In Silico Prediction of Metabolite in Petroselinum Crispum in Inhibiting Androgen Receptor as Treatment for Alopecia

Silviana Hasanuddin^1,4*, Dolih Gozali², Muhammad Arba³, Dwi Syah Fitra Ramadhan⁴,

Resmi Mustarichie¹

_{1Pharmaceutical Analysis and Medicinal Chemistry Department, Faculty of Pharmacy,}

_{Universitas Padjadjaran, Sumedang, Indonesia.}

_{2Pharmaceutical Department, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia.}

_{3Faculty of Pharmacy, Universitas Halu Oleo, Kendari, Indonesia.}

_{4Department of Pharmacy, Universitas Mandala Waluya, Kendari, Indonesia.}

ABSTRACT:

Introduction: Alopecia is a hair loss that occur continuously and may occur in men, women and children. The causes of alopecia, including the use of cosmetics, medication, stress, postpartum period, hormonal disorders, and scalp infection. The purpose of this research is to determine the compounds contained in Petroselinum crispum that have the potential as antialopecia agents by predicting ligand-receptor binding and binding modes, predicting ADME by using Lipinski's rule, and also comparing the effectiveness with native ligand and minoxidil. Methodology: The process starts with protein and ligand structure preparation, then docking using Autodock Vina. Afterward, analyzed and visualized of the ligands docking, and predicted the ADME according to lipinski's rules using SwissADME and toxicity using PASS predistion. Result: There were 24 compounds found in Petroselinum crispum. Molecular docking simulation showed that six compounds had better binding affinities than minoxidil. Based on the results of prediction of ADMET values using the Lipinski rule and PASS Prediction, compound that are thought to have good activity is (+)–Marmesin compared to minoxidil. Conclusion: (+)–Marmesin to have better interactions with the androgen receptor, but not better than native ligands. thus, (+)–Marmesin can be used as antialopecia agents alternative terapy.

INTRODUCTION:

Alopecia is known as baldness or hair loss, referring to the loss of some or all body hair. Alopecia related distress increases with lack of information, negative perception, and limited social support¹. Although hair loss is not fatal, hair loss often causes psychological distress, especially during the onset of symptoms, and negatively affects the patient’s self-image and self-esteem. The most common forms of adolescence are alopecia androgenetic alopecia, telogen effluvium, and alopecia areata.

Telogen effluvium may present suddenly to a variety of triggers. Androgenetic alopecia may begin in adolescence, and topical minoxidil is effective at retarding further hair loss² . The use of hair cosmetics, such as straightener, curler, and dye, can affect the strength, health, and volume of hair and become one of the causes of hair loss, which can become more severe, leading to baldness. As many as 95% of hair straightener users in the United States and 53% of hair straightener users in Africa reported hair damage or hair loss³. In addition, the habit of braiding and tying hair can also cause baldness or alopecia⁴. Prevalence of AA is approximately 2%, regardless of sex or ethnicity (Savavi, Miteva) and he prevalence increases with age and affects 57% of women and 30% for men in their 30s to 50% for men in their 50s⁵.

A normal hair growth cycle consists of the growth, lasting 3–5 years, cessation, lasting 3 weeks and rest. The  normal hair, papilla cells undergo cell division during the formation of new hair from resting hair, or new hair papilla cells are produced from hair root sheaths. Inhibition of the production of hair papilla cells by androgens can lead to the thickening of newly formed hair, shortening of the normal hair growth cycle, and eventually androgenic alopecia⁶. Alopecia or hair loss becomes a matter of concern when a person lose more than 100hairs/day. Although numerous medical treatments and many hair care products are available in the market, many people suffer from this dermatological disturbance globally⁷. Baldness (alopecia) is often associated with aging. However, in fact, baldness is also related to other factors, such as endocrine disorders, systemic abnormalities, genetic predisposition, disease, infection, medication, physiological abnormalities, autoimmune disease, and stress⁸. Conditions on the scalp affect hair native growth and hair retention. Pre-emergent hair can be affected by oxidative stress that occurs on an unhealthy scalp, possibly due to an incubatorial environment, specifically the persistent microbial metabolic activity⁸.

