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Quantification of lysine clonixinate in intravenous injections by NIR spectroscopy
Vibrational Spectroscopy 51 (2009) 255–262
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Quantification of lysine clonixinate in intravenous injections by NIR spectroscopy R. Lo´pez-Arellano a,*, E.A. Santander-Garcı´a a, J.M. Andrade-Garda b, G. Alvarez-Avila a, ˜ o-Rosas a, E.A. Morales-Hipo´lito a J.A. Gardun a LEDEFAR, Unidad Multidisciplinaria de Investigacio´n, FES Cuautitla´n Universidad Nacional Auto´noma de Me´xico, Km. 2.5 Carretera Cuautitlan, Teoloyucan Cuautitlan Izcalli 54700 Edo, de Me´xico, Mexico b Dept. of Analytical Chemistry, University of A Corun˜a, Campus da Zapateira s/n, 15071 A Corun˜a, Spain
A R T I C L E I N F O
A B S T R A C T
Article history: Received 12 May 2009 Received in revised form 7 July 2009 Accepted 8 July 2009 Available online 18 July 2009
Migraine is a very painful and somewhat unpredictable ache that affects millions of people worldwide and for which no definite medicine exists, yet. New drugs and/or pharmaceutical forms are being developed, for which new quantitation methods are required. Lysine clonixinate (LC) has proved very advantageous to alleviate migraine episodes although, so far, no analytical procedures have been reported to quantify it in pharmaceutical dosage forms usually employed by physicians, i.e., injectable solutions. In this paper a NIR spectral method was developed and validated against international pharmaceutical standard guidelines and a new UV-based method to quantify LC in intravenous injection solutions. Both methods are almost inexpensive, fast, simple and suitable for LC routine determination. In addition, they provide analytical protocols less time-consuming than other reported HPLC methods (developed for other matrices), proved to be specific, accurate, precise and linear within the typical working range, according to the Harmonized Tripartite Guideline of Validation of Analytical Procedures from the International Conference on Harmonization. Both methods yield equivalent results and they are useful to monitor the concentration of LC in injectable solutions in routine analysis. ß 2009 Elsevier B.V. All rights reserved.
Keywords: NIR PLS Lysine clonixinate Migraine Process analytical technology
1. Introduction Migraines are a type of vascular headache which affect women three times more often than men and despite they pose little risk of long-term damage to a person’s overall health, the debilitating pain can severely interfere with daily life. The frequencies in which migraines are experienced vary from person to person. Symptoms include nausea, vomiting, photophobia (increased sensitivity to bright light), and hyperacusis (increased sensitivity to noise). According to the US National Headache Foundation, more than 28 million Americans and 79 million Europeans experience migraines [1]. Lysine clonixinate (briefly, LC), 4-chloro-N-(2,6-dimethylpiperidino)-3-sulfamoylbenzamide lysine salt (CAS 55837-30-4 [2], Mw = 408.88) is a water-soluble non-steroidal drug with a powerful anti-inflammatory power, antipyretic and analgesic action [3]. LC has been studied for the acute treatment of migraine in controlled and open-label trials [4–6] and it was shown that injection of intravenous LC was effective and well tolerated in the treatment of severe migraine attacks, with best results than those obtained with oral formulations, as the latter were effective only
* Corresponding author. Fax: +52 55 56 23 20 91. E-mail address: [email protected] (R. Lo´pez-Arellano). 0924-2031/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.vibspec.2009.07.001
for migraine of moderate severity [7]. Clinical applications of LC include more than 60 studies, in Argentina, Chile, Brazil, Peru and Me´xico, as well as in Europe (Germany and Spain) [8]. LC has been registered as a generic drug in the form of both intravenous and intramuscular injection. So far, there are two registrations in Argentina, three in Chile, six in Mexico and one in Portugal [9]. Despite the increasing use of LC there are not, unfortunately, reported analytical methods to assure detection of changes in its identity and purity, nor to evaluate the concentration of LC in injectable pharmaceutical formulations. The two unique exceptions we found out deal with the quantitative determination of LC in water/oil microemulsions [10] and the measurement of clonixin in human plasma and urine samples [11] by HPLC, although those matrices are not within the scope of the present paper. HPLC-based methods are time-consuming, rather expensive, destructive and need highly skilled staff. On the contrary, spectral techniques like ultra-violet (UV) spectroscopy and near infrared (NIR) spectroscopy are fast, cheap, simple to implement in routine analysis and, therefore, they can be valuable tools for the quality control of LC pharmaceutical injectable formulations. On the other hand, pharmaceutical industries are nowadays involved in large methodological changes due to US and EU governmental initiatives for process analytical technology (PAT) procedures [12,13]. PAT initiatives boostered the development of
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analytical techniques capable of providing real-time process control and improving the understanding of the processes that occur at pharmaceutical unit operations [14]. Out of the many analytical techniques available in pharmaceutical laboratories, NIR has a long-standing success history and it became one of the most suitable tools for a variety of PAT applications and many studies have been developed using it [15]. Not in vain NIR has been described in the European Pharmacopeia since 1997, and it has promoted process understanding within the pharmaceutical industry thanks to its ability to perform remote measurements [16], monitor manufacturing objectives, and identify critical process parameters [17]. Nevertheless it is well-known that NIR has several pitfalls. In particular NIR spectra contain broad overlapping bands which cannot be always ascribed to an individual component. As a consequence, typical calibrations based on the traditional Lambert–Bouguer–Beer’s law are useless and hence whenever the NIR technique is used for quantitative purposes (e.g., the determination of active compounds in pharmaceutical injectable preparations) a calibration must be developed applying a multivariate calibration techniques [18–20]. Many multivariate quantitative regression models for a suite of NIR applications have been reported to quantify the active principle in pharmaceutical solid samples although much less works have been presented for liquid formulations. In both cases, the most common multivariate calibration method was partial least squares regression (PLS) [21]. As a matter of example, Forina et al. [22] presented the principles of multivariate calibration applied to the determination of a drug in a solid formulation and Blanco et al. [23] presented strategies to construct a calibration set to develop and validate a NIR method in solid pharmaceutical preparations. In order to comply with PAT initiatives, NIR methodologies must be challenged with a reference method at least once a year to ensure its ongoing validity and the maintenance of the reference method [24,25]. Since, so far, there is not a standard reference method to quantify LC in injectable solutions, a direct measurement of LC by UV spectroscopy was implemented as ‘reference method’ because of its excellent specificity without matrix interferences. The main objective of this paper is therefore to develop a NIR–PLS methodology to quantify LC in intravenous injection solutions and validate it against international pharmaceutical standards and against a off-line UV-based method (which will be used as an internal reference method). 2. Materials and methods 2.1. Apparatus and software NIR spectra were recorded on a Foss NIRSystems-6500 near infrared spectrophotometer (Raamsdonksveer, The Netherlands). The spectrum was obtained with a flat-bottom optical glass vessel (50 mm diameter) and gold-surface immersible diffusers, providing a fixed 1-mm total path length interactance, the nominal resolution was 10 nm. As one of the most significant sources of NIR spectral variation is random noise which, in turn, may affect the calibration models, the signal/noise ratio (SNR) was monitored at 1686 nm and 2302 nm. 100 scans were selected to get the spectra because of a satisfactory SNR (over 7000 at 1686 nm and over 20,000 at 2302 nm) and adequate time frame (8 min). The instrument was controlled by the Vision 3.1 software, which also provided the spectral pretreatments and multivariate PLS calibration algorithms (the PLS-1 block algorithm was employed throughout). UV spectra were recorded on a Cary 100 UV–vis spectrophotometer from Varian (Palo Alto, CA, US). The absorbance was measured at 283 nm using 1 cm path length quartz cells, in the transmittance mode.
