@article {2491, title = {Mucuna Pruriens Seeds Extract Loaded Phytosomal Intranasal Gel for the Effective Treatment of Parkinson{\textquoteright}s Disease}, journal = {Pharmacognosy Journal}, volume = {17}, year = {2025}, month = {April 2025}, pages = {129-154}, type = {Original Article}, chapter = {129}, abstract = {

One phytoconstituent derived from Mucuna pruriens (legumes) is levodopa. This medication{\textquoteright}s oral usage is limited by its high first-pass metabolism and low absorption. The goal of the present research was to develop a phytosomal gel filled with levodopa extract for better delivery and brain targeting. Various techniques, including solvent evaporation, salting out anti-solvent precipitation, direct egg yolk, and egg lipids methods, were used to create phytosomal formulations. Scanning electron microscopy, particle size, x-ray diffraction, and other techniques were used to characterize phytosomes. And added into gel formation, the more successful batch was examined for several parameters. The final batch underwent a variety of animal tests, including pharmacokinetic analysis, irritation to the nasal cavity testing. The most effective phytosomes were those made via the antisolvent precipitation approach. In this investigation, a 32-randomized complete factorial design was employed. Batch F4 had an entrapment efficiency of 70\%, a particle size of 15 (μg) and 60\% CDR. The gel-formulated batch F4G3 demonstrated improved results in terms of extrudability (90.82), amount of drug (89.32\%), viscosity (5421 cps at 100 rpm), and spreadability (25.18). Batch F4G3 of the Mucuna pruriens phytosome gel exhibited Higuchi{\textquoteright}s kinetics. According to the findings of the animal study, dopamine levels were significantly elevated. The pharmacokinetic and nasal irritation studies showed notable in vitro penetration of the nasal mucosa without resulting in skin irritation. For improving Parkinson{\textquoteright}s disease treatment, the phytosomal gel formulation delivered via the nasal route would be the ideal option.

}, keywords = {Gel, L-Dopa extract, Mucuna pruriens, Nasal gel, Optimization, Phytosome}, doi = {10.5530/pj.2025.17.18}, author = {Aniruddha Kulkarni and Manoj Tare and Meera Singh} } @article {680, title = {Improved Bioactive Metabolite Production by Saccharopolyspora halotolerans VSM-2 Using Response Surface Methodology and Unstructured Kinetic Modelling}, journal = {Pharmacognosy Journal}, volume = {10}, year = {2018}, month = {August 2018}, pages = {833-840}, type = {Original Article}, chapter = {833}, abstract = {

Background: This study targets to optimize and analyse the interactive effects of process variables for improved bioactive metabolite production using RSM and unstructured kinetic modelling by S. halotolerans VSM 2. Materials and Methods: RSM was applied to optimize the interactive effects of five variables, viz., time of incubation, pH, temperature, concentration of maltose and meat extract on bioactive metabolite production and its effect against the five responses viz., S. flexneri, S. marcescens, P. vulgaris, P. aeruginosa and E. coli. Models of Logistic and Luedeking-Piret were used to simulate the cellular increase and bioactive metabolite production. Results: RSM optimal conditions for the bioactive metabolite production recorded were incubation time (12days), pH (8), and temperature (250C), concentrations of maltose and meat extract (1 \% w/v) (each). The effect of the bioactive metabolite produced (zone of inhibition) against the responses were found to be 17 mm for S. flexneri, 17 mm for S. marcescens, 16 mm for P. vulgaris, 17 mm for P. aeruginosa and 18 mm for E coli. The data obtained from experimental values are in close agreement with the predicted values of RSM. Model adequacy was evaluated using ANOVA variance where the quadratic effect of p\<0.0001 which imply the significance of the model. The unstructured-, mathematical- kinetic models provided a better approximation of profiles of S. halotolerans VSM 2 growth, optimized media utilization and bioactive metabolite production. Conclusion: Optimization of the independent variables for the production of the bioactive metabolite using RSM by S. halotolerans VSM 2 and its effect against the five responses were documented. The predicted values are in good agreement with the experimental values. Unstructured models provided a better approximation of kinetic profiles for bioactive metabolite production by S. halotolerans VSM 2.

}, keywords = {Bioactive metabolites, Kinetic Modelling, Optimization, Response Surface Methodology, Saccharopolyspora halotolerans}, doi = {10.5530/pj.2018.5.142}, author = {Ushakiranmayi Managamuri and Muvva Vijayalakshmi and Mani Deepa Indupalli and Venkat Siva Rama Krishna Ganduri and Satish Babu Rajulapati and Sudhakar Poda} } @article {431, title = {Extracellular L-Asparaginase from Streptomyces labedae VSM-6: Isolation, Production and Optimization of Culture Conditions Using RSM}, journal = {Pharmacognosy Journal}, volume = {9}, year = {2017}, month = {September 2017}, pages = {932-941}, type = {Original Article}, chapter = {932}, abstract = {

Objective: The present study was intended to isolate actinomycetes VSM-6 from deep sea sediment samples of Bay of Bengal that is potent to produce L - asparaginase. Materials and Methods: The identification of the isolate was executed by polyphasic taxonomy. Optimization was carried out one factor at a time (O-F-A-T) for the production of the L - asparaginase. RSM was pledged to optimize the L - asparaginase production by S.labedae VSM-6. Central composite design was applied to study the influence of the variables and their interactive effects on the production of L - asparaginase. Unstructured Kinetic modelling for L - asparaginase production was adopted using Leudeking-Piret (LILP) and Logistic Incorporated Modified Leudeking-Piret (LIMLP) models. Results: Optimization using One-Factor-At-A-time registered a turnout of 8.92 IU/ml of L - asparaginase production. But results obtained from the statistical design are in agreement with the experimental results. The model followed the second order polynomial equation and the model adequacy was determined by the P value (\<0.0001), Coefficient determination (R2) with a value of 0.9942 and the adjusted R2 = 0.9087 which determines that the model was significant. The experimental values are in compliance with the model anticipated values and catalogued an escalation in yield of L - asparaginase (10.17 IU/ml) by RSM. Unstructured Kinetic modelling for L - asparaginase production adopting Leudeking-Piret (LILP) and Logistic Incorporated Modified Leudeking-Piret (LIMLP) models showed L - asparaginase production of (10.17 IU/ml), closer to model anticipated value (10.23 IU/ml) so unstructured models provided a better approximation for L - asparaginase production by S.labedae VSM-6. Conclusion: From our study we have reported for the first time the production of L - asparaginase from S.labedae VSM-6 using central composite design and kinetic modelling.

}, keywords = {Central Composite Design, Kinetic Modelling, L - asparaginase, Optimization, Response Surface Methodology, Statistical Analysis., Streptomyces labedae}, doi = {10.5530/pj.2017.6.146}, url = {http://fulltxt.org/article/199}, author = {Ushakiranamayi Mangamuri and Muvva Vijayalakshmi and Venkat Siva Rama Krishna Ganduri and Satish Babu Rajulapati and Sudhakar Poda} }