In a recent study, androgenetic alopecia (AGA) has been described as a consequence of the direct effect of dihydrotestosterone (DHT) on vulnerable hair follicle dermal papilla. DHT is a stronger androgen, derived from testosterone metabolism by the action of 5-alpha reductase. Compared with testosterone, DHT binds more strongly with androgen receptors in hair follicles, resulting in the upregulation of genes responsible for the gradual transformation of terminal follicles to miniaturized hair follicles⁸.

Some of the explanations above have mentioned that up to now, there are many causes of hair loss and many efforts have been made to solve the problem of hair loss. Until now, several attempts have been made in the treatment of alopecia by the use of various products derived from synthetic substances, for example, minoxidil. Minoxidil used as a comparison the study because topical that has been FDA approved as a remedy for hair loss. Minoxidil increases the amount of intracellular Ca2+, which had been shown to regulate enzyme adenosine triphosphate (ATP) enzymes that played a role in differentiation processes that facilitate hair growth⁹. However, the use of minoxidil allows the emergence of side effects, such as edema, vertigo, skin allergies, headaches, and hypotension, though the use of drugs for its side effect is not advisable, the drug of plant origin is necessary to replace the synthetic one¹⁰. Based on previous research, some medicinal plants, such as tea and hair tonic formulation of Angiopteris evecta extract have hair growth stimulating activities¹¹. The development of the role of traditional medicine needs to be supported by conducting various studies, both quantitatively and qualitatively, to ensure the safety in the use of traditional medicine. Therefore, until now, traditional medicine is still an area of interest to be studied.

Parsley is ethnomedicine that is used in Spain as a treatment for baldness⁸. Parsley (Petroselinum crispum Mill.) has also been used traditionally for the treatment of allergies, autoimmune disorders, and chronic inflammation. Parsley essential oil may be able to suppress cellular and humoral immune responses. It can also suppress the function of macrophages as the main innate immune cells¹². Parsley herb oil, which was analyzed by GC-MS, has been known to contain thirty-four compounds representing 93% of the identified oil. Volatile compounds, namely, myristicin (63.9%) and apiol (14.4%), are the main constituents¹³. Other constituents are timol (42.41%), p-cymene (27.71%), and γ-terpinene (20.98%)¹⁴.

In the process of drug discovery, the high failure of drug candidates in the final stage of the test is usually caused by poor pharmacokinetic profile, so that ADME study need to be conducted as early as possible to avoid the failure of the drug candidates. Pharmacokinetic profiles in question include absorption, distribution, metabolism, and excretion (ADME). ADME Screening of molecular properties can be performed use in vitro and in vivo methods¹⁵, but ADME prediction experimentally requires a large cost and thus¹⁶, insilico technique may be employed based on Lipinski’s rule or Veber’s rules. ADME results are predicted with a web-based online docking server program, SwissADME¹⁷. Molecular docking is a method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex¹⁸. Molecular docking study are used to find out compounds  interaction with the target site of the dihydrotestosteron hormon¹⁹.  The in silico method can be applied to study targets such as protein and DNA sequences, genomes, networks or pathways and even networks and organs like virtual liver, small molecules that don't contain amino acids or nucleic acids and small molecular complexes with targets protein or DNA or RNA. Based on this classification, in silico techniques have been used to analyze targets and small molecules interactions²⁰, quantitative structure of activity relationships²¹, analyzed the similarities between small molecules or targets²².

MATERIAL AND METHODS:

Materials:

The materials that were used in molecular docking were androgen receptor that was downloaded from Protein Data Bank (PDB Code: 4oez) and the structure of chemical compounds of parsley (Petroselinum crispum) that was obtained from the KNApSAck Family website KNApSAcK-3D (http://www.knapsackfamily.com/) as many as 24 compounds,  SwissADME. Mean while, the softwares that were used were AutoDock Tools, AutoDock Vina, BIOVIA Discovery Studio 2017,  OpenBabel, PyMOL.

