analysis of pramipexole and its five impurities

SPECIAL GUEST EDITOR SECTION

Validation of a Column Liquid Chromatographic Method for theAnalysis of Pramipexole and Its Five Impurities

ANDJELIJA MALENOVI�, BILJANA JAN�I�-STOJANOVI�, ANA VEMI�, and DARKO IVANOVI�

Vojvode Stepe 450, Faculty of Pharmacy, Department of Drug Analysis, 11152 Belgrade, Serbia MIRJANA MEDENICA Vojvode Stepe 450, Faculty of Pharmacy, Department of Physical Chemistry, 11152 Belgrade, Serbia

In this paper, a previously optimized method forHPLC analysis of pramipexole and its impuritieswas subjected to method validation in accordancewith official regulations. The optimizedchromatographic conditions were as follows:mobile phase acetonitrile–water phase [15 + 85,v/v, water phase contained 1% triethylamine (TEA),pH adjusted to 7.0 with orthophosphoric acid];detection at 262 nm for pramipexole, BI-II 751 xx,BI-II 786 BS, BI-II 820 BS, and 2-aminobenzo-thiazole and at 326 nm for BI-II 546 CL; columntemperature, 25°C; and flow rate, 1 mL/min.Acetonitrile and TEA content, pH of the waterphase, flow rate, column temperature, and columntype were factors studied in robustness testing.According to the experimental plan defined by aPlackett-Burman design, five dummy variableswere added in order to have 12 factors. As output,resolution factor was chosen. Robustness wasassessed by graphical (half-normal probabilityplots and Pareto charts) and statistical (t-test)methods. Also, nonsignificance intervals forsignificant factors were estimated, and limits forthe system suitability test were determined. Finally, linearity, accuracy, and precision of the proposedHPLC method were defined. LOD and LOQ valuesfor analyzed impurities were determined. Themethod was completely defined by theseexperiments.

The objective of any method used in pharmaceuticalanalysis is to generate reliable and accurate dataregardless of whether it is for the acceptance of raw

materials, release of the drug substances and products,in-process testing for QA, or establishment of the expirationdating period. Validation of a method is the process by whicha method is tested for reliability, accuracy, and precision for

its intended purpose. The method should also be validated toensure its robustness. Robustness testing could be done as theconcluding part of method optimization or after methodvalidation. Both approaches can be found in literature, butmany earlier studies concluded that robustness testingbefore validation has more advantages, e.g., less moneyand time would be spent (1). Generally, robustness testingenables anticipation and avoidance of different problemsduring a method transfer from one laboratory to another. Asdefined by the International Conference on Harmonization(ICH) of Technical Requirements for the Registration ofPharmaceuticals for Human Use, the robustness of ananalytical procedure refers to its capability to remainunaffected by small and deliberate variations in methodparameters (2). In accordance with this definition, the mainpoint of robustness testing is investigation of factors thatinfluence important parameters of a method.

In this paper, a previously optimized RP-HPLC method for chromatographic separation of pramipexole and its relatedsubstances was subjected to validation. Initially, robustnesswas tested using a Plackett-Burman design. Data obtainedfrom the design were estimated using graphical and statisticalmethods in order to give a better view of the influence of theanalyzed factors. Using the appropriate equations,nonsignificance intervals for significant factors wereestimated, and limits for system suitability tests weredetermined. Furthermore, the linearity, accuracy, andprecision of the proposed HPLC method were estimated.Concomitantly, LOD and LOQ values were determined, andthe method was completely validated.

Pramipexole constitutes a new drug in thetherapy for Parkinson’s disease. Chemically, it is(S)-2-amino-4,5,6,7-tetrahydro-6-(propylamino) benzo-thiazole. Chemical structures of the active substance and itsfive analyzed related substances are presented in Figure 1. Inour previous paper, determination of pKa values forpramipexole, BI-II 546 CL, BI-II 751 xx, and2-aminobenzothiazole (2-ABT) were determined employingRP-HPLC (3). Also, for the same mixture, experimentaldesign was applied for optimization of the RP-HPLCseparation (4). Some other papers dealing with pramipexoleanalysis were found in literature. Pramipexole and itsenantiomer were separated using normal-phase HPLC (5).

