The physicochemical analysis of B. juncea leaves powder revealed that the loss on drying was 2.60% thus moisture content was found to be within the hygroscopicity range limit. It contained total ash contents of 4.55%, water-soluble ash 1.05%, acid insoluble ash 3.60%, alcohol soluble contents 9.42%, water-soluble contents 1.05%. The alcohol and water contents of the plant revealed an aqueous-alcoholic cosolvent with advanced extractive capacity and purity while sulfated ash was found to be 5.1% contents.

Alkaloid contents of B. juncea extract were estimated by the linear regression equation y = 0.0009x (R${}^2$ = 0.9748) which was obtained by drawing a graph between concentration versus absorbance. The extract of B. juncea contained 42.66 $\mu$g atropine equivalent/mg of alkaloids. The phenolic contents were determined by the mean of linear regression equation y = 0.0014x (R${}^2$ = 0.9764) which was obtained by drawing a graph between concentration versus absorbance. The extract contained 107 $\mu$g gallic acid equivalent/mg of contents. The contents of flavonoids were estimated by the linear regression equation y = 0.0009x (R${}^2$ = 0.9711) which was obtained by drawing a graph between concentration versus absorbance. The plant extract contained 4 $\mu$g quercetin standard equivalent/mg of contents. In Fig. 1, the standard curves were demonstrated.

Fig. 1.

The standard curve of atropine (A), gallic acid (B) and quercetin (C). y = y-intercept, x = x-intercept, R${}^2$ = coefficient of determination.

Eleven compounds were identified and quantified in the HPLC. The compounds in the increasing order of quantity (mg/g extract) are catechol (25.34) $>$ hydroxybenzoic acid (23.84) $>$ rutin (12.47) $>$ chlorogenic acid (6.42) $>$ catechin (5.04) $>$ caffeic acid (3.63) $>$ sinapic acid (2.41) $>$ gallic acid (1.42) $>$ pyrogallol (1.38) $>$ ferulic acid (0.49) $>$ coumaric acid (0.22) (Fig. 2, Table 1).

Fig. 2.

HPLC chromatogram of B. juncea leaves extract and its standard phenolic compounds.

The 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging assay was used to assess the antioxidant potential of plant extract at various concentrations. The percentage radical scavenging activity of the plant extract was increased dose-dependently. The maximum % age scavenging was presented by plant extract at 1000 $\mu$g/mL (70%) which was notably varied from ascorbic acid (56%). The IC${}_{50}$ value of extract and ascorbic acid were calculated using linear regression equation was y = 0.0.0253x (R${}^2$ = 0.9232) and y = 0.0.0338x (R${}^2$ = 0.9272) respectively. The IC${}_{50}$ value of extract was 137.54 $\mu$g/mL while the IC${}_{50}$ value of ascorbic acid was 159.85 $\mu$g/mL.

Molecular Operating Environment (MOE) software package 2016.10 was used for molecular docking of AchE protein with selected 12 phytochemicals. Molecular docking analysis explains the functional residues involved in the inhibitory activity of AChE protein to design a potential drug against PD by using the dataset of 12 phytochemicals (Table 3).

Molecular docking interpretations show that the binding energies of best-bounded confirmation of the selected 12 phytochemicals were in the range of –7.8793 to –4.4390 Kcal/mol. As the molecule that has presented the lowest binding energy was considered the most significant AChE inhibitor. So that there are four ligands (sinapic acid, rutin, ferulic acid, and caffeic acid), that were considered to be the best AChE inhibitors based on their best-bounded confirmation, lowest binding affinities, and excellent binding interactions within the catalytic pocket of enzyme AChE (PDB ID: 4EY7) (Fig. 7). Other 7 phytochemicals have also presented significant interactions in terms of Vader Waals or hydrophobic interactions, hydrogen bonds, Pi-lone pair, Pi-Pi T-shaped, and Pi-alkyl interactions, but their binding energies were not up to the significant threshold value (Table 4).

Fig. 7.

Three-dimensional representation of the hydrophobic molecular surface of the Acetylcholine-esterase (AchE) protein (PDB ID: 4EY7) with respective docked phytochemicals; Sinapic acid. (a) Rutin, (c), ferulic acid (e), and caffeic acid (g) within an active binding site. While two-dimensional interaction plot presents significant binding interactions of the four top-scored bound ligands; sinapic acid (b), rutin (d), ferulic acid (f), and caffeic acid (h) within a distance range of 4Å. Interactions were shown by dotted lines and residues were classified by different colors, as indicated in the legend below the interaction plot.

