Chromatographic method development and metabolite profiling for biomass and extraction optimization of withametelin and daturaolone from D. Innoxia Mill.

2.1 Selection of sites and collection of samples

The sample location was chosen to signify the growing area of D. innoxia and showed a significant change in the edaphic and climatic factors affecting the growth of respective plant specie. Accordingly, D. innoxia was collected from two geographically different sites in Pakistan namely Islamabad (I) and Muzaffargarh (Mz). The sampling was carried out in two cities within a radius of 500 m over a period of six months (May to October). The selected (uniform) plants in the fruiting and flowering phase were sampled in order to do the collection on the same date of the month (15th). Each sample of the plant was placed in a plastic bag with appropriate labeling. Samples were returned to the laboratory within 24 h of field collection.

2.2 Weather parameter record collection

The detailed agroclimatology reports of 1 year (January 2018 to December 2018) of selected sites were downloaded from the authenticated source in CSV format and 6 month agroclimatology data was utilized in the current project. The data was obtained from the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) Prediction of Worldwide Energy Resource (POWER) Project funded through the NASA Earth Science/Applied Science Program (NASA, 2022) (https://power.larc.nasa.gov/).

2.3 Sample preparation

The collected plant was washed under tap water. Leaves (L), stem (S), roots (R), flowers (Fl), and fruits (Fr) were separated, and shade dried for up to 3 weeks. Samples were grounded to a fine powder. Pre-weighed (50 mg) each dried plant part in Eppendorf tubes was macerated (1 mL) in varied polarity solvents systems either alone or in 1:1 combination. Solvent systems and their combination include; n-hexane (nH), chloroform (C), acetone (A), ethyl acetate (Ea), methanol (M), ethanol (E) water (W), nH + C, nH + Ea, C + Ea, C + M, A + Ea, Ea + M and Ea + E. Occasional shaking and ultrasonication aided maceration were done for 3 days. Ultrasonication was performed thrice daily for 5 min each at a frequency of 40 kHz. Each sample was centrifuged, and the supernatant was separated. Solvent system and their combinations are given in Table 1. In brief, 5 plant parts were collected from two geographical locations for six months. Each plant part was macerated in 14 solvent systems respectively. A total of 840 samples were prepared for the TLC analysis. $$2locations\ast 6months\ast 5parts\ast 14solventsystems=840samples$$

Appropriate coding of each sample is given in Table 1.

Daturaolone and withametelin were isolated and purified in our previous work. Daturaolone (1 mg/ml) solution was prepared in chloroform. Withametelin (1 mg/ml) solution was prepared in ethyl acetate. 500 µl of the corresponding solution were mixed for co-detection and analysis on TLC plates. The final concentration of respective compounds was 0.5 µg/µl.

2.4 TLC detection method optimization and sample analysis

Normal phase TLC plates were used. Firstly, TLC method was optimized for the co-detection of withametelin and daturaolone. 1 µl of the standard solution was run in different mobile phases to select the best mobile phase for separation, elution and simultaneous detection of withametelin and daturaolone. Phosphomolybdic acid reagent was used for the final detection and analysis. After finalizing the TLC optimization of standards, samples were analyzed on 4 * 6.66 cm TLC plates. 1 µl of each plant sample was spotted on TLC plate and elution was done. TLC plate number, sample serial number, coding and sequence in which each sample was spotted on TLC plate along with the standard solution are given in Table 1. Each TLC analysis was performed in triplicate. Plant samples that gave detection of withametelin and daturaolone were selected for HPLC detection and quantification.

2.5 RP HPLC method development

2.6 Statistical analysis

Microsoft EXCEL 365 was used for statistical analysis. Graph Pad PRISM 5 was used for correlation analysis.

3.1 Area and time-dependent agroclimatology data variations were observed

A six-month period agroclimatic research has been carried out. The agroclimatic parameters differ between the two sites of Islamabad and Muzaffargarh (Fig. 1). The average surface temperature of the earth, and the average air temperature (dry bulbs) at 2 m in the six months were highest in Muzaffargarh in June while lowest in Islamabad in October. Withametelin content in D. innoxia was found to be correlated (P < 0.05) with temperature. High temperatures result in heat stress which affect plant secondary metabolites production. Cold stress also has a negative impact on plant growth and development, resulting in significant productivity constraints. It prevents plants from expressing their full genetic potential, directly inhibiting metabolic reactions, indirectly preventing water absorption and cell dehydration (Verma and Shukla, 2015). Our study showed that heat and cold stress had an impact on the variations in withametelin and daturaolone content. Humidity parameters were relatively high in the July, August and September in Islamabad region as compared to Muzaffargarh region. High humidity can exacerbate the harmful effects of high temperature by limiting transpiration. (i.e., moisture loss from leaves). This is essential to reduce leaf surface temperature and promote the absorption and mobility of water and minerals. Furthermore, high humidity increases the harmful effects of air pollution (such as ozone) and promotes infection spreading by increasing the size of the stomatal openings (Yang et al., 2012). Daturaolone content in D. innoxia was found to be correlated (P < 0.01) with humidity where its presence was found to be highest in August in I where the humidity value was also highest. Similarly, surface soil wetness in Multan was below 0.2 and root zone soil wetness was below 0.3 in six month period measurements. In a drought-stricken situation, the water available in the soil falls to critical levels, and atmospheric conditions increase the continuing loss of water. The severity of the water shortage is thought to reduce plant growth, but some studies have shown that water stress can increase secondary metabolites (Yang et al., 2012). Daturaolone content varied with soil wetness and quantified values showed significant (P < 0.01) value. Six month intra-variations in Islamabad were also observed for UVA irradiation. But no correlation was found between extent of UVA radiations and the quantified content of withametelin and daturaolone. The use of a controlled environment research to determine the quantitative relationship between various parameters with more accuracy is proposed.

