INTRODUCTION:

E. littorale, or "Mamejavo," treats diabetes mellitus in Gujarat and southern India¹. Its hot water extracts are integral to ayurvedic formulations for lowering blood sugar levels in north Gujarat^2-5. E. littorale significantly reduces blood glucose, antioxidants, and lipids in alloxan-induced diabetic rats^6-9 and is part of various natural diabetes treatments^10,11. The herb also addresses inflammation and cancer, with active compounds showing antioxidant and medicinal properties. Clinical trials can potentially reduce tumor growth and inhibit cancer cell proliferation, though further research is needed^12-15. It treats malaria, skin conditions, leprosy, and diabetes, offering hypoglycemic, antioxidant, hepatoprotective, hepatomodulatory, and weight management benefits¹⁶. The plant's compounds are nontoxic, eco-friendly, stable, and rich in minerals and vitamins^17,18.

E. littorale extract contains various compounds that contribute to its benefits. Pharmacokinetic studies show that swertisin (ST), a primary component, quickly distributes across tissues, with the liver accumulating the highest concentrations before renal elimination¹⁴.

E. littorale possesses numerous chemicals primarily identified from the plant. Tanna et al. reported a 34% yield from the dry alcoholic extract of the aerial parts and an ash content of 15.7%, containing minerals such as silica, phosphate, calcium, magnesium, iron, potassium, sodium chloride, sulfate, and carbonate¹⁵. Goshal et al. identified nine compounds in the alcoholic extracts: seven flavonoids (apigenin, genkwanin, isovitexin, swertisin, saponarin, 5-o glucosylswertisin, and 5-o glucosylisoswertisin) and two monoterpene alkaloids (enicoflavin and gentiocrucine)¹⁶.

Jahan et al. identified verticilliside, a new flavone C-glucoside, and catechins, saponins, steroids, triterpenoids, flavonoids, and xanthone¹⁷. Desai et al. found six phenolic acids: vanillic, syringic, p-hydroxy benzoic, protocatechuic, p-coumaric, and ferulic acids¹⁸. The methanol extract of E.

littorale includes amino acids like L-glutamic acid, tryptophan, alanine, serine, aspartic acid, L-proline, L-tyrosine, threonine, phenylalanine, methionine, isoleucine, DOPA, L-glycine, 2-amino butyric acid, valine, and monohydrochloride forms of L-histidine and L-arginine¹⁹. Swertiamarin is a key component in raw medicines in Japan and other countries, and it is used to assess the quality of medicinal products^4,20.

Fig.1 Swertisin chemical structure

Various literature reviews have highlighted sequential extraction using nonpolar to polar solvents^1,2. This method selectively transforms phytoconstituents, producing varied effects. Consequently, optimizing phytoconstituent extraction can be achieved by determining the most effective solvent and integrating this with experimental design²³.

Design of experimentation (DOE) systematically evaluates the influence and interactions of various factors on process outcomes, identifying optimal responses in the design matrix and reducing experimental trials, thereby saving time, costs, and effort while simplifying interpretation. Response Surface Methodology (RSM) uses DOE to predict optimal conditions^24,25. Practical statistical analysis, such as RSM, optimizes extraction conditions. The Box–Behnken design (BBD) is employed in food engineering, bioprocessing, and pharmaceutical development to enhance biologically active compound extraction^26-28.

The second-order, nearly rotatable design utilizes three levels of fractional factorial design. Its advantages include not detecting model fit, using blocks, building sequential designs, and estimating quadratic model parameters²⁹. This design avoids conducting experiments under extreme conditions, such as simultaneously using the lowest or highest levels of all factors, which can be expensive or impossible to test³⁰.

DOE requires fewer runs than One Factor At a Time (OFAT), but a complete factorial design still demands many runs, especially with numerous factors. The Box-Behnken design (BBD) was chosen for optimization due to its near-rotatable nature and reduced run requirements. The optimization factors include solvent mixture, sonication time, and solvent volume³¹.

This paper presents a more efficient method for enhancing the extraction and optimization of SWT phytoconstituents from E. littorale using the design of experiments (DOE), a systematic and statistically validated approach superior to OFAT³. DOE's advantages over OFAT include fewer experiments, cost-effectiveness, predictive capability for variable interactions, and robustness^4–6. DOE is commonly used to optimize processes such as formulation^7–11, dissolution parameters¹², chromatographic parameters^13–16, and MS ionization parameters^4,17–19.

