# **Neurotransmitter-Related Molecular Modeling Studies**

Edited by Alicja Nowaczyk and Grzegorz Grześk Printed Edition of the Special Issue Published in *Molecules*

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## **Neurotransmitter-Related Molecular Modeling Studies**

## **Neurotransmitter-Related Molecular Modeling Studies**

Editors

**Alicja Nowaczyk Grzegorz Grze´sk**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Alicja Nowaczyk Nicolaus Copernicus University Poland

Grzegorz Grzesk´ Nicolaus Copernicus University Poland

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Molecules* (ISSN 1420-3049) (available at: https://www.mdpi.com/journal/molecules/special issues/Neurotransmitter Molecular Modeling).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-4277-5 (Hbk) ISBN 978-3-0365-4278-2 (PDF)**

Cover image courtesy of Łukasz Fijałkowski

© 2022 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.

### **Contents**

Reprinted from: *Molecules* **2021**, *26*, 2838, doi:10.3390/molecules26102838 .............. **137**

### **Anna Pierzchli ´nska, Magdalena Kwa´sniak-Butowska, Jarosław Sławek, Marek Drozdzik ´ and Monika Białecka**

Arterial Blood Pressure Variability and Other Vascular Factors Contribution to the Cognitive Decline in Parkinson's Disease

Reprinted from: *Molecules* **2021**, *26*, 1523, doi:10.3390/molecules26061523 .............. **157**

### **About the Editors**

### **Alicja Nowaczyk**

Alicja Nowaczy is an associate professor at Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun. As an academic teacher and science popularizer, she holds ´ a postdoctoral degree in pharmaceutical sciences and a doctorate in chemical sciences. She is a member of the Pharmaceutical, Biomedical and Natural Products Analysis Team of the Analytical Chemistry Committee of the Polish Academy of Sciences, the Polish Pharmaceutical Society, and the Polish Chemical Society.

Her research focuses on medical and pharmaceutical sciences in the areas of pharmacometrics and molecular modeling. Her interests concern the predictive modeling of the detailed mechanisms of action and pharmacological efficacy of biologically active compounds in the areas of neuropharmacology, cardiovascular, and safety pharmacology as well as chemical reaction pathways. The following awards prove the importance and validity of her research: the Team Award of the Minister of Health of the Republic of Poland and a number of other awards, including the award of the Rector of the Nicolaus Copernicus University and the award of the Rector of the Medical University of Gdansk. The research she conducts is interdisciplinary and is assumed to be carried ´ out within larger research groups. Prof. Assoc. Nowaczyk actively cooperates with specialists from Poland, Germany, Denmark, and Spain.

### **Grzegorz Grze´sk**

Grzegorz Grzesk is a full professor at Ludwik Rydygier Collegium Medicum in Bydgoszcz, ´ Nicolaus Copernicus University in Torun. As a physician and university teacher, he combines ´ theoretical and clinical sciences. He is a specialist in internal medicine, cardiology, and clinical pharmacology. He is the head of the Cardiology Clinic of the Jan Biziel University Hospital No. 2 and the provincial consultant in the two clinical areas of cardiology and clinical pharmacology.

He is also an experienced researcher, especially in the field of experimental pharmacology, including studies in animal models. His interests are particularly focused on issues related to cardiology and the function of blood vessels and vascular endothelium, including the role of the mediators and modulators of reactions affecting the molecular level. It is the primary investigator in numerous clinical and experimental studies. He is a board member of the Polish Society of Clinical Pharmacology as well as a member of the European Society of Cardiology and the Society of Polish Internists. The research he conducts combines molecular and clinical approaches with clinical pharmacology methods in a unique way.

### **Preface to "Neurotransmitter-Related Molecular Modeling Studies"**

The year 2021 is the 100th anniversary of the confirmation of the neurotransmission phenomenon that was originally observed by Otto Loewi. For this reason, we have compiled some of what we consider to be the most interesting research reports and reviews in this field. The aim was to introduce the effects of experimental research into practical and clinical applications.

Neurotransmitters are chemicals that enable communication, i.e., the flow of nerve impulses between nerve cells or between nerve cells and muscles and glands. There are approximately 1011 nerve cells in a human brain, each of which can be connected to many other cells. In general, excitatory and inhibitory mediators have been distinguished, both of which are endogenous and exogenous compounds that control the function of the whole organism. Chemically, neurotransmitters belong to many different structural groups, such as amino acids (such as glycine), peptides (such as substance P, somatostatin), monoamines (such as noradrenaline or dopamine), purine derivatives (such as adenosine), gases (such as nitrogen, NO, carbon monoxide CO), and acetylcholine. From a medical point of view, disturbances in the concentration of neurotransmitters in the body mainly result in the occurrence of mood disorders and mental diseases. Thus, a sanguine personality type (sociable optimist) is a personality type that is mainly determined by dopamine. Serotonin mainly determines the temperament of a choleric person (vigorous chief), and acetylcholine implies the personality of a melancholic person (gifted analyst). On the other hand, the influence of the γ-ammino butyric acid determines the phlegmatic personality type (patient caregiver). For this reason, the deficit or excess of a specific neurotransmitter results in disturbances to the balanced mood of a person. Mental disorders such as depression, schizophrenia, and Parkinson's disease contribute to the occurrence of dementia (including Alzheimer's disease), among other disorders. However, the epidemiological problems are much wider. Neural conduction disorders can lead to many cardiovascular diseases and the development of vascular diseases of the brain as well as in many other organs. For example, the digestive system is the second largest cluster of neurons in our body—it has been estimated that it consists of at least 108 neuron cells. Enteric neurons are able to produce numerous neurotransmitters such as dopamine and serotonin. The neurons of the enteric nervous system are in regular contact with the microbes in the gut. In all respects, this communication system is very active. Many of the gut microbes produce the substances necessary for the proper functioning of the developing brain.

Year by year, pharmacological interventions try to influence regulatory processes. Such treatments improve survival, reduce the frequency of readmission, and improve patients' quality of life. We are deeply convinced that while 20 years ago, molecular research and its influence on therapeutic processes were the domain of the so-called researchers who only dealt with basic research, at present, knowledge, albeit basic, about the molecular basis of the effects of therapy allows for the better planning and personalization of therapy, which leads to greater effectiveness and a reduction in the number of side effects.

It is with great pleasure that we present readers with a book whose subject matter is very interesting for experienced and young researchers in the field of medical and health sciences: pharmacology and pharmacy and in the field of natural sciences: chemistry. We believe that the presented research results not only deserve the reader's attention but that they can also be helpful for further research development in the described area.

> **Alicja Nowaczyk and Grzegorz Grze´sk** *Editors*

### *Article* **Antiepileptic Drug Tiagabine Does Not Directly Target Key Cardiac Ion Channels Kv11.1, Nav1.5 and Cav1.2**

**Magdalena Kowalska 1, Łukasz Fijałkowski 1, Monika Kubacka 2, Kinga Sałat 2, Grzegorz Grze´sk 3, Jacek Nowaczyk 4 and Alicja Nowaczyk 1,\***

**Abstract:** Tiagabine is an antiepileptic drug used for the treatment of partial seizures in humans. Recently, this drug has been found useful in several non-epileptic conditions, including anxiety, chronic pain and sleep disorders. Since tachycardia—an impairment of cardiac rhythm due to cardiac ion channel dysfunction—is one of the most commonly reported non-neurological adverse effects of this drug, in the present paper we have undertaken pharmacological and numerical studies to assess a potential cardiovascular risk associated with the use of tiagabine. A chemical interaction of tiagabine with a model of human voltage-gated ion channels (VGICs) is described using the molecular docking method. The obtained in silico results imply that the adverse effects reported so far in the clinical cardiological of tiagabine could not be directly attributed to its interactions with VGICs. This is also confirmed by the results from the isolated organ studies (i.e., calcium entry blocking properties test) and in vivo (electrocardiogram study) assays of the present research. It was found that tachycardia and other tiagabine-induced cardiac complications are not due to a direct effect of this drug on ventricular depolarization and repolarization.

**Keywords:** tiagabine; cardiac voltage-gated ion channels; molecular modeling; ECG study

### **1. Introduction**

Epidemiological studies have consistently shown that people with epilepsy have a higher prevalence of structural cardiac disease than those without it [1]. The functioning of neurons, muscles and cardiac myocytes is based on action potentials (APs) generated by transmutational ion currents mediated mainly by sodium, calcium and potassium [2,3]. According to the guidelines of the Comprehensive in vitro Proarrhythmia Assay (CiPA), a set of six ion channels has been selected for which currents are important for both the repolarization and depolarization of the cardiac action potential (AP) [4]. There is some evidence, based on the effect of clinical drugs on cardiac APs, indicating the classification of cardiac ion channels into two classes [5–7]. The first class contains the most important cardiac ion channels, such as KV11.1, NaV1.5 and CaV1.2. The second class comprises KV4.3, KvLQT1/mink and Kir2.1 and is less critical for the assessment of all drugs under CiPA [7–9].

A common feature of both neurological disorders (e.g., epilepsy and chronic pain) and cardiac dysrhythmias is cell (neuronal cell and cardiac myocyte, respectively) hyperexcitability [2]. Therefore, drugs affecting cell excitability threshold within the nervous tissue

**Citation:** Kowalska, M.; Fijałkowski, Ł.; Kubacka, M.; Sałat, K.; Grze´sk, G.; Nowaczyk, J.; Nowaczyk, A. Antiepileptic Drug Tiagabine Does Not Directly Target Key Cardiac Ion Channels Kv11.1, Nav1.5 and Cav1.2. *Molecules* **2021**, *26*, 3522. https:// doi.org/10.3390/molecules26123522

Academic Editor: Diego Muñoz-Torrero

Received: 22 April 2021 Accepted: 8 June 2021 Published: 9 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

can also interact with cardiac cell APs and vice versa [10–12]. It has to be emphasized that this drug-induced effect might sometimes be harmful as it may lead to the occurrence of additional alterations in neuronal or cardiac cell reactivity, thus being a cause for additional drug-induced (iatrogenic) complications. From the safety pharmacology point of view, it is of key importance to recognize these potential risk factors as early as possible. The cardiac VGICs assay is an indispensable step and a high-quality assay must accompany any investigational new drug application. The in silico studies of drug binding to KV11.1, NaV1.5 and CaV1.2 may be valuable assays for drugs and drug candidates at present [13].

Currently, several antiepileptic drugs were reported to have cardiotoxic and metabolic adverse effects [1,14]. Tiagabine (TGB) is an anticonvulsant drug used to treat partial seizures in humans. Recent results of clinical trials and animal studies indicate that it might be also effective in patients suffering from pain, insomnia or mood disorders and these activities are attributed to its inhibitory effect on GABA uptake [15,16]. TGB is a 96% protein-bound molecule [17]. The drug is a potent inhibitor of [3H]GABA uptake into synaptosomal membranes (IC50 = 67 nM) or neurons (IC50 = 446 nM) and glial cells (IC50 = 182 nM) in primary cell cultures. The in vivo tests have shown that TGB neuronal inhibiting is 2.5-fold more potent than glial GABA uptake [18–20]. The enhancement of GABA neurotransmission due to GABA transporter subtype 1 (GAT-1) inhibition might also explain most of adverse effects of TGB, including drowsiness, confusion and dizziness [18,21]. However, some other TGB-induced complications do not to be directly related to its influence on GABA concentration in the brain and other tissues.

Several recent reports have demonstrated that tachycardia is observed in about 1.0% of patients treated with TGB [18,22] but the mechanisms underlying this cardiotoxic effect are not known. In the literature there is a limited amount of data regarding the influence of TGB on cardiovascular functions and metabolism [23].

Since tachycardia is most frequently caused by impaired ion channel functions [24–27], in the in silico part of the present study a detailed analysis of the interactions between the human KV11.1, NaV1.5, CaV1.2 and TGB was performed. In order to compare the strength of TGB's binding to individual ion channels terfenadine (TEF) [28], batrachotoxin (BTX) [29,30] and nifedipine (NFD) [31] were selected as compounds strongly affecting these molecular targets.

In the course of present study, in vivo tests in rats were performed. The in vivo assay comprised the assessment of TGB's proarrhythmic potential and its effects on ECG components were studied; i.e., we conducted in vivo evaluation of TGB to assess its influence on PQ, QRS, QT and QTc intervals. Its effect on heart rate was also investigated. The relevant changes in PQ, QRS, QT and QTc intervals were interpreted as the effect of the test drug predominantly on Nav, Kv and Cav channels. In contrast, the changes in heart rate were treated as a measure of the effect of the test compound, particularly, on the Nav1.5 channel or autonomic system function. Calcium blocking properties of TGB were tested in the isolated rat aorta contracted with depolarizing KCl solution.

### **2. Results**

### *2.1. Pharmacological Part*

### 2.1.1. The Effect on Normal Electrocardiogram

In vivo ECG study showed that TGB marked no significant effect on PQ, QRS, QT and QTc intervals. TGB also did not influence the heart rate significantly. It decreased the heart rhythm maximally at 30 min (by 6.9%) but this result did not reach statistical significance (Table 1). What is most important to note is that TGB at a dose as high as 100 mg/kg *i.p* did not prolong QT interval, which suggests that it did not prolong cardiac repolarization and probably did not block IKR currents. Similar observations have also been made in the published studies [32,33]. We also did not observe the prolongation of PQ interval and QRS widening, which reflects the slowed conduction and disturbances in ventricular depolarization, usually due to the INa block. This is in line with the results obtained and presented here from molecular docking studies, where we found that TGB did not bind to cardiac voltage-gated ion channels Kv11.1 (hERG) and Nav1.5.

**Table 1.** Effects of TGB (100 mg/kg *i.p.*) on the heart rate and ECG intervals in anesthetized rat (thiopental 75 mg/kg *i.p.*).

The data are the means of five experiments ± S.E.M. Statistical analysis: one-way analysis of variance (ANOVA) with repeated measurements and followed by Dunnett's post hoc test.