1102 MALENOVI� ET AL.: JOURNAL OF AOAC INTERNATIONAL VOL. 93, NO. 4, 2010

Guest edited as a special report on “New and Improved HPLC Methodsfor Drug Formulations and Clinical Analysis” by Danica Agbaba andJoseph Sherma.

Corresponding author’s e-mail: [email protected]

Analysis of pramipexole in biological samples wasperformed using HPLC with atmospheric pressure chemicalionization–tandem mass spectrometry (6) and withelectrochemical and UV detection (7). In this paper, the mainnovelty is the successful separation of pramipexole and its fiveimpurities, while in the two previously published papers (3, 4),only three impurities were determined. Also, for robustnesstesting a Plackett-Burman design was successfully applied, andthe respective statistical methods for objective evaluation of theresults were obtained.

Theory of Robustness Testing

According to Vander Heyden et al. (1), the followingsteps can be identified during the robustness study:(1) identification of the factors to be tested, (2) definition ofthe different levels for the factors, (3) selection of theexperimental design, (4) definition of the experimentalprotocol (complete experimental setup), (5) definition of theresponses to be determined, (6) execution of the experimentsand determination of the responses of the method,(7) calculation of effects, (8) statistical and/or graphicalanalysis of the effects, and (9) drawing chemically relevantconclusions from the statistical analysis and, if necessary,taking measures to improve the performance of the method.In this paper, a Plackett-Burman design was applied, and all

necessary data connected with the design and appropriatesteps are given below.

Plackett-Burman Design

A Plackett-Burman design can examine up to –1 factors inN experiments, with N being a multiple of 4. A design with12 experiments examines up to 11 factors. When the numberof examined factors is smaller than –1, the design is completed with dummy factors. These are imaginary factors, whoseeffect estimates occasionally can be used in a statisticalinterpretation of the factor effects (1, 8).

Analysis of a Plackett-Burman Design

Factor effects are calculated (1, 8–11) as:

EY Y

Nx =

+ - -åå ( ) ( )

2 (1)

where Ex is the effect of factor X; Y ( )+å and Y ( )-å are thesums of the responses, where factor X is at (+) or (–) level,respectively; and N is the number of design experiments.

For the identification of significant effects, both graphicaland statistical interpretation methods are described. Graphicalpresentation using normal and half-normal probability plotscould be made (8, 10, 11). Half-normal probability plots are

MALENOVI� ET AL.: JOURNAL OF AOAC INTERNATIONAL VOL. 93, NO. 4, 2010 1103

Figure 1. Chemical structures of the investigated substances.

more useful than normal probability plots. They follow thesame principle as the full normal probability plot, except thatthe sign of the effect is ignored in plotting. Therefore, in orderto create the half-normal plot, the n effects are ranked in asequence according to an increasing absolute size of theeffect. Unimportant factors are those that have near-zeroeffects, and important factors are those with effectsconsiderably removed from zero (12). Thus, unimportanteffects that tend to have a normal distribution centered nearzero are normally distributed around the straight line, whilesignificant effects deviate from it (1).

Although in the selection of statistically significant effectshalf-normal plots are strongly recommended, in some cases aPareto chart can be very valuable. In order to evaluate whether a given Ex is significantly different from zero, the followingtest statistic is calculated (1, 11):

( )t

E

S E

x

e

=. .

(2)

where Ex is the effect of the factor X and (S.E.)e is the standarderror of the effect. The calculated test statistic (Equation 2) iscompared with a tabulated t-value, ttab. An effect with t ³ ttab is significant, while t £ ttab suggests a nonsignificant effect.From Equation 2, it can be derived that:

( )E t S Ecritical tab e= . . (3)

where Ecritical is the critical effect for a response (1, 11). Factor effects are significant if the value of Ex is larger than or equalto Ecritical. The number of degrees of freedom (df) for (S.E.)and the applied significance level a determine ttab. The error(S.E.)e can be estimated in many ways (1, 11). In this paper,the estimations based on negligible effects and the algorithmof Dong were used, and for that reason it will be furtherexplained.

