HPLC method for the determination of thymoquinone in growth cell medium

Abstract

BACKGROUND:

Preclinical drug testing requires in vitro and in vivo assessments that are vital for studying drug pharmacokinetics and toxicity. Distinct factors that play an important role in drug screening, such as hydrophobicity, solubility of the substance and serum protein binding can be challenging by inducing result inconsistencies. Hence, establishing accurate methods to quantify drug concentrations in cell cultures becomes pivotal for reliable and reproducible results important for in vivo dosing predictions.

OBJECTIVE:

This research focuses on developing an optimized analytical approach via high-pressure liquid chromatography (HPLC) to determine thymoquinone (TQ) levels in monolayer cell cultures.

METHODS:

The method’s validation adheres to the International Council for Harmonisation (ICH) guideline M10, ensuring its acceptance and applicability. Using an HPLC system with a Diode Array Detector (DAD), the study fine-tuned various parameters to achieve an efficient separation of TQ. Validation covered specificity, sensitivity, matrix effects, linearity, precision, and accuracy, alongside assessing TQ stability in RPMI-1640 medium.

RESULTS:

The HPLC method exhibited remarkable TQ specificity, free from interfering peaks at the analyte retention. Sensitivity analysis at the lower limit of quantification (LLOQ) revealed 5.68% %CV and 98.37% % mean accuracy. Matrix effect evaluation showcased accuracy within 85–115%. Linearity spanned in the concentration range of 2–10 $\mu$ M with a correlation coefficient ( $r^{2}$ ) of 0.9993. Precision and accuracy were aligned with acceptance criteria. The proposed method was found to be greener in terms of usage of persistent, bioaccumulative, and toxic chemicals and solvents, corrosive samples, and waste production.

CONCLUSION:

The developed HPLC-DAD method emerges as specific, accurate, sensitive, and reliable for TQ determination in cell cultures. It ensures robust TQ quantification, enhancing precise in vitro assessments and dependable dosing predictions for in vivo studies. Further research is advocated to investigate TQ’s stability across diverse environmental conditions.

Keywords

Method validation RPMI-1640 thymoquinone concentration determination

1. Introduction

Preclinical testing is an essential phase in drug development that involves in vitro and in vivo evaluation [1, 2]. In vitro testing is commonly used to assess drug pharmacokinetics and toxicity [3], and different cell-based models are extensively utilized for this purpose. However, these assays can be influenced by various factors, such as compound hydrophobicity, solubility, and non-specific binding with serum proteins, leading to inconsistent data and incorrect dosing metrics. Therefore, it is critical to establish optimal analytical methods for the determination of drug concentrations in cell cultures to ensure accurate results and adequate dosing metrics for in vivo studies [4, 5].

One of the natural compounds that have shown significant pharmacological activities is thymoquinone (TQ), an active constituent of black cumin (Nigella sativa L.). TQ (Fig. 1) has demonstrated anti-inflammatory, anti-oxidant, anti-microbial, anti-tumor, analgesic, hepatoprotective, and neuroprotective activities [6, 7, 8, 9]. However, its instability in aqueous solutions makes it challenging to determine its stability and concentration in cell cultures accurately [10, 11].

Figure 1.

Structure of Thymoquinone.

To address this issue, this study aimed to establish optimal analytical conditions for TQ determination in monolayer cell cultures using high-pressure liquid chromatography (HPLC). The validation of the optimized method was carried out in accordance with the International Council for Harmonisation (ICH) guideline M10 on bioanalytical method validation and study sample analysis to ensure its acceptability and application [12].

By establishing reliable and accurate analytical methods for TQ determination in cell cultures, the results of in vitro evaluations can be adequately interpreted, and dosing metrics for studies can be determined accurately. This approach can help overcome some of the disadvantages of in vitro testing and give an insight into the importance of application of analytical methods in drug screening as numerous factors in cell culture media can influence the drug stability.