2.2. Calibration samples A stock placebo solution (simulated injectable solution matrix) provided by Mexican Rayere Company (which holds proprietary information on its detailed composition) was used to prepare all calibration standards. Twenty aliquots of placebo were spiked with solid commercial LC, 99.6% purity (Baselux, S.A. Lugano, Switzerland), to obtain a set of calibration samples whose concentrations ranged from 50% to 150%, the typical concentration of LC in commercial intravenous solutions. The pharmaceutical product under study consisted on commercial injectable solutions containing 100 mg/ 2 mL, nominal content of LC as active principle. These standards were employed to develop the UV and NIR calibration models. It is worth noting that even though the European Medicines Agency for the Evaluation of Medicinal Products (EMEA) recommend a calibration range between 80% and 120% the label claim [12], the working range was extended here from 50% to 150% the nominal value in order to detect out-of-specifications samples and, thus, get a NIR model capable of controlling the quality of a range of injectable solutions during the manufacturing processes and different production batches (with different specifications). 2.3. Validation samples The validation set was prepared in the same manner as the calibration set. In total, 15 LC samples were prepared to evaluate the linearity of the UV method and 20 ones to validate the NIR approach. These solutions bracketed a concentration range from 55% to 145% the label claim (100 mg/2 mL) in agreement with the calibration range. They corresponded to five concentration levels (prepared by triplicate) and they were used only to validate the two spectral methods. 2.4. UV measurement of LC In order to perform the UV measurements, the LC standard solutions were prepared in 0.1N sodium hydroxide solution to assure a total, optimal solubility of the LC. Two absorption maxima at 217 nm and 283 nm were obtained. Here the maximum at 283 nm was selected to quantify LC because of its smaller variation in the analytical response. Hence, analysis of routine samples by UV spectroscopy would be performed by diluting 1 mL of an injectable solution with 0.1N sodium hydroxide to obtain a final concentration of 40 mg/mL (25 mL). All standards were prepared on a daily basis. Three of them are detailed next as they were used to evaluate selectivity. To prepare the working placebo solution, an exact volume of the stock placebo solution (1 mL) was transferred to a 50 mL volumetric flask and dissolved in 0.1N sodium hydroxide, 1 mL of this solution was transferred to a 25 mL volumetric flask and the volume made up. A 40 mg/mL LC stock standard solution was prepared by accurately weighing 50 mg of pure LC, transferring them to a 50 mL volumetric flask and dissolving them in a 0.1N sodium hydroxide solution; 1 mL of this solution was withdrawn to a 25 mL volumetric flask and the volume made up. The 40 mg/mL LCspiked placebo solution was prepared by withdrawing an exact volume of the stock placebo solution (1 mL) and accurately weighing 50 mg of pure LC, they were transferred to a 50 mL volumetric flask and dissolved in 0.1N sodium hydroxide; 1 mL of the latter solution was transferred to a 25 mL volumetric flask and the volume made up. 3. Results and discussion The UV and NIR methods were validated with respect to their specificity, linearity, accuracy and precision. Validation was based
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on the guidelines of the International Conference on Harmonization (ICH) [26–28], the European Agency for the Evaluation of Medicinal Products (EMEA) [29] and the National School of Pharmaceutical Chemists and Biologists of Me´xico [30]. Then, the performance of the NIR method will be assessed against the UV one. 3.1. Specificity In order to evaluate the specificity of the spectral measurements, three solutions of LC were prepared (in accordance to the experimental procedure detailed above) and their associated UV and NIR spectra recorded. Fig. 1 shows the spectra of the three solutions detailed in Section 2.4. Remarkably the placebo solution did not interfere on the UV LC spectrum and, so, the UV method was capable of assessing the presence of LC in injectable solutions (matrix of interest), which corresponded to the definition of specificity given by ICH this permits a straightforward calibration process. As it is well-known, NIR spectra depend not only on the chemical structure of the analyte, but also on the physical and chemical properties of the matrix and its composition. As we were interested in measuring the NIR spectra of commercial LC injectable solutions directly, without any pretreatment, the calibration standards for NIR were designed by systematically varying the active principle in matrix-matched standard. Fig. 2 shows the raw NIR spectra of the calibration standards. It can be observed that only the spectral regions around 1200 nm and 1700 nm changed systematically with the LC concentrations. They corresponded to minor spectral bands, being most of the spectra dominated by the water bands and the spectral characteristics of the synthetic matrix or placebo (remarkably, the two broad bands centred at 1470 nm and 1940 nm correspond to water bands). To verify this situation the spectra of the placebo alone, of a 40 mg/mL LC solution and a spiked placebo were recorded and it was found that they overlapped almost totally (with minor differences at the LC standard). Therefore, it was concluded that the NIR measurements were not specific (according to EMEA [29]). 3.2. UV linearity A calibration line was established in the UV region to quantify LC in injectable solutions. Its performance was assessed using twenty-one standards (at seven concentration levels). All samples were prepared daily from scratch in a 0.1N sodium hydroxide solution by weighting different quantities of LC so as to obtain solutions in the 27.51–72.53 mg/mL concentration range. Table 1 presents the performance characteristics of the UV method. The relationship between the added LC concentrations and their
Fig. 1. UV spectra of a placebo (- - - -) injectable solution without LC, a standard LC reference solution with 40 mg LC/mL (—) and a placebo spiked with 40 mg LC/mL (- - -). The three solutions were prepared in 0.1N NaOH.
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corresponding recoveries (i.e., predicted concentrations) was investigated by linear regression. Its slope (0.9952 0.0153) estimates whether there was a bias on the predictions of the UV method whereas the intercept (0.1767 0.7743) provided an error inference of the measurement (note that the intervals correspond to 95% confidence intervals). As the intercept and slope were not statistically different from 0 and 1, respectively, it was concluded that there were neither bias nor systematic error (correlation coefficient = 0.9995). The relative standard deviation (RSD) of the three replicates was lower than 2 for each concentration level, as recommended by ICH. The results shown in Table 1 revealed satisfactory accuracy (all recoveries around 100%) and precision to measure LC in injectable forms. The graphical representation of the results employed for the evaluation of the linearity of the validation set is given in Fig. 4A and they demonstrated a linear relationship between the true LCadded concentrations and its corresponding predicted values. 3.3. NIR linearity To develop the PLS models four data pre-processing options were tested. Despite many options exist here, it was observed that our spectral characteristics were good, without artifacts nor relevant baseline displacements, nor other complex behaviors and, accordingly, it was decided to use only some four widely applied, common options: no pre-processing; smoothing (Savitzky–Golay algorithm; 11 points window, 2nd order polynomial) to increase the S/N ratio and, so, reduce noise; peak-to-peak baseline correction, to cope with possible slight baseline effects not visible for us; a combination of the latter two; along with different spectral ranges (namely, the whole spectral range (1100– 2500 nm), 1116–1278 nm, 1672–1838 nm and a combination of the latter two). These two regions were selected to avoid the undesired influence of the strong water absorption bands and to focus the models on the regions were a variation of the absorbances with the LC solutions appeared. No further efforts were made to select variables using other more objective methods like iPLS or UVE-PLS. Hereinafter, all results will refer to the latter option (1116–1278 nm plus 1672–1838 nm) as it yielded best predictive results after some preliminary assays. The fact that the 1100–2500 nm range yielded worst PLS predictive models than the two selected regions confirmed that the two bands around 1470 nm and 1940 nm did not contain useful information to quantify LC, as they depended on the large absorption of water. Indeed, the typical 1st overtones of the fundamental NH stretching bands would appear at the 1461–1470 range, as well as the overtones of the carbonyl and primary alcohol groups, which became masked by the strong absorption of water. The two spectral regions used to develop the PLS models can be linked to the molecular structure of LC in the following way. The band at 1116–1278 nm is originated by a complex mixture of different overtones of fundamental stretching bands present—in general—on the mid-IR region: the 2nd overtones of the CH bonds in aromatic structures (1142 nm), the 4th overtone of the (very intense) C5 5O bond (1160 nm), the second overtone of the CH in alkenes (1170 nm), the 2nd overtone of the CH in methyl groups (1194 nm) and the 2nd overtone of the CH in methylene groups (1211–1215 nm). The band at 1672–1838 nm is also related to a combination of several overtones of fundamental stretching vibrations: the 1st overtone of the aromatic CH bonds (1685 nm), the overtones of the CH bond in current terminal methyl groups and/or branched chains (1693 nm), the 1st overtone of the CH bond in methylene groups (1725 nm) and the 1st overtone of the CH bond in methylene groups (1762–1765 nm). The optimal number of factors (or latent variables) was established using the leave-one-out cross-validation strategy (on
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Fig. 2. Raw NIR spectra (a) and second-order derivatives (b) of the calibration standards prepared by diluting aliquots of a stock LC solution with the simulated matrix. LC concentrations ranged 50–150% the usual content in injectable commercial forms (i.e., 100 mg/2 mL). (a) Original spectra and (b) 2nd-order derivative spectra. (1) Placebo solution, (2) LC concentration 50%, (3) LC concentration 100% and (4) LC concentration 150%. The insets show the regions where the absorbances varied as a function of the concentration of LC.
