Aging in the human body is characterized by a decline in physiological conditions, a rise in disease risk, and, eventually, mortality1,2. This phenomenon occurs due to progressive alterations in the body's metabolic and hormonal function caused by the failure of deoxyribonucleic acid (DNA) transcription, chronic inflammation, and the instability of the body's homeostasis of death3,4. This type of homeostasis can emerge due to a progressive decline in the hormone estrogen, also known as estrogen deficiency, which typically happens in postmenopausal women5. This situation will create neuroendocrine alterations that disrupt several systems. Cellular and metabolic processes cause neurodegenerative diseases6,7.

Hormone replacement therapy, which is extremely effective and generally understood by the public, is a frequent therapy for overcoming health problems caused by estrogen shortages8. On the other hand, hormone replacement therapy has adverse effects that might lead to additional health concerns, such as hot flashes, cancer, ischemic stroke, and death9,10. Phytoestrogens are one of the medicines that have been made to work as well as hormone replacement therapy but with less risk11.

Phytoestrogens are natural chemicals with the same structure, activity, and affinity as estrogens found in mammals12. Flavonoids, including genistein, daidzein, glycitein, formononetin, biochanin A, and equol, are among the most abundant classes of chemicals with phytoestrogen activity. In addition to flavonoid molecules, there are non-flavonoid compounds with phytoestrogen activity, such as pinoresinol, 4-methoxypinoresinol, eudesmin, α-amyrin, and β-amyrin13-15. According to current literature13,16, the chemical is a polyphenolic compound with structural similarities to estradiol and estrogenic activity since it has a ring similar to estradiol and two hydroxyl groups with proper spacing between them.

Phytoestrogen chemicals have estrogenic activity after binding to their receptors (ER-dependent) and other pathways (ER-independent), allowing them to maintain brain homeostasis17,18. The ER-dependent pathway can deliver activity directly because one of its components binds to estrogen receptor α (ERα) and estrogen receptor β (ERβ)19. Many treatments for estrogen-related disorders can target the roles of ERα and ERβ these treatments show how important it is to understand ERα and ERβ mechanisms in order to get the most out of treatment20.

Based on this rationale, it is required to do in silico studies on widely encountered phytoestrogen chemicals from the flavonoid and non-flavonoid categories. To understand the mechanism of phytoestrogen drugs via the ER-dependent pathway, neurotoxic activity in ER must be observed. In silico observations have advantages such as being quick and affordable in determining a compound's estrogenic activity21.

Materials

A Lenovo AIAQH4R personal computer was utilized as the tool. For in silico testing, the software includes Autodock Vina (PyRx 0.8), ChemDraw Ultra 12.0, Avogadro 1.0.1, and Biovia Discovery Studio 2021, as well as SwissADME for physicochemical property testing, pkCSM, and Protox II for toxicity testing. The 11 phytoestrogen compound’s three-dimensional structures were created using the ChemDraw Ultra 12.0 software. The study discovered the following compounds: genistein, daidzein, glycitein, formononetin, biochanin A, equol, pinoresinol, 4-methoxypinoresinol, eudesmin, α-amyrin, and β-amyrin were all examples of phytochemicals. In addition to the 11 phytoestrogens substances, the structure of the native ligand (17-estradiol) and the protein (receptor) was created. A protein data database (www.rcsb.org) was used to figure out the three-dimensional crystal structure of the ERα (1A52) and ERβ (5TOA) receptors.

Methods

Preparation of samples22

Using the Biovia Discovery Studio 2021 application, the receptor was split into macromolecules and native ligands and saved in the Sybyl Mol 2. format. Biovia Discovery Studio 2021 is a free downloadable software. ChemDraw Ultra 12.0 was used to create a 3D structure from 11 compounds in mole format. This application is a product of http://www.cambridgesoft.com/. This software can be downloaded for free. Also, the compound was adjusted geometrically with Avogadro 1.0.1 to get a stable position using the MMFF94 method and saved in the Sybyl Mol 2 format19. This software can be downloaded for free.