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Fig. 1 Agroclimatology data (A = temperature and humidity parameters while B = drought stress, UV irradiance) was obtained for the study. The detailed agroclimatology reports of 1 year (January 2018 to December 2018) of selected sites were downloaded in CSV format and 6-month agroclimatology data was utilized in the current project. The data was obtained from the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) Prediction of Worldwide Energy Resource (POWER) Project funded through the NASA Earth Science/Applied Science Program.

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3.2 TLC method optimization showed Nh:EA (70:30) for co detection of withametelin and daturaolone

The robustness and sustainability of planar chromatography techniques play an important role in the quality assessment of pharmaceutical products in resource-limited countries (Kaale et al., 2011). Advantages of TLC methods that other techniques will never achieve include its simplicity, high throughput and simultaneous analysis of multiple complex samples (Ferenczi-Fodor et al., 2006) So, for the development of appropriate bands to detect withametelin and daturaolone, normal phase TLC technique was utilized. Various combined ratios (v/v) of n-hexane (nH) and ethyl acetate (Ea) were checked. It includes: nH: Ea (1:1), nH: Ea (3:2), nH: Ea (3.5:1.5), nH: Ea (4:1), nH: Ea (8.5:1.5), and nH: Ea (4.5:0.5). The mobile phase combinations i.e. nH: Ea (1:1), nHa: Ea (3:2), nH: Ea (4:1), nH: Ea (8.5:1.5) and nH: Ea (4.5:0.5) revealed unsatisfactory chromatographic separations and detection of the compounds. When mobile phase nH: Ea (70:30) was evaluated, it provided well-resolved and intact chromatographic detections for withametelin and daturaolone. Consequently, the nH:Ea (70:30) was selected for the co-detection of withametelin and daturaolone in all prepared samples for the TLC analysis.

3.2.1

3.2.1 TLC analysis showed the detections in 118/840 samples

TLC analysis of all 840 samples (Table) with standards were run using the mobile phase optimized for the co-detection of withametelin and daturaolone (Fig. 2). Detection of withametelin was mostly observed in leaf samples, especially in TLC 4, 13 and 14 (Fig. 2A) where ethyl acetate, ethyl acetate-methanol (1:1) and ethyl acetate-ethanol (1:1) are the extraction medium. All samples which show detection of withametelin in different samples of leaves are given in Table 3. None of the samples from the root, fruit, flower and stem portion showed the detection of withametelin. Whereas detection of daturaolone was observed in fruit samples, especially in TLC 34 and 42 where ethyl acetate and ethyl acetate-ethanol (1:1) are the extraction medium (Fig. 2B). None of the samples from root, leaves, flower and stem portion showed the detection of daturaolone. The visualizing effect depends on the chemical structure of the detecting reagent, detected substance, and the chromatographic adsorbent used. In particular, the application of visualization reagent reacts with the substances present in the analyzed mixture and gives diversified colors of chromatographic spots (Pyka, 2014).

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Fig. 2 TLC detection (red circled) of withametelin (W) and daturaolone (D) in selected samples of D. innoxia leaves (A) and fruits (B). TLC method was optimized for the co-detection of withametelin and daturaolone. 1 µl of the standard solution was run in different mobile phases to select the best mobile phase for separation, elution and simultaneous detection of withametelin and daturaolone. Phosphomolybdic acid reagent was used for the final detection and analysis.

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Fig. 2 TLC detection (red circled) of withametelin (W) and daturaolone (D) in selected samples of D. innoxia leaves (A) and fruits (B). TLC method was optimized for the co-detection of withametelin and daturaolone. 1 µl of the standard solution was run in different mobile phases to select the best mobile phase for separation, elution and simultaneous detection of withametelin and daturaolone. Phosphomolybdic acid reagent was used for the final detection and analysis.

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Table 3 Accuracy determination by analyzing withametelin and daturaolone at selected concentrations.