the calibration set), which was validated further using an external validation set (to verify that the selected model predicted new samples satisfactorily). The presence of outlying samples was assessed by studying the ‘t vs t’ scores plots (in the X-block) and the ‘t vs u’ plots (to inspect the relation between the X- and YTable 1 Performance characteristics of the UV and NIR methods for the calibration set. The percentages refer to the recoveries of the calibration standards. LC added (mg/mL)
27.51 37.09 42.53 47.05 52.51 62.50 72.53
Recovery by UV
Recovery by NIR
Mean, mg/mL (%)
RSD (%)
Mean, mg/mL (%)
RSD (%)
27.22 36.76 42.63 46.42 51.62 61.94 72.26
0.67 1.76 1.13 0.54 0.59 0.92 0.43
27.25 37.57 42.92 46.98 52.62 62.82 71.89
1.61 0.81 0.55 0.78 0.84 0.70 0.50
(98.92) (99.13) (100.23) (98.66) (98.32) (99.10) (99.63)
(99.04) (101.31) (100.91) (99.86) (100.22) (100.50) (99.12)
blocks); the leverage of the samples and the studentized residuals. Table 2 resumes the average errors for each calibration model, the root mean square error of calibration (RMSEC = [(1/ n k 1)(ypredicted yadded)2]1/2) and the root mean square error of prediction (RMSEP = [(1/n)(ypredicted yadded)2]1/2), where n is the number of samples and k is the number of latent variables (factors) in the PLS model. The multiple correlation coefficient (R), the slope and intercept of the regression line between the predicted and the added values (along with their associated 95% confidence intervals) are also displayed there. After selecting the most promising model by cross-validation, its predictive ability was evaluated by calculating the RMSEP for the external validation set. The most satisfactory model was obtained smoothing the combined spectral range (1116–1278 nm and 1672–1838 nm) and considering five factors, which resulted in the lowest average error of prediction for the validation standards (RMSEP = 0.36), the highest correlation coefficient (0.9996) and the tightest confidence intervals for the slope and intercept. The reason why five factors were needed may be explained because of the
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Table 2 PLS and MLR calibration models developed for the NIR spectra in the combined 1116–1278 nm plus 1672–1838 nm region and the overall spectral range (only MLR). Smoothing was performed employing the Saviztky–Golay algorithm, confidence intervals at 95% probability. BC = peak-to-peak baseline correction. Regression
PLS
Data treatment
Factors
Without pretreatment Smoothing BC Smoothing and BC
Regression
MLR (2nd order derivative)
5 4 5 4 5 4 5
Data treatment
Without pretreatment Smoothing BC Smoothing and BC Without pretreatment Smoothing BC Smoothing and BC Without pretreatment Smoothing BC Smoothing and BC
RMSEC (n = 19)
0.49 0.38 0.23 0.73 0.22 0.37 0.28
RMSEP (n = 19)
Correlation coefficient
0.99 0.44 0.36 1.72 0.33 0.42 0.51
0.9976 0.9993 0.9996 0.9984 0.9984 0.9993 0.9996
Wavelength (nm)
RMSEC (n = 20)
RMSEP (n = 20)
Correlation coefficient
1116–1278
2.96 2.70 4.06 4.32 2.80 2.88 3.28 3.25 1.86 1.87 3.37 3.48
5.48 3.32 2.24 5.68 3.96 4.09 5.44 5.61 2.50 2.06 1.34 3.80
0.8145 0.9323 0.8339 0.7808 0.9061 0.8979 0.8550 0.8483 0.989 0.9776 0.9215 0.9093
1672–1838
1100–2500
complex composition of the placebo (despite we did not know the proprietary information, we were informed that it contained EDTA, sodium hydroxide, water, propyleneglycol, sodium metabisulfite, ethanol and benzylic alcohol) and, therefore, the spectral information that has to be used to predict the LC concentrations was not only on the major absorbances of the NIR spectra, but also on subtle minor details. The predictions of the LC calibration standards were accurate and exhibited no systematic errors as the ‘predicted vs added concentrations’ plot yielded the regression line: predicted concentration ¼ ð0:4966 0:8005Þ þ ð0:9908 0:0158Þ added concentration Both the intercept and the slope were not statistically different from zero and one, respectively. Further, the slopes of the UV and NIR calibration lines were compared by a Student’s t-test, where from it was deduced that they agreed at 95% confidence (experimental t-value = 0.42, tabulated value t0.05;38 = 2.02). All PLS models were found to explain, at least, 99% of the spectral (Xblock) and LC concentrations (Y-block) variances, revealing that the models did establish a non-random relationship between the spectra and the LC concentration. Fig. 3 presents the regression coefficients of the final PLS model, where the most relevant variables which correlated positively to the LC concentration were those around 1124 nm and 1676–1740 nm. The spectral band around 1676 nm was the most important one, which was interpreted as the combination of the 1st overtone of the aromatic CH bonds (1685 nm) and the overtones of the CH bond in current terminal methyl groups and/or branched chains (1693 nm)—see above. Additional studies were made to verify that the PLS model was required instead of a simpler one (it is worth recall here that parsimony should be addressed in order to get stable prediction models). Hence, MLR (multiple linear regression) models were developed on the spectral second derivatives (preliminary assays using the original spectra were unsatisfactory). Fig. 2 gives the general appearance of the second-order derivatives compared to the original spectra. Two insets were included to show the regions where the absorbances varied rather clearly with the concentra-
Real vs predicted values Slope
Intercept
1.00 0.13 1.01 0.07 1.02 0.05 1.03 0.11 1.03 0.11 1.01 0.07 1.03 0.05
0.64 6.05 0.01 3.27 0.86 2.43 0.06 5.14 0.06 5.14 0.05 3.22 0.87 2.62
Real vs predicted values Slope
Intercept
0.71 0.93 1.01 0.72 0.72 0.87 0.64 0.93 0.99 0.85 0.97 0.87 0.97 1.08 0.93 1.07 1.22 0.34 0.87 0.35 1.04 0.81 0.98 0.83
10.99 44.64 0.90 34.40 11.48 41.60 14.96 44.65 0.26 40.55 0.64 4161 0.70 51.42 0.45 51.13 9.76 16.38 5.19 16.49 1.51 38.61 1.09 39.55
tion of the LC standards. They agreed very well with the spectral ranges used for the PLS studies. Trials were made using the overall spectral range and the two ranges employed for PLS (Table 2): the MLR predictions were clearly worse than those from PLS (this was expected and some technical explanations can be found at [31]) and, therefore PLS was the regression method of choice. Note also that multivariate PLS models will outperform traditional MLR ones whenever slight, uncontrolled changes occur on the samples due to, for instance, slight variations on the composition of the commercial LC solid standard or on the composition of the stock placebo solution, as well as other minor changes on the final injectable solutions caused by fluctuations in the manufacturing process (e.g., raw materials). This is called ‘the second-order advantage’ and it consists on the relative insensitiveness of PLS models to the presence of new ‘unexpected’ concomitants in the true unknown samples. Thus, classical MLR models would require—in the most favorable situation—a full assessment of the MLR regression models whenever new concomitants are supposed whereas in PLS this would be seldom
Fig. 3. Graphical representation of the vector of the regression coefficients for the PLS selected model. The horizontal segments denote the regions not used to perform the calibration and they have no meaning at all.