2. Experiments

2.1 Chemicals and cells

Pharmaceutical grade TQ (purity 99.8%) was purchased from Sigma-Aldrich Chemie GmbH, Germany. Acetonitrile (for HPLC-super gradient Reag. Ph. Eur., ACS water $<$ 30 ppm-suitable for UPLC/UHPLC instruments) was purchased from VWR Prolabo Chemicals Pennsylvania, United States of America (USA). Growth medium Roswell Park Memorial Institute (RPMI)-1640 basal medium (Sigma Aldrich, USA), supplemented with 10% Fetal Bovine Serum (FBS), 100 U mL^{- 1} penicillin, 100 $\mu$ l mL^{- 1} streptomycin, 10 mM HEPES, 1mM sodium pyruvate and 1% of non-essential amino acid $\alpha$ -Glutamine (Sigma Aldrich, USA) was used in this study. Dimethyl sulfoxide (DMSO) (Molecular Probes, USA) and phosphate-buffered saline (PBS) (Fisher Bioreagents, USA) were obtained from Life Technologies, USA. DHL-4 and HBL-1 lymphoma cell lines were a kind gift from Dr. Dimitar Efremov, ICGEB, Italy.

2.2 Apparatus

The Agilent Technologies HPLC 1260 Infinity II (Agilent Technologies, Inc., Santa Clara, USA) was used for method development and validation. This system was equipped with a binary pump integrated with a two-channel degasser, autosampler, column oven, and diode-array detector (DAD). The chromatographic separation was performed on a Zorbax SB-C18 5 $\mu$ m column, 4.6 mm $\times$ 150 mm (Agilent). OpenLAB CDS ChemStation and MS Excel software were used for statistical data processing and evaluation. HPLC grade water was obtained using Arium Mini (Goettingen, Germany) water purification systems.

2.3 HPLC conditions

Isocratic separation was performed at 30^∘C using a mixture of acetonitrile and water (50:50, $v/v$ ) at a flow rate 1 mL min^{- 1}. A sample injection volume was 10 $\mu$ L. Detection was performed at 254 nm. All solutions were filtered through 0.2 $\mu$ m membrane filter (Regenerated Cellulose 47 mm, pore size 0.2 $\mu$ m, Agilent Technologies, Inc., Santa Clara, USA).

2.4 Standard solution

To prepare a stock standard solution with a concentration of 100 $\mu$ M, the TQ reference standard was dissolved in 1 mL of DMSO. Stock solution was further dissolved in cell culture medium to obtain working standard solutions with concentrations of 2, 3, 4, 6, 8, and 10 $\mu$ M. All solutions were filtered through a 0.2 $\mu$ m membrane filter prior to analysis. Working standard solutions were utilized during the method optimization.

2.5 Samples

Cells were plated in 6-well plates at a concentration of 2 $\times$ 10⁵ cells m L^{- 1} of complete RPMI-1640 growth medium, and the TQ was added to the growth medium to achieve a final concentration of 10 $\mu$ M TQ.

In negative control, DMSO in final concentration 0.001 % was added to the cells. After half an hour, cells were centrifuged for 5 minutes at 320 g and supernatant was used for analysis of TQ concentration. Additionally, two solutions of 10 $\mu$ M TQ in growth medium were prepared: TQ in complete RPMI-1640 growth medium supplemented with fetal bovine serum (FBS) and TQ in RPMI-1640 growth medium without FBS. Concentration of TQ was determined in each sample within one hour from preparation.

2.6 Method validation

Following the ICH guidelines M10 for bioanalytical method validation and study sample analysis [12], we conducted a thorough validation procedure that encompassed multiple aspects. These included evaluating specificity and screening the biological matrix, assessing sensitivity, investigating the matrix effect, determining linearity, as well as examining precision and accuracy, and the method’s ruggedness in terms of precision, accuracy, and linearity.

System suitability was evaluated using a middle quality control (MQC) sample with a concentration of 6 $\mu$ M, involving six consistent injections. The calculation of coefficient of variation encompassed the retention time and response measurements of the analyte. The examination of autosampler carryover was performed through the injection sequence comprising a standard blank, an upper limit of quantification (ULOQ) concentration set at 10 $\mu$ M, and a lower limit of quantification (LLOQ) concentration established at 2 $\mu$ M.

