Analysis of Hydrocortisone Acetate and Lidocaine

DRUG FORMULATIONS AND CLINICAL METHODS

Optimization and Validation of an RP-HPLC Method forAnalysis of Hydrocortisone Acetate and Lidocaine inSuppositories

BILJANA JANCIC-STOJANOVI� and ANDJELIJA MALENOVI�

University of Belgrade, Faculty of Pharmacy, Department of Drug Analysis, Vojvode Stepe 450, 11 152 Belgrade, Serbia

SLAVKO MARKOVI�

Medicines and Medical Devices Agency of Serbia, Vojvode Stepe 458, 11 152 Belgrade, Serbia

DARKO IVANOVI�

University of Belgrade, Faculty of Pharmacy, Department of Drug Analysis, Vojvode Stepe 450, 11 152 Belgrade, Serbia

MIRJANA MEDENICA

University of Belgrade, Faculty of Pharmacy, Department of Physical Chemistry, Vojvode Stepe 450, 11 152 Belgrade,

Serbia

An RP-HPLC method has been optimized and

validated for the simultaneous determination of

hydrocortisone acetate and of lidocaine in

suppositories. For the method optimization,

response surface methodology was applied, and

the obtained model was tested using analysis of

variance. The optimal separations were conducted

on a Beckman-Coulter 150 � 4.6 mm, 5 �m particle-

size column at 20°C. The mobile phase was

methanol–water (65 + 35, v/v), pH adjusted to 2.5

with 85% orthophosphoric acid, with a flow rate of

1.0 mL/min. UV detection was performed at

250 nm. Phenobarbital was used as an internal

standard. The method was validated for selectivity,

linearity, precision, and robustness.

Hydrocortisone acetate is a natural corticosteroid

hormone with anti-inflammatory activity. Lidocaine,

a local anesthetic drug that is a derivative of

acetanilide, also can be used as an antiarrythmic drug. A

mixture of hydrocortisone acetate and lidocaine in

suppositories is usually used for treatment of hemorrhoids.

In the literature, various spectrophotometric methods for

the determination of hydrocortisone (1, 2) and

lidocaine (3–5), as well as their simultaneous determination in

suppositories (6), can be found. For the determination of

hydrocortisone in the different mixtures, a TLC method (7, 8)

was proposed. Lidocaine and other local anesthetic drugs

were determined by applying GC methods (9, 10).

Determination of hydrocortisone in pharmaceuticals (11–14)

and biological fluids (15–18) was performed using HPLC

with different methods of detection. Lidocaine, alone and in

mixtures with other drugs, was analyzed by HPLC (19–35).

Multivariate regression methods in support of HPLC

determination of hydrocortisone and lidocaine in

pharmaceuticals were used (36). The aim of this investigation

was optimization and validation of the new RP–HPLC

method for the simultaneous determination of hydrocortisone

acetate and lidocaine in a pharmaceutical dosage form.

Experimental

Reagents and Samples

All reagents used were of analytical grade. Methanol,

gradient grade (Merck, Darmstadt, Germany); water, HPLC

grade (Simplicity 185 Water Purification System; Millipore

Corp., Billerica, MA); and 85% orthophosphoric acid (Carlo

Erba, Milan, Italy) were used to prepare the mobile phase.

Xyloproct® suppositories (containing 5 mg hydrocortisone

acetate and 60 mg lidocaine) were manufactured by Astra,

Södertälje, Sweden. The standard of hydrocortisone acetate

was a chemical reference standard, and the working standard

of lidocaine was obtained from Astra.

Chromatographic Conditions

The Hewlett Packard 1100 chromatographic system

consisted of an HP 1100 pump, HP 1100 UV–Vis detector,

and HP ChemStation software. Separations were performed

on a Beckman-Coulter C18 (Fullerton, CA) 150 � 4.6 mm,

5 �m particle-size column at 20°C. The mobile phase for the

method validation was methanol–water (65 + 35, v/v) with a

flow rate of 1.0 mL/min; the pH of the mobile phase was

adjusted to 2.5 with orthophosphoric acid. UV detection was

performed at 250 nm, and phenobarbital was used as an

internal standard. The samples were introduced through a

Rheodyne injector valve with a 20 �L sample loop

(Perkin-Elmer Inc., Waltham, MA).