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Table 3 Resume of the validation parameters for the quantitation of LC using the UV and NIR regions. The acceptance criteria were extracted from the US Pharmacopoeia and Ref. [32]. Parameter
Acceptance criteria [32]
Linearity
n Concentration range (mg/mL) Intercept Slope Determination coefficient Correlation coefficient RSDy/x
– – 95% confidence interval include 0 95% confidence interval include 1 r2 0.98 – <3%
Accuracy
n Recovery interval RSDrecovery (%) Average differencea
97–103% <3%
Precision
a
n Intermediate precisionanalyst (%) Intermediate precisionday (%) Uveral RSD (%) Repeatability (%)
UV method 15 20.1–59.7 0.1565 0.7111 1.0025 0.0168 0.9992 0.9996 1.23 18 100.38 0.42 0.85 0.15 0.17 24 0.67 0.99 0.86 0.74
<3%
– –
NIR method 20 25.0–75.29 0.0271 0.6368 0.9992 0.0128 0.9993 0.9997 0.92 18 100.02 0.48 0.97 0.01 0.18 24 1.01 1.29 1.06 0.77
Student’s t-test demonstrated no statistical difference from zero.
the case and models will essentially be unaffected; more details can be found elsewhere [31–33]. Finally, the PLS model was tested for accuracy and precision when slight changes occurred on the solutions. Hence, 10 standards with the ‘same’ concentration of LC (equal to the nominal concentration of the commercial product) were prepared. Operational differences on the solutions would be linked to weighing, make-up of the volume, etc. Furthermore, the reproducibility of the spectral measurements was included in this overall study as the 10 spectra were measured on different days. All spectra were input to the PLS model and a 99.7% LC average value and a 1.3% standard deviation were obtained (i.e., the 95% confidence interval was 99.7 0.9%, which contained the nominal 100% concentration). The precision figures were well below the international acceptance criteria (see Table 3 and explanations below). The NIR method was validated further in accordance with the ‘Guidance on the Use of Near Infrared Spectroscopy by Pharmaceutical Industry’ [29]. The linearity of the NIR-based method was validated using an external set of 20 standards. The predictions of the PLS model were regressed against the ‘true’ concentrations (added concentrations of LC). According to EMEA the intercept and slope should be zero and one, respectively. Fig. 4B presents the results for the validation set, the correlation coefficient was greater than 0.999, indicating a very strong linear relationship between the true (added) and the predicted concentrations. As the confidence intervals of the slope and intercept include 1 and 0, respectively,
the predictions of the NIR method were unbiased. Besides, graphical analysis of the residuals was carried out in the 20– 60 mg/mL range and homoscedasticity was observed, this indicated that the method was linear within the studied working range. 3.4. NIR and UV accuracy As there are not certified reference materials nor standard reference methods to measure LC in injectable solutions, the accuracy of both methods was evaluated in accordance to ICH [34] by spiking a placebo to get a solution at the nominal concentration of the commercial product and, at least, at two other levels. We expanded this requisite for NIR so that the overall calibration range was tested. The UV method followed strictly the ICH rule as this acted as our internal ‘reference’ method. The 95% confidence intervals for the average recoveries of the two methods (and a Student’s t-test) showed that both methods were exact and precise (as required by ICH and EMEA) as the recoveries were around 100% and their relative standard deviations were lower than 2% (as required, [30]); see Table 3. 3.5. NIR and UV precision Precision was studied further as composed of two terms: shortterm precision (repeatability) and long-term precision (reproducibility). Common guides to evaluate the latter recommend the use
Fig. 4. Validation samples. Evaluation of the linearity of the (a) UV and NIR (b) methods following ICH [30]. See text for details.
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of different laboratories and/or equipments although this was not possible in our laboratory. Therefore we decided to evaluate the socalled ‘intermediate precision’ measuring the content of LC in six independent replicates of a sample in 2 different days and two different analysts using the same equipment. An analysis of variance (ANOVA) was performed to evaluate the repeatability as [29,35] pffiffiffiffiffiffiffiffiffiffiffiffi MSwg RSDt ð%Þ ¼ 100 X¯ where RSDt is the repeatability, within-group precision, expressed as percentage RSD, MSwg is the mean square within groups, obtained from the ANOVA table and X¯ is the grand mean of all observations. The intermediate precision or total precision was calculated as a combination of the within- and between-groups effects: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðMSbg þ ðn 1Þ MSwg Þ=n 100 RSDIðFÞ ¼ X¯ where RSDI(F) is the factor-different intermediate precision, expressed as percentage RSD, MSbg is the mean square between groups, MSwg is the mean square within groups, both values obtained from the ANOVA table, X¯ is the grand mean of all observations and ‘n’ is the number of measurements per cell (here n = 6). The RSDI(F) calculated in this way corresponds to the intermediate precision estimate obtained with the alternative design proposed by EURACHEM [36]. The results of the precision study are shown in Table 3. The repeatability, intermediate precision between analysts and days comply with the acceptance criterions defined by ICH, EURACHEM and IUPAC [26,37] for both the UV and NIR methods. 3.6. Comparison of the UV and NIR methods As detailed above, the performance of the NIR methodology was compared against the UV method either in calibration (see Table 1) and validation (see Table 2). The components of the precision (measured as relative standard deviations) were very similar and always lower than 2% for all concentration levels (as required by The National School of Pharmaceutical Chemists and Biologists of Mexico [30]). Table 3 resumes the most relevant results for the validation parameters, compared to the United States Pharmacopeia (USP) criteria. Both methods were found to meet all the ICH requirements [26], as well as those from the Mexican government [30], which confirms their suitability to measure LC in pharmaceutical injectable solutions. As a conclusion of all these studies, it can be stated that regarding the operational characteristics (cost and time), the NIR methodology is slightly cheaper and faster than the UV one because the samples can be measured directly, without adding sodium hydroxide and without diluting them and without a daily calibration (as required for UV analyses). Besides, as any pharmaceutical industry or laboratory will have a NIR system, the implementation of the PLS–NIR models will no add extra costs. Further, while measuring and executing remote quality control by NIR fiber optics is an established issue within the pharmaceutical PAT portfolio techniques, this is not the case with UV–vis fiber optics (despite recent advances allowed for up to 50 m length probes). Nevertheless, recall that a ‘reference’ method (alternately, certified reference materials) will still be needed to assess the NIR methodologies (and this is why a UV–vis procedure was proposed here). 4. Conclusion The UV and NIR methods presented in this work allow for a simple and fast quantitation of LC in injectable solutions. Both
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methods have proved to be specific, accurate, precise and linear within the range of the studied concentrations. They are equivalent and they are useful to monitoring the quality of final product in routine analysis. Besides, the NIR method opens up new possibilities to the quality control of LC in real-time on-line pharmaceutical production where the NIR technology is broadly available nowadays. Finally, these two methods provide more economical and less timeconsuming procedures than other reported HPLC methods (which are not still available for the problem considered here, as they were developed for other matrices). Acknowledgements The authors are grateful to the Mexican ‘Program to Support Innovation and Improvement Projects for Education’ (PAPIME), project EN215404, and the ‘Program to Support the Overcoming of the Academic Personnel’ (PASPA) of the Universidad Nacional Autono´ma de Me´xico for their funding. Also, ‘Farmace´uticos Rayere of Me´xico’ is acknowledged by its financial support. References [1] www.wrongdiagnosis.com/m/migraine/stats-country.htm, 2009 (accessed February 2009). [2] Ashgate Drugs 2.1, Scientific Databases CambridgeSoft Corporation, 2008. [3] E.H. Kramer, B. Sassetti, A.J. Kaminker, A.R. De los Santos, M.L. Marti, G. Di Girolamo, Medicina (B. Aires) 61 (2000) 301–307. [4] A.V. Krymchantowski, H. Carneiro, J. Barbosa, C. Jevoux, Arq. Neuro-Psiquiatr. 66 (2008) 216–220. [5] A.V. Krymchantowski, J. Barbosa, C. Cheim, L.A. Alves, Arq. Neuro-Psiquiatr. 