LC-MS/MS has been extensively employed in pharmaceutical analysis owing to its exceptional sensitivity and structural specificity. Researchers have utilized Various analytical techniques have been used in different studies, including HPTLC^41–46, HPLC⁴⁷, LC-MS/MS⁴⁸, UFLC-MS/MS⁴⁹, and CEC⁵⁰. These methods have been applied to quantify swertiamarin in E. littorale and swertisin in Enicostemma hyssopifolium, with HPLC and HPTLC specifically used for the latter.

No sensitive and rapid analytical methods have been published for determining swertisin in the aerial part of E. littorale by LC-MS/MS. This study is the first to systematically optimize extraction efficiency using the Box-Behnken Design and LC-MS/MS for swertisin in E. littorale. The results enhance understanding of isolation and extraction efficiency, aiding the development of new analytical methods for estimating swertisin in various plant species.

EXPERIMENTAL:

Chemicals and Reagents:

SD Fine Chemicals (Mumbai, India) supplied ultrapure water and methanol. Sigma-Aldrich provided swertisin (SWT) and quercetin (QUT), the latter serving as an internal standard. All remaining chemicals were analytical grade.

Plant identification:

A vendor (D.G. Ayurvedic Sangrah), in Mumbai, Maharashtra, India specializing in the Ayurvedic products, provided 10Kg of the whole plant powder of Enicostema littorale blume. The originality of this plant specimen was confirmed by Agharkar Research Institute, Pune, India where it received the identification number 3/485/137.

Crude Ethanolic Extract Preparation:

The plant powder was defatted with 200ml of petroleum ether by soxhlation. For crude ethanolic extract, defatted plant material was extracted with ethanol using a rotary evaporator at 60⁰C for 72 to 96hours. The extract was dried and stored in an airtight glass container at room temperature. The weight and yield percentage of the ethanolic extract were measured.

This formula was used to determine the percentage yield.

Weight of fraction after drying

% Yeild = -------------------------------------------- x 100

Initial weight of powder

Fractionation and Screening:

SWT was obtained by liquid-liquid solvent extraction of 10g crude methanolic extract of E. littorale. The crude extract was extracted sequentially using 60ml of the petroleum ether, chloroform, ethyl acetate, n-butanol and methanol. The mixture was filtered after every extraction and the solid residue stored to the next solvent, whereas the liquid filtrate was allowed to dry at room temperature, 24hours. The dried materials were diluted in acetonitrile and injected into LC-MS/MS analysis to determine SWT concentration in the samples.

Optimizing isolation of swertisin from E. littorale

Box–Behnken design (BBD) was applied to SWT extract from the ethanolic extract of E. littorale. Sequential fractionation identified n-butanol and methanol as the solvents maximizing SWT extraction. Therefore, one key parameter was the methanol and n-butanol mixture volume. The optimized independent parameters were the methanol and n-butanol ratio (BM), sonication time (ST), and extraction solvent volume (VS). Three levels of each factor in the BBD are detailed in Table 1.

Table. 1 Levels of each factor used in BBD

Level	Ratio of n-Butanol to Methanol(v/v) (BM)	Sonication Time (ST) (min)	Volume of Solvent (VS) (mL)
-1	1:9	15	5
0	5:5	30	10
1	9:1	60	15

A 500 mg ethanolic extract of E. littorale sample was weighed and placed in a 25ml volumetric flask. SWT extraction was conducted using the randomized protocol from Table 2, created with Design Expert® 11 software (Statease Inc., Minneapolis, MN, USA). Post-extraction, samples were centrifuged at 4000rpm for 5 minutes, and a 1µL aliquot was injected into the LC-MS/MS system for SWT quantification.

Table. 2: Runs for Extraction of Swertisin

Run	Ratio of n-Butanol to Methanol(v/v) (BM)	Sonication Time (ST) (min)	Volume of Solvent (VS) (mL)
1	0	0	0
2	0	1	1
3	-1	0	1
4	1	-1	0
5	-1	0	-1
6	-1	-1	0
7	1	1	0
8	1	0	1
9	0	0	0
10	0	0	0
11	0	-1	-1
12	0	0	0
13	1	0	-1
14	0	0	0
15	0	1	-1
16	0	-1	1
17	-1	1	0

BBD was considered the best optimization model for the extraction of SWT. It requires a smaller number of runs than a three-level full factorial design. The independent factors evaluated for the extraction of SWT were BM, ST, and VS. The samples were extracted randomly, as shown in the table. 2. The amount of SWT in each extraction run was recorded (Table. 1).