The error based on negligible effects is estimated as:

( )S EE

ne

N

N

. . =å 2

(4)


Figure 2. Chromatogram of BI-II 820 BS (1.435 min), pramipexole (1.738 min), unknown impurity 1 (2.010 min),unknown impurity 2 (2.338 min), BI-II 546 CL (2.929 min), BI-II 786 BS (3.439 min), BI-II 751 xx (5.112 min), and2-aminobenzothiazole (15.012 min).

Table 1. Experimental variables tested for robustness

Variable Lower value (–1) Nominal value (0) Upper value (+1)

Acetonitrile, % 14 15 16

Content of TEA, % 0.9 1.0 1.1

pH of the mobile phase 6.8 7.0 7.2

Flow rate, mL/min 0.9 1.0 1.1

Temperature, °C 20 25 30

Column C18a Zorbax Eclipse Zorbax Extend Zorbax Extend

Dummy 1 –1 0 +1

Dummy 2 –1 0 +1

Dummy 3 –1 0 +1

Dummy 4 –1 0 +1

Dummy 5 –1 0 +1

a 4.6 ´ 250 mm, 5 mm particle size column.

where EN represents the dummy effect from aPlackett-Burman design and nN the number of negligibleeffects. The number of df for ttab is equal to the number ofdummy effects used to estimate (S.E.)e. The value of ttab alsodepends on the significance level applied, usually a = 0.05 or0.01. Only for designs that include at least three negligiblefactor effects is it recommended to apply this error estimation(1, 11).

In the algorithm of Dong (1, 8, 11), from an initial estimateof error based on all effects, s0, a final estimation of standarderror, s1, is calculated (Equations 5 and 6) based on the effectsthat are considered not important. The latter allowsdetermination of critical effects, called the margin of error(ME; Equation 7):

s median E i0 15= ´. (5)

s m E j11 2= - å (6)

where Ei is the effect of the factor i, Ej an effect that in absolute value is smaller or equal to 2.5 ´ s0, and m the number of sucheffects, and:

( )E ME t scritical df= = ´-1 2 1a , (7)

where df = m and a = 0.05 or 0.01. Factor effects aresignificant if the value of Ex is larger than or equal to ME.

Estimating Nonsignificance Intervals for SignificantFactors

When a factor has a significant effect on a response, onecan wonder in which interval the factor levels should becontrolled to eliminate the effect (1). These levels can beestimated as:

XX X E

EX

X X Ecritical

X

critic

( )

( ) ( )

( )

( ) ( ),0

1 1

0

1 1

2-

-+

-- - al

XE2

é

ë

êê

ù

û

úú (8)

where X(0), X(1), and X(-1) are the real values of factor X for thelevels (0), (1), and (–1), respectively (1).


Table 2. Plan of Plackett-Burman design

Run

Factorsa

A B C D E F G H J K L

1 –1 –1 –1 –1 –1 –1 –1 –1 –1 –1 –1

2 +1 +1 +1 +1 +1 –1 +1 +1 –1 +1 +1

3 –1 +1 +1 –1 +1 +1 +1 –1 –1 –1 +1

4 +1 +1 –1 +1 +1 +1 –1 –1 –1 +1 –1

5 +1 –1 +1 +1 +1 –1 –1 –1 +1 –1 +1

6 +1 –1 +1 +1 –1 +1 +1 +1 –1 –1 –1

7 +1 +1 +1 –1 –1 –1 +1 –1 +1 +1 –1

8 –1 +1 +1 +1 –1 –1 –1 +1 –1 +1 +1

9 +1 +1 +1 +1 +1 +1 +1 +1 +1 –1 –1

10 –1 –1 –1 +1 –1 +1 +1 –1 +1 +1 +1

11 –1 –1 +1 –1 +1 +1 –1 +1 +1 +1 –1

12 +1 +1 –1 –1 –1 +1 –1 +1 +1 –1 +1

a A = Acetonitrile content, %; B = TEA content, %; C = dummy 1; D = pH of the mobile phase; E = dummy 2; F = flow rate, mL/min; G = dummy 3; H = temperature, °C; J = dummy 4; K = column; L = dummy 5.