102 JANCIC-STOJANOVI� ET AL.: JOURNAL OF AOAC INTERNATIONAL VOL. 93, NO. 1, 2010

Received May 27, 2008. Accepted by SW December 8, 2008.Corresponding author’s e-mail: [email protected]

Software

Data analysis and construction of three-dimensional (3-D)

graphs were performed by using Statistica 7.0.0. (StatSoft

Inc., Tulsa, OK).

Standard Solutions

For producing the calibration curves, eight solutions were

prepared at concentrations of 5, 10, 15, 20, 25, 30, 35, and

40 �g/mL for hydrocortisone acetate; and 50, 75, 100, 125, 150,

175, 200, and 225 �g/mL for lidocaine. Each solution contained

phenobarbital as an internal standard at a concentration of

150 �g/mL. All solutions were injected in triplicate.

Precision Evaluation

To prove the validity and applicability of the proposed

RP–HPLC method, a laboratory mixture of hydrocortisone

acetate and lidocaine was made at a ratio that corresponded to

the Xyloproct suppositories. For the quantitative analysis of

the mixture, three series (7.5, 10, and 15 �g/mL for

hydrocortisone acetate and 150, 180, and 200 �g/mL for

lidocaine) were prepared, with 10 solutions for each

concentration. Each solution contained internal standard at a

concentration of 150 �g/mL.

Sample Solutions

Using Xyloproct suppositories, 10 solutions were prepared

at concentrations of 10 �g/mL for hydrocortisone acetate and

180 �g/mL for lidocaine. Phenobarbital was added as an

internal standard (150 �g/mL). The resulting solutions were

injected onto the column.

Determination of LOQ and LOD

The series of solutions for determining LOQ and LOD

were made from the 5 �g/mL solution of hydrocortisone

acetate and 50 �g/mL solution of lidocaine until S/N values

of 3:1 for LOD and 10:1 for LOQ were achieved. Six

replications were done at appropriate concentrations.

JANCIC-STOJANOVI� ET AL.: JOURNAL OF AOAC INTERNATIONAL VOL. 93, NO. 1, 2010 103

Table 1. Plan of experiments for simultaneous determination of the influence of methanol content and pH of the

mobile phase on the selectivity factor

pH

MeOH, %a

2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

50 13.27 11.79 11.01 10.96 10.76 10.74 10.44 10.29

55 14.28 10.81 10.65 10.65 9.85 9.99 9.75 9.66

60 6.44 5.93 6.84 6.84 7.59 7.60 8.06 8.01

65 4.95 5.21 4.92 4.92 5.62 5.56 5.90 6.23

70 3.43 3.76 3.80 3.79 3.86 3.83 3.96 4.03

75 2.67 2.97 3.08 3.08 3.10 3.48 3.43 3.32

80 1.99 1.91 2.37 2.37 2.24 2.26 2.24 2.13

85 1.77 1.93 1.91 1.91 1.94 1.97 2.02 2.03

a MeOH = Methanol.

Table 2. Plan of experiments for simultaneous determination of the influence of methanol content and temperature

on the selectivity factor

T, �C

MeOH, % 20 25 30 35 40 45 50 55

50 13.27 11.88 10.73 10.21 10.22 7.69 8.42 6.32

55 11.28 10.20 9.26 8.50 7.72 7.28 6.74 6.34

60 6.23 5.95 5.31 5.07 4.64 4.56 4.19 4.18

65 4.95 4.75 4.39 4.17 3.89 3.75 3.48 3.44

70 3.43 3.45 3.11 3.01 2.87 2.82 2.65 2.63

75 2.60 2.57 2.47 2.44 2.29 2.28 2.17 2.14

80 1.99 1.98 1.89 1.87 1.80 1.78 1.73 1.77

85 1.77 1.79 1.74 1.73 1.68 1.68 1.61 1.63

Results and Discussion

Optimization is one of the most important steps in method

development. Depending on the method studied, several

optimization procedures may be applied. In this work,

optimization of chromatographic conditions was achieved by

applying response surface methodology (RSM). RSM is a

collection of mathematical and statistical techniques useful for

analyzing problems for which several independent variables

influence a dependent variable or response, and the goal is to

optimize this response (37).