The design of Experiment 11 was used to process the data. A reduced cubic model was chosen, the model was validated by analysis of variance (ANOVA) and was found to be significant with p<0.000001, and the lack of fit was found to be non-significant after elimination of insignificant factors (Table. 2). The adjusted R² was equal to 0.996 indicating that the polynomial equation fits the experimental data satisfactorily (Table. 3). The signal-to-noise ratio of 44.00 indicates better model discrimination and more accurate prediction in design space.

Perturbation plots help reveal the influence of a single independent factor when the other factors are fixed at a constant level. It can be observed that as the VS is increased from a lower value to a higher value, the amount of extraction considerably increases. However, BM and ST have negligible influence on extraction (Fig. 2).

Fig. 2 Perturbation plot for extraction of Swertisin

The interaction plot reveals that BM and ST show strong interaction (Fig .3). Factor BM and VS show mild interaction (Fig .3). However, VS and ST run almost parallel and have no interaction (Fig .2).

Fig. 3 Interaction plot between BM (Solvent) and ST (Time)

LC-MS/MS analysis of Swertisin

Quantitative analysis utilized Electrospray Ionization (ESI) in multiple reaction monitoring (MRM) mode, with MRM transitions for SWT and QUT in positive ionization mode at 447.12→297.15 and 303.11→157.85, respectively (Fig. 4, 5). ESI settings were as follows, a desolvation temperature of 400C degree, drying gas flow of 5mL/m min, and nebulizing gas flow of 3 mL/m min. The methanol: water (4:1, v/v) was the mobile phase and the separation was done on Kromasil C18 column (50 mm x 4.6mm, 3.5) with a flow rate of 0.6mL/min. The retention time of swertisin and quercetin was observed to be 1.39 and 1.38 min respectively (Fig. 6, 7).

Fig. 4. Mass spectrum of swertisin

Fig. 5. Mass spectrum of quercetin

Preparation of standard swertisin Solution:

Weighed about 1 mg of standard SWT and dissolved in methanol to prepare a 1000µg/mL solution. It was further diluted with methanol: water (1:1, v/v) to prepare six nonzero calibration curve standards ranging from 25 to 1000ng/Ml

Fig. 6. Standard chromatogram of swertisin and quercetin (IS)

Fig. 7. Extracted sample chromatogram of swertisin and quercetin (IS)

RESULTS AND DISCUSSION:

Calibration Curve of Swertisin:

The calibration curve demonstrated linearity within the 25–1000 ng/mL range, achieving an r2 value of 0.999. As shown in Fig. 8, the linear regression equation was y = 9E-05x - 0.0003.

Fig. 8. Calibration curve of swertisin

Quantitation of swertisin from the sequential fractionations of E. littorale:

Fig. 9 summarizes the quantitation of SWT from the sequential fractionations. It was observed that n-butanol (NB) had the maximum SWT, followed by methanol. Therefore, the mixture of these two solvents was chosen as one of the factors to be optimized using the DoE approach.

Fig. 9. Quantitation of SWT from the different solvents

Prediction of Optimized Solutions:

To understand the optimized solution, the independent parameters BM, ST, and VS were kept in range, and the response SWT content was kept at maximum with the highest priority. The software generated 100 solutions; solution 4 was selected for experimental verification as the extraction time was minimum, the yield was maximum, and desirability was 1 (Fig. 10). The prediction efficiency was calculated using the formula.

Practicle yield

% Efficiency= ------------------------- x 100

Predicted yield

Fig. 10. Desirability Plot

Application of Predicted solution:

A comparison was made between the optimized level (OL) and LL, ML, and HL, revealing a notable difference in the extraction efficiency. Statistical analysis was conducted using Dunnett’s test with a 95% confidence interval (α = 0.05). The mean difference between OL and LL, ML, or HL was statistically significant (p<0.001) (Fig.11). The OL mean SWT yield was 3.260 (±0.489)mg/g, and the predicted yield was 3.569mg/g. Thus, the percentage efficiency of the prediction can be calculated as 91.34% w/w.