For the entry into AChE, target protein’s through the active binding site, there is a narrow cleft linked with the pattern of aromatic amino acid residues, hence allow the substrate to enter into the catalytic triad of HIS, SER, and GLU amino acid residues. It is known as the hydrophobic amino acid residues are linked with the aromatic amino acids supervision that revealed the highest degree of flexibility and permits the ligands of significant size. Though, the opening and closing posture of the active site is associated with a fluctuating cleft, e.g., ligand atoms interacting with TYR337 residue [28]. The conformational investigation exposed that sinapic acid, ferulic acid, and caffeic acid, penetrated the active site cleft, and interrupted the aromatic amino acids, and inhibit the substrate-binding (Fig. 7). Moreover, these ligands accept the challenge of blocking the fluctuating cleft and overturned near the catalytic triad to inhibit the target protein activity [29].

Sinapic acid has achieved the lowest binding energy (–7.8793 Kcal/mol) of the best-bounded conformation and presented the highest potential towards inhibition of AChE protein expression. Functional residues of the active site such as THR83, ASN87, GLY121, SER125, TYR133, GLU202, ALA204, TRP236, TYR341, GLY448, and ILE451 are responsible for the weak Vader Waals and hydrophobic interaction network, while seven hydrogen bonds were generated by ASP74, GLY120, GLY122, SER203, TYR337, and TYR124 residues. TRP86 residue is applicable for Pi-lone pair association, Pi-Pi stacking with T-shaped geometry has been visualized by the interaction of TYR337 residue. PHE295, PHE297, PHE338, and HIS447 residues were found to establish the significant influence of Pi-alkylation. Hence, the atoms of sinapic acid are showing considerable interactions with the target protein residues in Fig. 7a,b.

Rutin has also presented significant binding energy (–7.0091 Kcal/mol) of the best-bounded conformation as a result of docking investigation with AChE protein, and it has been very good docked results. THR75, VAL282, ASN283, HIS284, TRP286, HIS287, GLN291, and SER293 residues are responsible for hydrophobic interactions and six hydrogen bonds were generated by TYR72, ASP74, LEU76, GLU292, TYR341, and GLY342 residues within the catalytic cleft of the enzyme AChE, most of the ligand surface was exposed towards the solvent shown by the blue blobs in Fig. 7c,d.

Ferulic acid exhibited the strongest binding affinity in terms of the lowest energy value (–7.0991 Kcal/mol), and also depicted the largest network of significant binding interactions within the vicinity of the active binding site of AChE protein. ASN87, GLY120, GLY121, GLY122, SER125, GLU202, SER203, PHE297, PHE338, HIS447, GLY448, and ILE451 are involved in hydrophobic interactions. ASP74, THR83, TYR124, TYR341, and GLY120, residues were responsible for the generation of five hydrogen bonds. TRP86 formed the Pi-lone pair. TYR337 residue was associated with Pi-Pi T-shaped bonding and TRP86 residue was showing Pi-alkylation. Fig. 7e,f shows the most of the interacting catalytic surface of the selected enzyme AChE is hydrophobic, and atoms of the ferulic acids make significant binding interactions with the residue present in the native binding pocket of the AChE target protein.

Caffeic acid has presented the –6.3780 Kcal/mol binding energy within the active site of target protein, most of the interacting residues such as ASP74, THR83, GLY120, GLY121, GLY122, TYR124, TYR133, GLU202, PHE297, TYR337, PHE338, TYR341, HIS447, and ILE451are involved in the hydrophobic interaction network, while GLY448 and GLY120 have generated two hydrogen bonds with the atoms of caffeic acid. Pi-lone pair association was observed between the ligand atom and TRP86 residue of target protein shown in Fig. 7g,h.