The stages in the application of RSM as an optimization

technique are as follows: (1) selection of independent

variables of major effects on the system through preliminary

studies and the delimitation of the experimental region,

according to the objective of the study and the experience of

the researcher; (2) definition of the plan of experiments and

carrying out the experiments; (3) mathematical–statistical

treatment of the obtained experimental data through the fit of a

polynomial function; (4) evaluation of the model`s fitness;

(5) verification of the necessity and possibility of performing

a displacement in direction to the optimal region; and

(6) obtaining the optimum values for each studied

variable (38).

The first step was selecting independent variables through

preliminary studies and the delimitation of the experimental

region. According to physical and chemical properties of

hydrocortisone acetate and lidocaine as well as literature data,

some chromatographic conditions were set. The lipophilic

character of the investigated compounds suggested a nonpolar

stationary phase and an acidic pH of the mobile phase. As an

organic modifier, both methanol and acetonitrile could be used,

but better performance was obtained using methanol. As

independent variables, methanol content in the mobile phase,

pH of the mobile phase, and temperature were chosen.

Selectivity factor, an important chromatographic parameter,

was selected to be the dependent variable that defines the level

of chromatographic separation and run time. Then, a region

over which each factor was to be studied was defined. Influence

of methanol content was investigated from 50 to 85%, pH of the

mobile phase from 2.0 to 5.0, and temperature from 20 to 50°C.

At the same time, influence of methanol content/pH of the

mobile phase and methanol content/temperature on the

selectivity factor were investigated.

The second step was selection and definition of the most

adequate plan of experiments. In order to get adequate surface for

each selected variable in the defined experimental region,

experiments were done according to Tables 1 and 2. On the basis

of our previously reported work (39–41), accomplishment of 64

experiments proved to be the most appropriate approach in the

examination of factor constraints. In that way, the possibility of

displacement of the maximum point outside the experimental

region is definitely avoided. The plans of experiments and

obtained data are given in Tables 1 and 2.

The obtained experimental data were subjected to

mathematical–statistical treatment through the fit of a

polynomial function. In case the two factors influence an

investigation, the relationship between the factors and the

response can be presented as a second-order polynomial of the

following form:

y = bo + b1x1 + b2x2 + b3x3 +....+ bNxN + bNxN +

b12x1x2 + b13x1x3 + b23x2x3 +...+

b(N–1)NxN–1xN+ b11x12 + b22x2

2 +...+bNNxN2


Figure 1. 3-D graph � = f (% methanol, pH); � =selectivity factor.

Figure 2. 3-D graph � = f (% methanol, temperature);

� = selectivity factor.

where y represents the estimated response (selectivity factor),

b0 is the constant term, the coefficients b1 to bN are the

estimated effects of the factors considered, and the extent to

which these terms affect the performance of the method is

called the main effect; the coefficients b12 to b(N-1)N are called

the interaction terms, the b11 to bNN represent the coefficients

of the quadratic terms, and the x1 to xN represent the variables.

The number of model parameters is defined by the number

of investigated factors (42, 43). On the basis of the performed

experiments, coefficients were calculated characterizing the

second-order polynomials, and 3-D graphs were constructed

as well.

For the methanol content/pH of the mobile phase, the

determined R2 value of 0.89 (89%) confirmed that

experimental data were well fit by the model (e.g., an R2 close

to 1.0 indicates that we have accounted for almost all of the

variability with the variables specified in the model). The

equation for � was obtained:

z = 61.268 – 1.261x – 2.287y + 0.007x2 +

0.021xy + 0.11y2

where x is the content of methanol, y is the pH of the mobile

phase, and z is the selectivity factor for hydrocortisone

acetate/lidocaine. The 3-D graph is presented in Figure 1.

For the methanol content/temperature of the system, the R2

of 0.84 (84%) confirmed that experimental data were well fit

by the model. The equation for � was obtained:

z = 70.958 – 1.494x – 0.43y + 0.008x2 +

0.005xy + 0.001y2

where x is the methanol content, y is the temperature, and z is

the selectivity factor for hydrocortisone acetate/lidocaine. The

3-D graph is presented in Figure 2.