Table 3. Obtained responses for resolution factorsa

Run R1 R2 R3 R4 R5

1 2.57 6.70 1.70 11.50 25.80

2 1.47 5.52 0.93 5.11 20.68

3 2.20 8.60 3.02 12.34 25.47

4 2.29 10.34 2.12 2.47 22.6

5 3.38 15.68 4.37 1.58 25.87

6 3.63 16.06 5.58 4.82 15.20

7 1.31 1.91 0.00 1.04 24.01

8 2.42 1.19 4.78 4.56 12.91

9 4.25 24.26 8.05 7.42 14.16

10 2.01 11.54 3.12 2.36 23.29

11 1.54 7.75 2.00 4.40 20.26

12 2.68 9.72 1.33 7.90 24.08

a R1 = Resolution factor between BI-II 820 BS and pramipexole, R2 = resolution factor between pramipexole and BI-II 546 CL, R3 = resolution factor between BI-II 546 CL and BI-II 786 BS, R4 = resolution factor between BI-II 786 BS and BI-II 751 xx, andR5 = resolution factor between BI-II 751 xx and 2-ABT.

Determination of System Suitability Test (SST)Limits from Robustness Test Results

According to ICH guidelines, SST limits are definedduring the robustness testing. Namely, the ICH guidelinesstate that “one consequence of the evaluation of robustnessshould be that a series of system suitability parameters(e.g., resolution tests) is established to ensure that the validityof the analytical procedure is maintained whenever used” (2).

The SST limit can be experimentally determined from theresult of one or several experiments performed at theseconditions. When the experiment is replicated, the SST limitcan be defined as the upper or lower limit from the one-sided95% confidence interval around the worst-case mean. Forresolution and retention factor, for instance, the lower limitwould be chosen, while for the tailing factor it would be theupper one. The confidence interval is defined as:

Y ts

nWorst case n- -- ¥

é

ëê

ù

ûúa , , ,1

when the lower limit has to be considered, and as:

0 1, ,Y ts

nWorst case n- -+ ×

é

ëê

ù

ûúa

when it is the upper one. If no significant effects wereoccurring for a response, then its SST limit can be determinedanalogously to the above situation, but the measurements willbe executed at nominal conditions (1).

Experimental

Chemicals

All reagents used were analytical grade. Acetonitrile(Lab Scan, Gliwice, Poland), orthophosphoric acid (CarloErba, Milan, Italy), triethylamine (TEA; Acros Organics,Geel, Belgium), and water (HPLC grade) obtained fromSimplicity 185 (Millipore, Billerica, MA) were used toprepare the mobile phase. Working standards of pramipexole,BI-II 751 xx, 2-ABT, BI-II 786 BS, BI-II 820 BS, and BI-II546 CL were kindly donated by Boehringer IngelheimPharma (Ingelheim am Rhein, Germany).

Standard Solutions

Stock solutions were prepared by dissolving the respectiveworking standard substances in mobile phase to obtain aconcentration of 1 mg/mL for pramipexole and 10 mg/mL forall of the analyzed impurities.

Solutions for Estimating the Selectivity

To prove the selectivity of the proposed RP-HPLCmethod, a placebo mixture that consisted of mannitol, maizestarch, anhydrous colloidal silica, povidone, and magnesiumstearate was prepared in mobile phase in the concentrationratio corresponding to the content in tablets. It was treated inthe same manner as the tablet mass used for the preparation ofthe sample solution. A standard solution mixture containing40 mg/mL pramipexole and 0.2 mg/mL of each impurity wasused to prove selectivity.


Table 4. Obtained factor effects and results for E critical and margin of error (ME)