### 2.1.2. Voltage-Dependent Calcium Channels

During the course of our study, we investigated the calcium entry blocking properties of TGB in vasculature by employing the isolated rat aorta contracted with depolarizing KCl solution. In this experiment, the KCl-induced contraction was caused by an increase in extracellular potassium that leaded to membrane depolarization, which increases calcium influx from extracellular sources involving voltage-dependent calcium channels [34] (Cav1.2). The reference compounds we used were verapamil [35] and NFD [36]. TGB was not able to relax KCl-precontracted aortic rings at the range of concentration 1–30 μM (Figure 1). At a higher concentration, TGB was not tested as it precipitated in Krebs– Henseleit solution. NFD, verapamil and voltage-dependent calcium channel blockers relaxed KCl (60 mmol/L)-precontracted aortic rings in a dose-dependent manner (Figure 1) by 95–97%, with the IC50 values of 4.7 ± 0.2 nM and 32.9 ± 7.4 nM, respectively [35,36]. On the basis of these results, we may state that TGB does not possess voltage-dependent calcium channel blocking properties at the tested range of concentrations.

**Figure 1.** Inhibitory potencies of TGB and reference drugs (NFD and verapamil) on sustained contraction of aortic rings induced by KCl (60 mM).

*2.2. Molecular Docking Studies*

### 2.2.1. Terfenadine

TEF is (RS)-1-(4-tert-butylphenyl)-4-{4-[hydroxy(diphenyl)methyl]piperidin-1-yl}-butan-1-ol and, from a chemical point of view, belongs to piperidine derivatives (Figure 2).

**Figure 2.** Chemical structure of the investigated compounds. Carbons at a tetrahedral stereogenic center are distinguished by \*.

The obtained results from molecular docking confirmed the strongest blocking effects of TEF on the hKv11.1 channel. R-TEF-hKv11.1 is the most stable complex in the studied set. Comparison of the data (such as EB and pKi, Table 2) obtained for R-TEF and S-TEF complexes leads to the conclusion that there are significant stereoselective differences in the potential interaction for all studied channels. Docking experimentation predicted more effective potential interactions of all studied channels and S-TEF than its counterpart R-TEF (Table 2). Nevertheless, all calculated h-bonds formed are weak interactions. Moreover, obtained data suggests that R/S-TEF displays non inhibitory effects for hCav1.2. According to data in Table 2, pKi < 3.5, while by convention pKi ≤ 4 indicates the lack of a biological effect. Based on the CiPA studies, including the examination of the effect of 30 clinical drugs on the 7-ion channel [7], it can be concluded that the risk level of torsade de pointes (TdP) is correlated with the blocking effects of hKv11.1, hNav1.5 and hCav1.2. Crumb et al. [7,37] in their research proposed a classification of drugs into three categories of TdP entry risk (high, medium and low). According to their studies, drugs belonging to the high and medium risk stand out with block hERG to a much greater extent than any other tested currents. The drugs belonging to the low risk category is distinct with the non-specific blocking of hKv11.1, hNav1.5 and hCav1.2 channels (Figure S1). These results clearly indicate the need for testing drug candidates on the ion channel panel. In our study it was found, on the molecular level, that the high risk of TdP is a result of TEF's strong blocking effects on hERG. R-TEF-hKv11.1 complex has one normal h-bond in which the hydroxyl group of the (4-tert-butylphenyl)methanol moieties of R-TEF donates energetically weak (−3.62 kcal/mol), short (2.12 Å) and almost linear interactions (158◦) to Tyr652 (Figure S2). Regarding the interaction of R-TEF and S-TEF with the hNav1.5 channel, it should be noted that both enantiomers of TEF practically strongly interact with this protein (pKi > 5, Table 2), which also indicates their arrhythmogenic effects through

the hNav1.5. This observation is also confirmed by previously presented pharmacological studies [38].

**Table 2.** The summary of hNav1.5, hCav1.2 and hKv11.1 (hERG) channel docking experiment results.

Abbreviations in Table. Components of the investigated complexes: protein-hNav1.5, hCav1.2 and hKv11.1 (hERG); and ligands, R/S-TGB (R/S-tiagabine), NFD (nifedipine), R/S-TEF (R/S-terfenadine), BTX (batrachotoxin). Other abbreviations: HB—hydrogen bond. acc—hydrogen bond acceptor. Hydrogen bond components: from the ligand % and from the protein #. EB—complex energy binding. θ—hydrogen bond angle. LHB—hydrogen bond length. EHB—hydrogen bond energy. pKi was calculated from the AutoDock4 and estimated inhibition constant Ki, which is reported in the AutoDock4 output.

### 2.2.2. Nifedipine

NFD is a 3,5-dimethyl 2.6-dimethyl-4-(2-nitrophenyl)-1.4-dihydropyridine-3,5 dicarboxylate (Figure 2). It is classified as a dihydropyridine subclass compound. It is a highly apolar photosensitive compound. The NFD docking experiment revealed that this molecule can interact with an active site of all the studied proteins (Table 2). It can be set to the following descending order of binding energies EB(NFD-hKv11.1) ≈ −4.42, EB(NFD-hNav1.5) ≈ −7.00 and EB(NFD-hCav1.2) ≈ −10.71 kcal/mol, respectively. The binding energies obtained in the docking experiment show that NFD forms a more stable complex in the case of the hCav1.2 channel. The in silico data obtained for blocking of

hCav1.2 channel (pKi = 7.85) are in line with data in the literature (pIC50 = 7.48) [35] and (pKi = 7.66) [39]. Based on the predicted binding affinity, the highest affinity was observed with the hCav1.2 and NFD in all cases of studied channels. Additionally, the blocking effect of NFD with hNav1.5 was also proven (Table 2). It has to be highlighted that in NFDhNav1.5 and NFD-hKv11.1 complexes, NFD interacts via one normal h-bond. In contrast, the complex NFD-hCav1.2 shows two h-bonds, both of which are short (≈1.68 Å) and strong (≈−3.8 kcal/mol) (Table 2.). The assessment of the data obtained for the hydrogen bonds clearly leads to the conclusion that they all have incomparable energies and bond lengths. Interestingly, NFD forms the strongest h-bond with hKv11.1 and the weakest one with hNav1.5 channel (Table 2. Figure S2). The h-bond with hCav1.2 is characterized by an indirect force of influence. However, taking into account the data from Table 2, there is no correlation between the binding affinities of NFD and h-bond energy. The obtained distribution of estimated pKi measure shows that in case of the hKv11.1 channel, NFD has no inhibitory effect for this channel (pKi < 4. Table 2). These data prove that NFD has potency only in inhibiting the hCav1.2 and hNav1.5 channel, which is in agreement with the pharmacological data previously presented in pharmacological literature [40].

### 2.2.3. Batrachotoxin

BTX as a steroidal alkaloid belongs to class A channel opening toxins [41]. Its molecule contains an oxazepane ring with tertiary amine and an aromatic pyrrole ring connected to the rigid polycyclic steroidal core via the ester group (Figure 2). Its 3D structure adopts a horseshoe conformation [42,43]. The outer surface of the horseshoe is hydrophobic, while the inner one is rather hydrophilic and forms the oxygen triad (at C3, C9 and C11) [44,45]. The outer surface of the horseshoe is hydrophobic, while the inner one is rather hydrophilic and forms oxygen triad (at C3, C9 and C11) [44]. It was suggested in the literature that this oxygen triad forms a hydrophilic arc, which can be regarded as a chelating site attracting some cations [46]. In the preliminary analysis of the docking, it was observed that BTX interacts with the active site of all studied proteins (Figure S2). The achieved data demonstrated that Nav1.5 forms one h-bond, while hCav1.2 and hKv11.1 form two h-bonds. The energies obtained in the present in silico experiment show that hCav1.2 and hKv11.1 form more and form stronger hydrogen bonds than the remaining one (hNav1.5). The predicted binding affinity can be arranged in the following increasing order: hKv11.1 < hCav1.2 < hNav1.5 (Table 2). Obtained results revealed that BTX-hNav1.5 is an energetically more stable complex with a binding energy of −9.01 kcal/mol. The stability of the complex with the hCav1.2 and hKv11.1 is slightly lower than that with hNav1.5 (i.e., EB BTX-hCav1.2 = −7.17 and EB BTX-hKv11.1 = −6.75 kcal/mol, respectively). The calculated BTX affinity value of pKi = 6.68 is consistent with the relevant inhibitory potential data known from the specialized literature (pIC50 = 6.71) [47]. The BTX-hNav1.5 complex is formed via a single key interaction with residues of hNav1.5. The hydroxyl group at position C-11 of the steroid skeleton donates one h-bond to the sulfur atom of Ser1458. This interaction is non-linear (157◦) and with weak energy, i.e., −3.43 kcal/mol and a small bond length equal to 2.18 Å. This is in line with the literature data according to which the atom included in oxygen triad is responsible for the toxic interaction of BTX with its molecular target [44]. We can treat these data as a strong argument proving that BTX acts on the cytoplasmic side of the channel just as other Class A neurotoxic compounds do, as it is shown in Figure S2.

For hCav1.2 and hKv11.1 channels, BTX has pKi ≈ 5.2, which suggests comparable blocking effects. The BTX-hCav1.2 complex is a more energetically stable form than the BTX-hNav1.5 complex (Table 2). This complex has one normal h-bond. In the bifurcated hbond, the major component is Ser1132 and the minor component is Gln1060; the associated energies are weak (EHB = −0.37 kcal/mol and −3.23 kcal/mol, respectively). The distances of the hydrogen bonds lay in the range from 1.69 to 2.22 Å and, due to this, the threecentered hydrogen bond is highly not symmetric. In the case of BTX-hCav1.2 complex, it is formed via one normal h-bond interaction (Table 2).

### 2.2.4. Tiagabine

As it can be seen from the data collected in Table 2, TGB complexes between hNav1.5, hCav1.2 and hKv11.1 have a calculated pKi ≤ 4, which is commonly used as a threshold and this drug can thus be considered as an inactive ligand for those proteins. These data refer to both R/S enantiomers of TGB (Figure 3). Docking TGB into the hNav1.5 channel showed the highest pKi value in Table 2, however, the value is still below the activity threshold level of pKi > 4. The lowest pKi was observed for hKv11.1. We can treat these data as a partial explanation of the reported adverse TGB interactions in the cardiovascular system. It also seems that a small percentage of the observed cardiac disorders can be attributed to the fact that TGB does not interact with hKv11.1, for which inhibition is responsible for QTc prolongation. In all analyzed cases, R-TGB shows slightly higher binding affinities than S-TGB. This observation is in line with many previous pharmacological studies indicating the greater biological activity of R enantiomers compared to the S ones [10–12]. It is also worth emphasizing that all h-bonds formed between hCav1.2 channels and R/S-TGB possess very favorable key interaction energy values (EHB ≈ −7.32 for hCav1.2-R-TGB and −5.54 kcal/mol for hCav1.2-S-TGB, Table 2) and geometrically nonlinear systems.

**Figure 3.** Pocket locations and binding modes of (R/S)-Tiagabine (R/S-TGB) and the investigated channels: hNav1.5, hCav1.2 and hKv11.1. Ligands (ball and stick model) and calculated hydrogen bonds (dashed green lines).

To sum up, the docking experiment revealed that R/S-TGB has lower intermolecular forces with all studied ion channels (EB ≈ −5.30 kcal/mol, Table 2). The combination

of the above-mentioned docking data with pharmacological as well as literature data regarding the risk for tachycardia due to TGB administration suggests that this adverse effect observed in humans is not likely to result from TGB interaction with molecular anti-targets (i.e., ion channels tested in this study) used for the cardiac risk assessment. The data obtained in this study allowed the supplementation of information on the impact of TGB on other than GAT-1 molecular targets. As is currently known, TGB has no significant affinity to other uptake systems, such as those for dopamine, noradrenaline, acetylcholine, adenosine, serotonin, histamine, opiate, glycine, glutamate or GABA [48,49]. It only has a weak affinity towards benzodiazepine receptors and does not affect K+ and Ca2+, while it slightly affects Na+ and cardiovascular channel function [19].

### *2.3. Validation Experiment*

The validation was carried out by the docking of molecules with no affinity to hNav1.5, hCav1.2 and hKv11.1 channels, such as progabide (PRG) [50,51] and acetylsalicylic acid (ASA) [52]. This choice of PRG and ASA was made based on their chemical and biological similarity to TGB (ATC code: N03AG06 [53]). PRG (ATC code: N03AG05 [53]) is a first-generation antiepileptic drug without analgesic properties [12,54]. ASA (ATC code: N02BA01 [52]) is a classical and peripherally-acting nonsteroidal anti-inflammatory drug that is inactive at GAT and recommended for the prevention of several cardiovascular diseases due to its antiplatelet activity [52].

The results of the validation experiments, such as the complex binding energies, the specific hydrogen bond components and detailed data of the hydrogen bond features (energies, lengths and angles) are gathered in Table 3. The binding modes between hNav1.5, hCav1.2 and hKv11.1; and the control compound are illustrated in Figure S3 in the supplementary file. As it can be observed from the data, all control complexes have a calculated pKi < 4, which is commonly used as the threshold and therefore the test compounds can be considered to be inactive ligands for those proteins. In addition, all control complexes have higher binding energy values and lower hydrogen bond energy than the TGB-hVGICs complex.

**Table 3.** The summary of validation experiment results.

Abbreviations in Table. Components of the investigated complexes: protein-hNav1.5, hCav1.2 and hKv11.1 (hERG); and ligands, ASA acetylsalicylic acid, PRG—Progabide. Other abbreviations: HB—hydrogen bond. acc—hydrogen bond acceptor. Hydrogen bond components: from the ligand % and from the protein #. EB—complex energy binding. θ—hydrogen bond angle. LHB—hydrogen bond length. EHB—hydrogen bond energy. pKi was calculated from the AutoDock4 and estimated inhibition constant Ki, which is reported in the AutoDock4 output [55].

### **3. Discussion**

Taking into consideration the new action profile of drugs already on the market, pharmacological safety is a particularly important issue. The main reason for this is the fact that for drugs with an extended therapeutic range (i.e., repurposed medications), the number of patients for whom a given drug is recommended will increase significantly. TGB discussed in this study belongs to this group of drugs. TGB is an anticonvulsant medication. It is also used in the treatment of anxiety-related disorders, as are a few other anticonvulsants [21]. In the case of this drug, the above mentioned point is particularly important because TGB was originally prescribed to a relatively small group of patients due to its narrow range of indicators (adjunctive treatment of partial seizures in adults and children 12 years of age and older) [56]. However, when we consider its analgesic and anxiolytic effects in our considerations, we include therapeutic indications for a significantly larger group of patients. Reports presented in the scientific literature indicate an approximate 70% increase in the number of patients on TGB therapy [57]. This is all the more important as the safety and efficacy of TGB have not been systematically evaluated for indications other than epilepsy.