The obtained equations gave valuable information about

the influence of the investigated factors on the

chromatographic separation. The 3-D graphs (Figures 1 and

2) present those influences. For the evaluation of the derived

model's fitness, the experimentally obtained values of the

Fisher ratio (F-ratio) were calculated, and the results for the %

methanol/pH of the mobile phase influence are presented in

Table 3. Analysis of variance (ANOVA) results for %

methanol/temperature are given in Table 4.

According to F-ratio values (F <Ftab), temperature

influence in the investigated range can be neglected and the

method can be considered to be robust from 20–55°C. A large

influence of methanol content in the mobile phase can be

observed by analyzing Figures 1 and 2, as well as on the basis

of the F-ratio value (F >Ftab). An optimal methanol content of

65% was chosen because optimal separation of the

investigated substances and separation time were obtained.

Also, lower methanol content resulted in deterioration of the


Table 3. ANOVA for the influence of methanol/pH on the selectivity factor

Source of variation Sum of squares d.f.a Mean square F-ratiob

Main effects 755.881 14 53.991 94.413

Methanol, % 753.761 7 107.680 188.296

pH 2.119 8 0.302 0.530

Residual 28.021 49 0.571

Total 783.902 63

a d.f. = Degrees of freedom.b Ftab = 2.203 (�1 = 7, �2 = 49).

Table 4. ANOVA for the influence of methanol/temperature on the selectivity factor

Source of variation Sum of squares d.f.a Mean square F-ratiob

Main effects 558.642 14 39.903 57.548

Methanol, % 528.226 7 75.460 108.829

Temperature, °C 30.416 8 4.345 6.267

Residual 33.975 49 0.693

Total 592.618 63

a d.f. = Degrees of freedom.b Ftab = 2.203 (�1 = 7, �2 = 49).

lidocaine peak, i.e., the basic character of the substance

caused its attachment to free silanol groups of the column

packing, and for that reason tailing appeared. Moreover,

acidic pH was convenient for chromatographic analysis of

both substances, and it was set at 2.5.

Method optimization is very important for its further

application; investigating and defining the chromatographic

behavior of investigated substances enables prediction of all

changes that can happen due to a change of investigated

factors. Nowadays, robustness is verified earlier in the

lifetime of a method, i.e., at the end of method development or

at the beginning of the validation procedure (44). For that

reason, robustness limits were defined as the experimental

region where the response of interest (selectivity factor) is not

influenced by changing significantly the levels of the

operating factors. Method robustness can be proven by visual

inspection of 3-D graphs for selectivity factor surface (38),

and it was concluded that the methanol content can vary

from 64 to 66% (v/v) and the pH of the mobile phase from 2.0

to 3.0.

After establishing the optimal conditions for the separation

and the definition of the robustness limits, the selectivity,

linearity, precision, LOD, and LOQ were determined. The

chromatogram of a laboratory mixture is presented in

Figure 3.

The assay was selective, because no significant interfering

peaks were observed at the retention times of hydrocortisone

acetate, lidocaine, and the internal standard. All excipients

were eluted at different times and did not interfere with the

analyzed compounds.

Linear relationships of the peak area over the concentration

range from 5 to 40 �g/mL for hydrocortisone acetate and 50 to

225 �g/mL for lidocaine were obtained. The important

calibration curve parameters, slope (a), intercept (b), R, and

SD of the intercept (Sb), are presented in Table 5.

The results for precision and accuracy of the proposed

RP–HPLC method are given in Table 6. The values of SD,

RSD, and R indicate that the assay was precise and accurate.

The validated method was then applied for content

determination, and the obtained results were 96.63% for

hydrocortisone acetate and 101.47% for lidocaine.

The detection sensitivity was demonstrated by

experimental determination of LOD. The LOQ is the lowest

concentration of substance that can be quantified with

acceptable precision and accuracy. The values for LOD and

LOQ are given in Table 5.


Figure 3. Representative chromatogram ofhydrocortisone acetate (a), lidocaine (b), and internalstandard (c).

