartimento di Scienze della Salute, Università “Magna Græcia” di

Catanzaro, Campus “S. Venuta”, Viale Europa, 88100 Catanzaro, Italy. E-mail: [email protected] Phone: +39 0961 3694197

ACS Paragon Plus Environment

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ABSTRACT

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In this study, for the first time, we have considered Essential Oils (EOs) as possible resources of

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Carbonic Anhydrase inhibitors (CAIs), in particular against the mitochondrial isoform VA that,

4

actually, represents an innovative target for the obesity treatment. In silico structure-based virtual

5

screening was performed in order to speed up the identification of promising anti-obesity agents.

6

The potential hit compounds were submitted to in vitro assays and experimental results,

7

corroborated by molecular modeling studies, showed EOs components as a new class of CAIs with

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a competitive mechanism of action due to the Zinc ion coordination within the active sites of these

9

metallo-enzymes.

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Keywords

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Essential Oils, Anti-obesity, Human Carbonic Anhydrase, Inhibition, Virtual Screening


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Introduction

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Obesity is ranked by the World Health Organization (WHO) as one of the top 10 global health

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problems, usually correlated with other pathologies such as cardiovascular and musculo-skeletal

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disorders, type 2 diabetes, depression and several cancers including colon, breast and

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endometrium.1 The pharmacotherapies for the obesity treatment play an important role for

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increasing the weight loss established by diet and exercise alone, but actually, only a few anti-

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obesity drugs are approved by the European Medicines Agency (EMA) and the US Food and Drug

19

Administration (FDA).2 In particular, five medications have been approved in the USA for chronic

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weight

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naltrexone/bupropion and liraglutide. Lorcaserin and phentermine/extended-release topiramate have

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not been approved in the European Union, in fact, several controversies are still being debated and

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one of the major uncertainties, for patients with obesity, is whether current pharmacological

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treatments increase, reduce, or are neutral with respect to cardiovascular events.3 Moreover, drugs

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acting on the metabolism have considerable side effects and, for these reasons, pharmacological

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interventions for weight loss remain limited.4,5

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Several studies have provided evidence that Carbonic Anhydrase inhibitors (CAIs) are emerging as

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anti-obesity drugs.6-8 To date 16 α-CA isoforms were reported in mammals with different tissue

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distribution and subcellular localization.9 These metallo-enzymes catalyze the reversible hydration

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of carbon dioxide to afford the ionic species bicarbonate and protons as follows: CO2 + H2O ⇆

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HCO3− + H+. The CA isoforms VA and VB, involved in lipogenesis, are expressed only in the

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mitochondria and the clinical observation of body weight reduction, for patients treated with the

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anticonvulsant topiramate and zonisamide (in phase II), is rationalized considering the potent

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inhibitory effect of these mitochondrial enzymes.10,11

management:

orlistat,

lorcaserin,

phentermine/extended-release


topiramate,

3


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In this study, for the first time, Essential Oils (EOs) components were considered as a new class of

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potential CAIs. Traditionally, EOs have been largely employed as antibacterial, antifungal and

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insecticidal agents12,13 and several studies have confirmed these biological activities.14-19 In

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addition, EOs have been also considered for their analgesic,20 sedative,21 anti-inflammatory,22-24

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spasmolytic25 and locally anesthetic properties.26 Finally, EOs seem to have a great potential as

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anticancer therapeutic agents but mechanisms of action are still unknown, considering that EO

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extract is a very complex mixture of low weight molecules (20 to 70 components) at different

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concentrations in the phytocomplex.27-31

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Conversely, our study was focused on a specific interaction between the EOs content and the CAs

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family. Considering EOs as new resources of CAIs, in particular of the mitochondrial isoforms VA,

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a Structure-Based Virtual Screening (SBVS) was performed in order to early identify in silico new

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potential anti-obesity drugs. Therefore, the Essential Oil University (EOU) chemoinformatics

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database32 was used to build a chemical library of natural ligands that were virtually screened

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against several crystallographic X-ray structures of CA, deposited into the bioinformatics Protein

49

Data Bank (PDB).33 The most relevant compounds selected on the basis of the predicted binding

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affinity results, due to the ligand-target interactions, were submitted to in vitro assays and new

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chemical scaffolds were discovered for CAIs.