59 (2001) 46–49. [6] C.T. Mesquita, J.C. Cac¸a˜o, P. Villas-Boas, L.F. Mattos, Rev. Bras. Med. 53 (1996) 1128–1131. [7] A.V. Krymchantowski, M.T.T. Silva, Curr. Ther. Res. 64 (2003) 505–513. [8] Ministerio de Salud Direccio´n General de Medicamentos Insumos y Drogas de la Repu´blica del Peru´, Medicamentos no incluidos en el petitorio nacional de medicamentos esenciales (Informe te´cnico 002), 2007 (available at: http:// www.digemid.minsa.gob.pe/daum/informes2007.htmL, accessed February 2009). [9] A.C. Moffat, M.D. Osselton, B. Widdop, Clarke’s Analysis of Drugs and Poisons, Pharmaceutical Press, 2004 (electronic version). [10] I. Pineros, P. Ballesteros, J.L. Lastres, J. Pharm. Biomed. Anal. 27 (2002) 747–754. [11] A. Bica, A. Farinha, H. Henning, C.M. Barbosa, J. Chromatogr. A 889 (2000) 135– 141. [12] The European Medicines Agency for the Evaluation of Medicinal Products (EMEA), Pre-authorization Evaluation of Medicines for Human Use, Report Biologics Working Party (BWP), Process Analytical Technologies (PAT) group and Industry Workshop on Process Analytical Technologies for Biologicals, London, 2007 (available at: www.emea.europa.eu/pdfs/human/bwp/18537007en.pdf, accessed February 2009). [13] A.A. Gowen, C.P. O’Donnell, P.J. Cullen, S.E.J. Bell, Eur. J. Pharm. Biopharm. 69 (2008) 10–22. [14] ASTM International Standards, Worldwide Committee E55 on Manufacture of Pharmaceutical Products, 2006 (available at: http://www.astm.org/COMMIT/ E55_fda.pdf, accessed February 2009). [15] Y. Roggo, P. Chalus, L. Maurer, C. Lema-Martı´nez, A. Edmond, N. Jent, J. Pharm. Biomed. Anal. 44 (2007) 683–700. [16] G. Reich, Adv. Drug Deliv. Rev. 57 (2005) 1109–1143. [17] FDA PAT Team Members (ORA, CDER, CVM), Guidance for industry pat—a framework for innovative pharmaceutical development, manufacturing, and quality assurance, in: Pharmaceutical CGMPs, 2004 (available at: http://www.fda.gov/ CDER/GUIDANCE/6419fnl.pdf, accessed February 2009). [18] H. Yang, J. Irudayaraj, J. Pharm. Pharmacol. 54 (2002) 1247–1255. [19] J.J. Workman, Appl. Spectrosc. Rev. 34 (1999) 1–89. [20] M. Blanco, J. Coello, H. Iturriaga, S. Maspoch, C. De la Pezuela, Analyst 123 (1998) 135R–150R. [21] J. Luypaert, D.L. Massart, Y. van-der-Heyden, Talanta 72 (2007) 865–883. [22] M. Forina, M.C. Casolino, C. De la Pezuela-Martı´nez, J. Pharm. Biomed. Anal. 18 (1998) 21–33. [23] M. Blanco, M.A. Romero, M. Alcala´, Talanta 64 (2004) 597–602. [24] C.P. Meza, M.A. Santos, R.J. Roman˜ach, AAPS. Pharm. Sci. Technol. 7 (2006) E1–E9. [25] D.A. Burns, E.W. Ciurczak, Handbook of Near-infrared Analysis, 3rd ed., CRC Press, 2007. [26] ICH Harmonised Tripartite Guideline, Validation of Analytical Procedures: Text and Methodology Q2 (R1), 2005 (available at: http://www.bioforum.org.il/ Uploads/Editor/karen/q2_r1_step4.pdf, accessed February 2009). [27] U.S. Food Drug Administration, Center for Drug Evaluation and Research, Guidance for Industry Analytical Procedures and Methods Validation, 2000 (avaliable at: http://www.fda.gov/CDER/GUIDANCE/2396dft.pdf, accessed February 2009).
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Contents lists available at ScienceDirect
Vibrational Spectroscopy journal homepage: www.elsevier.com/locate/vibspec
Quantification of lysine clonixinate in intravenous injections by NIR spectroscopy R. Lo´pez-Arellano a,*, E.A. Santander-Garcı´a a, J.M. Andrade-Garda b, G. Alvarez-Avila a, ˜ o-Rosas a, E.A. Morales-Hipo´lito a J.A. Gardun a LEDEFAR, Unidad Multidisciplinaria de Investigacio´n, FES Cuautitla´n Universidad Nacional Auto´noma de Me´xico, Km. 2.5 Carretera Cuautitlan, Teoloyucan Cuautitlan Izcalli 54700 Edo, de Me´xico, Mexico b Dept. of Analytical Chemistry, University of A Corun˜a, Campus da Zapateira s/n, 15071 A Corun˜a, Spain
A R T I C L E I N F O
A B S T R A C T
Article history: Received 12 May 2009 Received in revised form 7 July 2009 Accepted 8 July 2009 Available online 18 July 2009
Migraine is a very painful and somewhat unpredictable ache that affects millions of people worldwide and for which no definite medicine exists, yet. New drugs and/or pharmaceutical forms are being developed, for which new quantitation methods are required. Lysine clonixinate (LC) has proved very advantageous to alleviate migraine episodes although, so far, no analytical procedures have been reported to quantify it in pharmaceutical dosage forms usually employed by physicians, i.e., injectable solutions. In this paper a NIR spectral method was developed and validated against international pharmaceutical standard guidelines and a new UV-based method to quantify LC in intravenous injection solutions. Both methods are almost inexpensive, fast, simple and suitable for LC routine determination. In addition, they provide analytical protocols less time-consuming than other reported HPLC methods (developed for other matrices), proved to be specific, accurate, precise and linear within the typical working range, according to the Harmonized Tripartite Guideline of Validation of Analytical Procedures from the International Conference on Harmonization. Both methods yield equivalent results and they are useful to monitor the concentration of LC in injectable solutions in routine analysis. ß 2009 Elsevier B.V. All rights reserved.
Keywords: NIR PLS Lysine clonixinate Migraine Process analytical technology
1. Introduction Migraines are a type of vascular headache which affect women three times more often than men and despite they pose little risk of long-term damage to a person’s overall health, the debilitating pain can severely interfere with daily life. The frequencies in which migraines are experienced vary from person to person. Symptoms include nausea, vomiting, photophobia (increased sensitivity to bright light), and hyperacusis (increased sensitivity to noise). According to the US National Headache Foundation, more than 28 million Americans and 79 million Europeans experience migraines [1]. Lysine clonixinate (briefly, LC), 4-chloro-N-(2,6-dimethylpiperidino)-3-sulfamoylbenzamide lysine salt (CAS 55837-30-4 [2], Mw = 408.88) is a water-soluble non-steroidal drug with a powerful anti-inflammatory power, antipyretic and analgesic action [3]. LC has been studied for the acute treatment of migraine in controlled and open-label trials [4–6] and it was shown that injection of intravenous LC was effective and well tolerated in the treatment of severe migraine attacks, with best results than those obtained with oral formulations, as the latter were effective only
* Corresponding author. Fax: +52 55 56 23 20 91. E-mail address: [email protected] (R. Lo´pez-Arellano). 0924-2031/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.vibspec.2009.07.001
for migraine of moderate severity [7]. Clinical applications of LC include more than 60 studies, in Argentina, Chile, Brazil, Peru and Me´xico, as well as in Europe (Germany and Spain) [8]. LC has been registered as a generic drug in the form of both intravenous and intramuscular injection. So far, there are two registrations in Argentina, three in Chile, six in Mexico and one in Portugal [9]. Despite the increasing use of LC there are not, unfortunately, reported analytical methods to assure detection of changes in its identity and purity, nor to evaluate the concentration of LC in injectable pharmaceutical formulations. The two unique exceptions we found out deal with the quantitative determination of LC in water/oil microemulsions [10] and the measurement of clonixin in human plasma and urine samples [11] by HPLC, although those matrices are not within the scope of the present paper. HPLC-based methods are time-consuming, rather expensive, destructive and need highly skilled staff. On the contrary, spectral techniques like ultra-violet (UV) spectroscopy and near infrared (NIR) spectroscopy are fast, cheap, simple to implement in routine analysis and, therefore, they can be valuable tools for the quality control of LC pharmaceutical injectable formulations. On the other hand, pharmaceutical industries are nowadays involved in large methodological changes due to US and EU governmental initiatives for process analytical technology (PAT) procedures [12,13]. PAT initiatives boostered the development of