Specificity assessment involved utilizing six blank standards and LLOQ-level samples to screen the biological matrix. Our scrutiny aimed to uncover any potential interference between blank and sample responses. Sensitivity analysis revolved around utilizing the LLOQ-level sample to establish the lowest detectable limit. Subsequently, we calculated the % mean accuracy and % CV (coefficient of variation). Furthermore, we explored the matrix effect’s impact on analyte quantitation in relation to signal consistency, encompassing both suppression and enhancement effects. This investigation was conducted across six distinct lots of TQ cell growth medium. We prepared three replicates each of low quality control (LQC) and high quality control (HQC) levels from these cell growth medium samples, totaling 36 quality control (QC) samples. The accuracy was evaluated, and % bias was calculated across all QC samples. For testing of the dilution integrity, QC was prepared with analyte concentrations in the matrix that exceeded the ULOQ. The QC was then diluted with the blank matrix and six replicates per dilution factor ( $f=$ 3/10 and $f=$ 5/10) in a single run were tested to determine whether the concentrations were accurately and precisely measured within the calibration range.

To ascertain linearity, we examined standard plots corresponding to a 6-point standard calibration curve. This curve displayed linear behaviour within the range of 2–10 $\mu$ M. Precision and accuracy were evaluated within batches and between batches at four different concentration levels (LLOQ, LQC, MQC, and HQC), each with six replications for both analytes. Mean value was calculated from the resulting drug concentrations. Both, accuracy and precision, were calculated, expressed as % accuracy and coefficient of variation (%CV), respectively. According to the ICH guidelines, M10 concerning bioanalytical method validation and study sample analysis, it is recommended that a suitable internal standard (IS) is incorporated into all calibration standards, QC samples, and study samples during the sample processing phase. Alternatively, if an internal standard is not employed, a clear justification for its omission is required [12]. In our current analytical approach, the methodology is characterized by its simplicity, devoid of intricate sample preparation procedures or a multitude of potential error sources. Consequently, it is considered that introducing an internal standard could be avoided in this particular context.

2.7 Analytical tools for greenness assessment

The greenness of chemical processes, crucial in modern laboratories, is evaluated through specific tools considering environmental impacts. This assessment includes parameters like chemical hazards, energy consumption, occupational risks, and waste generation [13]. For our method, we assessed greenness using the National Environmental Methods Index (NEMI) tool [14].

3. Results and discussion

HPLC-DAD involves the use of a DAD in conjunction with HPLC, making it a highly utilized method for monitoring active substance concentrations in various media and matrices under different conditions [15]. In order to develop a method for direct determination of TQ in cell cultures, we developed the HPLC method and optimized some important parameters to achieve the best performance. Different combinations of solvent systems of acetonitrile–water and methanol-water were tested in order to determine the best conditions for the effective separation and optimization of TQ. Collectively, the best chromatographic condition was achieved using acetonitrile and water (50:50, $v/v$ ) as mobile phase, column temperature of 30^∘C, and flow rate 1 mL min^{- 1}. Applying these HPLC conditions resulted in a symmetrical and sharp TQ peak at 6.5 min. Analytical detection was done at 254 nm after scanning the UV spectrum of TQ and identifying the maximum absorption peak.

The appropriate validation of analytical methods has become an essential part of successful identification and quantification of active substances [16]. Method validation is the process of demonstrating that an analytical method is suitable for its intended purpose and can provide accurate and reliable results. It ensures that the results obtained from the method are trustworthy and can be used for making critical decisions related to the safety and efficacy of a product.

Validation of the analytical method involves examining the validation parameters and assessing their usability [17]. The most commonly used validation is performed according to ICH Guideline [12], which clearly points out the criteria by which a given analytical method is tested. The guidelines discuss the characteristics to be taken into account during the validation of analytical procedures included as part of registration applications submitted to distinct authorities.

The validation parameters typically include specificity, selectivity, calibration curve (response function), range (LLOQ to ULOQ), accuracy, precision, carry-over, dilution integrity, and stability. These parameters provide information on the ability of the method to produce accurate and precise results, its ability to detect and quantify the analyte at low levels. The proposed analytical method for TQ determination in cell growth media was further validated following the ICH Guidelines.