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Materials and Methods

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In silico Structure-Based Virtual Screening

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The 3D chemical structures of 2690 compounds, content in the EOU website,32 were downloaded

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and imported into the graphic interface of Maestro 9.734 in order to create the ligands database used

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for the SBVS. Duplicates were detected by the Canvas35 utility and Lipinski’s rule of five36,37 was

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used to filter 1771 compounds. For each molecule protonated and tautomeric forms at pH 7.4 were


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calculated, by the “LigPrep”38 module, ending up with a library of 2304 compounds. The lowest

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energy conformations, obtained by using the OPLS-2005 force field, represented the starting points

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for docking simulations. The X-ray crystallographic structures of CA I, CA II and CA VA isoforms

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were selected from the PDB33 with the respective entry codes 1AZM,39 4CQ040 and 1DMY,41 while

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for the CA VB enzyme no crystallographic models are deposited in the PDB. After the preparation

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procedure of each X-ray structure, the docking simulations were performed as reported in the

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experimental section of our previous SBVS study.42 Regarding the CA VA isoform, the best docked

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poses of compounds 1, 2, 6 and 11 were further submitted to a MD simulation of 100 ns, carried out

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by Desmond 3.7.43 Therefore, in order to analyze ligand-target interactions involved in the

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molecular recognition, we used the MD protocol already applied for investigating flavonoids

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inhibition of the monoamine oxidases isoforms.44 Pymol version 1.745 was used for create Figure 1.

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In vitro Carbonic Anhidrase Inibition assay

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All CA isoforms (I, II and VA) used for in vitro assays were purified and obtained as previously

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reported in other published works.46-48 An Applied Photophysics stopped-flow instrument has been

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used for assaying the CA-catalyzed CO2 hydration activity.49 For compounds 1-11 four inhibitor

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concentrations were used, starting from 10-5 M to 10-8 M, and testing them in triplicate. IC50 values

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were obtained from dose-response curves working at seven different concentrations of test

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compound, by fitting the curves using PRISM (www.graphpad.com) and nonlinear least squares

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methods; values represent the mean of at least three different determinations as described by us

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previously.50 The inhibition constants (Ki) were then derived by using the Cheng-Prusoff equation51

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as follows: Ki = IC50/(1 + [S]/Km), where [S] represents the CO2 concentration at which the

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measurement was carried out, and Km is the concentration of substrate at which the enzyme activity

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is at half-maximal. The concentrations of enzymes used in the assay were as follows: hCA I: 11.7


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nM, hCA II: 6.9 nM, hCA VA 10.5 nM. The kcat/KM values for these enzymes were as follows: 5.0

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x 107 M-1 x s-1 (hCA I); 1.5 x 108 M-1 x s-1 (hCA II); 2.9 x 107 M-1 x s-1 (hCA VA), respectively.

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Results and discussion

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Theoretical studies and in silico methods, by means of high performing computers, recently

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represent new innovative approaches in the drug discovery process. In fact, excluding the clinical

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trials, chemoinformatics and bioinformatics are playing important roles in every stage of the drug

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discovery pipeline.52 The EOU website is an example of a chemoinformatics database, providing

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accurate information about the world of EOs.

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In this study, we have considered for the first time EOs as resources of anti-obesity agents by

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inhibition of the mitochondrial CA VA/VB isoforms. With the aim of performing a SBVS, the 3D

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compounds used to build the ligands database were downloaded from the EOU website and

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prepared as reported in the experimental section. Regarding the X-ray crystal structures of the

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enzymes, we selected the PDB models of ubiquitous CA I, CA II and mitochondrial CA VA as

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previously reported for another SBVS study, performed in our laboratory for identifying new

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natural CAIs.42 Unfortunately, in the PDB there are no crystal structures for the CA VB isoform

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and, therefore, it was not considered for the SBVS. Molecular docking simulations and the virtual

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screening procedure, already published, allowed us to select the new hit compounds on the basis of

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the theoretical affinity values,42 reported in the Table 1S.

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Ten molecules, identified in silico as promising CA VA inhibitors, were submitted to in vitro

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assays using the human enzymes in order to confirm the ability to exert the predicted biological

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activity. In the Table 1 for compounds 1-10 and the positive control acetazolamide, also called

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AZA (11), are reported inhibition constant (Ki) values, calculated as displayed in the experimental

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section.