Modern technology provides us with many different possibilities, the use of which should create the conditions to learn about safety pharmacology. Undoubtedly, in silico research is one of many possibilities for seeking answers in this regard. It seems to us that the research presented above makes some important contributions to this issue. The study tried to answer the question about the cardiovascular safety assessment of TGB. This is all the more important in the light of the recent expert discussions focused on extending the pharmacological profile of TBG. Many modern studies indicate that, in addition to the therapeutic use of TGB in epilepsy, we should strongly consider its utilization for non-epileptic indications. On the other hand, it is known that drugs that show this type of biological activity might have a strong effect on a heart. This, in turn, undoubtedly forces us to increase the effort focused on assessing cardiac safety. Considering the assessment of the effect of the compound on so-called anti-targets adopted from CiPA in the cardiac risk assessment, the TGB interaction was investigated with the following channels: hKv11.1, hNav1.5 and hCav1.2. Drugs strongly affecting individual channels (such as TEF, BTX and NFD) were selected as reference compounds for this study. TEF is a prodrug metabolized by intestinal CYP3A4 to fexofenadine, the active form being a selective histamine H1-receptor antagonist with antihistaminic and non-sedative effects. As it is well known, antihistamines may increase the rate of heart beat [58,59]. TEF causes prolonged repolarization, as is reflected in the broadening of the electrocardiographic QT interval, with the potential for serious ventricular arrhythmia and death [60–62]. Due to this, in the U.S. TEF was superseded by its active metabolite fexofenadine in the 1990s [59]. Numerous studies have proven that the ability of TEF to extend the QT interval depends on its binding to the Kv11.1 (Figure S1) protein encoded by hERG [63,64]. Nevertheless, TEF does not readily cross the blood–brain barrier and due to this its CNS, depression is minimal. NFD is a calcium channel blocker, a specific antagonist of Cav1.2 channels [65]. It is used to treat hypertension and chronic stable angina. NFD binds directly to inactive calcium channels and stabilizes their inactive conformation. By inhibiting the influx of calcium in smooth muscle cells, NFD prevents calcium-dependent myocyte contraction and vasoconstriction [31,40]. BTX was chosen as a reference compound in our docking experiment study due to its extremely potent cardiotoxic and neurotoxic characteristics [29,42,46,47,66]. In animals, BTX inactivates sodium channels in nerve cells and muscle cells, thereby interfering with the electrical signals sent throughout the body and causing fibrillation, arrhythmias, cardiac failure and death [29]. It is worth emphasizing that the obtained data from molecular modeling confirmed the high selectivity of the reference compounds for the appropriate ion channels (Table 2). The response obtained from molecular studies also indicates that the mechanisms underlying tachycardia in patients treated with TGB appear to be unrelated to its effect on the hNav1.5, hCav1.2 and hKv11.1 heart ion channels. Moreover, it is known that one of the most common mechanisms of drug-induced ventricular tachycardia is the blocking of

hERG channels [67,68]. For this reason, additional evidence supporting the conclusions of the in silico study appears to provide epidemiological data (pharmacological reports) in which tachycardia is noted in approximately 1.0% of patients treated with TGB. In light of these facts, if TGB-induced tachycardia contacts the hKv11.1, hNav1.5 and hCav1.2. blocking mechanism, one would expect a higher rate of these side effects. In addition to the above-mentioned in vivo tests, as a part of safety pharmacology experiments, the TGB effects on PQ, QRS and QT intervals and the effects of TGB on heart rate were assessed. Many years of research have demonstrated that proarrhythmic effect, QT prolongation and hERG blocking cannot be treated as the only determinants of the occurrence of TdP. For instance, verapamil and ranolazines are examples of drugs that are strong inhibitors of the hERG channel and are simultaneously devoid of the risk of inducing arrhythmias and, vice versa, devoid of serious disorders of cardiomyocyte electrophysiology caused by drugs that are weak hERG inhibitors (e.g., sotalol and alfuzosin) [24,69]. Thus, this proves the insufficient specificity of the tests based only on the assessment of the hERG channel blocking potential. The risk of drug-induced TdP is rather balanced by multiple internal cardiac ionic currents that define ventricular repolarization. Therefore, studies that utilize the whole tissue seem to be a good option for the reflection of pharmacodynamics and potential adverse effects of a drug in a living organism. The in vivo results obtained from studies in rats showed that TGB did not prolong the QT interval or alter the QRS complex which suggests that it did not affect ventricular depolarization and repolarization. Taking into account that as in many other animal models of human diseases and also in this particular rat model, there might be basic translational problems. Some fundamental differences in the cardiac electrophysiology and myocyte calcium/potassium handling between rodents and humans have been suggested [70], but, nonetheless, the ECG in rats is still a widely applied experimental method in basic cardiovascular research. The technique of ECG recordings is simple; however, the interpretation of electrocardiographic parameters might be challenging. This is because the analysis may be biased by experimental settings, such as the type of anesthesia and the strain or age of animals. Furthermore, differences and similarities between rat and human ECG are frequently discussed in the context of translational cardiovascular research. Despite this, rat electrocardiography is an important investigational tool in experimental cardiology, even if the interpretation of electrocardiographic parameters is problematic [71]. In addition to this, a number of studies have shown that cardiotoxic drugs prolong QT interval in rodents and ECG recordings in rats have been used as a screening tool to assess the cardiotoxicity of various drugs. However, it needs to be stressed that the translation of the results of those studies to human application also possesses limitations. This is because rats' hearts do not express hERG, whereas the cardiotoxicity of drugs is strongly associated with the blockade of hERG-related potassium channels. However, rat hearts express a variant of Ether-à-go-go-Related Gene (rat ERG, also known as Kcnh2) [72,73], which may also play a key role in the assessment of druginduced cardiotoxicity. Taken together, we are aware that extrapolating the results from our rat model to humans should be performed extremely cautiously and this, of course, should be regarded as the main limitation of our study. Therefore, one can assume that its direct interaction with heart sodium and potassium channels can be neglected. TGB also marks no significant effect on the PQ interval, which suggests that it does not influence the atrio-ventricular conduction time. Consequently, we can postulate that TGB has low pro-arrhythmic potential, at least, after a single administration. Given that tachycardia may result due to a number of different mechanisms and not all of them directly affect ion channels, it is also necessary to evaluate the effects of TGB on several neurotransmitters/neuromodulators and the activity of the autonomic nervous system. However, these effects were not investigated in the present research. Further studies are necessary to assess the effect of TGB on the cardiovascular system, especially after chronic administration. On the basis of our research, we can state that TGB did not bind to voltage-gated ion channels and did not affect them directly. Furthermore, the observed accidents of tachycardia are probably not due to the direct effect of TGB on voltage-gated ion channels.

### **4. Methods**

### *4.1. Pharmacological Studies General Information*

The experiments were carried out using male Wistar rats (Krf:(WI) (WU), 200–250 g). The animals were housed in constant temperature facilities exposed to 12:12 h light/dark cycles and were maintained on a standard pellet diet with tap water given ad libitum. All procedures were conducted according to guidelines of ICLAS (International Council on Laboratory Animal Science) and approved by the Second Local Ethics Committee in Krakow, Poland (resolution No. 106/2016, 14 June 2016).

### 4.1.1. Voltage-Dependent Calcium Channels—Functional Assays

In order to investigate the calcium entry blocking properties of TGB, it was tested on isolated rat aorta precontracted with KCl. Rats were anaesthetized with thiopental sodium (75 mg/kg, i.p., Rotexmedica, Germany) and the thoracic aorta was dissected, cleaned, denuded of endothelium, cut and mounted as described earlier [35]. Briefly, aorta rings were incubated in 30 mL chambers filled with a Krebs–Henseleit solution (NaCl 118 mM, KCl4.7 mM, CaCl2 2.25 mM, MgSO4 1.64 mM, KH2PO4 1.18 mM, NaHCO3 24.88 mM, glucose 10 mM, C3H3O3Na 2.2 mM and EDTA 0.05 mM) at 37 ◦C and pH 7.4 with constant oxygenation (O2/CO2, 19:1) and connected to an isometric FDT10-A force displacement transducer (BIOPAC Systems, Inc., COMMAT Ltd., Ankara, Turkey). The aortic rings were stretched and maintained at an optimal tension of 2 g and permitted to equilibrate for 2 h. The aortic rings were contracted to submaximal tension with KCl (60 mmol/L). Once the plateau was attained, concentration-relaxation curves were obtained by the addition of cumulative doses of tiagabine to the precontracted preparations.

Concentration-response curves were analyzed using GraphPad Prism 5.0 software (GraphPad Software Inc., San Diego, CA, USA). Relaxations are expressed as a percentage of inhibition of the maximal tension obtained with the contractile agent (Emax = 100%). Data are the means ± SEM of at least 4 separate experiments.

### 4.1.2. The Effect on Normal Electrocardiogram

In vivo electrocardiographic investigations were carried out using an ASPEL ASCARD B5 apparatus (Aspel, Poland), standard lead II and paper speed of 50 mm/s. The ECG was recorded just prior to and also at 1, 5, 10, 20, 30, 40, 50 and 60 min following the i.p. administration of TGB at a dose of 100 mg/kg. The QTc was calculated according to the formula of Bazzett: QTc = QT/√RR [74]

### *4.2. In Silico Studies*

The calculation procedures applied in the study are typical for the processing of docking studies.

### 4.2.1. Ligand Preparation

For the 3D molecular structure calculations, the Gaussian 09 (version D.01. for Unix/Linux) package was used [75]. The initial acceptable 3D structures of 6 studied compounds (Figure 2) were downloaded (as mol2 file) from ZINC [76]. Later, the GaussView [75,77] was applied for preparation of Gaussian input files. All the molecules were geometry-optimized in water as described by the PCM (polarizable continuum model). DFT/B3LYP level of theory 6311 + G(d, p) basis set was used. After geometrical optimization, (the root-mean-square gradient value smaller than 10−6 a.u.) compounds were saved as mol2 files using the GaussView. Subsequently, torsionals and the number of active torsions for ligands were defined and the Gasteiger charges were assigned to each compound via AutoDockTools (ADT) [78]. Finally, ligands prepared for docking were saved as pdbqt files.

4.2.2. Voltage-Gated Ion Channels Preparation

• hNav1.5 preparation:

The lack of crystal structure of hNaV1.5 pore domain causes the need for preparing homology 3D models for this protein. For our research, the sequence for the hNav1.5 protein was gained from the Swiss Model Repository (SMR). SMR is a database which currently holds over 400,000 high quality 3D protein structure models generated by the automated SWISS-MODEL homology modeling pipeline [79]. The pdb file was downloaded from SWISS-MODEL SERVER (accession number Q14524) [80]. For this alignment, X-ray structure of human Nav1.2-β2-KIIIA ternary complex (PDB entry 6J8E) was employed. Sequence identity between template and the monomer of sodium channel protein type 5 subunit α is 66.70%. Subsequently, the pdb file was opened in ADT [78]. ADT read coordinates, added charges, merged non-polar hydrogens and assigned appropriate atom types. Before formatting a molecule for AutoDock, we removed 9Z9 ((3β,14β,17β,25R)-3- [4-methoxy-3-(methoxymethyl)-butoxyl]-spirost-5-en), which is irrelevant molecule in this experiment. Finally, the prepared protein was saved as a pdbqt file. In the computational part of the study, we pondered the interaction between the investigated ligands and the intracellular pore gate formed from the proper residues of chain A.

• hCav1.2 preparation

The dearth of a 3D structure of hCav1.2 proper region also causes need for preparing homology models of this protein. The sequence for the hCav1.2 was gained from the SMR as well (accession number Q13936). For this alignment, X-ray structures of nifedipine complex with rabbit Cav1.1 (PDB entry 6JP5) were employed. The sequence identity between template and isoform 4 of CAC1C\_HUMAN Voltage-dependent L-type calcium channel subunit alpha-1C is 70.31% and, according to the best of our knowledge, this is one of the highest identities currently available. Subsequently, similar to the hNav1.5 protein, the pdb file was opened in ADT [58]. The next steps were also analogous. Before the docking experiment, we removed C8U (methyl (4~{S})-2,6-dimethyl-5-nitro-4-[2- (trifluoromethyl)phenyl]-1,4-dihydropyridine-3-carboxylate)), which is a pointless ligand in this case. The pore forming and dihydropiridyne binding residues (from ARG1109 to LYS1198) were considered as the ligand binding site [81].

• hKv11.1 preparation:

The sequence for the potassium voltage-gated channel subfamily H member 2 protein was downloaded from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB entry 5va2) as the crystal structure [82]. As in previous proteins, the pdb file was opened in ADT, read coordinates, added charges, merged non-polar hydrogens and assigned the appropriate atom types. As usual, we also removed crystallographic waters from 5va2. The binding pocket of the studied molecule were composed of the pore forming segment H5 and transmembrane helical fragment-Segment S6 [83].

### 4.2.3. Molecular Docking

Molecular docking was performed using the AutoDockTools 4.2 suite of the program [55]. A grid box with a dimension of 60 × 60 × 60 Å3 and grid spacing of 0.375 Å, which is large enough for a free rotation of a ligands, was built in the middle of the binding pockets of the studied Voltage Gated Ion Channels (VGICs) channels, which are composed using the appropriate residues (Table 4).

Torsionals in the residuals of the binding pocket were not rotatable. The rigid docking was carried out using the Lamarckian genetic algorithm 4. The optimized docking parameters were set as default values, with the exception of the number of genetic algorithms run which was 100. Torsionals in the ligands were rotatable-6 active torsions in each ligand (except for terfenadine, where it was 11). A cluster analysis was performed using RMS tolerance of 2 Å. In each case, the best docking result was considered as the complex with the lowest binding energy. Interactions between ligands and the related channel models were analyzed using the AutoDockTools program (ADT. Version 1.5.4) [78].