Table 5. The important parameters for the calibration

curves

ParameterHydrocortisone

acetate Lidocaine

Concn range, �g/mL 5–40 50–225

y = ax + b 52.9x + 4.95 3.02x – 18.5

R2

1.000 0.9996

Sb 0.006 0.006

LOQ, �g/mL 0.45 5.6

LOD 50 ng/mL 0.37 �g/mL

Table 6. Precision and accuracy of the RP-HPLC method for assay of Xyloproct suppositories

Compound Injected, �g/mL Found, �g/mL � SD RSD, % R, %a t�b

Hydrocortisone acetate 7.5 7.52 ± 0.07c

0.93 100.25 0.09

10 10.03 ± 0.05 0.50 100.33 1.89

15 14.95 ± 0.08 0.53 99.66 1.97

Lidocaine 150 148.92 ± 1.60c

1.07 99.28 2.13

180 178.65 ± 1.58 0.88 99.25 2.34

200 200.97 ± 1.53 0.76 100.48 2.00

a R = Recovery.b t� = Certainty.c n = 10.

Conclusions

Method optimization is the best way to define optimal

chromatographic conditions and factors that most influence

the system. The applied mathematical model can be used for

detailed definition of the chromatographic behavior of the

investigated substances, giving the possibility for predicting

the influences of variations in chromatographic conditions.

Determined values of the coefficient of determination are able

to confirm if the model is good for fitting experimental data.

The proposed RP-HPLC method permits simultaneous

determination of hydrocortisone acetate and lidocaine

because of a good separation of the chromatographic peaks.

Because of its selectivity, the method is applicable to the

qualitative and quantitative analysis of Xyloproct

suppositories. The results obtained were in good agreement

with the declared contents. Results were accurate and precise,

as confirmed by the statistical parameters.

Acknowledgments

We thank the Ministry of Science for supporting these

investigations in Project 142077G.

References

(1) Bonazzi, D., Andrisano, V., Gatt, R., & Cavrini, V. (1995) J.

Pharm. Biomed. Anal. 13, 1321–1329

(2) Amin, A.S. (1996) Anal. Lett. 29, 1527–1537

(3) Cruz, A., Lopezrivadula, M., Bermejo, A.M., Sanchez, I., &

Fernandez, P. (1994) Anal. Lett. 27, 2663–2675

(4) Saleh, G., & Ascal, H.F. (1995) Anal. Lett. 28, 2663–2671

(5) Rizk, M., Issa, Y., Shoukry, A.F., & Atia, E.M. (1997) Anal.

Lett. 30, 2743–2753

(6) Medenica, M., Ivanovi�, D., Markovi�, S., Malenovi�, A., &

Janci�, B. (2004) Pharm. Ind. 66, 330–333

(7) Gagliardi, L., De Orsi, D., Giudice, M.R.D., Gatta, F., Porrá,

R., Chimenti, P., & Tonelli, D. (2002) Anal. Chim. Acta 457,

187–198

(8) Datta, K., & Das, S.K. (1994) J. AOAC Int. 77, 1435–1438

(9) Lorec, A.M., Banguerolle, B., Attolini, L., & Roucoules, X.

(1994) Ther. Drug Monit. 16, 592–595

(10) Demedts, P., Yauters, A., Franck, F., & Neels, H. (1996)

Ther. Drug Monit. 18, 208–209

(11) Miscicka, M., Sadlej-Sosnowska, N., & Wilczynska-

Wojutelewucz, I. (1990) Acta Pol. Pharm. 47, 25–28

(12) Chauhan, V., & Conway, B. (2005) Chromatographia 61,

555–559

(13) Hájková, R., Solich, P., Dvo�ák, J., & �ícha, J. (2003) J.


(14) Chen, D., Cao, G.Y., & Sun, C.H. (2007) Chinese Pharm. J.

42, 1431–1433

(15) Glowka, F.K., & Hermann, T.W. (1999) Chem. Anal. 44,

373–380

(16) Hay, M., & Mormed, P. (1997) J. Chromatogr. B 702, 33–39

(17) Grippa, E., Santini, L., Castellano, G., Gatto, M.T., Leone,

M.G., & Saso, L. (2000) J. Chromatogr. B 738, 17–25

(18) Majid, O., Akhlaghi, F., Lee, T., Holt, D.W., & Trull, A.