Protein Structure Preparation:

The coordinates of protein were downloaded via the website of www.rscb.org (PDB ID: 4oez) and then prepared using BIOVIA Discovery Studio 2017 to separate residual solvent (water), native ligand, and other non-standard residues in order to obtain native ligand and receptor files without solvent and other non standard residues in the PDB file extension. Next, each file in the PDB file extension was optimized using AutoDock Tools and stored in the PDBQT file extension.

Parsley (Petroselinum crispum) Compound Preparation:

Compounds were traced using the website KNApSAcK with the keyword “Petroselinumc rispum” and there were 24 compounds. The ID codes of the compound then downloaded on the website of KNApSAcK-3D, then the mol* format to PDB format using the software Open Babel 2.4.1²³.

Molecular Docking:

Molecular docking is a widely used, relatively fast, and economical computational tool for predicting in silico the binding modes and affinities of molecular recognition events²⁴. Protein–ligand docking, which is a branch of the molecular docking field, represents a particularly important methodology due to its importance in the current drug discovery process^24,25,26 i.e, virtual screening of large databases of available chemicals in order to select likely drug candidates²⁶. Molecular docking was performed using Autodock Vina with The coordinates of the grid box on the x=27.2510, y=2.2532, and z=4.3344 dimension was set to 30×30×30. All docking parameters follow the previous procedure.

Analysis interaction of Native Ligand and compound contain on Parsley:

The binding energy, amino acid interactions and the compound interaction distance of each of the tested compounds were then compared with the native ligand docking that was obtained from the previous docking protocol. The results of docking were analyzed and the ligand-receptor interaction model was visualized using BIOVIA Discovery Studio 2017. RMSD (Root Mean Square Deviation) values were obtained by using PyMOL software. If the result of native ligand docking has an RMSD value <2 Å, then the docking process can be accepted or declared valid²⁷.

ADME Prediction:

Parsley compounds were predicted using the website swissADME (http://swissadme.ch/index) by looking at the values of Molecular weight (MW), Hydrogen bond acceptors (nOH), Hydrogen bond donors (nOHNH) and Total polar surface area (TPSA) compared to Lipinski’s rules. If all compound showed a zero violations of the Lipinski’s rule  which indicates good bioavailability²⁸.

RESULT:

Molecular docking was validated by redocking native ligand with DHT. Docking of native ligand was used as protocol to predict protein ligands interactions. Molecular docking using AutoDock basically predicts the binding mode and binding free energy²⁹. Docking validation was conducted using AutoDock Vina by assessing the value of RMSD (Root Mean Square Deviation), which was used to measure the similarity of coordinates (docking poses) between two atoms³⁰. Figure 1 shows that the docking protocol used was valid as shown by the low Root Mean Square Deviation (RMSD) (0.4193Å)²⁹.

In tracking the compounds, there were 24 compounds in Pertroselinum crispum obtained from the KNApSAcK website and used as tested compounds. AutoDock Vina was employed to obtain the results of molecular docking of Pertroselinum crispum compounds from the metabolite compound ligands. The binding energies obtained were compared with native ligand and pharmaceutical product on the market for hair growth therapy, namely, minoxidil. The parameters that were used in analyzing molecular docking simulations were binding energy, which is expressed in kcal/mol, interactions between ligand atoms and amino acid residues from the target protein, and interaction distances.

The simulation results showed that six compounds of Pertroselinum crispum obtained had a better binding energy than minoxidil.  In the present study, the native ligand binding of amino acid residues Arg752, Asn705 dan Thr877 through hydrogen bonds. The docking conformations of several Pertroselinum crispum compounds  shown  interaction hydrogen bonds with Arg752, Asn705 dan Thr877.

