INTRODUCTION:

Parkinson's disease (PD) affects 3% of people who are 65 years old or older, making it the second most common disorder that harms neurons. By 2040, the number of cases is expected to go beyond 12 million¹. PD is a long-term neurodegenerative condition that causes significant loss of dopamine-producing neurons in the midbrain's substantia nigra pars compacta (SNpc) area, resulting in a decrease in dopamine levels.² Dopamine is a neuromodulator produced by nerve cells to communicate with other nerve cells, regulating movement and coordination. The most well-known motor symptoms associated with PD include tremor, unstable posture, bradykinesia (slow movement), and muscular rigidity³. Oxidative stress and the consequent build-up of Lewy bodies (LBs) in neuronal somata contribute to cellular dysfunction and neurodegeneration in PD. Reactive Oxygen and Nitrogen Species (RONS), which are reactive radicals that cause oxidative stress, are believed to be present at higher concentrations in dopaminergic neurons⁴and are responsible for chronic inflammation, making dopamine neurons particularly vulnerable⁵. Reactive radicals such as superoxide anion (O₂⁻), hydroxyl radical (OH⁻), and nitric oxide (NO) damage essential biological macromolecules, including proteins, nucleic acids, and lipids, contributing to further cellular disintegration⁶. Levodopa, commonly known as L-DOPA, is the most frequently prescribed psychoactive medication for PD. It functions as a prodrug of dopamine and crosses the blood-brain barrier (BBB), unlike dopamine itself⁷.

Mucuna monosperma (MM) is a climbing shrub with a twining stem, belonging to the Fabaceae family. As reported by Vadivel⁸, apart from L-DOPA, MM seeds also exhibit antioxidant and antibacterial activities⁹, as well as neuroprotective effects¹⁰, nephroprotective activity¹¹ and are used as an expectorant in cough and asthma¹². Additionally, the seeds are known to act as a nervine tonic, emmenagogue, and aphrodisiac, and are used in the treatment of leucorrhea, menstrual disorders, and paralysis¹³. Now a days plant-based drugs are widely used, as they possess antioxidant and anti-inflammatory properties, which help in the prevention of neurodegeneration¹⁴.

Vauzour¹⁵ and Renaud and Martinoli¹⁶have shown that natural phenolics and flavonoids are utilized for treatment of number of chronic conditions because of their strong free radical scavenging and anti-inflammatory activity.

For optimization of extraction processes, RSM is a beneficial statistical technique for assessing the impact of influential factors on one or more response variables¹⁷. RSM is fundamental framework for statistically planning studies and analyzing the impact of different parameters to determine ideal circumstances, which is validated by CCD^18,19. In this regard, optimizing L-DOPA content is prime objective of the current experiment along with evaluation of free radical scavenging potential of M. monosperma seeds²⁰.

MATERIAL AND METHODS:

Experimental chemicals and reagents:

L-DOPA, sodium nitrate, Ascorbic acid, 2,2-Diphenyl-1-picrylhydrazyl (DPPH), Gallic acid, Folin-Ciocalteu reagent, Quercetin, sodium carbonate, Methanol, potassium acetate, sodium nitrite, hydrochloric acid, sodium hydroxide, potassium acetate, aluminium chloride.

Plant material:

M. monosperma seeds were obtained from Gaganbawada, District Kolhapur, in western India. Prof. M G. Gavit, Head of Department, Dept. of Botany, K. A. Alias N. M. Sonwane Arts, Commerce and Science College, Satana, Nashik, is a famous taxonomist who identified and authenticated the collected plant species. The Herbarium Center is where the voucher specimen (BOT 803) was kept and placed. For further experimental research, dried and healthy M. monosperma seeds grounded to a fine powder. Then it was put in a clean, sealed container and refrigerated at 4 °C after by passage through a mesh that is typically less than 85µm.

Selection of solvent type:

As reported by Vora²¹the highest amount of L-DOPA was obtained when M. monosperma seeds were extracted using a 1:1 water: methanol solvent ratio, which maximized the compound's extraction from the seeds. Therefore, for further experimentation, we have also chosen the same solvent ratio for efficient extraction.

Selection of independent variables on RSM:

Developing a strong screening design is the first step in examining the influence of different factors on responses. Design of experiments (DOE) was done using three-factor central composite design, with Stat-Ease version 12 of Design-Expert software. To investigate the impact of the independent variables economically, RSM was employed in a shorter time, with fewer tests. These variables include ultrasonic time (A, min), incubator shaking time (B, hr), and extraction temperature (C, °C) (Table 1). These factors were then considered for further experimental analysis. Subsequently, 20 experimental runs were determined for getting L-DOPA content and total antioxidant activity (% inhibition).