3.1 Method validation

3.1.1 System suitability and auto sampler carryover

Six individual replicates of middle quality control samples, each featuring distinct concentrations, were subjected to injection. The coefficient of variation (%CV) was subsequently computed for these samples. Specifically, the %CV calculations encompassed the retention time of the analyte. The findings revealed that the %CV values for the retention time remained at or below 2%. These outcomes, falling within the predefined acceptance criteria, have been succinctly summarized in Table 1. To ascertain the accuracy and precision of the method, a carryover experiment was executed. This experiment aimed to ensure that carryover effects would not adversely impact these aspects. Importantly, the results of the carryover experiment demonstrated the absence of any detectable carryover, as presented in Table 2.

Table 1
System suitability data

Sample name	Analyte
	Area	Rt (min)
MQC	51.6	4.468
	51.7	4.470
	51.4	4.470
	51.1	4.469
	50.6	4.467
	49.1	4.469
MEAN	50.92	4.469
SD	0.97	0.001
%CV	1.91	0.026

Table 2

System suitability data

Sample ID	Peak area	% Carryover
	TQ	TQ
STD Blk	0	NA
ULOQ	80	0
STD Blk	0	NA
LLOQ	18.7	0

3.1.2 Specificity and screening of biological matrix

The assessment of selectivity aims to confirm the absence of substantial response arising from interfering components at the retention time (s) of the analyte or the internal standard in the blank samples. Interfering peaks present in the standard blank at the same retention time as the analyte were required to exhibit a response $\leqslant$ 20% of that in the LLOQ. During the analysis, all samples were confirmed to be devoid of interference at the analyte’s retention time in the blank samples. The outcomes are detailed in Table 3, indicating the method’s specificity towards TQ, demonstrated by the absence of interfering peaks.

Table 3
Specificity and screening of biological matrix

Sample	Response	%Interference	Pass/fail
	TQ	TQ
STD B1	0	0	Pass
LLOQ1	18.7
STD B2	0	0	Pass
LLOQ2	18.1	0
STD B3	0	0	Pass
LLOQ3	17.1
STD B4	0	0	Pass
LLOQ4	16.9
STD B5	0	0	Pass
LLOQ5	16.5
STD B6	0	0	Pass
LLOQ6	16.5

3.1.3 Sensitivity

At the LLOQ level, the precision and accuracy of TQ exhibited a coefficient of variation (%CV) of 5.68%, and the % mean accuracy was determined to be 98.37%. As per the acceptance criteria, a minimum of 67% (4 out of 6) of the samples should fall within the range of 80–120%. Moreover, the % mean accuracy is expected to be within the 80–120% range, and the %CV accuracy should not exceed 20%.

The obtained results align with the acceptance limits and are presented in Table 4. This confirms high sensitivity of the method towards TQ.

Table 4
Sensitivity data

Sample	LLOQ
	Nominal Concentration ( $\mu$ M)
	2 $\mu$ M
	Calculated Concentration ( $\mu$ M)
1	2.14
2	2.07
3	1.94
4	1.92
5	1.87
6	1.87
N	6
Mean	1.97
SD	0.11
%CV	5.68
% Mean Accuracy	98.37

3.1.4 Matrix effect

Table 5 displays the matrix effect information for HQC and LQC levels. To fulfil the acceptance criteria, a minimum of 67% (2 out of 3) of samples at each level should fall within the 85–115% range. Additionally, the acceptance criteria dictate that at least 80% (5 out of 6) of the matrix lots should meet these standards. Moreover, the % mean accuracy of the back-calculated concentration for LQC and HQC samples, which are derived from distinct biological matrix lots, is expected to be within the 85–115% range.