Fig. 11. Comparison between different levels and optimised level

Application of predicted solutions:

To understand the efficiency of extraction, the optimized level (OL) generated by the DoE software was compared against the Low-level (LL), Medium-level (ML), and Higher-level (HL) extraction factors. The extraction was carried out for each level in triplicate (n=3) (Table. 3).

Table. 3: Level of Extraction

Factors	Lower Level (LL)	Medium Level (ML)	Higher Level (HL)	Optimized level (OL)
BM	1:9	5:5	9:1	4.62:5.28
ST	30	45	60	30
VS	5	10	15	15

SUMMARY AND CONCLUSION:

Sequential extraction followed by BBD was effectively used to optimize the extraction efficiency of swertisin from the E. littorale. The most significant factor contributing to the extraction of swertisin from the E. littorale was the volume of solvent (VS). We therefore strongly recommend using optimized level (OL) parameters to make a swertisin-rich fraction for future experiments. We claim that screening and post-optimization would be a more innovative approach rather than direct optimization of extraction of phytoconstituents using the design of experiments. We further propose that researchers should consider BBD to enhance the extraction of phytoconstituents instead of relying on the traditional approach.

CONTRIBUTIONS OF THE AUTHORS: Venkata Suresh conducted the experiments, collected data, drafted the manuscript, edited the manuscript, and supervised the review and editing process. Prerna Mehta assisted in the writing process. All authors approved the final manuscript for publication.

DATA AVAILABILITY:

The datasets analyzed or generated in this study can be obtained from the corresponding author upon reasonable request.

ACKNOWLEDGMENT:

We thank the Chalapathi Institute of Pharmaceutical Sciences, Lam, Guntur, for providing technical support and guidance.

CONFLICT OF INTEREST:

The authors declare no conflict of interest.

REFERENCES:

1. Murali B, Upadhyaya UM, Goyal RK. Effect of chronic treatment with Enicostemma littorale in non-insulin-dependent diabetic (NIDDM) rats. Journal of Ethnopharmacology. 2002; 81(2); 199-204. https://doi.org/10.1016/S0378-8741(02)00077-6

2. Vijayvargia R, Kumar M, Gupta S. Hypoglycemic effect of aqueous extract of Enicostemma littorale Blume (chhota chirayata) on alloxan induced diabetes mellitus in rats. Indian Journal of Experimental Biology. 2000; 38(8); 781-4.

3. Maroo J, Ghosh A, Mathur R, Vasu VT, Gupta S. Antidiabetic efficacy of Enicostemma littorale methanol extract in alloxan-induced diabetic rats. Pharmaceutical Biology. 2003; 41(5); 388–391. https://doi.org/10.1076/phbi.41.5.388.15943

4. Vasu VT, Ashwinikumar C, Maroo J, Gupta S, Gupta S. Antidiabetic effect of Enicostemma littorale Blume aqueous extract in newly diagnosed non-insulin-dependent diabetes mellitus patients (NIDDM): a preliminary investigation. Oriental Pharmacy and Experimental Medicine. 2003; 3; 84–89. https://doi.org/10.3742/OPEM.2003.3.2.084

5. Srinivasan M, Padmanabhan M, Prince PS. Effect of aqueous Enicostemma littorale Blume extract on key carbohydrate metabolic enzymes, lipid peroxides and antioxidants in alloxan‐induced diabetic rats. Journal of Pharmacy and Pharmacology. 2005; 57(4); 497-503. https://doi.org/10.1211/0022357055722

6. Upadhyay UM, Goyal RK. Efficacy of Enicostemma littorale in Type 2 diabetic patients. Phytother Res. 2004 Mar; 18(3); 233-5. https://doi.org/10.1002/ptr.1434

7. Nampalliwar, A. R., & Godatwar, P. Randomised Controlled Trial on the efficacy of Mamajjaka (Enicostemma littorale Linn.) in the Management of Madhumeha (Diabetes Mellitus). International Journal of Ayurvedic Medicine. 2024; 15(1); 148–153. https://doi.org/10.47552/ijam.v15i1.4276

8. Zanwar, S. B., Patel, K. V., & Mandhane, S. N. In Vitro and In Vivo Anticancer Activity of E. littorale Extract on Hepatocellular Carcinoma. Journal of Natural Remedies. 2024; 24(4); 869–876. https://doi.org/10.18311/jnr/2024/36504