Table 1: The magnitude of independent factors for optimizing RSM

Independent variables (Unit) Factor level

Coded levels

-1.68 -1 0 +1 +1.68

A: Ultrasonic extraction time (UET) (min.) 5 7.5 15

B: Incubator shaking (IS) (hr.) 2 5 10

C: Extraction temperature (ET)(°C) 35 45 65

Three levels, denoted as -1, 0, and +1, were assigned to these independent variables. The complete CCD design matrix, comprising 20 runs of experiments with the experimental results of observed and predicted responses, such as L-DOPA content and total antioxidant activity, is displayed in Table 2.

Table 2: Central composite design (CCD) for process parameters, related quality characteristics, with anticipated and actual outcomes

	Independent variables			Response 1		Response 2
Run	A Ultrasonic extraction time (min)	B Incubator shaking (hr)	C Extraction temperature (°C)	L-DOPA (mg/g)		Antioxidant activity (mg GAE/g)
				Experimental	Predicted	Experimental	Predicted
1	10	6	24.77	6.56	5.68	51.32	47.71
2	10	12.72	50	19.22	16.67	78.36	78.88
3	10	6	50	18.59	17.77	72.54	71.69
4	1.59	6	50	6.32	6.27	60.02	59.78
5	15	2	35	6.15	6.11	35.74	36.49
6	15	2	65	4.9	4.88	40.68	37.84
7	18.4	6	50	11.3	10.05	55.31	57.56
8	15	10	65	13.9	15.15	65.32	61.81
9	5	2	35	5.42	5.09	39.42	41.51
10	10	6	50	17.84	17.77	70.45	71.69
11	5	10	65	10.71	11.67	61.59	59.42
12	10	6	50	15.9	17.77	73.53	71.69
13	5	2	65	4.3	3.56	35.68	33.60
14	10	6	75.22	8.3	7.88	32.02	37.64
15	10	-0.72	50	4.1	5.73	28.21	32.45
16	15	10	35	9.35	11.01	65.23	65.89
17	10	6	50	16.82	17.77	69.87	71.69
18	10	6	50	16.77	17.77	67.36	71.69
19	5	10	35	6.9	7.84	71.34	72.76
20	10	2	50	5.7	12.20	68.01	52.22

Response surface design of experiments:

RSM CCD has been employed to identify the ideal conditions and improvement of extraction efficiency of L-DOPA and total antioxidant activity. The work has been completed in accordance with the predictive models for the two parameters—L-DOPA content and total antioxidant activity—along with the three components that have been described.

Extraction of plant material:

To determine the L-DOPA content of M. monosperma, 10mg of the dried extract was transferred to a volumetric flask (25ml). Then, 10ml of distilled water: methanol (1:1) was administered in the container. The mixture then sonicated for different time intervals and incubator shaking is also done for different time intervals according to table number 2. Then strained with use of Whatman filter paper (No. 1). With the assistance of the previously approved spectrophotometric technique, the L-DOPA concentration of each concentrate was determined.

Analysis of the extract The 20 experimental runs obtained using Design Expert were analyzed for L-DOPA content and antioxidant activity. Quantitative estimation of L- DOPA done with the help of UV-visible spectrophotometric method and antioxidant activity measured by DPPH method for total antioxidant activity. Ultimately, the results were analyzed using ANOVA and fit statistics. In order to demonstrate the connection between the input factors along with their effects on dependent variables, 3D surface and contour plots were employed. Adequacy of model was evaluated with Coefficient of determination (R²), adjusted R² (R²ₐ_𝑑𝑗), predicted R² (R²ₚᵣₑ_𝒹), coefficient of variation (CV), and adequate precision (AP).

Spectroscopic quantification of L-DOPA:

By use of method proposed by Rahmani-Nezhad L-DOPA was quantified²², in which nitrosation of L-DOPA takes place in acidic medium in presence of sodium nitrate. This reaction produces a yellow solution that is unstable that subsequently converted into a stable red solution upon the addition of a base, measurable at 470 nm. This method is highly selective for L-DOPA as compared to other phenolic compounds due to formation of deep red color in solution²³. Our results are also consistent with these findings.

