Development, Optimization, and Validation of a Green Spectrofluorimetric method for the determination of Moxifloxacin using an Experimental design approach

 

Noha Ibrahim¹, Eman S. Elzanfaly², Ahmed E. El Gendy¹, Said A. Hassan²*

¹Analytical Chemistry Department, Faculty of Pharmacy, MISR International University, Cairo, Egypt.

²Analytical Chemistry Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt.

 

ABSTRACT:

Green chemistry is an emerging field concerned with safe practices in chemical method development. The purpose of the study is to develop a sensitive, environmentally friendly spectrofluorimetric method by studying and optimizing variables affecting the native fluorescence intensity of Moxifloxacin using experimental design approach. The analysis was divided into three phases: studying and screening the critical factors using 2-level full factorial design, optimization using central composite design and validation according to International Conference on Harmonization (ICH) guidelines. The optimal experimental conditions obtained from this study were 0.05 M phosphate buffer with an adjusted buffer pH of 9.7 and a temperature of 7.4°C±0.5, providing a sensitive measurement of MOX. The validated method providing to be linear over a range of 5-40ng/mL (r= 0.9999), precise, accurate, and robust (demonstrated by the Plackett-Burman design). It can be concluded that the validated method can be safe, optimum, and environmentally friendly alternative for the analysis of MOX.

 

 

 

INTRODUCTION:

Green chemistry is a compatible technique that can obtain the desired output without any harmful disposable of waste at the end of the chemical process¹. Such practices have become essential, especially in the African region, to reduce the production and use of hazardous substances in the pattern and may also reduce the bad impacts on the surround and homo health^2,3. Therefore, chemists must use their knowledge in developing methods with green solvents^4,5. Hence, there is an urgent need to develop method such as the proposed method that are less harmful to sense of human and the environment.

 

Moxifloxacin (MOX), 1-Cyclopropyl-6-fluoro-8-methoxy-7-[(4aS,7aS)-octahydro-6H-pyrrolo[3,4-b]pyridine-6-yl]-4-oxo-1,4-dihydro-3-quinolinecarboxylic acid⁶, is a broad-spectrum fluoroquinolone antibiotic active; against both gram-positive and gram-negative bacteria⁷.

 

Spectrofluorimetric is considered one of the most convenient analytical techniques because of its sensitivity, simplicity, and low cost. The measurement of a fluorescence signal is often considered to be a function of two variables, namely, the wavelength of excitation and the wavelength of emission. However, many variables, such as the pH, temperature, and chemical surroundings, can contribute to the measured fluorescence signal^8,9.

 

To maximize the fluorescence intensity, the above factors should be optimized. Fisher, Youden^10,11 and numerous others^12-15 have shown that carrying out a series of experiments where the factors are changed together; maximizes the amount of information gained while minimizing the amount of data to be collected. This approach is called statistical experimental design^16-17.

 

In the literature, MOX has been determined using spectrophotometric and spectrofluorimetric methods^18-23 HPLC methods^24-32. In previous work, variables were optimized via analysing each variable individually, which required a huge quantity of experimental runs. Further, such a strategy does not compare the interaction among elements and therefore does no longer depict the confounding results of different factors on the response. Some reported methods involved a fluorescence probe not dependent on the native fluorescence of MOX, which increased the sophistication of the method. To the best of our knowledge, no method based on green chemistry principles and implementing an experimental design has been described in the literature for the evaluation and optimization of variables affecting the native fluorescence of MOX in fluorescence spectroscopy.

 

The aim of this work was to optimize the fluorometric determination of MOX to reduce method costs and potential impacts on human health and the environment while maintaining high sensitivity. This goal was achieved using an experimental design approach to minimize the number of experiments, reducing the effort and time required.

 

MATERIAL AND METHODS:

Materials:

MOX was obtained from the Delta Pharma Company, (Egypt). The pharmaceutical-grade MOX used in this study were certified to be 98% pure. Avelox® tablets [labelled to contain 400 mg MOX; B. N. BXHAAA1] were purchased from a local drug store. Sodium dihydrogen phosphate, sodium hydroxide (highly diluted to produce an environmentally benign reagent) and glacial acetic acid were supplied by ADWIC, (Egypt). Ethanol, isopropanol, and acetone were purchased from Sigma-Aldrich; (Germany).

 

Instrumentation:

Fluorescence spectra were recorded using a Shimadzu spectrofluorometer (model: RF 5301 PC, Japan) equipped with a 150-watt xenon lamp. The slit widths of both the excitation and emission monochromators were set at 5 nm. A 1-cm quartz cell was used.

 

A sonicator (Grant Instruments™ XUBA Analog Ultrasonic Bath).

 

Software:

All the experimental designs and data analysis calculations were performed by using MODDE® 8.0.2 software.