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analytical techniques capable of providing real-time process control and improving the understanding of the processes that occur at pharmaceutical unit operations [14]. Out of the many analytical techniques available in pharmaceutical laboratories, NIR has a long-standing success history and it became one of the most suitable tools for a variety of PAT applications and many studies have been developed using it [15]. Not in vain NIR has been described in the European Pharmacopeia since 1997, and it has promoted process understanding within the pharmaceutical industry thanks to its ability to perform remote measurements [16], monitor manufacturing objectives, and identify critical process parameters [17]. Nevertheless it is well-known that NIR has several pitfalls. In particular NIR spectra contain broad overlapping bands which cannot be always ascribed to an individual component. As a consequence, typical calibrations based on the traditional Lambert–Bouguer–Beer’s law are useless and hence whenever the NIR technique is used for quantitative purposes (e.g., the determination of active compounds in pharmaceutical injectable preparations) a calibration must be developed applying a multivariate calibration techniques [18–20]. Many multivariate quantitative regression models for a suite of NIR applications have been reported to quantify the active principle in pharmaceutical solid samples although much less works have been presented for liquid formulations. In both cases, the most common multivariate calibration method was partial least squares regression (PLS) [21]. As a matter of example, Forina et al. [22] presented the principles of multivariate calibration applied to the determination of a drug in a solid formulation and Blanco et al. [23] presented strategies to construct a calibration set to develop and validate a NIR method in solid pharmaceutical preparations. In order to comply with PAT initiatives, NIR methodologies must be challenged with a reference method at least once a year to ensure its ongoing validity and the maintenance of the reference method [24,25]. Since, so far, there is not a standard reference method to quantify LC in injectable solutions, a direct measurement of LC by UV spectroscopy was implemented as ‘reference method’ because of its excellent specificity without matrix interferences. The main objective of this paper is therefore to develop a NIR–PLS methodology to quantify LC in intravenous injection solutions and validate it against international pharmaceutical standards and against a off-line UV-based method (which will be used as an internal reference method). 2. Materials and methods 2.1. Apparatus and software NIR spectra were recorded on a Foss NIRSystems-6500 near infrared spectrophotometer (Raamsdonksveer, The Netherlands). The spectrum was obtained with a flat-bottom optical glass vessel (50 mm diameter) and gold-surface immersible diffusers, providing a fixed 1-mm total path length interactance, the nominal resolution was 10 nm. As one of the most significant sources of NIR spectral variation is random noise which, in turn, may affect the calibration models, the signal/noise ratio (SNR) was monitored at 1686 nm and 2302 nm. 100 scans were selected to get the spectra because of a satisfactory SNR (over 7000 at 1686 nm and over 20,000 at 2302 nm) and adequate time frame (8 min). The instrument was controlled by the Vision 3.1 software, which also provided the spectral pretreatments and multivariate PLS calibration algorithms (the PLS-1 block algorithm was employed throughout). UV spectra were recorded on a Cary 100 UV–vis spectrophotometer from Varian (Palo Alto, CA, US). The absorbance was measured at 283 nm using 1 cm path length quartz cells, in the transmittance mode.
2.2. Calibration samples A stock placebo solution (simulated injectable solution matrix) provided by Mexican Rayere Company (which holds proprietary information on its detailed composition) was used to prepare all calibration standards. Twenty aliquots of placebo were spiked with solid commercial LC, 99.6% purity (Baselux, S.A. Lugano, Switzerland), to obtain a set of calibration samples whose concentrations ranged from 50% to 150%, the typical concentration of LC in commercial intravenous solutions. The pharmaceutical product under study consisted on commercial injectable solutions containing 100 mg/ 2 mL, nominal content of LC as active principle. These standards were employed to develop the UV and NIR calibration models. It is worth noting that even though the European Medicines Agency for the Evaluation of Medicinal Products (EMEA) recommend a calibration range between 80% and 120% the label claim [12], the working range was extended here from 50% to 150% the nominal value in order to detect out-of-specifications samples and, thus, get a NIR model capable of controlling the quality of a range of injectable solutions during the manufacturing processes and different production batches (with different specifications). 2.3. Validation samples The validation set was prepared in the same manner as the calibration set. In total, 15 LC samples were prepared to evaluate the linearity of the UV method and 20 ones to validate the NIR approach. These solutions bracketed a concentration range from 55% to 145% the label claim (100 mg/2 mL) in agreement with the calibration range. They corresponded to five concentration levels (prepared by triplicate) and they were used only to validate the two spectral methods. 2.4. UV measurement of LC In order to perform the UV measurements, the LC standard solutions were prepared in 0.1N sodium hydroxide solution to assure a total, optimal solubility of the LC. Two absorption maxima at 217 nm and 283 nm were obtained. Here the maximum at 283 nm was selected to quantify LC because of its smaller variation in the analytical response. Hence, analysis of routine samples by UV spectroscopy would be performed by diluting 1 mL of an injectable solution with 0.1N sodium hydroxide to obtain a final concentration of 40 mg/mL (25 mL). All standards were prepared on a daily basis. Three of them are detailed next as they were used to evaluate selectivity. To prepare the working placebo solution, an exact volume of the stock placebo solution (1 mL) was transferred to a 50 mL volumetric flask and dissolved in 0.1N sodium hydroxide, 1 mL of this solution was transferred to a 25 mL volumetric flask and the volume made up. A 40 mg/mL LC stock standard solution was prepared by accurately weighing 50 mg of pure LC, transferring them to a 50 mL volumetric flask and dissolving them in a 0.1N sodium hydroxide solution; 1 mL of this solution was withdrawn to a 25 mL volumetric flask and the volume made up. The 40 mg/mL LCspiked placebo solution was prepared by withdrawing an exact volume of the stock placebo solution (1 mL) and accurately weighing 50 mg of pure LC, they were transferred to a 50 mL volumetric flask and dissolved in 0.1N sodium hydroxide; 1 mL of the latter solution was transferred to a 25 mL volumetric flask and the volume made up. 3. Results and discussion The UV and NIR methods were validated with respect to their specificity, linearity, accuracy and precision. Validation was based
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on the guidelines of the International Conference on Harmonization (ICH) [26–28], the European Agency for the Evaluation of Medicinal Products (EMEA) [29] and the National School of Pharmaceutical Chemists and Biologists of Me´xico [30]. Then, the performance of the NIR method will be assessed against the UV one. 3.1. Specificity In order to evaluate the specificity of the spectral measurements, three solutions of LC were prepared (in accordance to the experimental procedure detailed above) and their associated UV and NIR spectra recorded. Fig. 1 shows the spectra of the three solutions detailed in Section 2.4. Remarkably the placebo solution did not interfere on the UV LC spectrum and, so, the UV method was capable of assessing the presence of LC in injectable solutions (matrix of interest), which corresponded to the definition of specificity given by ICH this permits a straightforward calibration process. As it is well-known, NIR spectra depend not only on the chemical structure of the analyte, but also on the physical and chemical properties of the matrix and its composition. As we were interested in measuring the NIR spectra of commercial LC injectable solutions directly, without any pretreatment, the calibration standards for NIR were designed by systematically varying the active principle in matrix-matched standard. Fig. 2 shows the raw NIR spectra of the calibration standards. It can be observed that only the spectral regions around 1200 nm and 1700 nm changed systematically with the LC concentrations. They corresponded to minor spectral bands, being most of the spectra dominated by the water bands and the spectral characteristics of the synthetic matrix or placebo (remarkably, the two broad bands centred at 1470 nm and 1940 nm correspond to water bands). To verify this situation the spectra of the placebo alone, of a 40 mg/mL LC solution and a spiked placebo were recorded and it was found that they overlapped almost totally (with minor differences at the LC standard). Therefore, it was concluded that the NIR measurements were not specific (according to EMEA [29]). 3.2. UV linearity A calibration line was established in the UV region to quantify LC in injectable solutions. Its performance was assessed using twenty-one standards (at seven concentration levels). All samples were prepared daily from scratch in a 0.1N sodium hydroxide solution by weighting different quantities of LC so as to obtain solutions in the 27.51–72.53 mg/mL concentration range. Table 1 presents the performance characteristics of the UV method. The relationship between the added LC concentrations and their
Fig. 1. UV spectra of a placebo (- - - -) injectable solution without LC, a standard LC reference solution with 40 mg LC/mL (—) and a placebo spiked with 40 mg LC/mL (- - -). The three solutions were prepared in 0.1N NaOH.