Calibration curve of Standard L-DOPA:

Ten milligrammes of L-DOPA were dissolved in a 100 millilitre flask for preparation of stock solution. Nine (25 ml) volumetric flasks were prepared, each holding 1, 2, 3, 4, 5, 6, 7, 8, or 9mL of the stock solution.

To each flask, 2mL of 3% sodium nitrite and 1ml of 1 mol/liter hydrochloric acid were administered. The mixtures were left to stand for 5minutes, during which it turned to yellow. Then, 3ml of 1mol/liter NaOH administered for rendering the solutions alkaline, followed by another 5-minute standing period, during which the solutions turned red. Then up to the mark each flask was diluted with the help of distilled water. By utilizing a blank solution (which contained the same components but without the analyte) λmax was determined, in range of 400–800 and Which was found to be in 470nm. And then absorbance of all samples was measured and a corresponding calibration curve was plotted²³.

Determination of antioxidant activity:

Total Antioxidant activity performed by method proposed by DPPH assay²⁴.

We examined how well different extracts could counteract DPPH free radicals using the modified methodology of Olanrewaju²⁵. A methanolic solution of 0.4mM DPPH radicals was combined with 1mL of extracts at different concentrations in methanol. By including 2mL of DPPH solution devoid of the test material, a blank control was also carried out. The absorbance at 516nm was determined spectrophotometrically after the mixes were exposed to the dark for 30minutes. The following equation was applied to determine the extract samples % inhibition of free radicals, which represents DPPH scavenging activity:

Ac - As

% Inhibition= ------------------ x 100

Where Ac and As stand for the sample's and blank control's absorbances, respectively. The test was repeated in triplicate.

The run showing the maximum L DOPA content and maximum antioxidant activity was evaluated for TPC and TFC.

Total phenolic content (TPC):

The Folin–Ciocalteu (FC) colorimetric assay was utilized to measure the TPC of Mucuna monosperma seed extract, following the proposed method by Khoirul et al²⁶. The procedure involved mixing 1.5mL of 10% FC reagent with 1.5ml of each extract solution. After five minutes, the mixture was vortexed, and 1.5mL of 10% Na₂CO₃ was addministered. The solutions were then allowed to stand in the dark for one hour at room temperature, and then the at 760nm absorbance was measured against blank reagent. Calibration curve also plotted using gallic acid as standard. TPC was recorded as milligrammes of gallic acid equivalents per gramme of extract (mg GAE/g extract).

Total flavonoid content (TFC):

As described by Khoirul²⁶ the AlCl₃ colorimetric assay was utilized to assess the TFC from M. monosperma seed extract. In brief, 4milliliters of extract were combined with 0.2milliliters of 0.1M potassium acetate and 10% (w/v) AlCl₃solutions. The resultant solutions were vortexed and allowed to sit for half an hour at room temperature in the dark. The solutions' maximum absorbance at 415nm was determined in comparison to a reagent blank. Quercetin was used for standard calibration curve and the amount of total flavonoids was expressed as milligrammes of quercetin equivalent per gramme of extract (mg QCE/g extract)²⁷.

Statistical analysis:

Regression analysis of data obtained after getting the results was performed by using 12^th version of Stat- Ease Design-Expert® software. The models' suitability was confirmed using analysis of variance (ANOVA) and a number of statistical measures, including coefficient of determination (R²), predicted (R²pred) and adjusted (R²adj) coefficient of determination. The results were presented as mean±SD, and all experimental analyses were performed in triplicate.

RESULTS AND DISCUSSION:

CCD model fitting:

Twenty sets of experiments were performed and associated responses are displayed in the table no 2. L-DOPA and antioxidant activity, which are the dependent variables, were shown to be highly significant (P< 0.001) according to the ANOVA statistics, presented in Table 3A and table 3B. At the p<0.05 model terms are significant, at p<0.01 model terms are highly significant and at p<0.001 model terms are remarkably significant.

However, while lack of fit was insignificant for both (L-DOPA and anti-oxidant activity) model responses (p > 0.05). The fact that there was no obvious lack of fit suggested that the model term adequately described the relationship between the process parameters and the model responses. The model responses showed a strong alignment between the observed and predicted values, as evidenced by the coefficient of determination (R²) and adjusted coefficient of determination (Adj. R²) being almost equal to one. Plots of the three-dimensional (3D) response surface demonstrated how independent factors interacted with the model responses.

L-DOPA estimate in relation to independent variables:

The following quadratic polynomial Eq. 1 for the response variable (L-DOPA) by multiple regression analysis performed on the experimental inputs.

