A statistical optimization tool, Central Composite Design (CCD) part of Response Surface design with the aid of the desirability function (DF) approach was employed to optimize and intensify the anti-Parkinson’s medicine L-3, 4-dihydroxyphenylalanine (L-DOPA), and bio-constituents extraction from Mucuna atropurpurea. The antioxidant and anti-inflammatory activities of an optimized plant extract were measured systematically using a series of assays to determine their relationship with total phenolic and flavonoid contents. Following multiple regression analyses of the experimental results, the optimal conditions were found for the maximum extraction of L-DOPA (112.68 mg/g), TPC (66.505 mgGAE/g), and TFC (132.44 mgQUE/g) were 10.35 min of ultrasonication time, 5.55 h of incubation (shaking) time with 52 °C of extraction temperature. The L-DOPA content was quantified using validated RP-HPLC under the optimized conditions. An estimate of the ANOVA and fit statistics “Suggested” the Quadratic polynomial model fit satisfactory. Validated response surface plots (Ramp function, cube plot, and bar graph of desirability) were the confirmation outputs showing significance to the target response. In conclusion, RSM provides the measures of the influence of factors on each quality characteristic to achieve actual outcomes at the optimal settings and model fit. It also revealed that a multi-target strategy consisting of L-DOPA therapy, antioxidants, iron chelation, and anti-inflammatory properties of Mucuna atropurpurea may provide a targeted new approach toward neuroprotection in Parkinson’s disease. The approach proposed in this research proved to be robust and reliable for L-DOPA investigations, demonstrating its enormous potential for industrial applications.
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