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corresponding recoveries (i.e., predicted concentrations) was investigated by linear regression. Its slope (0.9952 0.0153) estimates whether there was a bias on the predictions of the UV method whereas the intercept (0.1767 0.7743) provided an error inference of the measurement (note that the intervals correspond to 95% confidence intervals). As the intercept and slope were not statistically different from 0 and 1, respectively, it was concluded that there were neither bias nor systematic error (correlation coefficient = 0.9995). The relative standard deviation (RSD) of the three replicates was lower than 2 for each concentration level, as recommended by ICH. The results shown in Table 1 revealed satisfactory accuracy (all recoveries around 100%) and precision to measure LC in injectable forms. The graphical representation of the results employed for the evaluation of the linearity of the validation set is given in Fig. 4A and they demonstrated a linear relationship between the true LCadded concentrations and its corresponding predicted values. 3.3. NIR linearity To develop the PLS models four data pre-processing options were tested. Despite many options exist here, it was observed that our spectral characteristics were good, without artifacts nor relevant baseline displacements, nor other complex behaviors and, accordingly, it was decided to use only some four widely applied, common options: no pre-processing; smoothing (Savitzky–Golay algorithm; 11 points window, 2nd order polynomial) to increase the S/N ratio and, so, reduce noise; peak-to-peak baseline correction, to cope with possible slight baseline effects not visible for us; a combination of the latter two; along with different spectral ranges (namely, the whole spectral range (1100– 2500 nm), 1116–1278 nm, 1672–1838 nm and a combination of the latter two). These two regions were selected to avoid the undesired influence of the strong water absorption bands and to focus the models on the regions were a variation of the absorbances with the LC solutions appeared. No further efforts were made to select variables using other more objective methods like iPLS or UVE-PLS. Hereinafter, all results will refer to the latter option (1116–1278 nm plus 1672–1838 nm) as it yielded best predictive results after some preliminary assays. The fact that the 1100–2500 nm range yielded worst PLS predictive models than the two selected regions confirmed that the two bands around 1470 nm and 1940 nm did not contain useful information to quantify LC, as they depended on the large absorption of water. Indeed, the typical 1st overtones of the fundamental NH stretching bands would appear at the 1461–1470 range, as well as the overtones of the carbonyl and primary alcohol groups, which became masked by the strong absorption of water. The two spectral regions used to develop the PLS models can be linked to the molecular structure of LC in the following way. The band at 1116–1278 nm is originated by a complex mixture of different overtones of fundamental stretching bands present—in general—on the mid-IR region: the 2nd overtones of the CH bonds in aromatic structures (1142 nm), the 4th overtone of the (very intense) C5 5O bond (1160 nm), the second overtone of the CH in alkenes (1170 nm), the 2nd overtone of the CH in methyl groups (1194 nm) and the 2nd overtone of the CH in methylene groups (1211–1215 nm). The band at 1672–1838 nm is also related to a combination of several overtones of fundamental stretching vibrations: the 1st overtone of the aromatic CH bonds (1685 nm), the overtones of the CH bond in current terminal methyl groups and/or branched chains (1693 nm), the 1st overtone of the CH bond in methylene groups (1725 nm) and the 1st overtone of the CH bond in methylene groups (1762–1765 nm). The optimal number of factors (or latent variables) was established using the leave-one-out cross-validation strategy (on
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Fig. 2. Raw NIR spectra (a) and second-order derivatives (b) of the calibration standards prepared by diluting aliquots of a stock LC solution with the simulated matrix. LC concentrations ranged 50–150% the usual content in injectable commercial forms (i.e., 100 mg/2 mL). (a) Original spectra and (b) 2nd-order derivative spectra. (1) Placebo solution, (2) LC concentration 50%, (3) LC concentration 100% and (4) LC concentration 150%. The insets show the regions where the absorbances varied as a function of the concentration of LC.
the calibration set), which was validated further using an external validation set (to verify that the selected model predicted new samples satisfactorily). The presence of outlying samples was assessed by studying the ‘t vs t’ scores plots (in the X-block) and the ‘t vs u’ plots (to inspect the relation between the X- and YTable 1 Performance characteristics of the UV and NIR methods for the calibration set. The percentages refer to the recoveries of the calibration standards. LC added (mg/mL)
27.51 37.09 42.53 47.05 52.51 62.50 72.53
Recovery by UV
Recovery by NIR
Mean, mg/mL (%)
RSD (%)
Mean, mg/mL (%)
RSD (%)
27.22 36.76 42.63 46.42 51.62 61.94 72.26
0.67 1.76 1.13 0.54 0.59 0.92 0.43
27.25 37.57 42.92 46.98 52.62 62.82 71.89
1.61 0.81 0.55 0.78 0.84 0.70 0.50
(98.92) (99.13) (100.23) (98.66) (98.32) (99.10) (99.63)
(99.04) (101.31) (100.91) (99.86) (100.22) (100.50) (99.12)
blocks); the leverage of the samples and the studentized residuals. Table 2 resumes the average errors for each calibration model, the root mean square error of calibration (RMSEC = [(1/ n k 1)(ypredicted yadded)2]1/2) and the root mean square error of prediction (RMSEP = [(1/n)(ypredicted yadded)2]1/2), where n is the number of samples and k is the number of latent variables (factors) in the PLS model. The multiple correlation coefficient (R), the slope and intercept of the regression line between the predicted and the added values (along with their associated 95% confidence intervals) are also displayed there. After selecting the most promising model by cross-validation, its predictive ability was evaluated by calculating the RMSEP for the external validation set. The most satisfactory model was obtained smoothing the combined spectral range (1116–1278 nm and 1672–1838 nm) and considering five factors, which resulted in the lowest average error of prediction for the validation standards (RMSEP = 0.36), the highest correlation coefficient (0.9996) and the tightest confidence intervals for the slope and intercept. The reason why five factors were needed may be explained because of the
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Table 2 PLS and MLR calibration models developed for the NIR spectra in the combined 1116–1278 nm plus 1672–1838 nm region and the overall spectral range (only MLR). Smoothing was performed employing the Saviztky–Golay algorithm, confidence intervals at 95% probability. BC = peak-to-peak baseline correction. Regression
PLS
Data treatment
Factors
Without pretreatment Smoothing BC Smoothing and BC
Regression
MLR (2nd order derivative)
5 4 5 4 5 4 5
Data treatment
Without pretreatment Smoothing BC Smoothing and BC Without pretreatment Smoothing BC Smoothing and BC Without pretreatment Smoothing BC Smoothing and BC
RMSEC (n = 19)
0.49 0.38 0.23 0.73 0.22 0.37 0.28
RMSEP (n = 19)
Correlation coefficient
0.99 0.44 0.36 1.72 0.33 0.42 0.51
0.9976 0.9993 0.9996 0.9984 0.9984 0.9993 0.9996
Wavelength (nm)
RMSEC (n = 20)
RMSEP (n = 20)
Correlation coefficient
1116–1278
2.96 2.70 4.06 4.32 2.80 2.88 3.28 3.25 1.86 1.87 3.37 3.48
5.48 3.32 2.24 5.68 3.96 4.09 5.44 5.61 2.50 2.06 1.34 3.80
0.8145 0.9323 0.8339 0.7808 0.9061 0.8979 0.8550 0.8483 0.989 0.9776 0.9215 0.9093
1672–1838
1100–2500
complex composition of the placebo (despite we did not know the proprietary information, we were informed that it contained EDTA, sodium hydroxide, water, propyleneglycol, sodium metabisulfite, ethanol and benzylic alcohol) and, therefore, the spectral information that has to be used to predict the LC concentrations was not only on the major absorbances of the NIR spectra, but also on subtle minor details. The predictions of the LC calibration standards were accurate and exhibited no systematic errors as the ‘predicted vs added concentrations’ plot yielded the regression line: predicted concentration ¼ ð0:4966 0:8005Þ þ ð0:9908 0:0158Þ added concentration Both the intercept and the slope were not statistically different from zero and one, respectively. Further, the slopes of the UV and NIR calibration lines were compared by a Student’s t-test, where from it was deduced that they agreed at 95% confidence (experimental t-value = 0.42, tabulated value t0.05;38 = 2.02). All PLS models were found to explain, at least, 99% of the spectral (Xblock) and LC concentrations (Y-block) variances, revealing that the models did establish a non-random relationship between the spectra and the LC concentration. Fig. 3 presents the regression coefficients of the final PLS model, where the most relevant variables which correlated positively to the LC concentration were those around 1124 nm and 1676–1740 nm. The spectral band around 1676 nm was the most important one, which was interpreted as the combination of the 1st overtone of the aromatic CH bonds (1685 nm) and the overtones of the CH bond in current terminal methyl groups and/or branched chains (1693 nm)—see above. Additional studies were made to verify that the PLS model was required instead of a simpler one (it is worth recall here that parsimony should be addressed in order to get stable prediction models). Hence, MLR (multiple linear regression) models were developed on the spectral second derivatives (preliminary assays using the original spectra were unsatisfactory). Fig. 2 gives the general appearance of the second-order derivatives compared to the original spectra. Two insets were included to show the regions where the absorbances varied rather clearly with the concentra-