L – DOPA = + 17.77 + 3.25 A + 0.6529 B + 1.12 C + 1.34 AB + 0.5387 AC + 0.0763 BC – 2.32 A² – 3.89 B² –3.40 C²…………………………………..……..……..(1)

Fig. 1 displays the L-DOPA response surface plots that were produced by changing two parameters at a time. The maximal amount of L-DOPA recovered and the favorable influence of shaking hours (A) and ultrasonication duration (C), which agree with the regression analysis. As the ultrasonication time rose, the extraction efficiency first increased marginally. At around 10minutes of ultrasonication, the extracts possess the greatest L-DOPA content (19.22mg/g), indicated in Fig. 1(a) with an incubator shaking time of 12.72hours and at 50⁰C temperature. When the ultrasonication time exceeds 12.72 minutes, L-DOPA content declines. Due to sonication most of the bioactive constituents of plants enter into solvent by three different effects viz cavitation, mechanical agitation and thermology. Cavitation is a phenomenon that occurs during the sonication process when ultrasonic waves cause compression and expansion within a medium as they travel through it²⁸.

ANOVA Statistics for L-DOPA:

In a significant quadratic polynomial model, the model terms were indicated by two crucial parameters: a lower P-value and a larger F value. When compared to the pure error the P-value of 0.2274 for Lack of Fit indicates that the model is not significant. In this research, the regression coefficient (R²) is 0.9538, with an R² Adjusted of 0.9122 and a predicted R² of 0.7426, suggesting that the model is highly favorable. The regression coefficient obtained from the quadratic polynomial equation, which correlates L-DOPA with independent variables, is deemed relevant. The contour plot in Fig. 1 (b, d, f) shows a color gradient from green to yellow to red, indicating a progression from lower to higher L-DOPA transformation. These boundaries create smooth transitions between contours. The model's F-value of 22.92 indicates that a mere 0.01% chance of noise. In this case, A, C, AB, A², B², and C² are significant model terms, as indicated by P-values less than 0.0500. The Lack of Fit F-value of 2.24 indicates that, in comparison to the pure error, the Lack of Fit is not substantial. Noise has a 22.74% chance of causing such a high F-value. A non-significant mismatch is advantageous. Given that the difference is less than 0.2, the Predicted R2 of 0.7426 and the Adjusted R2 of 0.9122 are in reasonably good alignment. When the Adequate Precision is higher than 4, it is preferred.

An estimate of antioxidant activity (% inhibition) along with independent variables The effects of each independent variable on the antioxidant activity of the extracts was evaluated at the coded level using the regression equation. (Eq. no 2).

TAC = + 71.69 +13.80 A – 3.00 B – 0.6576 C – 1.36 AB – 0.4625 AC + 2.32 BC – 5.66 A² – 10.26 B² – 4.60 C².………………………………………………….... (2)

The DPPH radical, known for its stable non-radical form, is particularly utilized for free radical-scavenging capacity of antioxidants. When DPPH interacts with a substance that donates electrons or hydrogen, such as an antioxidant, the absorbance at 517 nm diminishes. A greater scavenging activity of the tested material is shown by a lower absorbance in the reaction mixture. Table 2 displays the DPPH scavenging activities of M. monosperma. We observed that the highest scavenging rate of 78.88% at ultrasonic extraction time 10 min, incubator shaking time 12.72 hour and extraction temperature 50⁰C. The interaction between incubator shaking (AB) and the duration of extraction significantly enhanced antioxidant activity (P <0.05), as shown in table 2. Figure 2illustrated that total antioxidant activity rises with increased shaking. However, when ultrasonication time exceeds 10 minutes, antioxidant activity diminishes. Likewise, the interaction between extraction temperatures and showed a similar positive relationship. Figure 2 demonstrated a summative effect for the terms AB and BC on antioxidant activity.

Lower P-value and Higher F value, which represent model terms, were the two crucial factors in a major quadratic polynomial model. The significance of the model was established using the F-value of 26.36. When compared to the pure error the 0.0739, Lack of Fit P-value, the model is not significant. The coefficient of regression (R²) in this investigation is 0.9596. R²predicted value is 0.8180, and R²adjusted is 0.9232. The model is quite desirable, as indicated by the computed regression coefficient of the quadratic polynomial equation for the relationship between the antioxidant activity and independent variables, which is near 1. The contour plot in Figure 2 gradually changes color from green to yellow to red, indicating a decrease in antioxidant activity and an increase in it. These boundaries gently change from one contour to another.