9. Wang X, Wang T. Swertiamarin exerts anticancer effects on human cervical cancer cells via induction of apoptosis, inhibition of cell migration and targeting of MEK-ERK pathway. Trop J Pharm Res. 2021; 20(1); 75-81. https://doi.org/10.4314/tjpr.v20i1.12

10. Roy S, Niranjan C, Jyothi T, Shankrayya M, Vishawanath K, Prabhu K, et al. Antiulcer and anti-inflammatory activity of aerial parts Enicostemma littorale blume. J Young Pharm. 2010; 2(4); 369–73. https://doi.org/10.4103/0975-1483.71629

11. Vaidya H, Rajani M, Sudarsanam V, Padh H, Goyal R. Antihyperlipidaemic activity of swertiamarin, a secoiridoid glycoside in poloxamer-407-induced hyperlipidaemic rats. Journal of Natural Medicines. 2009; 63(4); 437–442.

12. Saranya R, Thirumalai T, Hemalatha M, Balaji R, David E. Pharmacognosy of Enicostemma littorale: a review. Asian Pac J Trop Biomed. 2013 Jan; 3(1); 79-84. https://doi.org/10.1016/S2221-1691(13)60028-3

13. Maroo J, Vasu VT, Aalinkeel R, Gupta S. Glucose lowering effect of aqueous extract of Enicostemma littorale Blume in diabetes: a possible mechanism of action. J Ethnopharmacol. 2002; 81(3); 317-20. https://doi.org/10.1016/S0378-8741(02)00095-8

14. Dadheech N, Soni S, Srivastava A, Dadheech S, Gupta S, Gopurappilly R, Bhonde RR, Gupta S. A Small Molecule Swertisin from Enicostemma littorale Differentiates NIH3T3 Cells into Islet-Like Clusters and Restores Normoglycemia upon Transplantation in Diabetic Balb/c Mice. Evid Based Complement Alternat Med. 2013; 280392. https://doi.org/10.1155/2013/280392

15. Tanna S, Shukla VJ, Prajapati PK. Physico-phytochemical evaluation of aqueous extract of Mamajjaka Enicostemma littorale. Int J Pharm Bio Arch. 2010; 1(3); 309–312.

16. Ghosal SS, Sharma AK, Chaudhuri PV. Chemical constituents of Gentianaceae IX: natural occurrence of Erythrocentaurin in Enicostemma hissopifolium and Swertia lawii. J Pharm Sci. 1974; 63; 944–945.

17. Jahan E, Perveen S, Malik A. Verticilliside, a new flavone C-glucoside from Enicostemma verticillatum. J Asian Nat Prod Res. 2009; 11(3); 257-60. https://doi.org/10.1080/10286020802675019

18. Desai PD, Ganguly AK, Govindachari TR, Joshi BS, Kamat VN, Manmade AH, et al. et al. Chemical investigation of some Indian medicinal plants: Part II. Ind J Chem. 1966; 4; 457–459.

19. Leelaprakash G, Mohan Dass S. Antimicrobial activity and phytochemical screening of methanol extract of Enicostemma axillare. Int J Pharm Pharm Sci. 2012; 4(1); 342–348.

20. Sathiskumar R, Lakshmi PTV, Annamalai A. Comparative analyses of non-enzymatic and enzymatic antioxidants of Enicostemma littorale Blume. Int J Pharma Bio Sci. 2010; 1(2); 1–16.

21. Jaishree V, Badami S, Krishnamurthy PT. Antioxidant and hepatoprotective effect of the ethyl acetate extract of Enicostemma axillare (Lam). Raynal against CCL4-induced liver injury in rats. Indian J Exp Biol. 2010; 48(9); 896-904.