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Table 1 should be positioned here

105 106

Biological results showed all compounds as week inhibitors of the slow cytosolic isozyme CA I,

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demonstrated by Ki values in the range of 3.44-8.81 µM, and ineffective inhibitors of the rapid

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cytosolic isozyme CA II with Ki >100 µM, except for compounds 1, 3, 7 and 8. As regard the

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mitochondrial CA VA, it was poorly inhibited by compounds 8 and 10, with Ki values of 43.10 µM

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and 44.00 µM, while the other compounds showed a Ki in the range of 3.80-9.69 µM. The 2-

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hydroxyisobutyric acid (2) proved to be the best CA VA inhibitor, with a Ki value equal to 3.80

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µM, followed by 3Z-nonenoic acid (6) and β-thujaplicin (1) with Ki values 4.52 µM and 7.50 µM,

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respectively. Compound 9 has been already tested against several CA isoforms.53

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In order to analyze the ligand-target interactions, involved in the molecular recognition, and with

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the aim of taking into account the mechanism of action of new scaffolds, the best docked poses of

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compounds 1, 2 and 6, in complex with the CA VA X-ray structure 1DMY,41 were further

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investigated by a Molecular Dynamics (MD) simulation of 100 ns. Moreover, the reference 11 co-

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crystallized with the CA VA PDB model, was also used as positive control for the in silico

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simulations. In Figure 1, it is displayed a competitive mechanism of action for volatile compounds

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of EOs, coordinating the Zinc ion and establishing other non-covalent ligand-target interactions

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with amino-acid residues of the CA VA catalytic site.

122 123

Figure 1 should be positioned here

124 125

In particular, considering the different types of CAIs binding modes,54 all selected compounds

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showed metal-coordination binding to Zinc ion (Figures 1 and 1S). Docking poses were submitted


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to molecular dynamics simulations (MDs) in order to rationalize the physical basis of the

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complexes, as reported in the plots of Figure 2S. For compound 2, the carboxyl group in the

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dissociated form and the hydroxyl substituent at C2 are key moieties in the metal coordination that

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was maintained for all MD simulation time (Figure 2).

131 132


133 134

Moreover, stabilization of the best pose is attributed to H-bond contacts with THR199 and

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ARG209, hydrophobic interactions with TRP209 and HIS94/96/119, also involved in ionic and π-π

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contacts (Figures 1, 1S and 2S). The theoretical results showed a stabilization of compound 2,

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better than 1 and 6, but not than AZA (11), due to the great number of H-bond contacts. These

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findings are summarized in Figure 3, as timeline representation of H-bond contacts established

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between enzyme and ligand over the course of the MD trajectory.

140 141


142 143

Enzyme activity analysis is only a preliminary investigation but it is the first step, in the drug

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discovery process, for identifying new potential anti-obesity agents targeting the CA V isoforms.

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Actually, there are several patents with mitochondrial CAIs, also in combination therapy,

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confirming that inhibition of enzymes involved in de novo lipogenesis represents a new valid

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approach for the obesity treatment.1In addition, from a polypharmacological prospective, EOs

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contents and their derivatives may be considered Multi-Target Ligands (MuTaLig), in fact, other

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experimental results reported in literature have shown EOs compounds as potential Histone


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Deacetylase inhibitors (HDACIs), promising targets in the cancer therapy, by binding the Zinc ion

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also present in this metallo-enzyme.55,56

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In conclusion, in this work, docking and MDs enabled us to investigate the binding modes of

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ligands against the CA VA target, confirming the important role of molecular modeling techniques

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to speed up the identification of bioactive compounds, reducing time and costs of research

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procedures and taking into account multi target interactions. Moreover, EOs and other natural

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products could be investigated in silico in order to early identify new potent anti-obesity drugs

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and/or new scaffolds to focus the lead optimization studies, considering that it has been

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demonstrated that CA VA inhibition with sulfonamides changes the metabolism of pyruvate,

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acetate, and succinate in mitochondria and reduces dramatically lipogenesis.57

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ABBREVIATIONS

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EOs, Essential Oils; CAIs, Carbonic Anhydrase inhibitors; WHO, World Health Organization;

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EMA, European Medicines Agency; FDA, Food and Drug Administration; SBVS, Structure-Based

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Virtual Screening; EOU, Essential Oil University; PDB, Protein Data Bank; AZA, acetazolamide;

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Ki, inhibition constant; MD, Molecular Dynamics; MDs, Molecular Dynamics simulations; hCA,

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human Carbonic Anhydrase; MuTaLig, Multi-Target Ligands; HDACIs, Histone Deacetylase

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inhibitors.