Factor R1a R2 R3 R4 R5

Acetonitrile, % –0.0383 –0.1350 –1.3890 –3.277 1.758

TEA, % 0.0917 –1.2050 0.2665 0.9933 –1.3120

Dummy 1 –0.1320 –2.815 0.4160 –1.3370 –1.1480

pH 1.0350 6.4780 3.1730 –0.3180 –4.3480

Dummy 2 0.0850 4.1720 0.6630 0.1900 0.6250

Flow rate, mL/min –0.1750 1.4580 –0.4440 0.5130 1.2450

Dummy 3 –0.0017 2.7520 0.7340 0.1130 –1.4520

Temperature, °C 0.3712 1.6220 1.3900 0.4870 –6.6250

Dummy 4 0.0983 3.7420 0.1230 –2.6830 1.5020

Column –1.2780 –7.1280 –1.850 –4.2700 –1.1400

Dummy 5 –0.2380 –2.462 –0.3160 0.3670 1.7120

E critical (a = 0.05) 0.2717 6.5583 1.0151 2.7288 2.7150

E critical (a = 0.01) 0.4537 10.9523 1.6953 4.5570 4.5340

ME (0.975), mb 1.1446 8.1810 2.0927 3.9547 3.2247

ME (0.995), m 1.6152 11.5448 2.9766 5.6250 4.7709

a See Table 3 for definitions of R1 to R5.b m = Number of absolute effects smaller than 2.5 ´ s0, where s0 is the initial estimate of error based on all effects.

Solutions for Estimating the Linearity

For the calibration curve, eight solutions containingpramipexole as well as eight solutions for each of itsimpurities were prepared in mobile phase, from thecorresponding standard solution, in the concentration rangefrom 4 to 400 mg/mL for pramipexole and from 0.02 to2 mg/mL for all of the impurities.

Solutions for Estimating the Precision

To check the precision of the proposed RP-HPLC method,six standard solutions were prepared in mobile phase from thecorresponding standard solution. The mixture was preparedcontaining 40 mg/mL pramipexole and 0.2 mg/mL of eachimpurity.

Solutions for Estimating the Accuracy

A laboratory mixture containing the above-mentionedplacebo components and pramipexole was prepared in mobilephase in the ratio corresponding to the investigated tablets.Also, the investigated impurities were added at aconcentration corresponding to the maximal allowed content.The laboratory mixture was treated in the same manner as thetablet mass used for the preparation of sample solution. Forthe quantitative analysis of the laboratory mixture, three series of dilutions calculated as 80, 100, and 120% of theconcentrations corresponding to those in tablets wereprepared, with three solutions for each concentration mixture:Mixture 1 contained 32 mg/mL pramipexole and 0.16 mg/mL

of each impurity, Mixture 2 contained 40 mg/mL pramipexoleand 0.2 mg/mL of each impurity, and Mixture 3 contained48 mg/mL pramipexole and 0.24 mg/mL of each impurity.

Sample Solutions

A quantity of pulverized tablet mass corresponding to25 mg pramipexole was placed into a 25 mL volumetric flaskand extracted with the mobile phase using an ultrasonic bathfor 15 min. The volumetric flask was filled with mobile phaseto the mark, and the solution was filtered. From that stocksolution, six solutions containing 40 mg/mL pramipexole were prepared. The resulting solutions were injected into thecolumn.

Chromatographic Conditions

The Waters Breeze HPLC system consisted of a Waters1525 binary HPLC pump, a Waters 2487 UV-Vis dualabsorbance detector, and Breeze Software Windows XP fordata collection (Milford, MA). Separations were performedon a Zorbax Extend C18 4.6 ´ 150 mm, 5 mm particle sizecolumn with UV detection at 262 nm for pramipexole, BI-II751 xx, 2-ABT, BI-II 786 BS, and BI-II 820 BS, and at326 nm for BI-II 546 CL. The mobile phase consisted of anacetonitrile–water phase (15 + 85, v/v). The water phasecontained 1% TEA, and the pH was adjusted to 7.0 withorthophosphoric acid. The flow rate was 1.0 mL/min andcolumn temperature was 25°C. The samples were introducedthrough a Rheodyne injector valve with a 20 mL sample loop.


Figure 3. Half-normal probability plots (A: R1, C: R3, E: R5) and Pareto charts (B: R1, D: R3, F: R5).