ANOVA Statistics for Anti-oxidant activity (% inhibition):

In a significant quadratic polynomial model, the model terms were indicated by two crucial parameters: a lower P-value and a larger F value. The model is not significant in relation to the pure error, as indicated by the Lack of Fit P-value of 0.0739. For the present study, the coefficient of determination (R²) is 0.9596, along with adjusted R² of 0.9232 and a predicted R² of 0.8180, suggesting that the model is highly desirable. The regression coefficient obtained from the quadratic polynomial equation, which correlates antioxidant activity with independent variables, is deemed relevant. The contour plot in Fig. 2(b, d, f) exhibits a gradual color transition from green to yellow to red, indicating an increase in antioxidant activity. These transitions show when one contour ends and another begins. The F-value of 26.36 for the model suggests that it is significant, with a 0.01% possibility that noise might be the cause of such a high F-value. In the case of A, B, A², B², and C², model terms are indicated by P-values below 0.0500. At the same time, values above 0.1000 imply non-significant model terms. The Lack of Fit F-value of 4.86 shows a 7.39% probability that such a large number may be the consequence of noise, which is undesirable as a good model fit is desired. The probability is extremely low (less than 10%), which is concerning. Since the discrepancy between the predicted R2 of 0.8180 and the revised R2 of 0.9232 is less than 0.2, there is a reasonable degree of agreement. Preferably Adequate Precision needs to be more than 4. Adequate signal is indicated by ratio 1

Determination of TFC and TPC:

The extract from the plant is abundant in phytochemicals that have the ability to donate hydrogen atoms, which helps neutralize free radicals and prevent oxidative harm. Phenolic compounds in plants are vital due to their hydroxyl groups, which enhance their radical scavenging properties²⁹. Both phenolics and flavonoids are acknowledged as effective scavengers of numerous oxidizing molecules³⁰. The TPC was quantified using the FC reagent and corresponds to gallic acid equivalents (GAE). M. monosperma demonstrated a phenolic content of 43.13mg GAE/g, determined through the calibration curve of gallic acid, expressed by the equation y = 0.009x + 0.0572, R2 = 0.9856. The TFC was measured using a quercetin standard curve, with results represented as milligrams of quercetin equivalent per 100 grams of dry weight (mg QE/100g dry weight). The flavonoid content was quantified using the quercetin calibration curve, y = 0.0012x + 0.0538, R2 = 0.969, and was found to be 26.25mg/g of dry powder. The findings from our study suggest that M. monosperma possesses a notable capacity for radical scavenging, which is linked to its total phenolic and flavonoid content, indicating its antioxidant potential.

CCD cube plot optimization of predictive models:

The cube plot illustrates how independent variables cumulatively affect responses. The response surface system in three dimensions is shown in the graph. To help in response prediction, the cube plot is a helpful tool for examining the relationship between three parameters.

By combining three important parameters on L-DOPA, and antioxidant activity is shown in a cube plot (Figure 3). Each effect's highest and lowest values are shown in the corner. A negative sign denotes the lower limit, whereas a positive sign highlights the upper limit. In order to maximize response variables while adhering to model limitations, the optimal set of variables for the extraction of functional chemicals from M. monosperma was determined. Amounts of input factors that satisfied all requirements for every quality characteristic and factor simultaneously was sought after via the desirability cube plot.

Fig. 3: The relationship between input and response variables and their attractiveness are displayed by cube plots.

Once the experimental and predicted results were connected, there was a significant matching among the responses with independent variables, confirming the reliability of RSM with a significant correlation. Cube plot determines a suitable set of parameters that will satisfy the design. The best value for each answer is predicted by a cube plot, which solves for controllable parameters.

DISCUSSION:

Herbal medicine tends to focus on not only treating the root cause of illness but also promotes wellbeing and balance and alleviates the symptoms. Here dopamine depletion can be overcome by M. monosperma seeds. And for this reason, we focused on use of M. monosperma seeds which consist of L-DOPA can be used for treatment of PD. In present study RSM- CCD was used for optimized L-DOPA extraction and antioxidant activity from M monosperma. In this study at 10-minute ultrasonication time got the optimized results. Due to sonication most of the bioactive constituents of plants enter into solvent by three different effects viz cavitation, mechanical agitation and thermology. Cavitation is the process by which ultrasonic waves cause compression and expansion in the medium as they travel through it during sonication³¹.