 

Preparations:

Preparation of stock standard solution:

An accurately weighed amount of 10mg of MOX was transferred into a 10mL volumetric flask; dissolved in 5 mL distilled water; and brought to volume with the same solvent to produce concentration of 1000μg/mL of MOX.

 

Preparation working standard solution:

From the stock standard solution an aliquot 0.1mL was transferred to a 10mL volumetric flask and brought to volume with distilled water to obtain a concentration equivalent to 10µg/mL. Suitable aliquots were accurately transferred from the standard working solution into a series of 25mL volumetric flasks. This solution was further diluted according to the objective and then analysed following the proposed procedure.

 

A freshly prepared 40ng/mL standard solution of the analyte was prepared daily in the appropriate solvent for screening and optimization.

 

Preparation of Dosage form solution:

Ten tablets of Avelox® [labelled to contain 400mg MOX] were accurately weighed and finely powdered. An accurate weight of powder equivalent to 10mg MOX was transferred into a 100mL volumetric flask; sonicated with 30mL 0.05 M phosphate buffer, pH 9.7, for 15 min; and brought to volume with the same solvent. The solution (100µg/ml) was filtered, and an aliquot 0.1mL was transferred to a 10mL volumetric flask and brought to volume with the same solvent to obtain a concentration equivalent to 1000ng/ml. This solution was further diluted in a 10mL volumetric flask according to the objective and then analysed following the proposed procedures. The concentration of MOX in tablets was calculated from the previously plotted calibration curve.

 

Design of experiment:

2-level full factorial design with four variables was used for screening the significant and non-significant independent variables and to detect the interaction between them. The dependent, independent variables and levels selected for the screening procedure are listed in Table 1. For method optimization, Central composite design (CCD) was used with the 2 variables that were significant at 95% confidence interval according to the ANOVA results obtained from the 2-level full factorial design. The variables and levels selected for the optimization procedure were temperature (X1; 5, 25, 45 ̊С±0.5) and buffer pH (X3; 4, 8, and 12%). Because of the nonlinearity of the CCD, a polynomial equation containing second-order model is given in Eq. (1) for the two variables.

 

Y_i=β_◦+β₁X1+β₂X3+β₁₂X1X3 +β₁₁X²1+β₂₂X²3 Eq. 1

 

where Y_i is the predicted dependent variable; β_◦ is the coefficient constant; X1 and X3 represents the factors; β₁ and β₂ is the regression coefficient calculated by the model by considering the average response of changing one variable at a time from its lower to higher level. The interaction term: β₁₂ demonstrates how the response changes when two variables are simultaneously changed; and β₁₁ and β₂₂ are the quadratic coefficients.

 

Table1: Variables used for the Screening testing.

Independent variables	Lower value (−1)	Nominal value (0)	Upper value (1)	Constraints
X1: Temperature ( ̊С±0.5)	5	25	45	In the range
X2: Degassing time using a sonicator (min)	0	5	10	In the range
X3: Buffer pH	4	8	12	In the range
X4: Phosphate buffer concentration (M)	0.05	0.15	0.25	In the range
Dependent variable				Constrains
Fluorescence intensity (MOX intensity)				Maximum

 

Method validation:

The proposed method was validated according to the guidelines set by the International Conference on Harmonization (ICH guidelines) ³³. The evaluated parameters were, linearity of the calibration curve, limit of detection (LOD), limit of quantification (LOQ), precision, accuracy and robustness which was demonstrated using Plackett-Burman design (PBD). The method for the determination of MOX was linear over a concentration range of 5-40ng/mL. The LOD and LOQ were calculated as LOD 3𝑥𝜎/𝑆 and LOQ 10𝑥𝜎/𝑆, where 𝜎is the standard deviation of the intercept and 𝑆 is the slope. To test the prediction performance of the proposed method, the intra-day (repeated three times within the same day with the same conditions) and inter-day (repeated three times on three successive days) studies were performed at three different concentrations (20,25 and 30ng/mL for MOX). The accuracy of the method was determined by a recovery study at the same concentrations used for precision analysis. PBD was used to determine robustness and the effect of the 3 factors at different levels at 95% confidence interval. The variables and levels selected for the robustness procedure were, temperature (X1; 7 ± 2±0.5 ̊С), buffer pH (X3; 9.7 ± 0.1 pH unit) and buffer concentration (X4; 50 ± 20mM).

 

RESULTS AND DISCUSSION:

The proposed method utilized an experimental design to optimize the variables and maximize the fluorescence intensity. The optimization process was carried out by using two designs: a 2-level full factorial design to evaluate which variables were significant and a central composite design CCD to obtain the response surface and optimize the significant variables to maximize the response.