**Table 4.** The composition of the binding pocket of the analyzed channel models: hNav1.5, hCav1.2 and hKv11.1.

**Supplementary Materials:** The following are available online, Figure S1: Model transport proteins selected for the study: hNav1.5, hCav1.2, Kv11.1. Figure S2: Figure S3: Binding modes between hNav1.5, hCav1.2 and Kv11.1 and the four tested compounds () and two validated compounds

**Author Contributions:** A.N. and M.K. (Magdalena Kowalska) conceived and directed the project. K.S., Ł.F., M.K. (Magdalena Kowalska) and A.N. designed the study. K.S., Ł.F., M.K. (Magdalena Kowalska), M.K collected the data and carried out the experiments. K.S., Ł.F., M.K. (Magdalena Kowalska), M.K. and A.N. analyzed the data. A.N., M.K. (Magdalena Kowalska), Ł.F., M.K., G.G., J.N. and K.S. interpreted the results and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by a research grant from the National Science Centre UMO-2015/17/B/NZ7/02937.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board the Second Local Ethics Committee in Krakow, Poland (resolution No. 106/2016, 14 June 2016).

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** None of the authors declare any conflicts of interest with respect to this study.

**Sample Availability:** Samples of the compounds are not available from the authors.

### **Abbreviations**

### **References**

### *Article* **Development of Halogenated Pyrazolines as Selective Monoamine Oxidase-B Inhibitors: Deciphering via Molecular Dynamics Approach**

**Aathira Sujathan Nair 1,†, Jong-Min Oh 2,†, Vishal Payyalot Koyiparambath 1, Sunil Kumar 1, Sachithra Thazhathuveedu Sudevan 1, Opeyemi Soremekun 3, Mahmoud E. Soliman 3, Ahmed Khames 4, Mohamed A. Abdelgawad 5,6, Leena K. Pappachen 1,\*, Bijo Mathew 1,\*,† and Hoon Kim 2,\***

**Abstract:** Halogens have been reported to play a major role in the inhibition of monoamine oxidase (MAO), relating to diverse cognitive functions of the central nervous system. Pyrazoline/halogenated pyrazolines were investigated for their inhibitory activities against human monoamine oxidase-A and -B. Halogen substitutions on the phenyl ring located at the fifth position of pyrazoline showed potent MAO-B inhibition. Compound 3-(4-ethoxyphenyl)-5-(4-fluorophenyl)-4,5-dihydro-1*H*-pyrazole (**EH7**) showed the highest potency against MAO-B with an IC50 value of 0.063 μM. The potencies against MAO-B were increased in the order of –F (in **EH7**) > –Cl (**EH6**) > –Br (**EH8**) > –H (**EH1**). The residual activities of most compounds for MAO-A were > 50% at 10 μM, except for **EH7** and **EH8** (IC50 = 8.38 and 4.31 μM, respectively). **EH7** showed the highest selectivity index (SI) value of 133.0 for MAO-B, followed by **EH6** at > 55.8. **EH7** was a reversible and competitive inhibitor of MAO-B in kinetic and reversibility experiments with a Ki value of 0.034 ± 0.0067 μM. The molecular dynamics study documented that **EH7** had a good binding affinity and motional movement within the active site with high stability. It was observed by MM-PBSA that the chirality had little effect on the overall binding of **EH7** to MAO-B. Thus, **EH7** can be employed for the development of lead molecules for the treatment of various neurodegenerative disorders.

**Keywords:** halogenated pyrazolines; monoamine oxidase inhibitors; kinetics; reversibility; molecular dynamics

### **1. Introduction**

Monoamine oxidases (MAOs) are the principal metabolizing enzymes responsible for the oxidative degradation of various biogenic amines that are related to diverse cognitive functions of the central nervous system (CNS) [1]. Considering the difference in the structure, substrate specificity, biological functions, and catalytic mechanism of MAOs,

**Citation:** Nair, A.S.; Oh, J.-M.; Koyiparambath, V.P.; Kumar, S.; Sudevan, S.T.; Soremekun, O.; Soliman, M.E.; Khames, A.; Abdelgawad, M.A.; Pappachen, L.K.; et al. Development of Halogenated Pyrazolines as Selective Monoamine Oxidase-B Inhibitors: Deciphering via Molecular Dynamics Approach. *Molecules* **2021**, *26*, 3264. https:// doi.org/10.3390/molecules26113264

Academic Editors: Alicja Nowaczyk and Grzegorz Grze´sk

Received: 29 April 2021 Accepted: 27 May 2021 Published: 28 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

two types of isoforms are available, namely, MAO-A and MAO-B [2]. Ammonia, aldehyde, and hydrogen peroxide are the major intermediate products formed during MAO-based catalyzed oxidative deamination [3]. These can produce substantial issues, such as astrocyte swelling, unbalancing of the signaling process in the neurotransmission, neuronal loss, and mitochondrial dysfunctions [4]. Eventually, these toxic by-products can lead to numerous neurodegenerative disorders, such as Alzheimer's disease (AD) and Parkinson's disease (PD) [5]. Selegiline is a selective/irreversible MAO-B inhibitor used as an adjuvant therapy with L-DOPA, which is considered a safe therapy for PD [6,7]. The latest research has also documented that potent, reversible dual targeted MAO-B/AChE inhibitors can decrease the production of β-amyloid peptide levels in AD-related neurons [8]. In 2017, safinamide, as the first selective and reversible MAO-B inhibitor approved by the USFDA for PD treatment, along with levodopa, has promising neuroprotective effects on MPTP-treated mice [9,10].

In the exploration of novel, selective, and reversible MAO-B inhibitors, a general blueprint of drug design has been widely accepted recently, and it consists of a molecular framework of two hydrophobic rings of phenyl/heteroaryl, which are separated by an electron-rich and flexible short spacer unit (Figure 1) [11]. Many of the molecules from this diverse class, such as pyrazolines, enamides, carboxamides, and α, β-unsaturated ketones, were identified as potent MAO-B inhibitors [12–21]. The presence of a rotatable bond and electron-rich Michael acceptor unit in the spacer can efficiently make appropriate orientations and binding affinities in the entrance and substrate cavity of MAO-B. The flexibility of the molecules provides good recognition of aromatic systems of the ligand to the hydrophobic cavity of the inhibitor binding cavity of MAO-B by Pi–Pi stacking or Pi–cation interactions. The binding requirements of the selective MAO-B inhibitors include one or more hydrophobic ring, preferably an aromatic or heteroaromatic nucleus with small lipophilic substituents, such as dimethylamino, ethoxy, methoxy, and halogens. The presence of a hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) linker with optimal rotatable bonds between these hydrophobic units had a pivotal role in the design of potent MAO-B inhibitors [22–26].

**Figure 1.** General blueprint of the design of MAO-B inhibitors.

Pyrazoline, or dihydropyrazole, is the partially reduced form of pyrazole, and 2 pyrazoline is the most stable tautomeric from in this class [27]. The presence of an asymmetric carbon atom at the C5 position of pyrazoline was revealed to be due to two epimers of the *R* and *S* forms [28]. The ring closure reaction of α, β-unsaturated ketones with hydrazine derivatives in the presence of basic medium is the commonly used synthetic methodology for 2-pyrazolines [29]. The separation of the *R* and *S* forms from the racemic mixture of 2-pyrazoline is a considerable task in the identification of biologically more active isomers [30]. Many studies of the literature have documented that the substitution of the 3rd and 5th position of pyrazolines with aryl/heteroaryl units can favor MAO-B

inhibition [31]. At the same time, the length and bulkiness of the groups anchoring from the N1 position of pyrazolines can shift the selectivity from MAO-B to MAO-A. The presence of lipophilic groups, such as halogens, hydroxyl, and methoxy groups, on the phenyl ring of the third and fifth positions of the pyrazoline nucleus, can cause significant MAO-B inhibition [32–35].

Regarding the role of halogens in MAO inhibition, it is generally known that FDAapproved MAO inhibitors like clorgyline, moclobemide, safinamide, and lazabemide are chlorine-containing drugs [36]. The evidence shows that more than 40% of drugs in FDAapproved or clinical trials are in the halogenated form [37]. Recently, our group emphasized the presence and orientation of various halogens on chemical scaffolds, such as chalcones, chromones, coumarins, hydrazothiazoles, and pyrazolines, thereby providing in-depth knowledge on MAO inhibition and B isoform selectivity. It was noted that this can have a new type of impact on the drug–target interaction of these MAO inhibitors in order to fill the small lipophilic pockets in the inhibitor binding cavity (IBC) of MAOs [38].

The objective of the present study was focused on the in vitro evaluation of MAO-A and MAO-B inhibitions of pyrazoline/halogenated pyrazoline derivatives. The lead molecules were further studied for kinetics, reversibility, and blood–brain barrier (BBB) permeation. Detailed molecular dynamics established precisely the protein–ligand binding interactions of the halogenated molecules in their respective active sites of enzyme targets and the role of chirality towards MAO-B inhibition.

### **2. Results and Discussion**

### *2.1. Inhibitory Activities against MAO Enzymes*

At 10 μM, all derivatives in the **EH** sequence had potent inhibitory action against MAO-B, with residual activities of about 50% (Table 1). Fluorine-containing compound **EH7** showed the greatest inhibitory activities against MAO-B with an IC50 value of 0.063 μM, followed by chlorine-containing **EH6** (IC50 = 0.40 μM), and bromine-containing **EH8** (IC50 = 0.69 μM). Comparing **EH1** (IC50 = 5.38 μM) with **EH7**, the potencies for MAO-B greatly increased, i.e., by 85.4 times, based on their IC50 values, with the presence of –F instead of –H. Most of the compounds weakly inhibited MAO-A at 10 μM, with residual activities of >50%, except **EH7** and **EH8**. Among them, **EH7** showed the highest selectivity index (SI) value for MAO-B with 133.0, followed by **EH6** with 55.8.

**Table 1.** Inhibitions of recombinant human MAO enzymes by compounds of the **EH** series a.

a Results are expressed as the means ± standard errors of duplicate or triplicate experiments. b SI values are expressed for IC50 of MAO-B/IC50 of MAO-A. Values for reference compounds were determined after preincubation for 30 min with enzymes.

> The enzyme inhibition results obtained in the current study revealed that all of the halogenated pyrazoline derivatives showed good selective MAO-B inhibitory activity. The study mainly confirmed the effect of halogens on the *para*-position of the **B** ring of ethoxylated pyrazolines. Among the halogens, fluorine had the greatest impact on MAO-B inhibition with a sub-micromolar level range. It is worth noting that a highly electronegative fluorine atom-containing compound is two times more potent than marketed drugs

for the inhibition of MAO-B, such as reversible-type lazabemide (IC50 = 0.11 μM) and irreversible pargyline (IC50 = 0.14 μM). Recent reports state that the anisotropy of charge circulation around the fluorine atom, which is the reason for the directional and stabilizing property of the active site, proves that halogen enhances the analogy of drugs of new molecules [38].

Recently, we evaluated MAO-B inhibitory activities for ethoxylated head of chalcone derivatives and found that chlorine-, fluorine-, or bromine-containing chalcones had good IC50 values of 0.57, 0.053, and 2.01 μM, respectively [39]. Cyclization with hydrazine hydrate of the same halogenated molecules retained higher MAO-B inhibitory activities than the unsubstituted one. This finding clearly shows the importance of halogen incorporation both in ethoxylated chalcones and their pyrazolines moieties for MAO-B inhibition. Interestingly, in both series, fluorine-containing compounds showed dominant MAO-B inhibition profiles. For the purpose of the development of compounds with good MAO-B inhibitory activity, the bonding of fluorine to the molecule was an auspicious lead.

### *2.2. Kinetics of MAO Inhibition*

Kinetic studies on MAO-A and MAO-B inhibitions by **EH6** and **EH7** were performed. Lineweaver–Burk plots and secondary plots showed that **EH6** was a competitive inhibitor of MAO-A and MAO-B (Figure 2A,C), with Ki values of 10.8 ± 1.03 and 0.19 ± 0.016 μM, respectively (Figure 2B,D). Lineweaver–Burk plots and secondary plots exhibited that **EH7** was a competitive inhibitor of MAO-A and MAO-B (Figure 3A,C), with Ki values of 3.64 ± 0.18 and 0.034 ± 0.0067 μM, respectively (Figure 3B,D). These results suggest that **EH6** and **EH7** are selective and competitive inhibitors of MAO-B.

**Figure 2.** Lineweaver–Burk plots for MAO-A and MAO-B inhibitions by **EH6** (**A**,**C**) and their respective secondary plots (**B**,**D**) of slopes vs. inhibitor concentrations.

**Figure 3.** Lineweaver–Burk plots for MAO-A and MAO-B inhibitions by **EH7** (**A**,**C**) and their respective secondary plots (**B**,**D**) of slopes vs. inhibitor concentrations.

### *2.3. Reversibility Study*

Reversibility studies on MAO-B inhibition were carried out on **EH7**. In these experiments, inhibition of MAO-B by **EH7** was recovered from 12.5% (value of AU) to 89.5% (value of AD). The recovery value was similar to that of the reversible reference lazabemide (from 35.3% to 75.7%); however, it was different from that of the irreversible inhibitor pargyline (from 41.3% to 44.9%), which was not recovered (Figure 4). Similar to the reversible reference level, inhibition of MAO-B by **EH7** was reversed in these experiments, indicating that **EH7** is a reversible inhibitor of MAO-B.

**Figure 4.** Recovery of MAO-B inhibition by **EH7**, using dialysis experiments. The concentrations used were **EH7** at 0.15 μM, lazabemide (a reversible reference inhibitor) at 0.20 μM, and pargyline (an irreversible reference inhibitor) at 0.30 μM.

### *2.4. Blood–Brain Barrier (BBB) Permeation Studies*

The CNS bioavailability of the molecules was confirmed by a parallel artificial membrane permeability assay (PAMPA) [40]. From the assay, highly effective permeabilities and high CNS bioavailabilities were observed for the halogenated pyrazolines with Pe ranges of 14.13 ~ 14.56 × 10−6 cm/s (Table 2). All halogenated substituted derivatives showed higher CNS permeabilities than unsubstituted ones.

**Table 2.** BBB assay of standard and **EH** compounds.

CNS+ (high): Pe (10−6 cm/s) > 4.00; CNS− (low): Pe (10−6 cm/s) < 2.00; CNS± (uncertain): Pe (10−6 cm/s) from 4.00 to 2.00. a From [40].