Table 5
Matrix effect

S. No.		Cell growth medium	LQC	HQC
			Nominal Concentration ( $\mu$ M)
			4	8
			Nominal Concentration Range ( $\mu$ M)
			3.40–4.60	6.80–9.20
			Calculated Concentration ( $\mu$ M) ( $n=$ 3)
1		LOT1	4.00	8.09
2		LOT2	4.12	8.11
3		LOT3	3.96	7.79
4		LOT4	3.93	7.88
5		LOT5	3.95	7.88
6		LOT6	3.97	7.78
	Mean		3.99	7.92
	SD		0.07	0.15
	%CV		1.71	1.83
	%Mean Accuracy		99.67	99.02
	No. of QC Failed		0	0

3.1.5 Linearity

The linearity of the method was assessed across six concentration levels, encompassing the LLOQ. The calibration curve exhibited linearity within the range of 2–10 $\mu$ M, displaying a high correlation coefficient ( $r^{2}$ ) of 0.9993. The calibration curves were established through three separate runs in triplicates. The linear representation can be observed in Fig. 2. Every back-calculated standard concentration fell within the acceptance criteria. The calculated calibration standards are outlined in Table 6.

Table 6
Linearity

S. No.	Conc. ( $\mu$ M)	Back Calculated Concentration ( $\mu$ M)			Avg.	%CV	%Mean accuracy
		1	2	3
1	2	2.14	2.07	1.94	2.05	4.88	102.49
2	3	2.65	2.75	2.62	2.67	2.45	89.07
3	4	3.99	4.12	3.97	4.03	1.97	100.71
4	6	6.18	6.14	6.08	6.14	0.81	102.3
5	8	8.09	8.11	7.88	8.03	1.61	100.33
6	10	9.52	10.33	10.09	9.99	4.16	99.85

Figure 2.

Calibration plot for concentration v/s area ratio..

3.1.6 Precision

Interday and intraday precisions were quantified as relative standard deviations and expressed as percentages of the TQ concentration [18]. The %CV of estimated concentrations across all four quality control samples, each replicated six times for the analyte, ranged from 0.18% to 4.80%. In terms of % mean accuracy, LLOQ, LOQ, MOQ, and HQC values ranged from 94.52% to 105.80%.

For inter-day precision and accuracy, the %CV and accuracy results across all quality control samples fell within the ranges of 0.42–4.08% and 95.29–102.56%, respectively. Adhering to acceptance criteria, a minimum of 67% of the QC samples were $\leqslant$ 15.00%, and for the LLOQ, this was $\leqslant$ 20%. The % mean accuracy for LQC, MQC, and HQC samples was expected within the 85–115% range, while for the LLOQ sample, it should be within 80–120%.

Detailed precision and accuracy data can be found in Table 7. Figure 3–7 show the chromatograms of the quality control samples. The obtained data collectively indicate favorable precision for all tested concentrations in both intraday and inter-day samples.

Table 7
Precision interday and intraday data

Nominal Concentration ( $\mu$ M)
	HQC	MQC	LQC	LLOQ
	8	6	4	2
Day 1 ( $n=$ 6)
Mean	7.68	5.82	3.88	2.12
SD	0.26	0.03	0.03	0.08
%CV	3.44	0.47	0.81	3.66
%Mean Accuracy	96.05	97.00	97.12	105.80
Day 2 ( $n=$ 6)
Mean	7.56	5.76	3.81	1.98
SD	0.36	0.02	0.03	0.04
%CV	4.80	0.36	0.72	1.84
%Mean Accuracy	94.52	96.08	95.24	98.86
Day 3 ( $n=$ 6)
Mean	7.62	5.80	3.86	2.06
SD	0.31	0.02	0.01	0.02
%CV	4.02	0.43	0.18	1.20
%Mean Accuracy	95.31	96.74	96.43	103.03
Between Batch Precision and Accuracy
N	18	18	18	18
Mean	7.62	5.80	3.85	2.05
SD	0.31	0.02	0.02	0.05
%CV	4.08	0.42	0.57	2.23
%Mean Accuracy	95.29	96.61	96.26	102.56

Figure 3.

Chromatogram of LLOQ.

Figure 4.

Chromatogram of LQC.

Figure 5.

Chromatogram of MQC.

3.1.7 Dilution integrity

Both dilution factors (3/10 and 5/10) met the precision requirement ( $\leqslant$ 15% CV) and the accuracy requirement (within $\pm$ 15% of nominal concentration) which indicate that the “dilution integrity” testing has successfully fullfiled the specified requirements for both dilution factors, demonstrating that the sample dilution procedure does not impact the accuracy and precision of the measured analyte concentration (Table 8).