Physicochemical examination

ChemDraw Ultra 12.0 was used to format each compound into a simplified molecular-input line-entry system (SMILES). The SMILES form was used to evaluate molecules' physical properties and compare them to compounds in IUPAC nomenclature23. The format was then copied one by one onto the SwissADME (http://www.swissadme.ch) and run to find out the compound's pharmacokinetics and pharmacodynamics in the form of the topological polar surface area (TPSA), molecular weight, log P, hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), and the statement "Yes" or "No" in meeting Lipinski's rule of five parameters.

Molecules docking

Internal validation of the receptor and native ligand was performed first using AutoDock Vina (PyRx 0.8). Internal validation was used to determine the root mean square deviation (RMSD). The RMSD number is one of the characteristics that must be met to evaluate its applicability. The RMSD value reported in this study was less than 2.0, indicating that the application is suitable. This stage was completed by altering the grid box location where the ligand interacts with the target receptor to identify the coordinates of the receptor binding site, which was then saved using the csv menu24. Grid box determination for ERα (PDB ID 1A52) includes setting the location according to the grid center x = 90.4083, y = 14.0333, and z = 72.2361 with dimensions of 4.1 x 10.9 x 7.8 Å, and for ERβ (5TOA) location settings according to the grid center x = 19.8271, y = 43.3538, and z = 15.4885 with dimensions of 4.5 x 7.4 x 9.9 Å. Each chemical was docked to the ERα and ERβ receptors using AutoDock Vina (PyRx 0.8), and the interaction was visualized using Biovia Discovery Studio 2021 to analyze the distance between the pharmacophores and the bound amino acids. The molecular docking data of these compounds were compared to those of native ligands to see if they had similar interactions25.

Toxicity test

The pkCSM (http://biosig.unimelb.edu.au/pkcsm/prediction) and ProTox II (http://tox.charite.de/protox II/) were used to predict LD50 values of each ligand using the SMILES format26.

Validation of methods

The method was validated by anchoring the ERα receptor with PDB ID 1A52 and native ligand using AutoDock Vina (PyRx 0.8) and obtaining an RMSD of 1.761 Å. The RMSD result for the ERβ receptor with PDB ID 5TOA and native ligand using AutoDock Vina (PyRx 0.8) was 1.831 Å. An RMSD of less than 2.0 shows that the application is suitable for molecular anchoring processes that provide outcomes similar to experimental results27-29. Figures 1 and 2 show the procedure validation findings.

Physicochemical examination

The SwissADME was used to forecast the pharmacokinetic and pharmacodynamic potentials, and it was discovered that only phytoestrogens from the flavonoid group satisfied the parameters and could be accepted by the body (Table I). Genistein, daidzein, glycitein, formononetin, biochanin A, and equol all meet the requirements of Lipinski's rule of five, so the body recognizes them. Lipinski's rule of five parameters with 500 g/mol molecular weight, hydrogen bond acceptors (HBA) ≤5, hydrogen bond donors (HBD) ≤5, and log P ≤5. A molecular weight of less than 500 g/mol suggests that the molecule can penetrate biological membranes. The log P value shows the compound's dissolved capacity in a liquid membrane. The hydrogen capacity of the H-acceptor and H-donor is shown, and the higher the value, the more energy is required for the absorption process30. Daidzein, formononetin, and equol have topological polar surface area (TPSA) values of 79 Å2, indicating that these compounds can cross the blood-brain barrier and have an effect31,32, indicating that these compounds require further processing to examine the outcomes of molecular docking with the estrogen receptor.

A B

C

Figure1. The results of the validation of the internal ligand method with ERα (A), 2D (B), and 3D (C) overlay of crystallographic ligand.

A B

C

Figure2. The results of the validation of the internal ligand method with ERβ (A), 2D (B), and 3D (C) overlay of crystallographic ligand.

TableI. Physicochemical analysis of phytoestrogen compounds analyzed.

Molecules docking

Internal validation studies with 17β-estradiol ligand showed an average RMSD value of 1.761 Å for ERα receptors with PDB ID 1A52 and 1.831 Å for ERβ receptors with PDB ID 5TOA, indicating that they can be used for docking simulations because they have met the RMSD value requirements of <2.033. Simulated compounds are then screened for prospective compounds that can be used as estrogen replacements. Tables II and III show the analysis results on the interaction between amino acids and the pharmacophore distance of the selected flavonoid compounds. Based on these findings, the three flavonoid compounds were found to be agonists of the 1A52 and 5TOA proteins. The amino acids bound by each chemical were used to categorize the compounds. The simulation results show that the chosen flavonoid compounds can function as agonists against ERα and ERβ and produce estrogenic effects (Figure 2).