The active content of B. juncea, sinigrin has also been studied to check its binding potential with acetylcholine-esterase (AchE) protein (PDB ID: 4EY7) and bounded at the best conformation at –6.2601 Kcal/mol and interacted with TYR72, THR75, LEU76, TYR124, LEU285, TRP286, HIS287, GLY289, GLN291, GLU292, SER293, VAL294, PHE295, ARG296, PHE297, TYR341, and GLY342 functional residues of the active site of the target protein. TRP286 has generated Pi-sulfur contact, TYR124, PHE297, and TYR341 have generated Pi-alkyl contacts, TYR72, GLU292, and GLY342 have generated hydrogen bonds, and TYR75, LEU76, HIS287, LEU289, GLN291, Val294, PHE295, and ARG296 residues are involved in the Vander Waals interactions with atoms of sinigrin (Table 3).

The docking results of sinapic acid, ferulic acid, and caffeic acid are excellent in terms of binding interactions with functional residues and their docked complex binding energies are above 7 Kcal/mol and shown the strongest efficacy towards the AChE target protein among the selected 12 phytochemicals. Moreover, the other contents of Brassica juncea extracts, caffeic acid, and sinigrin has exhibited binding energies of less than 7 Kcal/mol which is less than the good threshold values, and shown moderate protein-ligand interaction results and energy values in the range of 6 to 7 Kcal/mol. The other six phytochemicals; Pyrogallol, gallic acid, catechol, catechin, hydroxybenzoic acid, chlorogenic acid, coumaric acid, and rutin have presented binding energies less than 6 Kcal/mol that is less than the threshold values. It means the docking results show less potential of these six compounds towards inhibition of AChE target protein.

Calculation of in silico pharmacokinetic parameters is significant to narrow down the large dataset to highly significant drugs with the minimum risk of rejection in the early drug development procedures [29]. Four best-scored phytochemicals were subjected for the estimation of in silico pharmacokinetic properties by Swiss ADME and toxicity estimations by Osiris molecular property explorer. Hence, three phytochemicals, sinapic acid, ferulic acid, and caffeic acid showed the maximum results in the acceptable range shown in Tables 4 and 5.

The drug or compound we selected should be metabolized by the body or not, it is required to be understood because the only pharmacokinetics properties won’t be enough to understand the membrane permeability of selected phytochemicals. After all, the high logP and molecular weights of the compounds from natural sources could not accept the drug-likeness parameters, although in several studies in silico drug-like calculations have been performed [30] while in this study three phytochemicals have shown that they have accepted the rules of drug-likeness, and only one compound rutin has violated the rule due to its large chemical compound size.

Additionally, cytochromes P450 were calculated to support the pharmacokinetics of the selected phytochemicals, it is reported that the essential isoforms, e.g., CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4 are important for drugs activity within the body. The mechanism of isoforms of the cytochrome P450 superfamily is becoming a challenge for the natural chemicals concerning the metabolism and excretion of drugs through the liver [31]. Chemicals that could inhibit the cytochromes P450 assist the interactions of selected natural compounds and are attributable to drug administration that may not be able to encompass the metabolism-related activity and accumulate as toxic substantials inside the body.

Lipophilicity (LogP) values are also calculated for the four putative phytochemicals because it is important to explain the bioavailability of the drug, some minor violations were observed in three selected phytochemicals other than rutin, these drug-like parameters assist the oral administration of chemicals, such as poor lipophilic drugs corresponding to poor gastrointestinal absorption and owns the lowest BBB permeability, so it’s critical to understand the protein-ligand interactions intricated in the disease of the central nervous system [32]. The water solubility (LogS) of the phytochemical was calculated to describe the absorption and distribution, all four chemicals have presented very good solubility and all these properties were explained well in Tables 4 and 5.

The undesired or unnecessary structural issues known as Pan assay interference compounds (PAINS) and Brenk alerts were also studied in the four selected phytochemicals, it is thought to be eliminated before moving towards the early drug development procedures [28]. All four phytochemicals have displayed PAINS and Brenk alerts, which could be a preliminary hypothesis and could be helpful for the improvement of structural issues. Osiris molecular property explorer demonstrated that four phytochemicals results are positive, there are no harmful effects on four major grounds of tumorigenic, mutagenic, irritant, and reproductive properties, Moreover, drug-likeness and drug scores are important to understand the ADMET profile of a chemical, that is also very good and acceptable result are shown in Table 5 and Fig. 8. Hence, it is demonstrated that the ADMET profile of the selected phytochemicals along with the protein-ligand interaction results is most acceptable, so our proposed chemical compounds could be potential inhibitors of the AChE protein target and treat the PD well [31].