Table 8
Dilution integrity

	Diluting factor 3/10	Diluting factor 5/10
Nominal Concentration ( $\mu$ M)
	3	5
Number of replicates $n=$ 6
Mean	2.71	5.12
SD	0.06	0.13
%CV	2.20	2.59
%Mean Accuracy	90.37	102.33

Figure 6.

Chromatogram of HQC.

Figure 7.

Chromatogram of ULOQ.

Figure 8.

Degradation kinetics of TQ in RPMI-1640 medium. Curves of thymoquinone kinetics: a) Zero- order model, b) First- order model, c) Second- order model.

3.1.8 TQ solution stability

Previous studies have shown that thymoquinone (TQ) is susceptible to degradation in aqueous solutions, especially at an alkaline pH [19, 20]. To assess the stability of TQ under the proposed experimental conditions of TQ (HPLC, we evaluated its degradation kinetics, as shown in Fig. 8. The determination of the degradation kinetics was based on fitting the experimental data to different kinetic models (zero-order, first-order, and second-order). The quality of the fitting was assessed using the coefficient of determination ( $R^{2}$ ), which measures the fit goodness of the data to the model. The $R^{2}$ values obtained for the zero-order and second-order kinetics were much lower than those of the first-order kinetics, indicating that the first-order model was the most appropriate to describe the degradation kinetics of TQ in RPMI-1640 medium. Thus, it can be concluded that TQ degradation in RPMI-1640 medium follows first-order kinetics, indicating that degradation is primarily dependent on the available TQ concentration. The degradation rate constant was calculated as $k=$ 310–4 L/min, and the half-life time for a 10 $\mu$ M TQ solution under defined conditions was 38.5 hours. Our findings are consistent with those of Salmani et al., who found that TQ degradation also followed first-order kinetics at higher pH values in aqueous media. It should be noted that the theoretical pH value of RPMI-1640 medium is 8.2 $\pm$ 0.3 [21].

The findings presented in this study are preliminary and were obtained under specific experimental conditions. To fully understand the degradation kinetics of TQ in various environmental conditions, more extensive studies are needed. However, our results provide valuable insight into the stability of TQ in RPMI-1640 medium under the experimental conditions tested.

Table 9
TQ concentration in analyzed samples

	Concentration of TQ [ $\mu$ M] $\pm$ SD ( $R$ %)
Negative control ( $\mu$ M)	TQ in growth medium without serum ( $\mu$ M)	TQ in growth medium with serum ( $\mu$ M)	TQ in growth medium with serum and HBL-1 cell line	TQ in growth medium with serum and DHL-4 cell line
ND^a	9.71 $\pm$ 1.06 (95.64–98.50)	6.17 $\pm$ 1.46 (58.67– 65.15)	2.62 $\pm$ 1.72 (22.29–29.88)	2.57 $\pm$ 1.52 (20.65– 40.97)

^aND – not detected.

Figure 9.

NEMI pictograms’ description.

3.2 Method application

Table 9 shows the TQ concentrations in samples with cells in growth medium supplemented with serum ( $n=$ 2), only growth medium supplemented with serum ( $n=$ 1), and growth medium without serum spiked with 10 $\mu$ M TQ ( $n=$ 1). The proposed analytical method for TQ determination demonstrated good reproducibility in the samples of growth medium without serum, with R% values ranging from 95.64% to 98.50%. However, a slightly lower concentration of TQ was observed in the samples of growth medium supplemented with serum, with R% values ranging from 58.67% to 65.15%. This could be explained by TQ binding to serum proteins, which could affect its availability for analysis. Similarly, the TQ concentrations in the samples with cells in growth medium supplemented with serum were also lower (R% range between 20.65% and 40.97%) compared to the samples without cells, indicating that TQ binding to cellular components could also affect its availability for analysis. However, further studies are needed to precisely elucidate the reasons for the lower TQ concentrations observed in these samples.