TableII. Molecular docking of compounds against ERα receptors.

TableIII. Molecular docking of compounds against ERβ receptors.

Toxicity test

Toxicity experiments were carried out on drugs with agonist interactions to the ERα and ERβ utilizing the ProTox II. Toxicity testing is classified into several types. To predict the toxicity level of the substances, the toxicity class of LD50 is applied. According to the Globally Harmonized System of Classification and Labelling of Chemicals (GHS), six toxicity classes exist. Class I (LD50 ≤5 mg/kg) is deadly if swallowed, class II (5 <LD50 ≤50 mg/kg) is fatal if swallowed, class III (50 <LD50 ≤300 mg/kg) is toxic if eaten, class IV (300 <LD50 ≤2000 mg/kg) is harmful if swallowed, class V (2000 <LD50 ≤5000 mg/kg) is hazardous if swallowed, and class VI (LD50 >5000 mg/kg) is non-toxic34. The higher the LD50 value, the less dangerous the substance is to the body, and vice versa35. The results showed that three chemicals were in classes IV and V, which means they were not very dangerous.

The pkCSM was employed to estimate the values of hepatoxicity, skin sensitization, and Ames toxicity, whereas the ProTox II was used to forecast the toxicity class of compound LD50. Hepatotoxicity is one type of toxicity used to discover hazardous substances in the liver36. Skin sensitization is a hypersensitivity reaction triggered by reactive substances that penetrate the stratum corneum layer of the skin37. Ames toxicity is used to determine various chemicals' mutagenic and carcinogenic potency38. Table IV shows that equol compounds are non-toxic for hepatotoxicity, cutaneous sensitization, and Ames toxicity. The findings of the toxicity test indicate that the equol chemical is not harmful.

TableIV. Toxicity test results for daidzein, formononetin and equol.

* pkCSM; ** ProTox II

The findings of this study reveal that equol molecules can replace estrogen in the body in neurodegenerative disorders. Equol (4',7 isoflavandiol) is a metabolite of daidzein that is an isoflavone derivative. Equol (C15H14O3) is a non-polar isoflavones phenolic chemical that may be responsible for its physiological activity39-41. Equol has an asymmetric carbon atom at the C3 position, which gives rise to the R(-)- and S(-)-equol enantiomers. Equol is more stable, easier to absorb, and has a lower clearance than daidzein, its precursor molecule. It is also more estrogenic than other isoflavones or isoflavone-derived metabolites40,42. This equol molecule exhibits estrogenic action similar to 17β-estradiol and works via the ER-dependent pathway by binding to ERα and ERβ. ERα and ERβ are membrane-associated proteins found in synaptic terminals, dendritic spines, dendritic shafts, axons, and glial cell processes43,44.

Neuroinflammation is the most common cause of neurodegenerative disorders45-47. These phytoestrogen chemicals will bind to the ER and substitute estrogen, inhibiting neuroinflammation via four mechanisms: (1) suppression of IK activation; (2) inhibition of IB phosphorylation; (3) direct inhibition of NF-κB activation; and (4) induction of HLA-B27 gene downregulation. This causes the expression of MHC II to go down and Arg1 to go up. This prevents neuroinflammation and neuronal cell death through necrosis or apoptosis48.

This NF-κB activation causes M1 polarity in microglia cells and the production of inflammatory cytokines (TNF, IL-1, IL-6, and others)49-52. This increased synthesis of inflammatory cytokines will impact the production of brain-plasticity-related molecules such as brain-derived neurotrophic factor (BDNF) and glial cell line-derived neurotrophic factor (GDNF)53,54. This disease causes mitochondrial malfunction and apoptosis of dopaminergic neuron cells through mediating mitogen-activated protein kinase (MAPK)/extracellular signal-regulation kinase (ERK) signaling55.

Equol has estrogenic effects like 17β-estradiol and works by binding to ERα and ERβ, which is part of the ER-dependent pathway.

This research received no external funding.

All authors have an equal contribution to carrying out this study.

None.

The authors declare there is no conflict of interest in this research.

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