(2001) Ther. Drug Monit. 23, 163–168

(19) Klein, J., Fernandes, D., Gazarian, M., Kent, G., & Koren, G.

(1994) J. Chromatogr. B 655, 83–88

(20) Sattler, A., Kramer, I., Jage, J., Vrana, S., Kleemann, P.P., &

Dick, W. (1995) Pharmazie 50, 741–744

(21) Venkateshwaran, T.G., & Steward, J.T. (1995) J. Liq.

Chromatogr. 18, 565–578

(22) Achilli, G., Callerino, G.P., Deril, G.V.M., & Tagliaro, F.

(1996) J. Chromatogr. A 729, 273–277

(23) Antz, O., & Oztop, F. (1997) Anal. Lett. 30, 565–584

(24) Drewe, J., Eufer, S., Huwyler, J., & Kusters, E. (1997) J.

Chromatogr. B 691, 105–110

(25) Sattler, A., Jage, J., & Kramer, I. (1998) Pharmazie 53,

386–391

(26) Wiberg, K., Hagman, A., & Jacobsson, S.P. (2003) J. Pharm.

Biomed. Anal. 30, 1575–1586

(27) Laiwruangrath, S., Laiwruangrath, B., & Pibool, P. (2001) J.


(28) Gebauer, M.G., McClure, A.F., & Vlahakis, T.L. (2001) Int.

J. Pharm. 233, 49–54

(29) Dal Bo, L., Mazzucchelli, P., & Marzo, A. (1999) J.

Chromatogr. A 854, 3–11

(30) Kang, L., Jun, H.W., & McCall, J.W. (1999) J. Pharm.

Biomed. Anal. 19, 737–745

(31) Salas, S., Talero, B., Rabasco, A.M., & González-Rodríguez,

M.L. (2008) J. Pharm. Biomed. Anal. 47, 501–507

(32) Dincel, A., & Basci, N.E. (2007) Chromatographia 66,

S81–S85

(33) Zivanovic, L., Zecevic, M., Markovic, S., Petrovic, S., &

Ivanovic, I. (2005) J. Chromatogr. A 1088, 182–186

(34) Malenovi�, A., Medenica, M., Ivanovi�, D., Janci�, B., &

Markovi�, S. (2005) Il Farmaco 60, 157–161

(35) Malenovi�, A., Ivanovi�, D., Medenica, M., & Janci�, B.

(2004) Acta Chim. Slov. 51, 559–566

(36) Lemus Gallego, J.M., & Pérez Arroyo, J. (2002) Anal.

Bioanal. Chem. 374, 282–288

(37) Douglas, C.M. (1976) Design and Analysis of Experiments,

John Wiley & Sons, Inc., Hoboken, NJ

(38) Bezzera, M.A., Santelli, R.E., Oliveira, E.P., Villar, L.S., &

Escaleira, L.A. (2008) Talanta 76, 965–977

(39) Ivanovi�, D., Medenica, M., Janci�, B., Malenovi�, A., &

Markovi�, S. (2004) Chromatographia 60, S87–S92

(40) Medenica, M., Janci�, B., Ivanovi�, D., & Malenovi�, A.

(2004) J. Chromatogr. A 1031, 243–248

(41) Janci�, B., Ivanovi�, D., Medenica, M., & Malenovi�, A.

(2005) Chromatographia 62, 233–238

(42) Brereton, R.G. (2003) Chemometrics–Data Analysis for the

Laboratory and Chemical Plant, John Wiley & Sons Ltd,

The Atrium, Chichester, UK

(43) Deming, S.N., & Morgan, S.L. (1993) Experimental Design:

A Chemometric Approach, Elsevier, Amsterdam, The

Netherlands

(44) Vander Heyden, Y., Nijhuis, A., Smeyers-Verbeke, J.,

Vandeginste, B.G.M., & Massart, D.L. (2001) J. Pharm.

Biomed. Anal. 24, 723–753


Analysis of Hydrocortisone Acetate and Lidocaine

Documents

Transcript of Analysis of Hydrocortisone Acetate and Lidocaine