3.3 National environmental methods index (NEMI)

The National Environmental Methods Index (NEMI) uses a pictogram with four quadrants to represent greenness. A method is greener if: a) reagents aren’t persistent, bio-accumulative, and toxic (PBT) by the Environment Protection Agency’s Toxic Release Inventory (EPA-TRI); [22] b) none of the chemicals applied in the procedure is listed on D, F, I or C hazardous wastes EPA list; [23] c) pH is between 2 and 12; d) waste is under 50 g. NEMI is qualitative, marking quadrants as meeting certain criteria. Our method, shows three green quadrants, indicating high greenness, as detailed in Fig. 9.

4. Conclusion

In conclusion, this manuscript highlights the significant achievements in developing and validating a high-performance liquid chromatography (HPLC) method for the direct determination of Thymoquinone (TQ) in cell cultures. The research successfully identified the optimal chromatographic conditions, notably using a 50:50 v/v mixture of acetonitrile and water, a column temperature of 30^∘C, and a flow rate of 1 mL min^{- 1}. This setup enabled the achievement of a sharp and symmetrical TQ peak at 6.5 minutes, with analytical detection at 254 nm.

The method validation was rigorous, adhering to the ICH Guideline and encompassing various parameters like specificity, sensitivity, linearity, accuracy, precision, and matrix effect. The validation process demonstrated that the method could reliably produce accurate and precise results, with the capability to detect and quantify TQ at low levels. The method’s sensitivity and linearity were particularly noteworthy, with the calibration curve showing a high correlation coefficient.

Furthermore, the study addressed TQ’s stability, a critical factor given its known susceptibility to degradation in aqueous solutions. The degradation kinetics of TQ in RPMI-1640 medium were assessed, revealing that TQ degradation follows first-order kinetics. This insight is crucial for understanding TQ’s behavior in various environmental conditions and its interaction with cellular components.

The application of this method in actual cell culture scenarios showed good reproducibility, though variations were observed when serum was added to the growth medium, suggesting possible binding of TQ to serum proteins or cellular components. These findings open avenues for further research to elucidate these interactions more clearly. The proposed method was found to be greener in terms of usage of persistent, bioaccumulative, and toxic chemicals and solvents, corrosive samples, and waste production.

Overall, this research makes a significant contribution to the accurate measurement of TQ in cell cultures.

Footnotes

Conflict of interest

None to report.

References

Doskey

van’t Erve

Wagner

Buettner

. Moles of a Substance per Cell Is a Highly Informative Dosing Metric in Cell Culture. PLOS ONE. 2015; 10: e0132572.

Zhang

Guan

Mais

Wang

. Quality control of cell-based high-throughput drug screening. Acta Pharmaceutica Sinica B. 2012; 2: 429-38.

Omeragic

Dedic

Elezovic

Becic

Imamovic

Kladar

, et al. Application of direct peptide reactivity assay for assessing the skin sensitization potential of essential oils. Sci Rep. 2022; 12: 7470.

Joseph

Malindisa

Ntwasa

. Two-Dimensional (2D) and Three-Dimensional (3D) Cell Culturing in Drug Discovery [Internet]. Cell Culture. IntechOpen; 2018; [cited 2022 Mar 22]. Available from: https//www.intechopen.com/chapters/64565.

Teuscher

Zhang

. A Versatile Method to Determine the Cellular Bioavailability of Small-Molecule Inhibitors. J Med Chem. 2017; 60: 157-69.

Majdalawieh

Fayyad

Nasrallah

. Anti-cancer properties and mechanisms of action of thymoquinone, the major active ingredient of Nigella sativa. Crit Rev Food Sci Nutr. 2017; 57: 3911-28.

Gomathinayagam

Jayaraman

Song

Isidoro

Dhanasekaran

. Chemopreventive and Anticancer Effects of Thymoquinone: Cellular and Molecular Targets. J Cancer Prev. 2020; 25: 136-51.

Taka

Mendonca

Mazzio

Reed

Reams

Soliman

. Molecular Targets Underlying the Anti-inflammatory Effects of Thymoquinone in LPS activated BV-2 Cells. The FASEB Journal. 2018; 32: 40.2-40.2.

Armutcu

Akyol

. The interaction of glutathione and thymoquinone and their antioxidant properties. ELECTRON J GEN MED. 2018; 15: em59.