 

Screening phase, 2-level full factorial design:

Screening was performed using 2-level full factorial design, this design was selected because all possible combinations of factors and levels are created and tested³⁴. This approach prevents any interaction between variables from being missed. Table 2 gives the design matrix for these experiments with the fluorescence response obtained. Analysis of the results reveals which factors affect the fluorescence intensity. These factors are illustrated in the coefficient plot shown in Fig. 1. Each green bar represents a factor in the reaction, showing the average effect on the fluorescence intensity upon increasing the factor from the midpoint value (either positive or negative) and the confidence interval as error bars. The two factors that were significant (temperature and buffer pH) at a 95% confidence interval according to ANOVA, as shown in Table 3. In addition, a significant interaction between the two significant factors was detected.

 

Table 2: The plan of 2-level full factorial design and experimentally obtained results.

Symbol	X1	X2	X3	X4
Run number	Temperature (±0.5 ºC)	Degassing time (Min)	Buffer pH	Buffer concentration (M)	Fluorescence intensity
1	5	0	4	0.05	175
2	45	0	4	0.05	105
3	5	10	4	0.05	152
4	45	10	4	0.05	106
5	5	0	12	0.05	198
6	45	0	12	0.05	137
7	5	10	12	0.05	181
8	45	10	12	0.05	138
9	5	0	4	0.25	166
10	45	0	4	0.25	110
11	5	10	4	0.25	167
12	45	10	4	0.25	109
13	5	0	12	0.25	175
14	45	0	12	0.25	137
15	5	10	12	0.25	170
16	45	10	12	0.25	138
17	25	5	8	0.15	154
18	25	5	8	0.15	153
19	25	5	8	0.15	155

 

 

Table 3: ANOVA results for 2-level full factorial design and 5% level of significance.

	Fluorescence Intensity
Factors	Coefficient	P value
X1	-25.250	1.287ͯ 10-7*
X2	-2.625	0.110
X3	11.500	4.93ͯ 10-5*
X4	-1.250	0.417
X1X2	1.875	0.084
X1X3	3.500	0.043*
X1X4	2.250	0.162
X2X3	0.125	0.933
X2X4	2.125	0.184
X3X4	-3.000	0.074

Coefficient: Is the coefficient values obtained by the model.

*P value less than 0.05 at a (95%) confidence interval, indicate significance

X1; temperature (̊С), X2; degassing time (min), X3; buffer pH and X4; buffer concentration(M).

 

 

Fig. 1: Regression coefficient plot of factors studied in 2-level full factorial design for MOX, factors outlined with circle are the significant factors. X1; temperature (̊С), X2; degassing time (min), X3; buffer pH, and X4; buffer concentration (M).

 

One of the variables studied is the degassing using the sonicator for the expel of oxygen which is a cause of a quenching effect on the fluorescence activity³⁵. From the preliminary studies, degassing time and buffer concentration had no effect on the fluorescence intensity, and accordingly they were set at its lowest levels. The influence of temperature on the fluorescence intensity was examined from 5°C to 45°C, and the results are illustrated in the main effect plot in Fig. 2. It became determined that florescence intensity decreases while temperature increases; this result is explained by the predicted theory of the effect of temperature on fluorescence intensity, which states that increasing temperature reduces the fluorescence intensity because of increased collision quenching³⁶.

 

The effect of buffer pH on the fluorescence intensity was examined from 4.0 to 12.0. Any increase in pH was accompanied by an increase in fluorescence intensity. The main effect plot for pH in Fig. 2 shows that the pH directly influences the fluorescence intensity. This was due to the presence of the carboxylic group in the MOX structure (COOH), when the MOX in acidic medium (COOH) presence causes a large decrease in the fluorescence intensity, while in basic medium the carboxylic group dissociates to carboxylate ions that are become fully dissociated in alkaline medium and not neutral because the carboxylic acid is a weak acid so the fluorescence intensity increases.

 

Fig. 2: a, b, c, d represents the main effect plots of the studied variables on MOX intensity.

 

 

Interactions:

An interaction effect between factors is the relationship between a factor and the target that depends on the values of other factors³⁷. According to the P value, there is a significant interaction between temperature(X1) and buffer pH (X3). The influence of this interaction could be illustrated by the interaction plot in Fig. 3. The relationship between the fluorescence intensity and temperature is based on the buffer pH. At high and low buffer pH, there is a negative relationship between temperature and fluorescence intensity. When the temperature decreases the fluorescence intensity increases depending on the upper and lower level of buffer pH .

 

Fig. 3: Interaction plot demonstrating the relationship between buffer pH and temperature on MOX intensity.