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ACKNOWLEDGMENTS

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This work was supported by Interregional Research Center for Food Safety and Health at the

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Magna Græcia University of Catanzaro (MIUR PON a3_00359). The authors thank Teresa Piperno,

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biotech student at Magna Græcia University of Catanzaro, for her availability and support to the

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experimental section and Dr. Simon Cross, Molecular Discovery Ltd, for the corrections in the

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language style of manuscript. The authors also thank the COST action CA15135 (Multi-target

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paradigm for innovative ligand identification in the drug discovery process MuTaLig) for the

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support.

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ASSOCIATED CONTENT

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Supporting Information. In the Table 1S are reported the theoretical affinity results of molecular

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docking for compounds 1-11. 2D structures of 1, 2, 6 and 11 with ligand-target interactions of the

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best docked poses are shown in Figure 1S. These contacts were also monitored during MDs and

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reported in Figure 2S. This material is available free of charge via the Internet at

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http://pubs.acs.org.”

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Notes


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This project won the first national prize of the Italian Society for the Research of Essential Oils

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(SIROE), presented from the Dr. Maria Concetta Gidaro during the 3rd Congress to Rome on

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November 2015.

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Figure captions Figure 1. Best docked poses into the catalytic site of CA VA Xray model (PDB code 1DMY41) with metal coordination of the Zinc ion (Zn) and H-bond contacts for: acetazolamide 11 (a), cocrystallized inhibitor of the PDB model, and compounds 1 (d), 2 (b) and 6 (c). All ligands are rendered as cyan carbon sticks, THR199 amino-acid residue involved in the H-bond is shown as yellow carbon sticks, while histidines coordinating Zn sphere as yellow carbon lines. The enzyme is represented as a grey cartoon with transparent surface and all non-carbon atoms are colored according to atom types. Figure 2. A schematic representation of the metal coordination between compound 2 and Zn ion within the CA VA catalytic site. The ligand-target interaction was maintained for all MD simulation time, indicated by the percentage value of 100. Figure 3. Timeline representation of H-bond contacts that CA VA target makes with ligands 1 (green), 2 (red) and 6 (yellow) compared to AZA 11 (blue) over the course of the MD simulations.


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Table 1. 2D chemical structures of ten hit compounds (1-10) selected in silico, by a SBVS study, for the inhibition assays against CA I, II and VA isoforms. The traditional names of the new hit compounds are: 1. β-thujaplicin; 2. 2-hydroxyisobutyric acid; 3. 4-isopropylbenzoic acid; 4. methyl geranate; 5. 3-phenylpropyl benzoate; 6. 3Z-nonenoic acid; 7. Z-geranic acid; 8. 2-methylhexanoic acid; 9. ferulic acid; 10. 5-methylfuran-2-carboxylic acid. 11. Acetazolamide is the CA inhibitor used as positive control. Biological data are reported as inhibition constant (Ki) values in µM. TARGET COMPOUND CAI CAII CA VA 1

HO

4.98

90.60

7.50

5.18

>100

3.80

3.44

85.40

7.38

8.27

>100

8.36

3.75

>100

7.83

6.09

>100

4.52

8.81

18.90

9.07

5.06

3.71

43.10

6.82

>100

9.69

4.89

>100

44.00

0.25

0.01

0.06

O O OH

2 HO

HO

3 O

O

4

O

O

5 O OH

6 O O

7 OH

8

HO O O

9

O

HO

OH

HO

10

O O N N

O

11 N H

S

O S O NH2


20

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Figure 1.


21


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Figure 2.


22

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Figure 3.


23


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For Table of Contents Use Only

Active Components of Essential Oils as Anti-Obesity Potential Drugs investigated by in silico techniques Giosuè Costa† , Maria Concetta Gidaro†,*, Daniela Vullo‡, Claudiu T. Supuran‡, Stefano Alcaro†


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Active Components of Essential Oils as Anti-Obesity Potential Drugs

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