Increase in temperature causes cyclic wave compression and expansion which is known as thermology. Softening of particle surfaces and increased diffusivity are favored by in situ and limited temperature elevation, both of which are advantageous for increased mass movement. Therefore, cavitation can be considered the fundamental process that favors thermology and agitation as cavitation-driven events in a larger sense³². Also, according to Shen⁵³. UAE increases rate of mass transfer through bubbles in solid/liquid extraction to explode and produce local pressure required for rupturing the tissue of plant for release of components outside the cell in the solvent⁵⁴. Increase in temperature of extraction from 40°C to 50°C, the yield of levodopa grew dramatically, but as the temperature rose above 50°C, the yield rapidly fell (Fig. 1(c)). Elevated temperature facilitates their solubilisation and encourages diffusion out of the cell. A slight rise in temperature of solvent for extraction has the potential to enhance L-dopa solubility and diffusion, leading to an improvement in L-DOPA content. So, by use of these three different variables, we got optimized extract at 12.72 hour of shaking, 10 minutes of ultrasonication at 50⁰C. The phenolic compounds found in plants are particularly significant due to their hydroxyl groups, which enhance their ability to scavenge free radicals. Both phenolics and flavonoids are highly effective in neutralizing most oxidizing agents. Here also 43.13 mg GAE/g phenolic content and 26.25 mg Quercetin/g flavonoid content would be responsible for showing a very good antioxidant activity.

CONCLUSION:

In this research, a streamlined and proficient technique was utilized for extraction for quantifying L- DOPA content and antioxidant activity of Mucuna monosperma seeds. The optimal extraction conditions were as follows: ultrasonic extraction time 10 min, incubator shaking time 12.72 hour and extraction temperature 50⁰C. It was found that the observed experimental data closely aligned with the anticipated results. RSM-CCD offers significant insights for predicting the best factor combinations for L-DOPA and total antioxidant activity. Fitting the second-order polynomial model, the ANOVA output revealed that the time required for ultrasonication, incubator shaking along with the controlled temperature considerably impacted the response variables. 3-D surface plots and contour plots can also be used to analyze how independent variables affect overall performance. The polynomial quadratic model is significantly fitted, and our research shows a high agreement between adjusted R²and expected R². The phenolics and flavonoids present in M. monosperma have a involved in halting the production of free radicals as our findings showed their good antioxidant property.

ACKNOWLEDGMENTS:

The authors express their sincere thanks and acknowledgment to Marathwada Mitramandals College of Pharmacy, Thergaon, Pune 411033 for rendering their support in instrumental analysis of extracts. Also, Prabha Bhong thankful to Dr. Suvarna Pramod Ingale madam for providing valuable guidance time to time.

ETHICAL STATEMENTS:

This research does not require ethical statement/clinical trial registration number or informed consent.

CONFLICT OF INTEREST STATEMENT:

The authors declare that there is no conflict of interest.

REFERENCES:

1. Bhong PN, Ingale SP, Jadhav PT. Molecular Mechanisms Involved in Pathogenesis of Parkinson's Disease. Research Journal of Pharmacy and Technology. 2024; 17(10): 5167-4. doi: https://doi.org/10.52711/0974-360X.2024.0079

2. Evancho A, Do M, Fortenberry D, Billings R, Sartayev A, Tyler WJ. Vagus nerve stimulation in Parkinson’s disease: a scoping review of animal studies and human subjects research. npj Parkinson's Disease. 2024 Oct 24; 10(1): 199. https://doi.org/ 10.1038/s41531-024-00803-1

3. Rajesh Kumar Reddy P, Saravanan J and Praveen T K. Evaluation of Neuroprotective Activity of Melissa officinalis in MPTP Model of Parkinson’s Disease in Mice. Research J. Pharm. and Tech. 2019; 12(5): 2103-2108. doi: https://doi.org/10.5958/0974-360X.2019.00349.4

4. Wei Z, Li X, Li X, Liu Q, Cheng Y. Oxidative stress in Parkinson's disease: a systematic review and meta-analysis. Frontiers in Molecular Neuroscience. 2018 Jul 5; 11: 236. https://doi.org/10.3389/fnmol.2018.00236

5. Isik S, Yeman Kiyak B, Akbayir R, Seyhali R, Arpaci T. Microglia Mediated Neuroinflammation in Parkinson’s Disease. Cells. 2023; 12: 1012. https://doi.org/10.3390/cells12071012