Fig. 8.

Schematic representation of ADMET profile of top score four phytochemicals; sinapic acid (a), rutin (b), ferulic acid (c) and caffeic acid (d). The pink-colored area depicted the acceptable pharmacokinetic parameters. All four extracted chemical compounds have demonstrated very good results, fulfilling the drug-likeness rules except rutin because of its large size it has a high molecular weight, moreover, the drug scores are in the acceptable range, while sinapic acid has presented the best result.

The B. juncea leaves were collected in the winter season from Toba Tek Singh district, Pakistan. The herbarium of B. juncea plant was prepared and identified from the Department of Botany, Government College University Faisalabad by Taxonomist Dr. Qasim Ali under authentication number Z245-BOT-19.

The physicochemical analysis of B. juncea leaves powder including loss on drying, total ash, acid-insoluble, water-insoluble and sulfated ash contents, alcohol and water-soluble contents were performed by following United States Pharmacopoeia-National Formulary (2003) methods [67]. Das et al. [68] method was adopted for the quantification of alkaloids and Saleem et al. [69] methods were followed for the flavonoids and phenolic contents determination.

The methanolic Plant extract was filtered through a 0.2 $\mu$m membrane filter (Millex-HV) and injected into HPLC. The HPLC system (Shimadzu LC-20AT) was used. Mobile phase composed of (A) 0.1% acetic acid and (B) methanol and the composition gradient was: 25% (B) for 10 min; 40% (B) for 15 min; then 50%, 60%, 80% and 100% (B) for every 10 min. The flow rate was 0.8 mL/min. Phenolic moieties were acknowledged with the spectra of standard compounds at 280 nm [70].

B. juncea leaves were washed thoroughly with tap water and the leaves were dried in a hot air oven at 40 ${}^{\circ }$C temperature. Then, the leaves were crumbled to fine powder by utilizing an electronic blender. The powder was passed over a sieve and deposited in an airtight container. The 100 g powder of B. juncea was weighed and taken in each of 3 beakers that contain 750 mL ethanol each. All three beakers were placed in a microwave oven already adjusted at 9000 watt, and then the oven heated up for 2 min, stopped the oven, and opened it for 30 sec. This specific method was repeated 5 times. In the end, the supernatant was filtered through filter paper. In the 2nd cycle again 500 mL ethanol was added in each beaker containing residue. Again all the beakers were placed in a microwave oven. The oven was heated up for 2 min and then stopped for 30 sec. The same method was repeated 5 times. The supernatant solution was filtered through filter paper. In the 3rd cycle again 500 mL ethanol was added to each beaker and repeated the same method 5 time. Filtrates were pooled in the same reservoir. Excess solvent was removed from the filtrate/extract using a rotary evaporator at 40 ${}^{\circ }$C and semisolid ethanolic leaves extract (ELE) was obtained. Lipids were removed by washing the extract with n-hexane. It was stored in a refrigerator at 4 ${}^{\circ }$C till further use.

The in vitro antioxidant properties of B. juncea ethanol extract were determined by 2,2-diphenyl-1-picryl-hydrazyl (DPPH) free radical scavenging assay by the earlier procedure [68]. The extract solution at different concentrations (25-, 50-, 100-, 150- and 200 $\mu$g/mL) was prepared in methanol by two-fold dilution method while ascorbic acid solution (1–100 $\mu$g/mL) that was prepared in methanol, used as standard. For the test, 500 $\mu$L DPPH solution (0.1 mM) was added in 250 $\mu$L of extract solution and 250 $\mu$L of methanol. The solution was allowed to stand at 25 ${}^{\circ }$C for half an hour in a dark place and absorbance was measured at 517 nm using an ultraviolet-visible (UV-Vis) spectrophotometer. The sample was kept for. The percentage for DPPH-radical scavenging activity of the extract was determined by the following formula:

(2)$$\text{Percentage Inhibition}(\%)=\frac{\text{Blank absorbance}-\text{Sample absorbance}}{\text{Blank absorbance}}*100$$

Extract IC${}_{50}$ was measured using linear regression equation y = 0.253x (R${}^2$ = 0.9232) by drawing graph between absorbance verses concentration.