Results and Discussion

The aim of this investigation was the analysis ofpramipexole and its five impurities under the optimizedchromatographic conditions published in our previouspaper (4). Three previously analyzed impurities (BI-II546 CL, BI-II 751 xx, and 2-ABT) and two additionalimpurities (BI-II 786 BS and BI-II 820 BS) wereconsidered (Figure 2). Under the optimizedchromatographic conditions, the order of elution was:BI-II 820 BS (1.43 min), pramipexole (1.74 min), BI-II546 CL (2.94 min), BI-II 786 BS (3.44 min), BI-II 751xx (5.11 min), and 2-ABT (15.01 min). The proposedRP-HPLC method enabled satisfactory separation, andthe method could be used for the analysis ofpramipexole and its five impurities. After the definitionof acceptable chromatographic conditions, the nextstage was robustness testing. Initially, robustness testing was done at the end of the method validation process toindicate important factors that could affect the results(reproducibility estimates) of an interlaboratory study.However, performing a robustness test late in thevalidation procedure involves the risk that, when amethod is found not to be robust, it should beredeveloped and optimized. At this stage, much effortand money have already been spent in the optimizationand validation, and therefore one wants to avoid this.Nowadays, robustness verification is shifted to anearlier point in the development of the method (1). So,with the aim of avoiding possible problems, robustnesstesting was done first. In our previous papers, theconclusions about a method’s robustness were madeemploying Response Surface Methodology (12–15) oran appropriate combination of experimental design andartificial neural networks (16, 17). Comparing all of theapplied approaches, priority could be given to aPlackett-Burman design because of the most useful data, including the most significant effects, nonsignificantinterval for significant effects, SST, etc., that can beextracted from it. All necessary data important forrobustness testing employing a Plackett-Burman designare given above in the Theory of Robustness Testingsection.

Testing was started by choosing the factors that aregoing to be analyzed. Factors and their lower, upper, and nominal values are given in Table 1. As can be seenfrom Table 1, six real factors were chosen and fivedummy variables added in order to set thePlackett-Burman design for 11 factors. The plan ofexperiments was created using DesignExpert 7.0software, and it is presented in Table 2.

As the most important parameter, resolution factorwas selected for evaluation. Results for resolutionfactors (R1—resolution factor between BI-II 820 BSand pramipexole; R2—resolution factor betweenpramipexole and BI-II 546 CL; R3—resolution factorbetween BI-II 546 CL and BI-II 786 BS; R4—resolution


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ahc

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aP

morf

R1

——

K—

—D

+ K

R2

K—

——

——

R3

K +

H +

D +

AK

+ D

DD

—D

R4

K +

D +

A—

K—

——

R5

D—

H +

DH

—H

+ D

a

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Table 6. Nonsignificant intervals for significant variables (dummy variable and Dong¢s methods)

Responsesa Significant factorbNonsignificant interval obtained from

Ecritical from dummies at a = 0.05Nonsignificant interval obtained from

Ecritical from Dong¢s algorithm at a = 0.05

R1 — — —

R2 — — —

R3 A 14.27–15.73 —

D 6.94–7.06 6.87–7.13

H 21.35–28.65 —

R4 A 14.17–15.83 —

D 6.83–7.17 —

R5 D 6.88–7.12 6.85–7.15

H — 21.35–28.65

a See Table 3 for definitions.b See Table 2 for definitions.

Table 7. Factor levels for the worst-case situation

Responsesa

Factors R1 R2 R3 R4 R5

Acetonitrile, % +1 –1 +1 +1 –1

Content of TEA, % +1 +1 +1 +1 +1

pH of the mobile phase –1 +1 –1 –1 +1

Flow rate, mL/min –1 –1 –1 –1 –1

Temperature, °C –1 +1 –1 –1 +1

Column +1 +1 +1 +1 –1

a See Table 3 for definitions.

Table 8. SST limits obtained from the worst-case situation

Run R1a R2 R3 R4 R5

1 1.31 1.19 0.0 1.04 14.16

2 1.42 1.23 0.15 1.17 14.35

3 1.36 1.30 0.10 1.20 14.27

Mean 1.36 1.24 0.08 1.14 14.26

SD 0.055 0.056 0.076 0.085 0.095

n 3 3 3 3 3

Limits

1.36 – 2.92 0055

3

. = 1.27 1.24 – 2.92

0056

3

. = 1.15 0.08 – 2.92

0076

3

. = 0.0 1.14 – 2.92

0085

3

. = 0.99 14.26 – 2.92

0095

3

. = 14.09

a See Table 1 for definitions.

factor between BI-II 786 BS and BI-II 751 xx; andR5—resolution factor between BI-II 751 xx and 2-ABT)obtained from 12 experiments are presented in Table 3.