### *2.5. Molecular Dynamics Studies*

2.5.1. **EH1**, **EH6**, **EH7**, and **EH8** Exert Differential Impacts on the Stability of MAO-A and MAO-B

The root mean square deviation (RMSD) can be used to estimate the conformational stability of a system during a production run [41]. The differential structural perturbatory effect of the ligands on the structure of MAO-B relative to MAO-A was first explored. MAO-B\_**EH6**, MAO-B\_**EH7**, and MAO-B\_**EH8** attained equilibration at around 20 ns (Figure 5). However, MAO-B\_**EH1** achieved convergence earlier in the simulation run but eventually elicited a somewhat high structural movement at around 45 ns. In comparison, **EH1**, **EH6**, **EH7**, and **EH8** did not elicit similar stabilizing effects on the MAO-A protein; this could be due to the IC50 values experimentally reported, suggesting their low inhibitory potential towards MAO-A. The superior stabilizing effect elicited by the ligands against MAO-B when compared to MAO-A binding points to the high selective targeting of the ligands for MAO-B. As indicated by the IC50, **EH7** had the highest stabilizing impact on MAO-B with an average RMSD value of 1.49 Å, while **EH1**, **EH6**, and **EH8** had 2.29, 1.61, and 1.79 Å, respectively (Table 3).

**Table 3.** Average RMSD, RMSD\_AS, RMSF, and IC50 of the Apo and bound systems.

**Figure 5.** Comparative root mean square deviations (RMSDs) of the Cα carbon atoms of the compounds in whole systems for MAO-B (**A**) and MAO-A (**B**) and in active sites for MAO-B (**C**) and for MAO-A (**D**). (**A**,**C**) **EH1**, black; **EH6**, green; **EH7**, blue; and **EH8** red. (**B**,**D**) **EH1**, black; **EH6**, red; **EH7**, green; and **EH8**, blue.

Zeroing on structural influence of the ligands on the mechanistic behavior of the active site could provide more clues regarding their activity. The RMSD of the MAO-A and MAO-B active sites upon **EH1**, **EH6**, **EH7**, and **EH8** binding revealed that **EH6** and **EH7** had the highest active site-stabilizing effect on MAO-B, with overall average RMSD\_AS values of 1.61 and 1.49 Å, respectively.

The orientation and position of a ligand are considerably indicative of how reactive the ligands can be within the active site. The characteristics of the ligands were therefore investigated. Interestingly, while **EH7** elicited some motional movement within the active site, which made it flexible enough to interact with crucial amino acid residues, it also exhibited good stability—just enough to be adequately anchored for overall binding (Figure 6).

**Figure 6.** Stability plot of compounds **EH1**, **EH6**, **EH7**, and **EH8**.

2.5.2. **EH7** Exhibited Low Residual Motion and Trajectory Movement upon Binding to MAO-A and MAO-B

Principal component analysis (PCA) is a common molecular dynamic simulation analysis used in the estimation of the essential production run of a system when computed on a low-dimensional free energy space [42]. This estimation is associated with correlated vibrational modes; the translation of the MD trajectory towards the geometric center abolishes the initial rotational and translational movements of the atoms. The decomposition of the trajectory along PC1 and PC2 reveals that upon **EH7** binding to MAO-B, the system exhibits very low diagonalization when compared with other systems (Figure 7A). This further corroborates the results discussed above in the stability section, which attest to the superior activity of **EH7** over other ligands in the series. In contrast, due to the low specificity of **EH1**, **EH6**, **EH7**, and **EH8** towards MAO-A, less high trajectory motion was observed in the ligand-bound MAO-A (Figure 7B).

The root mean square fluctuation (RMSF) computes the residual deviation of a protein over a period of time using a reference position, such as the averaged residual position. RMSF estimates the level of fluctuation of residue from its original position [43]. As depicted in the RMSF plot, the average RMSF values of MAO-B\_**EH1**, MAO-B\_**EH6**, MAO-B\_**EH7**, and MAO-B\_**EH8** were 0.88, 0.83, 0.68, and 0.75 Å, respectively (Figure 7C), while those of MAO-A\_**EH1**, MAO-A\_**EH6**, MAO-A\_**EH7**, and MAO-A\_**EH8** were 1.47, 1.15, 1.07, and 1.40 Å, respectively (Figure 7D).

2.5.3. Estimation of Free Binding Energies of **EH1**, **EH6**, **EH7**, and **EH8** on MAO-A and MAO-B

The estimation of free binding energy using an MM/PBSA protocol can be employed to gain insight into the models and further obtain the semiquantitative values of their stability. We therefore used a representative snapshot and active site residue decomposition to further explore the time-wise bond interaction occurring between major residues in the active sites of MAO-A/MAO-B and the ligands. MM/PBSA has found useful application in the drug design space used in the estimation of binding affinity between ligands and biomolecules [44].

As indicated in Table 4, **EH7** had higher binding affinity (−36.29 kcal/mol) towards MAO-B than the other ligands, while the binding of **EH1**, **EH6**, and **EH8** was recorded to be −4.11, −11.5, and −5.48 kcal/mol, respectively. Of note is the surprising high binding affinity (−32.55 kcal/mol) of **EH7** for MAO-A. This could be due to the high electronega-

tivity potential of fluorine, which is characteristic of compound **EH7**. In agreement with the IC50, the binding affinity of the compounds also followed the same trend.

**Figure 7.** Principal component analysis of the compounds bound to MAO-B (**A**) and to MAO-A (**B**), and root mean square fluctuation (RMSF) of the compounds bound to MAO-B (**C**) and to MAO-A (**D**). (**A**,**B**) **EH1**, black; **EH6**, green; **EH7**, blue; and **EH8**, red. (**C**,**D**) **EH1**, black; **EH6**, green; **EH7**, blue; and **EH8**, red.

The individual energy contributions of the active sites were determined using the MM/PBSA per residue energy decomposition protocol [45]. The high binding energy exhibited by **EH7** was contributed by electrostatic interactions of Arg42, Arg87, Arg98, Lys296, and Lys386, with energy values of −28.4, −12.8, −15.3, −41.9, and −20.6 kcal/mol, respectively (Figure 8C). In the interaction between compound **EH6** and MAO-B, residues Arg42, Gly58, Tyr60, Arg87, Pro98, and Arg100 contributed an electrostatic energy of −2.68, −0.04, −0.12, −15.17, −0.14, and −17.27 kcal/mol, respectively (Figure 8B). Despite the electrostatic interactions and other favorable bonds elicited by **EH1**, **EH6**, and **EH8**, compound **EH7** still had the best binding energy and highest contribution of electrostatic interactions. This behavior may not be unrelated to the impact of fluorine found on compound **EH7**. Furthermore, the energetically favored disposition assumed by **EH7** could be attributed to the hydrogen bond formed between the fluorine of **EH7** and hydrogen of Trp386 (Figure 9C). The ligand interaction profile of the interaction of **EH1** (A), **EH6** (B), **EH7** (C), and **EH8** (D) with MAO-B is shown in Figure 9.

**Table 4.** The total free binding energy contributions of **EH**, **EH6**, **EH7**, and **EH8**.

**Figure 8.** Per residue decomposition of **EH1** bound to MAO-B (**A**), **EH6** bound to MAO-B (**B**), **EH7** bound to MAO-B (**C**), and **EH8** bound to MAO-B (**D**) systems.

**Figure 9.** Ligand interaction profile of the interaction of **EH1** (**A**), **EH6** (**B**), **EH7** (**C**), and **EH8** (**D**) with MAO-B.

2.5.4. Estimation of Free Binding Energies of **EH1**, **EH6**, **EH7**, and **EH8** on MAO-A and MAO-B

Since our compounds contain a pyrazoline moiety, it is assumed that they possess an asymmetric carbon atom and, hence, exist as enantiomers. We therefore generated the R and S enantiomers of **EH7** as a representative structure of the compounds, taking into consideration the fact that it has the best IC50 and total free energy values. We explored the differential binding and estimated the total free binding energy of the R isomeric form of **EH7** (MAO-B\_**EH7**R) and its S counterpart (MAO-B\_**EH7**S) using MM/PBSA calculations. Our calculations revealed that MAO-B\_**EH7**R and MAO-B\_**EH7**S possess binding energies of −36.29 and −36.17 kcal/mol (Table 5). This suggests that the chirality does not change considerably in the overall binding of **EH7** to MAO-B. However, MAO-B\_**EH7**S has more van der Waals interactions than MAO-B\_**EH7**R. Despite having total free binding energy values that are close, dissimilar residues present in the active sites are responsible for the binding interactions. Chiefly among the residues are Tyr435, Tyr398, and Leu328 (jointly shared by MAO-B\_**EH7**R), which contribute van der Waals binding energies of −2.93, −1.58, and −1.00 kcal/mol, respectively (Figure 10).

### *2.6. Target Predicition and ADME Analysis*

The biological target and ADME predictions of the lead molecule of **EH7** were assessed by the online web-based program SwissTarget. The lead molecule showed good prediction results on biological targets of MAO and AChE (Figure 11A). The bioavailability radar gave saturation, size, flexibility, insolubility, and polarity of the molecule, which provided a quick judgement of the drug-likeness of the molecule. The lead molecule was recognized to be within the pink region (optimal ranges) and could be considered a compound endowed with drug-like characteristics (Figure 11B). The BOILED-Egg model also predicted the passive human BBB permeation and gastrointestinal absorption (HIA) for the compound (Figure 11C). The results revealed that **EH7** had comparatively higher probability of BBB and HIA.

**Table 5.** The differential free binding energy of MAO-B\_**EH7**R and MAO-B\_**EH7**S enantiomers.

**Figure 10.** Per residue decomposition of MAO-B\_**EH7**S enantiomer.

**Figure 11.** Swiss target/ADME prediction: (**A**) Biological target prediction of lead molecule **EH7**; (**B**) bioavailability radar of **EH7**; (**C**) the BOILED-Egg construction of **EH7**.

### **3. Materials and Methods**

*3.1. Synthesis*

A series of ethoxylated chalcone (0.0078 M) was refluxed with (0.0629 M) hydrazine hydrate under ethanol medium. After 14–16 h of reflux, the product was cooled, acidified with dil. HCl, washed thoroughly with water, filtered, dried, and recrystallized from ethanol. The progress of the formation of pyrazoline product was monitored by thin-layer chromatography (TLC; ethylacetate–hexane = 1:9). The synthetic route is depicted in Scheme 1.

**Scheme 1.** Synthetic route of the compounds under the present study.

*3-(4-ethoxyphenyl)-5-phenyl-4,5-dihydro-1H-pyrazole (EH1)*: 1H NMR (400 MHz, CDCl3) δ: 1.47–1.45 (t, 3H, J = 8.0 Hz, CH3), 4.15–4.13 (d, 2H, J = 8.0 Hz, CH2), 3.76 (d, 1H, HA), 4.02 (d, 1H, HB), 5.51 (d, 1H, HX), 7.89–7.10 (d, 9H, ArH), 9.21(S, NH). MS (ESI) m/z calcd. for C17H18N2O: 266.3376, found 266.3345.

*5-(4-chlorophenyl)-3-(4-ethoxyphenyl)-4,5-dihydro-1H-pyrazole (EH6)*: 1H NMR (400 MHz, CDCl3) δ: 1.47–1.45 (t, 3H, *J* = 8.0 Hz, CH3), 4.14–4.12 (D, 2H, *J* = 8.0 Hz, CH2), 3.75 (d, 1H, HA), 4.01 (d, 1H, HB), 5.51 (d, 1H, HX), 7.99–7.08 (d, 8H, ArH) 9.24 (S, NH). MS (ESI) m/z calcd. for C17H17N2OCl: 284.3280, found 284.3276.

*3-(4-ethoxyphenyl)-5-(4-fluorophenyl)-4,5-dihydro-1H-pyrazole (EH7):* 1H NMR (400 MHz, CDCl3) δ: 1.47–1.45 (t, 3H, *J* = 8.0 Hz, CH3), 4.15–4.13 (d, 2H, *J* = 8.0 Hz, CH2), 3.72 (d, 1H, HA), 4.04 (d, 1H, HB), 5.51 (d, 1H, HX), 7.96–7.03 (d, 8H, ArH) 9.23 (S, NH). MS (ESI) m/z calcd. for C17H17N2OF: 266.3376, found 266.3345.

*5-(4-bromophenyl)-3-(4-ethoxyphenyl)-4,5-dihydro-1H-pyrazole (EH8):* 1H NMR (400 MHz, CDCl3) δ: 1.47–1.45 (t, 3H, *J* = 8.0 Hz, CH3), 4.15–4.13 (q, 2H, *J* = 8.0 Hz, CH2), 3.76 (d, 1H, HA), 4.02 (d, 1H, HB), 5.51 (d, 1H, HX), 7.89–7.70 (d, 8H, ArH), 9.24(S, NH). C17H17N2OBr: 345.2336, found 345.2340.

### *3.2. Enzyme Assays*

MAO activities were assayed as described previously, using recombinant human MAO-A and MAO-B and kynuramine (0.06 mM) and benzylamine (0.3 mM) as substrates, respectively; both substrate concentrations were 1.5 × Km (Km = 0.041 and 0.20 mM, respectively). The enzymes and chemicals were purchased from Sigma-Aldrich (St. Louis, MO, USA) [17].

### *3.3. Analysis of Enzyme Inhibitions and Kinetics*

The inhibitory activities of the four compounds against MAO-A and MAO-B were first observed at a concentration of 10 μM. IC50 values for MAO-B by compounds showing residual activities of <50% were determined. Kinetic studies were performed on the most potent inhibitors, i.e., **EH6** and **EH7** for MAO-A and MAO-B, at five concentrations of the substrates and three inhibitor concentrations, as previously described [18].

### *3.4. Analysis of Inhibitor Reversibilities*

Reversibilities of **EH7** were analyzed using a dialysis method after preincubating with MAO-B for 30 min, as previously described. The concentrations used were **EH7** at 0.15 μM, lazabemide (a reversible MAO-B reference inhibitor) at 0.20 μM, and pargyline (an irreversible MAO-B reference inhibitor) at 0.30 μM. The relative activities for undialyzed (AU) and dialyzed (AD) samples were compared to determine the reversibility patterns [19].