22. Varghese A, Saboo P, Wairkar S. Bioactivity guided fractionation of methanolic extract of Terminalia arjuna for its CYP3A and CYP2D inhibition in rat liver microsomes. Biopharmaceutics & Drug Disposition. 2018 Mar; 39(3): 143-51. https://doi.org/10.1002/bdd.2121

23. Rolta R, Kumar V, Sourirajan A, Upadhyay NK, Dev K. Bioassay guided fractionation of rhizome extract of Rheum emodi wall as bio-availability enhancer of antibiotics against bacterial and fungal pathogens. Journal of ethnopharmacology. 2020 Jul 15; 257:112867. https://doi.org/10.1016/j.jep.2020.112867

24. Elsayed EW, El-Ashmawy AA, Mursi NM, Emara LH. Optimization of gliclazide loaded alginate-gelatin beads employing central composite design. Drug Dev Ind Pharm. 2019; 45(12); 1959-1972. https://doi.org/10.1080/03639045.2019.1689992

25. Toyota H, Asai T, Oku N. Process optimization by use of design of experiments: Application for liposomalization of FK506. Eur J Pharm Sci. 2017; 102; 196-202. https://doi.org/10.1016/j.ejps.2017.03.007

26. Abu-Dahab, R. & Afifi, F. Antiproliferative activity of selected medicinal plants of Jordan against a breast adenocarcinoma cell line (MCF7). Sci. Pharm. 2007; 75(3); 121–146.

27. Perez RM. Anti-inflammatory activity of compounds isolated from plants. Scientific World Journal. 2001; 1; 713-84. https://doi.org/10.1100/tsw.2001.77

28. Hwang ES, Thi ND. Effects of Extraction and Processing Methods on Antioxidant Compound Contents and Radical Scavenging Activities of Laver (Porphyra tenera). Prev Nutr Food Sci. 2014; 19(1); 40-8. https://doi.org/10.3746/pnf.2014.19.1.040

29. Ferreira SL, Bruns RE, Ferreira HS, Matos GD, David JM, Brandão GC, da Silva EG, Portugal LA, dos Reis PS, Souza AS, dos Santos WN. Box-Behnken design: an alternative for the optimization of analytical methods. Anal Chim Acta. 2007; 597(2); 179-86. https://doi.org/10.1016/j.aca.2007.07.011

30. Ahmad A, Alkharfy KM, Wani TA, Raish M. Application of Box-Behnken design for ultrasonic-assisted extraction of polysaccharides from Paeonia emodi. Int J Biol Macromol. 2015; 72; 990-7. https://doi.org/10.1016/j.ijbiomac.2014.10.011

31. Nalawade V, Vora A. Box–Behnken design directed optimization for sensitivity assessment of anti-platelet drugs. Drug Development and Industrial Pharmacy. 2019 Sep 2; 45(9): 1515-22. https://doi.org/10.1080/03639045.2019.1634092

32. R.H. A.C. Myers, Response Surface Methodology: Process and Product Optimization Using Designed Experiments. 4th+Edition, ISBN: 978-1-118-91601-8, Wiley, 2016; 865 pages.

33. Gullberg, P. Jonsson, A. Nordström, M. Sjöström, T. Moritz, Design of experiments: An efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry, Analytical Biochemistry. 2004; 283–295. https://doi.org/10.1016/j.ab.2004.04.037

34. Barmpalexis P, Grypioti A, Eleftheriadis GK, Fatouros DG. Development of a new aprepitant liquisolid formulation with the aid of artificial neural networks and genetic programming. Aaps Pharmscitech. 2018 Feb; 19; 741-52. https://doi.org/10.1208/s12249-017-0893-z

35. Yadav KS, Sawant KK. Formulation optimization of etoposide loaded PLGA nanoparticles by double factorial design and their evaluation. Current Drug Delivery. 2010 Jan 1; 7(1): 51-64. http://dx.doi.org/10.2174/156720110790396517

36. Nalawade VV, Peepliwal AL, Shidhaye SS, Pandey S. Formulation and evaluation of oral sustained release dry suspension of metformin hydrochloride. Eur J Pharm Med Res. 2016; 3; 447-57.

37. Ogbonna JD, Attama AA, Ofokansi KC, Patil SB, Basarkar GD. Optimization of formulation processes using Design Expert® Software for preparation of polymeric blends-artesunate-amodiaquine HCl microparticles. Journal of Drug Delivery Science and Technology. 2017 Jun 1; 39: 36-49. https://doi.org/10.1016/j.jddst.2017.02.011

38. Ganorkar SB, Dhumal DM, Shirkhedkar AA. Development and validation of simple RP-HPLC-PDA analytical protocol for zileuton assisted with design of experiments for robustness determination. Arabian Journal of Chemistry. 2017 Feb 1; 10(2): 273-82. https://doi.org/10.1016/j.arabjc.2014.03.009.