The next step was the calculation of the factor’s effectsusing DesignExpert 7.0 software. The obtained results arepresented in Table 4. Using the appropriate equations, givenin the Theory of Robustness Testing section, Ecritical fora = 0.05 and a = 0.01 were calculated using dummyvariables, and ME for a suitable df was obtained employingthe algorithm of Dong (Table 4).

Concomitantly, graphical evaluation employinghalf-normal probability plots and Pareto charts was done. Theobtained graphs are presented in Figure 3. The sum ofestimation of effects obtained from all of the proposedmethods is presented in Table 5. By analyzing Table 5, it canbe noticed that completely different factors influence thechosen responses. Of course, it depends on the df when Ecritical

and ME were analyzed. The most influential factor appears tobe factor K (column): it was influential not only on R5. Theimportance of column selection is expected in HPLC.However, the column is a qualitative factor, and testing twocolumns with similar characteristics from the samemanufacturer would probably lead to the conclusion thatthe column is an important factor. The second important factor is pH of the mobile phase (factor D), which is connectedwith the chemical structure of the analyzed substances.It is very well known that pH has a stronger

influence on the chromatographic behavior of substances

with basic characteristics compared to substances with

acidic characteristics. In this case, all substances (except

impurity BI-II 751 xx) have basic character, and the influence

of pH could be considered as very important. Its significance

must be confirmed through definition of the significance

interval. Acetonitrile content (factor A) and temperature

(factor H) are important for some responses, and their

significance interval also must be considered.

According to Vander Heyden et al. (1), significance

interval can only be calculated for quantitative factors, and

extreme levels must be symmetrically situated around the

nominal level, while a linear behavior of the response as a

function of the factor levels is assumed. In this study, besides a

quantitative factor (pH of the mobile phase), the authors have

chosen to calculate nonsignificant intervals for acetonitrile as

a mixture factor and temperature as a process-related factor, if

their significance should be confirmed, of course. The

obtained intervals for all analyzed responses were calculated

and are given in Table 6.

It is obvious that R1 and R2 present the most stable

responses, which means that the factors in investigated

regions will not significantly change their value. On the other

hand, other responses are very sensitive to pH and acetonitrile

changes, so those factors must be strictly controlled to save the

method’s performance. However, according to Table 6,


Table 9. Important parameters for the calibration curves

Parameter Pramipexole BI-II 820 BS BI-II 546 CL BI-II 786 BS BI-II 751 xx 2-ABT

Concentration range,

mg/mL

4–400 0.02–2.0 0.02–2.0 0.02–2.0 0.02–2.0 0.02–2.0

Slope 26.62 100.72 105.80 51.95 61.25 166.7

Intercept 6.38 0.51 0.22 –0.051 0.259 –2.26

r 0.9999 0.9989 1.000 0.9997 0.9999 0.9995

tba 0.332 0.325 1.625 0.122 1.117 1.292

a tb = Statistical parameter having a lower value than the tabular value, ttab = 2.364 (P = 0.05, n = 8).

Table 10. Precision of the RP-HPLC method

Compound Injected, mg/mL Found, mg/mL ± SDa RSD, %

Pramipexole 40.0 39.4 ± 0.31 0.77

BI-II 820 BS 0.2 0.198 ± 0.003 1.52

BI-II 546 CL 0.2 0.203 ± 0.004 1.97

BI-II 786 BS 0.2 0.195 ± 0.004 2.05

BI-II 751 xx 0.2 0.201 ± 0.003 1.49

2-ABT 0.2 0.204 ± 0.004 1.96

a n = 6.

temperature could be changed in quite a wide range, but itsinfluence is important only for R3 and R5 responses.

Finally, SST limits for resolution factors were determined.As mentioned in the Theory of Robustness Testing section,one of the methods for SST determination is execution ofexperiments under worst-case experimental conditions. Thatmeans experiment 7 (Table 2) for R1, R3, and R4 output;experiment 8 for R2 output; and experiment 9 for R3 output.Factor levels for the worst-case situation are presented inTable 7 and the obtained results from experiments, as well ascalculated values for the appropriate limits, are in Table 8.