### *3.5. Molecular Dynamics Study*

The 3D structures of MAO-A (PDB ID: 2Z5X and MAO-B: 2V5Z), hereafter referred to as MAO-A and MAO-B, respectively, were retrieved from the Protein Data Bank (PDB) [46,47]. Molecules that were co-crystallized with MAO-A and MAO-B were deleted with the exception of the co-enzyme FAD. Missing residues were filled with the aid of MODELLER tools found in the graphic user interface of Chimera [48,49]. Marvin Sketch was employed to draw the 2D structures of **EH1**, **EH6**, **EH7**, and **EH8**; afterwards, geometrical and structural optimizations of these ligands were carried out using B3LYP/6-311 ++G (d,p) and the UFF forcefield of Gaussian 16, respectively [50]. Active site identification was achieved by centering the grid box around a co-crystallized ligand found on the MAO-A and MAO-B proteins downloaded from PDB. Molecular docking was then carried out using AutoDock Vina [51]. The systems were then subjected to a simulation run of 50 ns using Amber18 software (San Francisco, CA, USA) [52]. Eight systems were set up for simulation (MAO-A:**EH1**, MAO-A:**EH6**, MAO-A:**EH7**, MAO-A:**EH8**, MAO-B:**EH1**, MAO-B:**EH6**, MAO-B:**EH7**, and MAO-B:**EH8**). The simulation process was carried out in accordance with an in-house protocol. The CPPTRAJ module of Amber18 software was used to analyze the trajectories emanating from the simulation process [53,54].

### *3.6. Target Predicition and ADME Analysis*

The biological target and ADME predictions of the lead molecule of **EH7** were assessed at http://www.swisstargetprediction.ch (access on 23 May 2021) [55,56].

### **4. Conclusions**

A series of four halogenated pyrazolines with ethoxylated heads (**EH1**, **EH6**, **EH7**, and **EH8**) was synthesized from their corresponding chalcones. All four compounds were explored for their MAO-A and MAO-B inhibitory activities. On the basis of previous studies regarding the role of halogens in various scaffolds with MAO inhibition activities, we mainly focused on the effect of various halogens on the pyrazoline core with the ethoxylated head. According to the results of this study, the synthesized halogenated pyrazolines exhibited good selective MAO-B inhibition. Fluorine-containing pyrazoline (**EH7**) exhibited an improved selectivity profile against the MAO-B isoform. Kinetics and reversibility studies revealed that **EH7** is a selective and competitive inhibitor of MAO-B, with a recovery value similar to that of the reversible reference drug lazabemide. Pyrazolines are said to be optically active compounds, and, from the MM/PBSA values obtained, it can be clearly observed that chirality has minimal influence on the binding of the compound to MAO-B. It may be suggested that the absence of bulky groups on the N1 position of pyrazoline limited the role of chirality in the binding mode of the lead molecule in the current study.

**Author Contributions:** Conceptualization: L.K.P., B.M., and H.K.; biological activity: J.-M.O., A.K., and M.A.A.; kinetics: J.-M.O.; molecular dynamics: O.S. and M.E.S.; synthesis: A.S.N., V.P.K., S.K., S.T.S., and B.M.; data curation: B.M. and J.-M.O.; writing—original draft preparation: J.-M.O. and B.M.; writing—review and editing: L.K.P., B.M., and H.K.; supervision: H.K.; funding acquisition: H.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by the National Research Foundation of Korea (NRF) grant provided by the Korean Government (NRF-2019R1A2C1088967 to H. Kim), Republic of Korea, and by a grant from Amrita Vishwa Vidyapeetham University (Seed Grant Number K-PHAR-20-628 to B. Mathew).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** Authors acknowledge Taif University Researchers Supporting Project number (TURSP-2020/68), Taif University, Taif, Saudi Arabia.

**Conflicts of Interest:** The authors have no conflict of interest to declare.

**Sample Availability:** Samples of the compounds are available from the authors.

### **References**

### *Article* **Cardiometabolic Risk Factors in Rosuvastatin-Treated Men with Mixed Dyslipidemia and Early-Onset Androgenic Alopecia**

**Robert Krysiak \*, Marcin Basiak and Bogusław Okopie ´n**

Department of Internal Medicine and Clinical Pharmacology, Medical University of Silesia, 40-752 Katowice, Poland<EMAIL_ADDRESS>(M.B<EMAIL_ADDRESS>(B.O.) **\*** Correspondence<EMAIL_ADDRESS>Tel./Fax: +48-322-523-902

**Abstract:** Men with early-onset androgenetic alopecia are characterized by hormonal profiles similar to those observed in women with polycystic ovary syndrome. The purpose of this research was to investigate levels of cardiometabolic risk factors in 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA)-treated men with early-onset androgenic alopecia. We studied two matched rosuvastatintreated groups of men with mixed dyslipidemia: subjects with early-onset androgenic alopecia (group A) and subjects with normal hair growth (group B). Plasma lipids, glucose homeostasis markers, and levels of sex hormones, uric acid, hsCRP, homocysteine, fibrinogen, and 25-hydroxyvitamin D were measured before entering the study and six months later. Both groups differed in insulin sensitivity and levels of calculated bioavailable testosterone, dehydroepiandrosterone-sulfate, uric acid, hsCRP, fibrinogen, and 25-hydroxyvitamin D. Though observed in both study groups, treatment-induced reductions in total cholesterol, LDL cholesterol, hsCRP, and fibrinogen were more pronounced in group B than group A. Moreover, only in group A did rosuvastatin deteriorate insulin sensitivity, and only in group B did the drug affect uric acid, homocysteine, and 25-hydroxyvitamin D. The impact of rosuvastatin on cardiometabolic risk factors correlated with insulin sensitivity, calculated bioavailable testosterone, and dehydroepiandrosterone-sulfate. The obtained results suggest that men with early-onset androgenic alopecia may benefit to a lesser degree from rosuvastatin treatment than their peers.

**Keywords:** androgenetic alopecia; 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors; mixed dyslipidemia; risk factors

### **1. Introduction**

The presence of polycystic ovary syndrome (PCOS) in women is associated with an increased arterial stiffness, increased carotid intima-media thickness, endothelial dysfunction, thrombotic complications, cerebrovascular events, and possibly also cardiovascular events [1]. Compared with brothers of healthy women, brothers of women with PCOS are characterized by increased levels of two-hours post-challenge glucose, increased values of insulin resistance markers, and higher values of systolic blood pressure, as well as higher prevalences of impaired glucose tolerance, metabolic syndrome, and type 2 diabetes [2–8]. Brothers of PCOS probands are also characterized by elevated levels of dehydroepiandrosterone-sulfate (DHEA-S) levels and lower concentrations of bioavailable testosterone [5–7]. Therefore, it is reasonable to assume that male relatives of PCOS probands should be screened, identified, and appropriately treated [9,10].

Interestingly, a very similar hormonal profile to that observed in male siblings of women with PCOS was reported in men with early-onset androgenic alopecia. The prevalence of androgenic alopecia increases with age, and it is estimated that up 30% of Caucasian men have androgenic alopecia by the age of 30, and 80% of Caucasian men are affected by this disorder in the course of their life [11,12]. The classic male-pattern of hair loss involves the temporal and vertex region while leaving a rim of hair at the sides and sparing the occipital region [12]. Male pattern hair loss probably results from both genetic susceptibility

**Citation:** Krysiak, R.; Basiak, M.; Okopie ´n, B. Cardiometabolic Risk Factors in Rosuvastatin-Treated Men with Mixed Dyslipidemia and Early-Onset Androgenic Alopecia. *Molecules* **2021**, *26*, 2844. https:// doi.org/10.3390/molecules26102844

Academic Editors: Alicja Nowaczyk and Grzegorz Grze´sk

Received: 17 April 2021 Accepted: 10 May 2021 Published: 11 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and excessive androgen action [13]. The estimated heritability of early-onset androgenic alopecia in men is about 80%, while genetic variability in the androgen receptor gene and ectodysplasin A2 receptor gene is a prerequisite for the development of this disorder [9,13]. Compared with control subjects, men with early androgenetic alopecia have shown increased levels of testosterone, DHEA-S, LH, and prolactin; decreased levels of FSH and sex hormone-binding globulin; and increased values of the free androgen index and the LH/FSH ratio [14]. In comparison with healthy subjects, the risk of metabolic syndrome was found to be 2.3 times higher in men with early-onset androgenetic alopecia [15]. There is also evidence that early-onset androgenic alopecia is associated with obesity, insulin resistance, dyslipidemia, hypertension, and atherosclerosis [10]. Genetic, hormonal, and metabolic similarities to PCOS mean that early-onset androgenic alopecia is regarded as a male equivalent of PCOS [16].

The results of a recent study indicated that cardiometabolic effects of atorvastatin are less pronounced in brothers of women with polycystic ovary syndrome than in the male siblings of unaffected women [17]. Moreover, high-dose treatment with rosuvastatin, the latest and the most potent statin that is currently available on the market and frequently used in clinical practice, was found to reduce circulating testosterone levels but not to affect levels of DHEA-S in men with coronary artery disease [18]. Because no previous study has investigated statin action in subjects with male-pattern hair loss, the purpose of this study was to investigate whether early-onset androgenic alopecia determines the impact of rosuvastatin therapy on cardiometabolic risk factors in men with mixed dyslipidemia.

### **2. Results**

At study entry, there were no differences between both groups in age, smoking, body mass index, and blood pressure, as well as in the plasma levels of total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, glucose, estradiol, and homocysteine. Both groups differed in HOMA1-IR and levels of calculated bioavailable testosterone, DHEA-S, uric acid, hsCRP, and fibrinogen, which were higher in group A than in group B, as well as in 25-hydroxyvitamin D, which was higher in group B than in group A (Tables 1 and 2).

**Table 1.** Baseline characteristics of patients.

a Men with early-onset androgenic alopecia. b Control men.

Rosuvastatin did not result in serious adverse events, and all patients completed the study. Body mass index and blood pressure remained at a similar level throughout the study.

In both study groups, rosuvastatin decreased the levels of total cholesterol, LDLcholesterol, hsCRP, and fibrinogen. Treatment-induced changes in total and LDL cholesterol, hsCRP, and fibrinogen were more pronounced in group B than group A. Rosuvastatin increased HOMA1-IR exclusively in group A, while the drug only reduced levels of triglycerides, uric acid and homocysteine and increased levels of HDL cholesterol, and 25 hydroxyvitamin D only in group B. Rosuvastatin treatment did not affect glucose, DHEA-S, total testosterone, bioavailable testosterone, estradiol, and the estimated glomerular filtration rate. After six months of treatment, there were differences between both groups in total

cholesterol, LDL cholesterol, HOMA1-IR, DHEA-S, calculated bioavailable testosterone, uric acid, hsCRP, homocysteine, fibrinogen, and 25-hydroxyvitamin (Table 2).

**Table 2.** The effect of rosuvastatin on plasma lipids, glucose homeostasis markers, hormones, and the investigated cardiometabolic risk factors in young men with mixed dyslipidemia.

a Men with early-onset androgenic alopecia. b Control men. \* The impact of rosuvastatin (percent changes from baseline after adjustment for baseline values) stronger than in the second group.

At entry, total cholesterol and LDL cholesterol correlated with levels of uric acid, hsCRP, homocysteine, and fibrinogen (group A: r values between 0.24 (*p* = 0.0499) and 0.40 (*p* = 0.0011); group B: r values between 0.30 (*p* = 0.0345) and 0.47 (*p* = 0.0001)), and there were inversely correlated with levels of 25-hydroxyvitamin D (group A: r = −0.32 (*p* = 0.0285) and r = −0.41 (*p* = 0.0007); group B: r = −0.35 (*p* = 0.0122) and r = −0.44 (*p* = 0.0002)). Moreover, there were positive correlations between HDL cholesterol and 25 hydroxyvitamin D (group A: r = 0.48 (*p* < 0.0001); group B: r = 0.50 (*p* < 0.0001), triglycerides or HOMA1-IR and hsCRP and fibrinogen (group A: r values between 0.26 (*p* = 0.0385) and 0.47 (*p* = 0.0001); group B: r values between 0.29 (*p* = 0.0403) and 0.49 (*p* = 0.0001)), and there were inverse correlations between triglycerides or HOMA1-IR and 25-hydroxyvitamin D (group A: r = −0.35 (*p* = 0.0071) and r = −0.43 (*p* = 0.0008); group B: r = −0.41 (*p* = 0.0014) and r = −0.49 (*p* < 0.0001)), as well as between HDL cholesterol and hsCRP and fibrinogen (group A: r = −0.34 (*p* = 0.0087) and r = −0.40 (*p* = 0.0025); group B: r = −0.39 (*p* = 0.0037) and r = −0.47 (*p* = 0.0001)). Treatment-induced changes in uric acid, hsCRP, fibrinogen, homocysteine, and 25-hydroxyvitamin D inversely correlated with calculated bioavailable testosterone levels (group A: r values between −0.32 (*p* = 0.0298) and −0.42 (*p* = 0.0006); group B: r values between −0.35 (*p* = 0.0281) and −0.48 (*p* < 0.0001)) and DHEA-S (group A: r values between -0.24 (*p* = 0.0488) and −0.37 (*p* = 0.0046); group B: r values between −0.31 (*p* = 0.0011) and −0.47 (*p* < 0.0001)). All other correlations were not significant.

### **3. Discussion**

In comparison with the control subjects, men with androgenic alopecia had increased plasma concentrations of DHEA-S and increased levels of calculated bioavailable testosterone. Because of the exclusion criteria and selection procedure, these findings could not be attributed to differences in body mass index, blood pressure, plasma lipids, concomitant disorders, or drug interactions. The hormonal profile of individuals with early-onset alopecia differed from that observed in the male siblings of PCOS probands, in whom elevated concentrations of DHEA-S coexisted with lower levels of calculated bioavailable testosterone [17]. Unlike bioavailable testosterone, in both studies, mean total testosterone levels were similar to those observed in control groups. This discrepancy may be explained by the fact that bioavailable testosterone (denoting the sum of the free and free weakly bound testosterone) calculated by Vermeulen's formula (used in the current study) correlates with free testosterone levels when assessed by equilibrium dialysis [19,20]. Because only unbound testosterone binds the androgen receptor in target tissues in order to exert its activity, the obtained results seem clinically relevant. Under physiological conditions, DHEA-S is converted to testosterone by three enzymes: steroid sulfatase, 3β-hydroxysteroid dehydrogenase, and 17β-hydroxysteroid dehydrogenase type 3 [21,22]. Therefore, it is possible that in brothers of PCOS women, but not in men with early-onset androgenic alopecia, the activity of at least one these enzymes is slightly disturbed. In addition to increased levels of HOMA1-IR, uric acid, and hsCRP and a decreased concentration of 25-hydroxyvitamin D, as observed in both brothers of PCOS probands and men with early-onset alopecia,

individuals with early-onset alopecia were characterized by elevated levels of fibrinogen. Interestingly, fibrinogen levels were found to correlate with the incidence rates of myocardial infarction and stroke and with cardiovascular mortality, while the risk of coronary artery disease in individuals with hyperfibrinogenemia was comparable to or higher than that in subjects with elevated total cholesterol levels [23]. Based on the obtained results, at least three conclusions may be drawn. Firstly, from the phenotype point of view, men with early-onset androgenic alopecia and male siblings of women with PCOS represent different clinical entities or constitute different spectra of the same entity. Secondly, they may differ in cardiometabolic risk, which seems to be higher in subjects with alopecia. Finally, because men with early-onset androgenic alopecia were more insulin-resistant than body mass index-, plasma lipid- and blood pressure-matched peers, this state may predispose subjects to type 2 diabetes, metabolic syndrome, and other conditions associated with insulin resistance.