Fig. 1. RMSD result of standard ligand (RMSD value = 0.4193Å)

_{Table 1. Molecular compounds of secondary metabolites and 2D structures built by KNApSAcK}

Table 2. Analysis of Molecular Simulations of Native Ligand, Parsley (Pertoselinum crispum) Compounds, and Minoxidil against Alopecia

The negative sign or the lowest bonding energy is considered to be the easiest to interact with receptors. Hydrogen bonds are bonds between H atoms, which have a partial positive charge, and other atoms that are electronegative and have a pair of free electrons that have complete octets, such as O, N, and F³¹. A good range of hydrogen bonding distance of the docking simulation results is around 1.72-2.85 Å [24] and amino acid bonds around 4-6 Å will form Van der Waals interactions. The docking results obtained five Petrocelinum crispum compounds which have binding energy below the native ligand -11.16kcal/mol but better than minoxidil, namely psoralen -7.3kcal/mol forms hydrogen bonds with Arg752 (2.59Å), bergaptol -7.7 kcal/mol hydrogen bonds Pro682 (2.38 Å) and Val685 (2.10 Å), 1-Methyl-4- (1-methylethenyl) -2,3-dioxabicyclo [2.2.2] oct-5-ene in Arg752 with a hydrogen bond distance of 2.39 Å while xanthotoxin -7.4kcal/mol and xanthotoxol -7.8 do not form hydrogen bonds. These five compounds have good binding energy, so it is predicted that they can interact with the androgen receptor but of course with further experimental research.. Xantothoxin and xanthotoxol form Van der Waals bonds with amino acids. Van der Waals interactions are the attractions between molecules and uncharged atoms.

Fig. 2. Androgen receptor’s target amino acids that were involved in interactions with native ligand (DHT), mermesin compound, and minoxidil

Although Van der Waals forces are weak, the number of Van der Waals bonds is a substantial binding factor, especially for compounds with high molecular weight. Van der Waals interactions also have an effect on solubility of lipid ligands. The more Van der Waals interactions occur, the more easily ligand will besolluble in lipid that can penetrate the cell membrane to bind to the receptor.

Table 2. Predictions of the compounds in Petroselinum crispum by using Lipinski’s rule

In analyzing ADME, the assessment parameters used adjusted to Lipinski’s rule. This rule is used as a reference to determine the theoretical effectiveness and bioavailability of oral drugs. The Lipinski’s rule considers the physical properties of a compound, namely, the value of the n-octanol/water partition coefficient, characterizing lipophilicity (LogP). A good range of log P values is −2 <log P <5, which means the two compounds are predicted to easily penetrate. Molecular weight (MW) expressed in Dalton <500, nOH is the number of hydrogen bond acceptors <10, nOHNH is the number of hydrogen bond donors <5, and TPSA is total polar surface area. As shown in Table 2, some compounds showed values that were not included in the Lipinski’s rule, including LogP sgreater than 5.0 as in bergaptol, Xanthotoxol, apiin, beta-D-Apiose, Petroselinic acid, 2-Phenylethanol and 1,2: 3,4-Diepoxy-p-menth-8-ene. Molecular weights greater than 500 kcal/mol and Hydrogen acceptor (nOH) greater than 10 in apiin, Apigenin 7- (6 '' - malonylglucoside), Luteolin 7-apiosyl- (1-> 2) –glucoside, Luteolin 3'-methyl ether 7-malonylglucoside, Isorhamnetin 3,7-di-O-beta-glucopyranoside. Hdrogen bond donor (nOHNH) greater than 5 in Psoralen, Xanthotoxin, Apiin, Apiol, Myristicin, Apigenin 7- (6 '' - malonylglucoside), Luteolin 7-apiosyl- (1-> 2) –glucoside, Luteolin 3'-methyl ether 7-malonylglucoside, Isorhamnetin 3,7-di-O-beta-glucopyranoside, 1,2: 3,4-Diepoxy-p-menth-8-ene, 1-Methyl-4- (1-methylethenyl) -2,3-dioxabicyclo [2.2.2] oct-5-ene, (+/-) - beta-Phellandrene and p-Mentha-1,3,8-triene. TPSA greater than 140 Å as in apiin, apigenin 7- (6 '' - malonylglucoside), Luteolin 7-apiosyl- (1-> 2) –glucoside, Luteolin 3'-methyl ether 7-malonylglucoside, Isorhamnetin 3,7-di-O-beta-glucopyranoside, (+/-) - beta-Phellandrene and p-Mentha-1,3,8-triene. Four compounds showing the values included in the Lipinski rule are (+) – Marmesin , Falcarindiol, vaginatin and 1alpha-Angeloyloxycarotol. PASS Prediction of 22 compounds for alopecia treatment and their toxicity can be seen in table 3.