6. Juan CA, Pérez de la Lastra JM, Plou FJ, Pérez-Lebeña E. The chemistry of reactive oxygen species (ROS) revisited: outlining their role in biological macromolecules (DNA, lipids and proteins) and induced pathologies. International Journal of Molecular sciences. 2021 Apr 28; 22(9): 4642. https://doi.org/10.3390/ ijms22094642

7. Armstrong MJ, Okun MS. Diagnosis and treatment of Parkinson disease: a review. JAMA. 2020 Feb 11; 323(6): 548-60. doi:10.1001/jama.2019.22360

8. Vadivel V. Nutrient composition and antioxidant content of Mucuna monosperma DC. EX Wight seeds. International Journal of Recent Scientific Research. 2019 Aug; 10(10): 35649-54. http://dx.doi.org/10.24327/ijrsr.2019.1010.4145

9. Mallaiah GK, Thirupathi K, Ganapaty S, Tirumala Rao P, Mohan GK. Phytochemical and Antimicrobial Studies on the Seeds of Mucuna monosperma DC. Current Trends in Biotechnology and Pharmacy. 2008; 2(2): 442-6.

10. Hareshchandra DY, Mundugaru R, Shridhara Bairy T, Ravikrishna S, Ravi Shankar B. Neuro-protective role of seeds of Mucuna pruriens BEK and Mucuna monosperma DC in wistar albino rats. The Journal of Phytopharmacology. 2015; 4(6): 276-81.

11. Naik V. An Experimental Evaluation of Kapikacchu (Mucuna Pruriens; Bak.) and Kakandika (Mucuna Monosperma. DC) WSR to its Vrukka Prasadaka (Nephroprotective) Activity–A Comparative Study (Doctoral dissertation, Rajiv Gandhi University of Health Sciences (India)).

12. Nadkarni KM, 1976. India Materia Medica Vol I. Bombay popular Prakashan, India. pp 817-818.

13. Khory RN, and Katrak NN (1999) Materia medica of India and their therapeutics. Komal Prakashan, Delhi pp 218-219.

14. Feng J, Zheng Y, Guo M, Ares I, Martínez M, Lopez-Torres B, Martínez-Larrañaga MR, Wang X, Anadón A, Martínez MA. Oxidative stress, the blood–brain barrier and neurodegenerative diseases: The critical beneficial role of dietary antioxidants. Acta Pharmaceutica Sinica B. 2023 Oct 1; 13(10): 3988-4024. https://doi.org/10.1016/j.apsb.2023.07.010

15. Vauzour D, Rodriguez-Mateos A, Corona G, Oruna-Concha MJ, Spencer JP. Polyphenols and human health: prevention of disease and mechanisms of action. Nutrients. 2010 Nov 8; 2(11): 1106-31. https://doi.org/10.3390/nu2111106

16. Renaud J, Martinoli MG. Considerations for the use of polyphenols as therapies in neurodegenerative diseases. International Journal of Molecular Sciences. 2019 Apr 16; 20(8): 1883. https://doi.org/10.3390/ijms20081883

17. Kumar R, Reji M (2023) Response surface methodology (RSM): An overview to analyze multivariate data. IJMR 9: 241–248. https://doi.org/10.18231/j.ijmr.2022.042

18. Subbaiya R, Raja VV, Balachandar R, Suresh D, Chozhavendhan S, Vinoth S, Devi GK. Optimization of Process Parameters for Total Phenol Extraction from Wood Waste using Response Surface Methodology. Research Journal of Pharmacy and Technology. 2019; 12(3): 1096-104. doi: 10.5958/0974-360X.2019.00180.X

19. Sundararaman S, Narendrakumar G, Sundari N, Amarnath M, Thayyil PJ. Extraction of Pectin from used Citrus Limon and optimization of process parameters using Response Surface Methodology. Research Journal of Pharmacy and Technology. 2016 Dec 1; 9(12): 2246. doi: 10.5958/0974-360X.2016.00453.4

20. Subbaiya R, Priyanka S, Suresh D, Selvam MM, Balachandar R, Chozhavendhan S. Application of Response Surface Methodology in Process Parameter Optimization of Media for Production of Amylase. Research Journal of Pharmacy and Technology. 2018; 11(12): 5273-81. doi: 10.5958/0974-360X.2018.00961.7