The current study also showed that subjects with alopecia differed from the control group in the impact of rosuvastatin on lipids, insulin sensitivity, and levels of cardiometabolic risk factors. Individuals with early-onset alopecia were characterized by less pronounced changes in plasma lipids and cardiometabolic risk factors, as well as by a potentially unfavorable effect on insulin sensitivity. These findings indicate that men with early-onset male-pattern hair loss may benefit to a lesser degree from rosuvastatin therapy than subjects with normal hair growth, at least in the primary prevention of cardiovascular disease. This observation is of clinical significance because early-onset androgenic alopecia can be easily diagnosed based on anamnesis and basic clinical signs.

There are different possible explanations for dimorphism in rosuvastatin action between both study groups. Some our observations seemed to indicate that they are probably partially related to differences in insulin sensitivity. Both groups differed in baseline HOMA1-IR and in the impact of rosuvastatin on HOMA1-IR, present only in men with alopecia. Moreover, treatment-induced changes in circulating levels of uric acid, hsCRP, fibrinogen, homocysteine, and 25-hydroxyvitamin D levels correlated with HOMA1-IR. If this explanation is accurate, non-pharmacological treatment or metformin may restore the pleiotropic effects of rosuvastatin in subjects with early-onset androgenic alopecia, and we intend to verify this hypothesis in our future studies. Despite numerous clinical cardiovascular benefits of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors in type 2 diabetes, statin therapy is associated with a slightly increased risk of developing new-onset diabetes [24]. Considering the results of our study, this risk may be higher in men with early-onset androgenic alopecia than in other groups of patients with indications for treatment with HMG-CoA reductase inhibitors.

According to another explanation, dimorphism in the lipid-lowering and extra-lipid effects of statins may be associated with differences in the baseline levels of endogenous androgens, particularly testosterone. Over the entire study period, the levels of DHEA-S and calculated bioavailable testosterone were higher in subjects with early-onset androgenic alopecia than in individuals without it, while the impact of rosuvastatin on all assessed cardiometabolic risk factors correlated with calculated bioavailable testosterone and, though to a lesser extent, with DHEA-S. Because HMG-CoA reductase inhibitors do not affect androgen levels [18], except for high-dose statin therapy, between-group differences in the impact of rosuvastatin cannot be attributed to their effect on plasma androgens. They are also not associated with differences in the conversion of androgens to estrogens because estradiol levels were similar in both study groups. It is much more probable that statins and testosterone may interact at the level of conversion of HMG-CoA to mevalonate. The pleiotropic properties of statins result from their inhibitory effect on the biosynthesis of some isoprenoids, particularly farnesyl pyrophosphate and geranylgeranyl pyrophosphate, that play essential roles in the posttranslational modification of numerous key proteins that act as molecular switches (especially Ras, Rac, and Rho) [25]. This mechanism may be attenuated by high testosterone levels. Even a single dose of exogenous testosterone was found to induce the expression of HMG-CoA reductase [26]. Moreover, testosterone has

recently been shown to increase levels of key enzymes of the mevalonate pathway, and its high levels may stimulate the posttranslational prenylation and farnesylation of numerous small signaling proteins [27].

This study had some study limitations that require consideration. The study population exceeded the required sample size, but due to a relatively small number of participants, our findings should be verified in a large prospective study. We did not investigate hard clinical endpoints, such as morbidity and mortality rates. The study design did not allow us to conclude whether the obtained results may be explained by a "class effect" of HMG-CoA reductase inhibitors or only characterize rosuvastatin or some statins. Finally, because the study included only men with normal glucose homeostasis or prediabetes, it cannot be totally ruled out that the impact of rosuvastatin is different in individuals with coexisting type 2 diabetes.

In conclusion, compared with age-, body mass index-, and blood pressure-matched subjects with isolated mixed dyslipidemia, men with early-onset androgenic alopecia were characterized by impaired insulin sensitivity; elevated levels of DHEA-S, calculated bioavailable testosterone, uric acid, hsCRP, and fibrinogen; and lower levels of 25-hydroxyvitamin D. In addition to less expressed lipid-lowering properties, men with early-onset androgenic alopecia were characterized by less pronounced cardiometabolic effects of rosuvastatin. Contrary to the control subjects, the rosuvastatin treatment of men with alopecia deteriorated insulin sensitivity. The obtained results suggest that men with early-onset androgenic alopecia may benefit from rosuvastatin treatment to a lesser degree than other subjects with mixed dyslipidemia.

### **4. Materials and Methods**

The study was conducted in accordance with the 1964 Helsinki Declaration and its later amendments. The experimental protocol was reviewed and approved by the Institutional Review Board. All participants provided written informed consent prior to enrolment in the study.

### *4.1. Patients*

We studied two groups of young men (aged 18–35 years) with mixed dyslipidemia, defined as total cholesterol levels above 200 mg/dL, low-density lipoprotein (LDL) cholesterol levels above 115 mg/dL, and triglyceride levels of more than 150 mg/dL, despite complying with the lifestyle modification for more than 3 months before entering the study. Group A included 25 individuals with early-onset androgenic alopecia, while group B (which was a control group) included 25 matched men with isolated mixed dyslipidemia. Early-onset androgenic alopecia was defined as grade 3 vertex or more alopecia on the Hamilton and Norwood scale, which is used to classify the stages of male pattern baldness as diagnosed before the age of 30 years [28,29].

The study population was selected among a larger group of subjects with mixed dyslipidemia (*n* = 112), diagnosed and treated in our department. A power calculation using 80% power and a type I error of 0.05 indicated that at least 23 individuals would need to be enrolled in each group to detect a 20% difference in the effects of treatment on the measured cardiometabolic risk factors between the groups. The selection procedure, performed using the freely available PEPI-for-Windows computer program, was intended to obtain two groups matched for age, blood pressure, and body mass index. To minimize the impact of seasonal fluctuations in the study outcomes, similar numbers of participants were included in January or February (*n* = 26) and in July and August (*n* = 24).

The exclusion criteria were as follows: cardiovascular disease (with the exception of mild arterial hypertension), impaired renal or hepatic function, diabetes, thyroid disorders or other endocrine disorders, acute and chronic inflammatory processes, malabsorption syndromes, any other serious disorder, and any pharmacotherapy.

### *4.2. Study Design*

Rosuvastatin was administered at a dose of 10 mg once daily at bedtime for 6 months. All participants were also given detailed advice about how to achieve the goals of lifestyle modification, which were a total fat intake of less than 30% of total energy intake, a saturated fat intake of less than 7% of energy consumed, a cholesterol intake of less than 200 mg per day, and an increase in fiber intake to 15 g per 1000 kcal. They were also recommended to do at least 150 min of moderate-intensity aerobic physical activity per week, as well as muscle-strengthening activities that were of moderate intensity and involved all major muscle groups on two or more days a week. Medication adherence was assessed every 6 weeks by means of a four-item Morisky–Green test and by pill count.

### *4.3. Laboratory Assays*

All laboratory assays were carried out at baseline and 6 months later. Venous blood samples for laboratory were collected between 7.00 and 8.00 a.m. after 12-h overnight fasting in a quiet and air-conditioned room (constant temperature of 23–24 ◦C), and they were assessed in duplicate. Standard laboratory techniques were used to measure plasma glucose levels, plasma lipids (total cholesterol, LDL cholesterol, HDL cholesterol, and triglycerides), uric acid, and creatinine (Roche Diagnostics, Basel, Switzerland). The plasma levels of insulin, DHEA-S, total testosterone, estradiol, sex-hormone binding globulin, homocysteine, and 25-hydroxyvitamin D were assayed by direct chemiluminescence using acridinium ester technology (ADVIA Centaur XP Immunoassay System, Siemens Healthcare Diagnostics, Munich, Germany). The plasma levels of high-sensitivity C-reactive protein (hsCRP) were measured using an immunoassay with chemiluminescent detection (Immulite 2000XPi, Siemens Healthcare, Warsaw, Poland), while fibrinogen levels were measured by the Clauss technique using an automated BCS XP analyzer (Siemens Healthcare, Warsaw, Poland). The homeostasis model assessment 1 of insulin resistance index (HOMA1-IR) was calculated by multiplying plasma insulin (mIU/L) by plasma glucose (mg/dL) and dividing that by 405.  The estimated glomerular filtration rate was calculated using the Modification of Diet in Renal Disease equation.

### *4.4. Statistical Analysis*

Outcome variables were log-transformed for analysis to obtain a better approximation of the normal distribution, and they were transformed back for reporting in the tables. All analyses were adjusted for age, smoking, body mass index, and blood pressure as potential confounding factors. Both groups and differences between percent changes from baseline after adjustment for baseline values (reflecting rosuvastatin action) were compared by Student's *t*-tests for independent samples. The differences between the means of variables within the same group were analyzed using Student's paired *t*-test. Qualitative data were compared using the χ2 test. Correlations were assessed using Pearson's correlation coefficient (r). Two-tailed *p*-values corrected for multiple testing below 0.05 were considered statistically significant.

**Author Contributions:** R.K. conceived of the study, participated in its design, performed the statistical analysis, and drafted and edited the manuscript. M.B. conducted the literature search, carried out the assays, and performed the statistical analysis. B.O. participated in its design and coordination and provided critical input during manuscript preparations. All authors read and approved the final manuscript.

**Funding:** The study was supported by the statutory grant of the Medical University of Silesia (PCN-1-185/N/9/O).

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (the Bioethical Committee of the Medical University of Silesia [KNW/0022/KB/188/16]; 19 October 2016).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Sample Availability:** Samples of the compounds are not available from authors.

### **Abbreviations**

DHEA-S: dehydroepiandrosterone-sulfate; HDL: high-density lipoproteins; HMG-CoA: 3 hydroxy-3-methylglutaryl coenzyme A; FSH: follicle-stimulating hormone; HOMA1-IR: the homeostasis model assessment 1 of insulin resistance index; hsCRP: high sensitivity C-reactive protein; LDL: low-density lipoproteins; LH: luteinizing hormone; PCOS: polycystic ovary syndrome; SD: standard deviation

### **References**

### *Article* **Serotonergic Neurotransmission System Modulator, Vortioxetine, and Dopaminergic D2/D3 Receptor Agonist, Ropinirole, Attenuate Fibromyalgia-Like Symptoms in Mice**

**Kinga Sałat \* and Anna Furgała-Wojas**

Department of Pharmacodynamics, Faculty of Pharmacy, Jagiellonian University Medical College, 9 Medyczna St., 30-688 Krakow, Poland<EMAIL_ADDRESS>
**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** Fibromyalgia is a disease characterized by lowered pain threshold, mood disorders, and decreased muscular strength. It results from a complex dysfunction of the nervous system and due to unknown etiology, its diagnosis, treatment, and prevention are a serious challenge for contemporary medicine. Impaired serotonergic and dopaminergic neurotransmission are regarded as key factors contributing to fibromyalgia. The present research assessed the effect of serotonergic and dopaminergic system modulators (vortioxetine and ropinirole, respectively) on the pain threshold, depressive-like behavior, anxiety, and motor functions of mice with fibromyalgia-like symptoms induced by subcutaneous reserpine (0.25 mg/kg). By depleting serotonin and dopamine in the mouse brain, reserpine induced symptoms of human fibromyalgia. Intraperitoneal administration of vortioxetine and ropinirole at the dose of 10 mg/kg alleviated tactile allodynia. At 5 and 10 mg/kg ropinirole showed antidepressant-like properties, while vortioxetine had anxiolytic-like properties. None of these drugs influenced muscle strength but reserpine reduced locomotor activity of mice. Concluding, in the mouse model of fibromyalgia vortioxetine and ropinirole markedly reduced pain. These drugs affected emotional processes of mice in a distinct manner. Hence, these two repurposed drugs should be considered as potential drug candidates for fibromyalgia. The selection of a specific drug should depend on patient's key symptoms.

**Keywords:** reserpine-induced fibromyalgia model; vortioxetine; ropinirole; serotonin and dopamine in fibromyalgia; mouse

### **1. Introduction**

According to the American College of Rheumatology, fibromyalgia (FM) is a common neurological health problem that causes widespread musculoskeletal pain accompanied by fatigue, sleep, memory, and mood issues [1–4]. The development of FM results from a complex dysfunction of the nervous system, and therefore, not only its diagnosis but also treatment and prevention based on combined pharmacological, alternative medicine [5,6], and educational methods [7] are a serious challenge for contemporary medicine [8].

FM affects 2–4% of the population and it is more prevalent in the population of people from urban than rural areas [8–13]. Other risk factors comprise female sex, stress, genetic factors, and comorbid inflammatory diseases (e.g., osteoarthritis, rheumatoid arthritis, lupus, and ankylosing spondylitis) [8,14–16]. Clinical symptoms of FM arise from the central sensitization [17] due to the neuroendocrine dysfunction and fluctuations in the concentration of neurotransmitters, namely decreased levels of biogenic amines, accompanied by cytokine abnormalities, increased concentrations of excitatory neurotransmitters, and substance P. Impaired functions of the hypothalamic–pituitary–adrenal axis and the autonomic nervous system are also observed in FM patients [18,19].