Table 3. Prediction of alopecia activity and toxicity of the Petrocelinum crispum Mill compound using Pass Prediction

No

Compounds name

Biological activity

Pharmacology activity

Toxicity

Pa

Pi

Pa

Pi

1

Psoralen

0,422

0,091

0,809

0,005

2

Xanthotoxin

0,315

0,183

-

-

3

Bergaptol

0,449

0,075

0,783

0,008

4

Xanthotoxol

0,449

0,075

0,863

0,004

5

(+) – Marmesin

0,336

0,161

0,387

0,102

6

Apiin

-

-

-

-

7

beta-D-Apiose

0,384

0,118

-

-

8

Petroselinic acid

0,578

0,025

0,909

0,004

9

Falcarindiol

0,132

0,026

0,934

0,003

10

2-Phenylethanol

0,604

0,019

-

-

11

Apiol

-

-

-

-

12

Myristicin

-

-

-

-

13

Apigenin 7- (6 '' - malonylglucoside)

14

Luteolin 7-apiosyl- (1-> 2) –glucoside

15

Luteolin 3'-methyl ether 7-malonylglucoside

16

Isorhamnetin 3,7-di-O-beta-glucopyranoside

17

1,2: 3,4-Diepoxy-p-menth-8-ene

18

1-Methyl-4- (1-methylethenyl) -2,3-dioxabicyclo [2.2.2] oct-5-ene

19

(+/-) - beta-Phellandrene

20

p-Mentha-1,3,8-triene

21

Vaginatin

22

1alpha-Angeloyloxycarotol

From table 3 it can be seen that compounds that have activity as alopecia treatment both as hair growth and as a therapy that improves hair follicles are Psoralen, Xanthotoxin, Bergaptol, Xanthotoxol, (+) - Marmesin, beta-D-Apiose, Petroselinic acid, Falcarindiol, 2 -Phenylethanol, 1,2: 3,4-Diepoxy-p-menth-8-ene, 1-Methyl-4- (1-methylethenyl) -2,3-dioxabicyclo [2.2.2] oct-5-ene, ( +/-) - beta-Phellandrene, p-Mentha-1,3,8-triene, and Vaginatin. This compounds were predicted to have activity as an alopecia therapy. In the PASS prediction, compounds that have an irritating toxicity effect to moderate and high dermatitis are Psoralen, Bergaptol, Xanthotoxol, (+) - Marmesin, Petroselinic acid, Falcarindiol and 1-Methyl-4- (1- methylethenyl) -2,3-dioxabicyclo [2.2.2] oct-5-ene

CONCLUSION:

Based on the results of molecular docking simulation and ADMET prediction using Lipinski's rule and Pass Pediction, compounds that were predicted to have good activity is (+) - Marmesin, where marmesin has the best affinity compared to other compounds, marmesin also meets the five lipinski rules and is reported to have a low toxicity effect. Compared with minoxidil therapy, (+) - Marmesin is predicted to have good interactions with androgen receptors as hair growth / antialoecia agents, but not better than native ligand. Because the compounds that were studied were based on literature surveys, the compounds must pass in vivo testing for anti-alopecia activity. The compounds must also be explained and examined whether those with good results are responsible for antialopecia activity.

ACKNOWLEDGEMENT:

The author thanks to the Chancellor of Mandala Waluya University for the research facilities that have been provided, especially the Unit Head and staff of the Phytochemical Pharmacognosy Laboratory for completing this research.

CONFLICT OF INTEREST:

The authors declare no conflict of interest.

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Accepted on 16.08.2021           © RJPT All right reserved

Research J. Pharm.and Tech 2022; 15(3):1211-1218.