39. Moreiras G, Leão JM, Gago‐Martínez A. Design of experiments for the optimization of electrospray ionization in the LC‐MS/MS analysis of ciguatoxins. Journal of Mass Spectrometry. 2018 Nov; 53(11): 1059-69. https://doi.org/10.1002/jms.4281

40. Patel TP, Soni S, Parikh P, Gosai J, Chruvattil R, Gupta S. Swertiamarin: An Active Lead from Enicostemma littorale Regulates Hepatic and Adipose Tissue Gene Expression by Targeting PPAR- γ and Improves Insulin Sensitivity in Experimental NIDDM Rat Model. Evid Based Complement Alternat Med. 2013; 2013: 358673. https://doi.org/10.1155/2013/358673

41. Vishwakarma S, Bagul M, Rajani M, Goyal R. A sensitive HPTLC method for estimation of Swertiamarin in Enicostemma littorale Blume, Swertia chirata (Wall) Clarke, and in formulations containing E. littorale. JPC-Journal of Planar Chromatography-Modern TLC. 2004; 17(2); 128-31. https://doi.org/10.1556/jpc.17.2004.2.8

42. Vishwakarma S, Rajani M, Bagul M, Goyal R. A rapid method for the isolation of swertiamarin from Enicostemma littorale. Pharmaceutical Biology. 2004 Jan 1; 42(6); 400-3. https://doi.org/10.1080/13880200490885095

43. Bhandari P, Gupta AP, Singh B, Kaul VK. HPTLC determination of swertiamarin and amarogentin in Swertia species from the Western Himalayas. JPC–Journal of Planar Chromatography–Modern TLC. 2006 Jun; 19: 212-5. https://doi.org/10.1556/JPC.19.2006.3.8

44. Alam P, Ali M, Singh R, Shakeel F. A new HPTLC densitometric method for analysis of swertiamarin in Enicostemma littorale and commercial formulations. Natural Product Research. 2011; 25(1); 17-25. https://doi.org/10.1080/14786411003754348

45. Sawant L, Prabhakar B, Pandita N. A validated quantitative HPTLC method for analysis of biomarkers in Enicostemma littorale Blume. JPC-Journal of Planar Chromatography-Modern TLC. 2011; 24(6); 497-502. https://doi.org/10.1556/jpc.24.2011.6.8

46. Ahamad J, Hassan N, Amin S, Mir SR. Development and Validation of a High-Performance Thin-Layer Chromatographic—Densitometric Method for the Quantification of Swertiamarin in Traditional Bitters and Formulations. JPC–Journal of Planar Chromatography–Modern TLC. 2015; 28; 61-6. https://doi.org/10.1556/JPC.28.2015.1.10

47. Rana VS, Dhanani TU, Kumar SA. Improved and rapid HPLC-PDA method for identification and Quantification of swertiamarin in the aerial parts of Enicostemma axillare. Malaysian J. Pharma Sci. 2012; 10; 1-10.

48. Li HL, Peng XJ, He CJ, Feng EF, Xu GL, Rao GX. Development and validation of an LC-ESI-MS/MS method for the determination of swertiamarin in rat plasma and its application in pharmacokinetics. Journal of Chromatography B. 2011; 879; 1653–1658. https://doi.org/10.1016/j.jchromb.2011.04.003

49. Feng B, Zhu H, Guan J, Zhao L, Gu J, Yin L, Fawcett JP, Liu W. A rapid and sensitive UFLC-MS/MS method for the simultaneous determination of gentiopicroside and swertiamarin in rat plasma and its application in pharmacokinetics. J Pharm Pharmacol. 2014; 66(10); 1369-76. https://doi.org/10.1111/jphp.12266

50. Akei H, Nakauchi K, Yoshizaki F. Analysis of swertiamarin in swertia herb and preparations containing this crude drug by capillary electrophoresis. Anal Sci. 2001; 17(7); 885-8. https://doi.org/10.2116/analsci.17.885

51. Patel, M. B. and Mishra, S. H. Quantitative analysis of marker constituent swertisin in Enicostemma hyssopifolium Verdoon by RP-HPLC and HPTLC. Acta Chromatographica. 2012; 24(1); 2083-5736. https://doi.org/10.1556/achrom.24.2012.1.8