 

Optimization phase, central composite design:

A CCD with 2 variables, temperature, and buffer pH, was established. This design involved 8 runs plus three central points. Table 4 gives the design matrix for these experiments and the fluorescence response obtained. The two rejected variables in the screening design were fixed at their lowest levels. The buffer concentration and the degassing time were (0.05 M and 0 min, respectively). The following equation, Eq. 2 was obtained after computing the coefficients of the second-order polynomial model.

 

Y=8.50-6.76X1+6.76X3-8.28X1X3+10.41X1²-10.41X3² Eq. 2

 

Where (Y, X1 and X3) are the fluorescence intensity, temperature, and buffer pH, respectively. By simply replacing X1 and X3, the response can be anticipated for any viable putting, even for experiments that have not been accomplished.

 

Table 4: Optimization experimental plan and response applying central composite design.

	X1	X3
Run number	Temperature (±0.5 ºC)	Buffer pH	Fluorescence intensity
1	5	4	174
2	45	4	104
3	5	12	190
4	45	12	148
5	5	8	181
6	45	8	133
7	25	4	120
8	25	12	164
9	25	8	154
10	25	8	153
11	25	8	153

 

Validation of the optimized factors:

The model was found to be statistically significant (P <0.05) at 95% confidence interval according to ANOVA. Determination coefficient values indicates how strong is the linear relationship between the factors with each other. The model showed a sufficiently good determination coefficient (R² = 0.98) and a sufficiently good adjusted determination coefficient (R² adjusted = 0.96) that represents the proportion of the variance for a dependent variable that is explained by an independent variable, and a reasonable prediction determination coefficient was obtained (Q² = 0.82), showing that the prediction overall performance of the proposed model is appropriate.

 

Polynomial equations are graphically represented in Fig. 4 by a response surface and a contour plot generated with Eq. (2). A response surface plot displays the interaction between 2 elements and their effect on the response in a 3 dimensions shape. A more simplified plot is the contour plot, which reviews the interactions in 2 dimensions which makes it easier to read. Both the response surface plot and the contour plot in the proposed method have a saddle shape. The red region in both plots represents the region of maximum fluorescence intensity. The optimal experimental conditions obtained from this study were (0.05 M) phosphate buffer with an adjusted buffer pH of (9.7) and a temperature of (7.4°C±0.5).

 

Fig. 4: Effects of temperature and buffer pH on the fluorescence intensity (a) contour plot and (b) response surface plot.

 

 

Spectrofluorimetric method validation:

Linearity and range:

The method was linear over a concentration range of 5-40ng/mL. The high value of the correlation coefficient = 0.9992 displays a good linear relationship. Descriptive information of the regression line confirmed low widespread mistakes of the estimation, slope (S_b)= 0.02, intercept (S_a) = 0.57 and residuals (S_y/x) = 0.68, which demonstrate an acceptable accuracy with low deviations of the calibration points.

 

Accuracy and precision:

The accuracy and inter-day and intra-day precision of the proposed method were determined by analysing standard solutions of MOX with concentrations of 20, 25 and 30 ng/ml. The satisfactory recovery % of 100.4-100.6% showed that the method was accurate.

 

The inter-day precision and intra-day precision were expressed as % RSD values not exceeding 2, indicating the high reproducibility of the results and the precision of the method.

 

Limits of detection (LOD) and quantitation (LOQ):

LOD and LOQ of MOX obtained by the proposed method were 1.80 and 5.45 ng/mL, respectively, proving the high sensitivity of the proposed method.

 

Robustness using Plackett-Burman design:

A second level PBD was used to determine the effect of 3 factors in only 11 experiments. The model was found to be statistically non-significant (P>0.05) at the 95% confidence interval.

 

Analysis of pharmaceutical samples:

A tablet of the commercial pharmaceutical Avalox® (with MOX content of 400 mg) was treated according to the procedure illustrated below sample preparation. The prepared samples were analysed by the proposed fluorescence method. To assess the accuracy of the method, a standard addition technique was carried out. The results were satisfactory, indicating that the additives in the tablets did not interfere with the analysis. The results of the pharmaceutical dosage form and standard addition are presented in Table 5.

Table 5: Results obtained by applying the proposed method for the determination of MOX in Avalox® tablets and the applying standard addition technique.

*Average of three determinations.

CONCLUSION:

In this study, a chemometric strategy was demonstrated to optimize the spectrofluorimetric determination of MOX. A 2-level full factorial design, CCD design and Plackett-Burman design were selected based on the experimental objective to develop a validated and highly sensitive method for the determination of MOX and evaluate the significant factors influencing the detection and the interactive effect among them. The advantages of this green method allow its application to the analysis of MOX in pharmaceutical dosage form and in quality control laboratories with no adverse environmental impacts.

 

CONFLICT OF INTEREST:

The authors declare that they have no conflict of interest.

 

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Received on 15.05.2020            Modified on 29.06.2020

Accepted on 27.07.2020         © RJPT All right reserved