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
104 JANCIC-STOJANOVI� ET AL.: JOURNAL OF AOAC INTERNATIONAL VOL. 93, NO. 1, 2010
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
JANCIC-STOJANOVI� ET AL.: JOURNAL OF AOAC INTERNATIONAL VOL. 93, NO. 1, 2010 105
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.
106 JANCIC-STOJANOVI� ET AL.: JOURNAL OF AOAC INTERNATIONAL VOL. 93, NO. 1, 2010
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.
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