On the molecular level, the development of FM is based on the impaired serotonin-, noradrenaline-, and dopamine-mediated neurotransmission [20]. This dysregulated neu-

**Citation:** Sałat, K.; Furgała-Wojas, A. Serotonergic Neurotransmission System Modulator, Vortioxetine, and Dopaminergic D2/D3 Receptor Agonist, Ropinirole, Attenuate Fibromyalgia-Like Symptoms in Mice. *Molecules* **2021**, *26*, 2398. https:// doi.org/10.3390/molecules26082398

Academic Editors: Alicja Nowaczyk and Grzegorz Grze´sk

Received: 13 March 2021 Accepted: 19 April 2021 Published: 20 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

rotransmission is likely to be responsible not only for painful symptoms of FM but also mood control deficits, sleep dysregulation, and cognitive dysfunction as these neurotransmitters serve as neurochemical substrates for a wide range of central nervous system activities [21–23].

In view of potential neurochemical mechanisms underlying FM, for years many attempts have been made to search for effective therapies for FM and drugs targeting at serotonergic, noradrenergic, and dopaminergic neurotransmission systems, including serotonin/noradrenaline reuptake inhibitors (duloxetine and desvenlafaxine) [24–27], or ligands of the α2-δ subunit of voltage-gated calcium channels [7,8] have been recommended for FM treatment but their efficacy seems to be modest [25,28]. On the other hand, selective serotonin reuptake inhibitors turned out to be ineffective in the treatment of other than depression symptoms of FM [29].

In the present study we focused on the assessment of two potential drug candidates for FM treatment. The first one affects serotonergic signaling in the brain, while the second one stimulates dopaminergic neurotransmission in the central nervous system. We used a mouse model of FM, i.e., the reserpine (RES) model, and we compared the effects of vortioxetine (VORT), a serotonin transporter (SERT) inhibitor, a 5-HT3, 5-HT7 receptor antagonist, and a 5-HT1A receptor agonist [30], and ropinirole (ROP), a dopaminergic D2 and D3 receptor agonist [31], on the pain threshold, depressive-like and anxiety-like responses, locomotor activity and muscle strength of mice exposed to RES to assess if these drugs are able to attenuate the key symptoms resembling those of human FM. The anti-FM effectiveness of multitarget serotonergic agents (e.g., VORT) has not been established, yet. Additionally, the effectiveness of dopamine-mimetics in FM patients requires further confirmation [32] because at present, such compounds are mainly used in the treatment of Parkinson's disease and restless legs syndrome [33,34]. ROP, because of its previously shown significant impact on decreasing tenderness without causing side effects [35], and pramipexole, which affects muscle pressure and tactile allodynia [36], seem to be promising drugs for FM, even though their therapeutic potential in FM has never been thoroughly investigated.

### **2. Results**

The experimental design for in vivo tests is presented in Figure 1. A general procedure used for the induction of FM-like symptoms (Figure 1A) and the behavioral testing protocol used in this study (Figure 1B) are described in Sections 4.3 and 4.4.

Since this set of experiments, which focused on the assessment of the influence of VORT and ROP on FM-like symptoms, was the first-in-animal study, we investigated pharmacological activities of both drugs at only one time point of testing and we did not carry out time-effect studies. In our study only one route of drug administration was used. Available literature indicates that in pain tests utilizing mechanical and thermal stimulation the maximum effect of oral VORT can be achieved 60–240 min after its administration [37], and oral ROP reaches its peak concentration within approximately 60–120 min [38]. Therefore, in this study we chose 60 min after drug administration as the time point for behavioral testing. We used the intraperitoneal route because the pharmacokinetics of substances administered intraperitoneally resembles that seen after oral administration [39].

### *2.1. Effect on Tactile Allodynia (Von Frey Test)*

In the von Frey test repeated-measures ANOVA revealed an overall effect of treatment (F[5, 108] = 62.72, *p* < 0.0001). Time effect and drug × time interaction were also significant (F[1, 108] = 38.25, *p* < 0.0001 and F[5, 108] = 10.98, *p* < 0.0001, respectively).

**Figure 1.** Experimental design used in the present study. FM-like model was induced in mice by a 3-day administration of subcutaneous reserpine (RES; 0.25 mg/kg). Twenty-four hours after the last administration of RES, behavioral tests were carried out (**A**). Vortioxetine (VORT) and ropinirole (ROP) were injected intraperitoneally at doses 5 and 10 mg/kg (**B**). Before test compound administration and 1 h later, the effects of test drugs on tactile allodynia and heat hyperalgesia were measured in the von Frey test (vFT) and the hot plate test (HPT), respectively. Antidepressant-like and anxiolytic-like activities of VORT and ROP were measured in the forced swim test (FST) and the four-plate test (FPT), respectively. Effects of test drugs on the muscle strength were measured using the grip strength test (GST), while the effects of VORT and ROP on the locomotor activity of mice were measured using the locomotor activity test (LA).

In this assay (Figure 2A), Tukey's post hoc analysis showed that RES significantly lowered the mechanical nociceptive threshold in mice (*p* < 0.0001 vs. predrug paw withdrawal of VEH group). VORT and ROP influenced the paw withdrawal threshold of mice treated with RES and compared to RES + VEH group a statistically significant (*p* < 0.001) elevation of the mechanical nociceptive threshold was noted in RES + VORT 10 mg/kg and RES + ROP 10 mg/kg groups. The dose 5 mg/kg of VORT or ROP did not affect the mechanical nociceptive threshold of mice treated with RES (Figure 2A).

### *2.2. Effect on Heat Hyperalgesia (Hot Plate Test)*

Repeated measures ANOVA did not reveal an effect of treatment on the hot plate test results (F[5, 108] = 1.994, *p* > 0.05). Time effect was significant (F[1, 108] = 6.455, *p* < 0.05) but the drug × time interaction was not (F[5, 108] = 0.3449, *p* > 0.05). Post hoc analysis did not reveal statistically significant antihyperalgesic properties of VORT or ROP in this assay (Figure 2B).

### *2.3. Effect on Depressive-Like Symptoms (Forced Swim Test; FST)*

In the FST one-way ANOVA showed an overall effect of treatment on the duration of immobility in mice (F[5, 51] = 25.00, *p* < 0.0001).

**Figure 2.** Antiallodynic and antihyperalgesic activities of intraperitoneally administered vortioxetine (VORT; 5 and 10 mg/kg) and ropinirole (ROP; 5 and 10 mg/kg) in the mouse FM-like model induced by a 3-day administration of subcutaneous reserpine (RES; 0.25 mg/kg). The effect of test drugs on tactile allodynia was measured in the von Frey test (**A**) and their effect on heat hyperalgesia was measured in the hot plate test (**B**) 1 h after drug administration. Results are shown as the mean paw withdrawal threshold (g) (**A**), or the mean latency to pain reaction (s) (**B**) ± SEM for *n* = 10. Statistical analysis: repeated measures analysis of variance, followed by Tukey's post hoc comparison. Significance vs. predrug paw withdrawal of VEH group: #### *p* < 0.0001 and significance vs. postdrug paw withdrawal of RES + VEH group: \*\*\* *p* < 0.001, \*\*\*\* *p* < 0.0001.

In the FST Dunnett's post hoc test revealed that although RES prolonged immobility in mice this effect was not specific, i.e., the effect of RES on the duration of immobility compared to that of VEH was not statistically significant (Figure 3). In contrast to this, in mice treated with RES, both doses of ROP significantly reduced the duration of immobility (*p* < 0.001 vs. RES + VEH group; Figure 3). In this test, VORT did not influence immobility of animals, which were previously treated with RES (Figure 3).

**Figure 3.** Antidepressant-like properties of intraperitoneally administered vortioxetine (VORT; 5 and 10 mg/kg) and ropinirole (ROP; 5 and 10 mg/kg) in the mouse FM-like model induced by a 3-day administration of subcutaneous reserpine (RES; 0.25 mg/kg). The effect of test drugs on the duration of immobility was measured in the FST carried out 1 h after drug administration. Results are shown as the mean duration of immobility (s) ± SEM for *n* = 8–10. Statistical analysis: one-way analysis of variance, followed by Dunnett's post hoc test. Significance vs. RES + VEH group: \*\*\* *p* < 0.001.

### *2.4. Effect on Anxiety-Like Symptoms (Four-Plate Test; FPT)*

One-way ANOVA demonstrated a statistically significant overall effect of treatment on the number of punished crossings in the FPT (F[5, 52] = 7.953, *p* < 0.0001).

Dunnett's post hoc analysis revealed that the number of punished crossings in the RES + VEH group was significantly lower as compared to the VEH group (*p* < 0.05), RES + VORT 5 mg/kg (*p* < 0.01), and RES + VORT 10 mg/kg groups (*p* < 0.0001, Figure 4). ROP at doses 5 and 10 mg/kg did not show anxiolytic-like properties in the mouse FPT (Figure 4).

### *2.5. Effect on Muscle Strength (Grip Strength Test)*

The grip strength test was carried out to assess if RES, VORT, or ROP affect muscle strength of mice. Repeated measures ANOVA showed that neither drug effect, time effect, nor the drug × time interaction were significant (F[5, 108] = 0.4860, *p* > 0.05, F[1, 108] = 0.1589, *p* > 0.05, and F[5, 108] = 1.639, *p* > 0.05. respectively) and none of the drugs used affected muscle strength of mice (Figure 5).

**Figure 4.** Anxiolytic-like properties of intraperitoneally administered vortioxetine (VORT; 5 and 10 mg/kg) and ropinirole (ROP; 5 and 10 mg/kg) in the mouse FM-like model induced by a 3-day administration of subcutaneous reserpine (RES; 0.25 mg/kg). The effect of test drugs on the number of punished crossings was measured in the FPT performed 1 h after drug administration. Results are shown as the mean number of punished crossings ± SEM for *n* = 8–10. Statistical analysis: one-way analysis of variance, followed by Dunnett's post hoc test. Significance vs. RES + VEH group: \* *p* < 0.05, \*\* *p* < 0.01, and \*\*\*\* *p* < 0.0001.

**Figure 5.** Effect of intraperitoneally administered vortioxetine (VORT; 5 and 10 mg/kg) and ropinirole (ROP; 5 and 10 mg/kg) on the muscle strength in the mouse FM-like model induced by a 3-day administration of subcutaneous reserpine (RES; 0.25 mg/kg). The effect of VORT and ROP was measured in the grip strength test carried out 1 h after drug administration. Results are shown as the mean force (expressed in (g)) that evoked the animal's reaction ± SEM for *n* = 10. Statistical analysis: repeated-measures analysis of variance, followed by Tukey's post hoc comparison. Significance vs. RES + VEH group at the respective time point of testing: *p* > 0.05.

### *2.6. Effect on Locomotor Activity*

Repeated measures ANOVA demonstrated an overall effect of treatment on the locomotor activity of mice (F[3, 84] = 9.104, *p* < 0.0001). Time effect was also statistically significant (F[2, 84] = 78.90, *p* < 0.0001) but the drug × time interaction was not (F[6, 84] = 1.421, *p* > 0.05).

Locomotor activity did not differ in RES + VEH group compared to RES + VORT group or RES + ROP group. However, locomotor activity of RES + VEH group was significantly decreased as compared to the VEH group (*p* < 0.01 in the 5th min of the test, and *p* < 0.001 in the 6th min of the test, Figure 6).

**Figure 6.** Effect of intraperitoneally administered vortioxetine (VORT; 10 mg/kg) and ropinirole (ROP; 10 mg/kg) on the locomotor activity of mice in the FM-like model induced by a 3-day administration of subcutaneous reserpine (RES; 0.25 mg/kg). The effect of test drugs on the number of light beam crossings was measured in the locomotor activity test carried out 1 h after drug administration. Results are shown as the mean number of light beam crossings ± SEM for *n* = 8. Statistical analysis: repeated-measures analysis of variance, followed by Tukey's post hoc comparison. Significance vs. RES + VEH group at the respective time point of testing: \*\* *p* < 0.01 and \*\*\* *p* < 0.001.

### **3. Discussion**

FM is a disease that results from the impaired neurotransmission mediated by serotonin and catecholamines [40–42]. This altered neurotransmission underlies many symptoms of this disease, including chronic pain, mood and sleep disorders, and cognitive decline [43]. In FM dysregulated dopaminergic neurotransmission was shown and a strong correlation between dopamine metabolism and reduced gray matter density was reported. These phenomena contributed to the enhanced pain perception, cognitive deficits and abnormal stress reactivity [44]. Additionally, a disruption of presynaptic dopaminergic neurotransmission in those brain regions where dopamine plays a putative role in analgesia was shown in FM patients [45]. Recent human and animal studies have revealed that a decreased level of dopamine or the hypofunction of the dopaminergic system might lead to a significantly lowered pain threshold [46,47] and chronic pain intensification [48]. In patients suffering from FM, abnormal dopamine function may be associated with differential processing of pain perception [49,50]. It has also been shown that the polymorphism in the serotonergic 5-HT3 receptor gene (HTR3) might modulate the striatal dopamine D2/D3 receptor availability in FM patients [15].

Considering this key role of the abnormal serotonergic and dopaminergic neurotransmission in the development of FM in humans, in the present research we investigated the influence of VORT and ROP on the pain threshold, depressive-like symptoms, anxiety, and motor functions in a rodent model of FM caused by RES.

In this mouse study we used RES, a natural alkaloid [51,52], which acts by inhibiting the sequestration of monoamine neurotransmitters, namely noradrenaline, dopamine, and serotonin into storage vesicles [53,54]. This action triggers increased metabolism of neurotransmitters resulting in their marked decrease in the brain and the spinal cord [55].

Peripheral administration of RES at doses 1–10 mg/kg produces a significant (70–95%) depletion of monoamine content in the central nervous system. This monoamine depletion occurs 30 min after RES injection and may last up to 2 weeks, finally returning to physiological levels after 21 days of retrieval [56].

As proposed by Nagakura and colleagues [36], repeated-dose RES-induced depletion of biogenic amines in the nervous system underlies the development of sensory hypersensitivity accompanied by depressive-like and anxiety-like behavior in rodents [57,58]. Behavioral studies also proved the effect of RES on sleep architecture [59], muscle strength [60], and cognitive impairment [61]. Hence, RES is regarded as a reliable tool compound to induce FM-like symptoms in laboratory animals. It offers good construct validity, face validity, and predictive validity [41] because its use mimics disease biochemistry, many symptoms that occur in FM patients and it is also useful in the search for new biologically active compounds with potential therapeutic effectiveness in FM.