After robustness estimation, the other parameters of themethod validation were done. The assay proved to beselective: no significant interfering peaks were observed at the retention time of pramipexole or any of the impurities.Excipients were eluted at different times and did not interferewith analyzed compounds.

Linear relationships of the peak area over the mentionedconcentration range for the investigated substances wereobtained (Table 9). As the r value for the calibration curves ofpramipexole and its impurities were greater than 0.9990 andthe statistical parameter (tb) was lower than the tabular value(ttab), it was concluded that the calibration curves were withinthe linearity acceptance criteria.

For the evaluation of the method’s intermediate precision(Table 10) and accuracy (Table 11), the important statistical

values SD, RSD, and recovery were calculated. All of thecalculated parameters were within the acceptance criteria.

Being important for the quantitative analysis, LOD andLOQ for the impurities were experimentally determined based on an S/N approach. S/N was determined by comparingmeasured signals from standard solutions with known lowconcentrations of analyte with those of blank samples, andLOD and LOQ were defined as the minimum concentration atwhich the analyte can be reliably detected and quantified,respectively. An S/N of 3:1 and 10:1 is generally consideredacceptable for estimating the LOD and LOQ, respectively.For all of the analyzed impurities, the obtained values forLOD and LOQ were 5 and 15 ng/mL, respectively.

The validated RP-HPLC method was then applied forassay of pramipexole and its impurities in Mirapexinâ

(Boehringer Ingelheim, Ingelheim, Germany) tablets. Thecontent of pramipexole was 96.8%. Impurity BI-II 820 BSwas found at a level of 0.46%, which was within the requiredcriterion of less than 0.5%. The content of all other impuritieswas below the LOD of the validated RP-HPLC method.

Conclusions

A Plackett-Burman design was successfully applied forrobustness testing of an RP-HPLC method for separation andsimultaneous determination of pramipexole and its relatedsubstances. Data extracted from the design enabled a


Table 11. Accuracy of the RP-HPLC method

Compound Injected, mg/mL Found, mg/mL ± SDa Recovery, %

Pramipexole 32 32.70 ± 0.13 102.2

40 40.28 ± 0.05 100.7

48 49.25 ± 0.43 102.6

BI-II 820 BS 0.16 0.165 ± 0.001 103.2

0.20 0.196 ± 0.005 97.9

0.24 0.241 ± 0.005 100.4

BI-II 546 CL 0.16 0.160 ± 0.004 100.1

0.20 0.202 ± 0.008 101.2

0.24 0.237 ± 0.007 98.9

BI-II 786 BS 0.16 0.162 ± 0.003 101.4

0.20 0.203 ± 0.002 101.6

0.24 0.242 ± 0.002 100.7

BI-II 751 xx 0.16 0.163 ± 0.004 101.6

0.20 0.202 ± 0.004 101.0

0.24 0.236 ± 0.004 98.5

2-ABT 0.16 0.157 ± 0.003 98.2

0.20 0.202 ± 0.002 101.1

0.24 0.245 ± 0.003 102.2

a n = 6.

complete view of the possible changes in the proposedmethod. The biggest influencing factor appears to be factor K(column), which was influential not only on R5. The secondimportant factor is pH of the mobile phase (factor D), which isconnected with the chemical structure of the analyzedsubstances. Nonsignificant intervals for significant variableswere then calculated. As acetonitrile content (factor A) andtemperature (factor H) proved to be important for someresponses, their significance interval was also considered.Finally, SST limits for resolution factors using anexperimental approach were defined. The rest of thevalidation parameters were tested, and the method proved tobe selective, precise, and accurate, so it can be used forseparation, identification, and simultaneous determination ofpramipexole and its impurities. The proposed method is alsorapid and sensitive, and it presents significant improvement inHPLC analysis and routine application in drug analysis.

Acknowledgments

We thank the Ministry of Science, Republic of Serbia, forsupporting these investigations in Project 142077.

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analysis of pramipexole and its five impurities

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