Wistar albino rats weighing 180–200 g were purchased from the Animal House of the University of Agriculture, Faisalabad. They were kept in an animal house (under standard laboratory conditions; room temperature: 25 ${}^{\circ }$C, 12 h light and dark cycle, humidity 60%) of Govt. College University, Faisalabad for one week before the start of the experiment to acclimatize the animals. They had free access to food and water.

The in vivo study was executed after getting ethical approval from Institutional Review Board (Reference No. GCUF/ERC/2035; 25-1-2019) ruled under the regulation of the Institute of Laboratory Animal Resources, Commission on Life Sciences University, National Research Council (1996).

Thirty-six healthy rats of both sexes were divided into 6 groups (n = 6). Group I was normal to control (NCG), to whom the vehicle was given orally. Group II served as disease control (DCG), receiving haloperidol 1 mg/kg intraperitoneally [71, 72]. Group III was the standard group, received L-Dopa 100 mg/kg + carbidopa 25 mg/kg orally. Groups IV–VI treatment groups received extract at 200, 400, and 600 mg/kg dose levels respectively via the oral route. All the groups were given their relevant treatments for 21 days using a gavage needle.

Behavioral evaluation of the NCG was started from day one whereas in other groups behavioral observations were started from the eighteenth day of the study to evaluate the anti-PD effect of the extract.

On the 22nd day, all the animals were humanely killed by cervical dislocation under mild chloroform anesthesia. Brains were excised from all the animals for biochemical and histopathological analysis to examine the anti-PD effect of the plant extract. For the preparation of tissue homogenate (10% w/v) and biochemical assays brain was isolated and the homogenate was prepared with phosphate buffer.

Molecular docking assists to understand and visualize the binding interactions of extracted phytochemicals with the enzyme acetylcholine-esterase, which is one of the remarkable drug targets for PD [79]. For the analysis of protein-ligand interactions, preliminary requirements were the three-dimensional structure of protein and ligand dataset, X-rays crystallographic structure of AChE protein with PDB ID: 4EY7 with the resolution of 2.35 Å was used for molecular docking interpretations [80]. While 12 phytochemicals; pyrogallol (CID: 1057), gallic acid (CID: 370), catechol (CID: 289), catechin (CID: 73160), hydroxybenzoic acid (CID: 135), chlorogenic acid (CID: 1794427), caffeic acid (CID: 689043), sinapic acid (CID: 637775), coumaric acid (CID: 637542), ferulic acid (CID: 445858), rutin (CID: 5280805) and sinigrin (CID: 6911854) in three-dimensional conformations were downloaded from PubChem database [35]. Molecular Operating Environment (MOE) software package (version 2016.10 Chemical Chemical Computing Group Montreal, Quebec, Canada) was used for molecular docking of AchE protein with selected 12 phytochemicals. The chemical structures were directed to the system to prepare and optimize the files before the docking procedure. The integral issues were found in the co-crystallized macromolecule and corrected, missing residues were added, the irrelevant molecular moieties such as water elements, co-factor, ions were removed. Polar hydrogen atoms were added. The protonate function was applied for the optimization. By using the Dock module of MOE, molecular docking was performed, the native binding site was identified from the co-crystallized protein structure. The energy minimization was done by the Amber: 10EHT force field. The triangular matcher placement method was selected with the London dG scoring function. The initially docked poses were refined by the induced-fit docking protocols and selected the GBVI/WSA dG scoring function. Docked complex with the lowest binding energies/highest binding affinities was subjected for interaction analysis.

In silico pharmacokinetic parameters were evaluated by the SwissADME web tool (http://www.swissadme.ch/), major properties calculated were medicinal chemistry-related parameters such as pharmacokinetics, lipophilicity, water-solubility, drug-likeness, identification of undesirable structural moieties, and toxicity alerts of the selected phytochemicals. Osiris property explorer (https://www.organic-chemistry.org/prog/peo/) was used to estimate the toxicity on the four major grounds such as tumorigenic, irritant, mutagenic, and reproductive properties. It also calculated the drug-likeness and drug score, which explains the ADMET (absorption, distribution, metabolism, elimination, and toxicity) profile of the extracted phytochemicals as explained in our previous study [32].

Results were expressed as the mean $\pm$ S.E.M. the data was analyzed by the application of two-way ANOVA followed by the Bonferroni test by the GraphPad prism (version 5.0, GraphPad Software, San Diego, California USA), the value of p 0.05 was set as the statistical significant parameter value.