10.

Pathan

Jain

Zaidi

SMA

Akhter

Vohora

Chander

, et al. Stability-indicating ultra-performance liquid chromatography method for the estimation of thymoquinone and its application in biopharmaceutical studies. Biomed Chromatogr. 2011; 25: 613-20.

11.

Salmani

JMM

Asghar

Zhou

. Aqueous solubility and degradation kinetics of the phytochemical anticancer thymoquinone; probing the effects of solvents, pH and light. Molecules. 2014; 19: 5925-39.

12.

EMA. ICH M10 on bioanalytical method validation - Scientific guideline [Internet]. European Medicines Agency. 2019 [cited 2023 Aug 29]. Available from: https://www.ema.europa.eu/en/ich-m10-bioanalytical-method-validation-scientific-guideline.

13.

Tobiszewski

Marć

Gałuszka

Namieśnik

. Green Chemistry Metrics with Special Reference to Green Analytical Chemistry. Molecules. 2015; 20: 10928-46.

14.

Green Analytical Methodologies

|

Chemical Reviews [Internet]. [cited 2023 Nov 24]. Available from: doi: 10.1021/cr068359e.

15.

Malviya

Bansal

Pal

Sharma

. High performance liquid chromatography: A short review. Journal of Global Pharma Technology. 2010; 2: 22-6.

16.

Bridwell

Dhingra

Peckman

Roark

Lehman

. Perspectives on Method Validation: Importance of Adequate Method Validation. The Quality Assurance Journal. 2010; 13: 72-7.

17.

Validation of Analytical Methods

|

IntechOpen [Internet]. [cited 2023 Mar 17]. Available from: https://www.intechopen.com/chapters/57909.

18.

Mowafy

Alanazi

El Maghraby

. Development and validation of an HPLC–UV method for the quantification of carbamazepine in rabbit plasma. Saudi Pharmaceutical Journal. 2012; 20: 29-34.

19.

Soliman

Abdel Salam

Eid

Khayyat

Neamatallah

Mesbah

, et al. Stability study of thymoquinone, carvacrol and thymol using HPLC-UV and LC-ESI-MS. Acta Pharm. 2020; 70: 325-42.

20.

Salmani

JMM

Asghar

Zhou

. Aqueous solubility and degradation kinetics of the phytochemical anticancer thymoquinone; probing the effects of solvents, pH and light. Molecules. 2014; 19: 5925-39.

21.

biowest_tds_rpmi_1640_p0883.pdf [Internet]. [cited 2023 Mar 17]. Available from: https://www.biowest.net/media/biowest_tds_rpmi_1640_p0883.pdf.

22.

US EPA O. Persistent, Bioaccumulative, and Toxic (PBT) Chemicals under TSCA Section 6(h) [Internet]. 2017 [cited 2023 Nov 24]. Available from: https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/persistent-bioaccumulative-and-toxic-pbt-chemicals.

23.

40 CFR Part 261 – Identification and Listing of Hazardous Waste [Internet]. [cited 2023 Nov 24]. Available from: https://www.ecfr.gov/current/title-40/part-261.

HPLC method for the determination of thymoquinone in growth cell medium

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2.1 Chemicals and cells

2.2 Apparatus

2.3 HPLC conditions

2.4 Standard solution

2.5 Samples

2.6 Method validation

2.7 Analytical tools for greenness assessment

3. Results and discussion

3.1 Method validation

3.1.1 System suitability and auto sampler carryover

Table 1 System suitability data

Table 3 Specificity and screening of biological matrix

Table 4 Sensitivity data

Table 5 Matrix effect

Table 6 Linearity

Table 7 Precision interday and intraday data

Table 8 Dilution integrity

Table 9 TQ concentration in analyzed samples

3.3 National environmental methods index (NEMI)

4. Conclusion

Footnotes

Conflict of interest

References

Table 1
System suitability data

Table 3
Specificity and screening of biological matrix

Table 4
Sensitivity data

Table 5
Matrix effect

Table 6
Linearity

Table 7
Precision interday and intraday data

Table 8
Dilution integrity

Table 9
TQ concentration in analyzed samples