Analyte/Initial Concentration	Theoretical concentration after dilution added in the sample (µg/mL)	Amount recovered (µg/mL)	Recovery (%)	Mean (%)	RSD (%)
Withametelin (Concentration measured in the sample (LEa + EMzMay) = 3.96 µg)	0.5	4.48	100.65	100.74	0.33
		4.51	101.20
		4.47	100.38
	1.0	4.94	99.62	100.41	0.72
		4.97	100.25
		5.02	101.38
	1.5	5.45	99.94	100.62	0.65
		5.48	100.42
		5.54	101.51
Daturaolone (Concentration measured in the sample (FrEa + EIJuly) = 4.55 µg)	0.7	5.21	99.25	99.68	0.83
		5.29	100.84
		5.19	98.95
	1.4	6.02	101.25	100.52	0.51
		5.95	100.08
		5.96	100.24
	2.1	6.64	99.90	100.59	0.60
		6.74	101.38
		6.68	100.51

3.3 RP HPLC method was developed

High-performance liquid chromatography (HPLC) is a modern, powerful, and flexible separation technology that is usually used to separate, identify and quantify components of herbal mixtures to obtain their chemical profiles (Sarker and Nahar, 2015). The parameters for analysis of withametelin and daturaolone were determined for the first time by adjusting their analytical parameters respectively. It is aimed at identifying the best conditions for the analysis of compounds. Optimization was carried out using gradient elution for each compound. Subsequently, the time and composition of the eluent were adjusted until the optimal conditions were achieved. Moreover, gradient time changes are also used as an optimized parameter. Standard solutions of withametelin and daturaolone were injected. Data is processed using software linked to the HPLC system. Chromatogram met the criteria necessary to identify withametelin and daturaolone. In the absence of a valid method, a new method for analyzing new products is being developed. These methods are optimized and verified by test runs. An alternative method is proposed to replace the existing methodology in comparison laboratory data and implement it in practice, including all available benefits and disadvantages (Patil, 2017).

3.4 Two samples showed maximum quantification of withametelin and daturaolone via RP HPLC

The quantitative method developed here had been successfully applied to quantification analysis of withametelin and daturaolone in dry powders of D. innoxia. Based on the results of the study, the proposed method can be used easily for analysis. The quantitative results of the two compounds are shown in Table 3, Fig. 3(1) and Fig. 3(2). It appears that the distribution of withametelin is mostly found in leaves with a maximum quantified value of 5.12 ± 0.28 µg/mg dry powder when collected in June from the arid Mz region and extracted with Ea + E. During this period, earth temperature is at maximum. On contrary, the lowest humidity, soil wetness and UVA irradiance was noted. Quantity lowers down in months when the temperature falls whereas humidity and soil wetness rise. Withametelin quantity was also less in the mountainous Islamabad (I) region where soil wetness and UVA irradiance were high. Mainly, a positive correlation (P < 0.05) with temperature was observed. Temperature modulation is reported to cause the accumulation of alkaloids and their biological synthesis is promoted by high temperatures. Morphinane, phthalisoquinoline and benzylisoquinoline in Papaver somniferum was limited at low temperatures (Bernáth and Tetenyi, 1981). Similarly, the distribution of daturaolone is mostly found in fruits with a maximum quantified value of 5.18 ± 0.45 µg/mg dry powder when collected in August from the mountainous I region and extracted with Ea + E. Highest humidity and soil wetness were observed, and high UVA irradiance was noted. The quantity of daturaolone also lowers in months with a decline in humidity and soil wetness. Daturaolone quantity was less in the arid (Mz) region where soil wetness and UVA irradiance were low. Mainly, a positive correlation with soil wetness (P < 0.01) and humidity (P < 0.01) was noted. Extraction in green solvents i.e., EA: E (1:1) gave maximum results. Ethyl acetate is an environmentally benign green solvent (Häckl and Kunz, 2018). The updated GSK solvent selection guide also places it as relatively greener than most. But this does not mean that the end decision of solvent greenness is finally and definitively achieved (Byrne et al., 2016). Similarly, bio-solvents, i.e. solvents from renewable sources such as ethanol from sugar-containing feed fermentation, starch feeds and lignocellulosic feeds are used to avoid the use of fossil resources and CO₂ emissions from fossil fuels into the environment (Capello et al., 2007).

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Fig. 3 RP HPLC chromatograms of selected samples for detection (Red colour) of withametelin (1) and daturaolone (2). 1 = withametelin (W) blank (A), standard withametelin (B) LEa + EMzJune (C) LEMzJune (D) and LC + EaMzJune (E). 2 = daturaolone (D) blank (A) standard daturaolone (B) FrEa + EIJune (C), FrEa + MIJune (D) and FrA + EaIJune (E) of Datura innoxia.

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Fig. 3 RP HPLC chromatograms of selected samples for detection (Red colour) of withametelin (1) and daturaolone (2). 1 = withametelin (W) blank (A), standard withametelin (B) LEa + EMzJune (C) LEMzJune (D) and LC + EaMzJune (E). 2 = daturaolone (D) blank (A) standard daturaolone (B) FrEa + EIJune (C), FrEa + MIJune (D) and FrA + EaIJune (E) of Datura innoxia.

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