21. Vora R, Joshi AN, Joshi NC. Comparison Of L-Dopa Content In Three Species Of Genus Mucuna By Different Extraction Techniques. AnnPlSci 2018; 7:1973. https://doi.org/10.21746/ aps.2018.7.1.10

22. Rahmani-Nezhad S, Dianat S, Saeedi M, Tehrani MB, Ghadiri A, Hadjiakhoondi A. Evaluating the accumulation trend of L-dopa in dark-germinated seeds and suspension cultures of Phaseolus vulgaris L. by an efficient UV-spectrophotometric method. Química Nova. 2018 Apr; 41(4): 386-93. https://doi.org/10.21577/ 0100-4042.20170193

23. Shah PB, Bijal Joshi BJ. Estimation of L-dopa from Mucuna pruriens Linn and formulations containing M. pruriens by spectrofluorimetric method. International Journal of PharmTech Research. 2010; 2(2): 1033-1036.

24. Hemalatha CN, Keerthana V, Mehrunisha K, Shalini S, Geetha B, Harikrishnan N. Comparative of Antioxidant property of Actinidia deliciosa extracts by DPPH Assay. Research Journal of Pharmacy and Technology. 2024; 17(3): 1246-9. doi: https://doi.org/ 10.52711/0974-360X.2024.00194

25. Mukhia R, Basistha B, Chhetri DR. Variation in Antioxidant Activity of a Rattan Species, Plectocomia himalayana Griff. by DPPH assay based on two different methods of Methanol Extraction. Research Journal of Pharmacognosy and Phytochemistry. 2018; 10(2): 175-178. doi: https://doi.org/ 10.5958/0975-4385.2018.00027.4

26. Ngibad K, Herawati D, Aisyah SD, Triarini LJ, Mohammad Rizki Fadhil Pratama. Total Flavonoid, Total Phenolic contents and Antioxidant activity of Methanol and n-hexane extract from purple passion fruit peel. Research Journal of Pharmacy and Technology 2023; 16(3): 1247-3. doi: https://doi.org/10.52711/ 0974-360X.2023.00206

27. Maestre-Hernández AB, Vicente-López JJ, Pérez-Llamas F, Candela-Castillo ME, García-Conesa MT, Frutos MJ, Cano A, Hernández-Ruiz J, Arnao MB. Antioxidant activity, total phenolic and flavonoid contents in floral saffron bio-residues. Processes. 2023 May 5; 11(5): 1400. https://doi.org/10.3390/pr11051400

28. Naik AS, Suryawanshi D, Kumar M, Waghmare R. Ultrasonic treatment: A cohort review on bioactive compounds, allergens and physico-chemical properties of food. Current Research in Food Science. 2021 Jan 1; 4: 470-7. https://doi.org/10.1016/ j.crfs.2021.07.003

29. Suja C, Shuhaib. B, Abdurahman M, Khathoom H, Simi K. A Review on Dietary Antioxidants. Research Journal Pharmacy and Technology., 2016; 9(2): 196-202. doi: https://doi.org/10.5958/ 0974-360X.2016.00035.4

30. Bravo L. Polyphenols: chemistry, dietary sources, metabolism, and nutritional significance. Nutrition reviews. 1998; 56(11): 317-33. https://doi.org/10.1111/j.1753-4887.1998.tb01670.x

31. Garg J, Pathania K, Sah SP, Pawar SV. Nanostructured lipid carriers: a promising drug carrier for targeting brain tumours. Future Journal of Pharmaceutical Sciences. 2022; 8(1): 25. https://doi.org/10.1186/s43094-022-00414-8

32. Chakraborty S, Uppaluri R, Das C. Optimization of ultrasound-assisted extraction (UAE) process for the recovery of bioactive compounds from bitter gourd using response surface methodology (RSM). Food and Bioproducts Processing. 2020; 120: 114-22. https://doi.org/10.1016/j.fbp.2020.01.003

Received on 02.06.2025 Revised on 11.10.2025

Accepted on 03.12.2025 Published on 20.05.2026

Available online from May 25, 2026

Research J. Pharmacy and Technology. 2026;19(5):1961-1968.

DOI: 10.52711/0974-360X.2026.00281

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.


Fig. 1: The interactive effects of UET, IS, and ET on L-DOPA are displayed in(2D) contour (b, d, f) plots and (3-D) surface response (a, c, e).	Fig. 2: The interactive effects of UET, IS, and ET on antioxidant activity are displayed in (2D) contour (b, d, f) plots and (3-D) surface response (a, c, e).