Real vs predicted values Slope
Intercept
1.00 0.13 1.01 0.07 1.02 0.05 1.03 0.11 1.03 0.11 1.01 0.07 1.03 0.05
0.64 6.05 0.01 3.27 0.86 2.43 0.06 5.14 0.06 5.14 0.05 3.22 0.87 2.62
Real vs predicted values Slope
Intercept
0.71 0.93 1.01 0.72 0.72 0.87 0.64 0.93 0.99 0.85 0.97 0.87 0.97 1.08 0.93 1.07 1.22 0.34 0.87 0.35 1.04 0.81 0.98 0.83
10.99 44.64 0.90 34.40 11.48 41.60 14.96 44.65 0.26 40.55 0.64 4161 0.70 51.42 0.45 51.13 9.76 16.38 5.19 16.49 1.51 38.61 1.09 39.55
tion of the LC standards. They agreed very well with the spectral ranges used for the PLS studies. Trials were made using the overall spectral range and the two ranges employed for PLS (Table 2): the MLR predictions were clearly worse than those from PLS (this was expected and some technical explanations can be found at [31]) and, therefore PLS was the regression method of choice. Note also that multivariate PLS models will outperform traditional MLR ones whenever slight, uncontrolled changes occur on the samples due to, for instance, slight variations on the composition of the commercial LC solid standard or on the composition of the stock placebo solution, as well as other minor changes on the final injectable solutions caused by fluctuations in the manufacturing process (e.g., raw materials). This is called ‘the second-order advantage’ and it consists on the relative insensitiveness of PLS models to the presence of new ‘unexpected’ concomitants in the true unknown samples. Thus, classical MLR models would require—in the most favorable situation—a full assessment of the MLR regression models whenever new concomitants are supposed whereas in PLS this would be seldom
Fig. 3. Graphical representation of the vector of the regression coefficients for the PLS selected model. The horizontal segments denote the regions not used to perform the calibration and they have no meaning at all.
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Table 3 Resume of the validation parameters for the quantitation of LC using the UV and NIR regions. The acceptance criteria were extracted from the US Pharmacopoeia and Ref. [32]. Parameter
Acceptance criteria [32]
Linearity
n Concentration range (mg/mL) Intercept Slope Determination coefficient Correlation coefficient RSDy/x
– – 95% confidence interval include 0 95% confidence interval include 1 r2 0.98 – <3%
Accuracy
n Recovery interval RSDrecovery (%) Average differencea
97–103% <3%
Precision
a
n Intermediate precisionanalyst (%) Intermediate precisionday (%) Uveral RSD (%) Repeatability (%)
UV method 15 20.1–59.7 0.1565 0.7111 1.0025 0.0168 0.9992 0.9996 1.23 18 100.38 0.42 0.85 0.15 0.17 24 0.67 0.99 0.86 0.74
<3%
– –
NIR method 20 25.0–75.29 0.0271 0.6368 0.9992 0.0128 0.9993 0.9997 0.92 18 100.02 0.48 0.97 0.01 0.18 24 1.01 1.29 1.06 0.77
Student’s t-test demonstrated no statistical difference from zero.
the case and models will essentially be unaffected; more details can be found elsewhere [31–33]. Finally, the PLS model was tested for accuracy and precision when slight changes occurred on the solutions. Hence, 10 standards with the ‘same’ concentration of LC (equal to the nominal concentration of the commercial product) were prepared. Operational differences on the solutions would be linked to weighing, make-up of the volume, etc. Furthermore, the reproducibility of the spectral measurements was included in this overall study as the 10 spectra were measured on different days. All spectra were input to the PLS model and a 99.7% LC average value and a 1.3% standard deviation were obtained (i.e., the 95% confidence interval was 99.7 0.9%, which contained the nominal 100% concentration). The precision figures were well below the international acceptance criteria (see Table 3 and explanations below). The NIR method was validated further in accordance with the ‘Guidance on the Use of Near Infrared Spectroscopy by Pharmaceutical Industry’ [29]. The linearity of the NIR-based method was validated using an external set of 20 standards. The predictions of the PLS model were regressed against the ‘true’ concentrations (added concentrations of LC). According to EMEA the intercept and slope should be zero and one, respectively. Fig. 4B presents the results for the validation set, the correlation coefficient was greater than 0.999, indicating a very strong linear relationship between the true (added) and the predicted concentrations. As the confidence intervals of the slope and intercept include 1 and 0, respectively,
the predictions of the NIR method were unbiased. Besides, graphical analysis of the residuals was carried out in the 20– 60 mg/mL range and homoscedasticity was observed, this indicated that the method was linear within the studied working range. 3.4. NIR and UV accuracy As there are not certified reference materials nor standard reference methods to measure LC in injectable solutions, the accuracy of both methods was evaluated in accordance to ICH [34] by spiking a placebo to get a solution at the nominal concentration of the commercial product and, at least, at two other levels. We expanded this requisite for NIR so that the overall calibration range was tested. The UV method followed strictly the ICH rule as this acted as our internal ‘reference’ method. The 95% confidence intervals for the average recoveries of the two methods (and a Student’s t-test) showed that both methods were exact and precise (as required by ICH and EMEA) as the recoveries were around 100% and their relative standard deviations were lower than 2% (as required, [30]); see Table 3. 3.5. NIR and UV precision Precision was studied further as composed of two terms: shortterm precision (repeatability) and long-term precision (reproducibility). Common guides to evaluate the latter recommend the use
Fig. 4. Validation samples. Evaluation of the linearity of the (a) UV and NIR (b) methods following ICH [30]. See text for details.
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of different laboratories and/or equipments although this was not possible in our laboratory. Therefore we decided to evaluate the socalled ‘intermediate precision’ measuring the content of LC in six independent replicates of a sample in 2 different days and two different analysts using the same equipment. An analysis of variance (ANOVA) was performed to evaluate the repeatability as [29,35] pffiffiffiffiffiffiffiffiffiffiffiffi MSwg RSDt ð%Þ ¼ 100 X¯ where RSDt is the repeatability, within-group precision, expressed as percentage RSD, MSwg is the mean square within groups, obtained from the ANOVA table and X¯ is the grand mean of all observations. The intermediate precision or total precision was calculated as a combination of the within- and between-groups effects: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðMSbg þ ðn 1Þ MSwg Þ=n 100 RSDIðFÞ ¼ X¯ where RSDI(F) is the factor-different intermediate precision, expressed as percentage RSD, MSbg is the mean square between groups, MSwg is the mean square within groups, both values obtained from the ANOVA table, X¯ is the grand mean of all observations and ‘n’ is the number of measurements per cell (here n = 6). The RSDI(F) calculated in this way corresponds to the intermediate precision estimate obtained with the alternative design proposed by EURACHEM [36]. The results of the precision study are shown in Table 3. The repeatability, intermediate precision between analysts and days comply with the acceptance criterions defined by ICH, EURACHEM and IUPAC [26,37] for both the UV and NIR methods. 3.6. Comparison of the UV and NIR methods As detailed above, the performance of the NIR methodology was compared against the UV method either in calibration (see Table 1) and validation (see Table 2). The components of the precision (measured as relative standard deviations) were very similar and always lower than 2% for all concentration levels (as required by The National School of Pharmaceutical Chemists and Biologists of Mexico [30]). Table 3 resumes the most relevant results for the validation parameters, compared to the United States Pharmacopeia (USP) criteria. Both methods were found to meet all the ICH requirements [26], as well as those from the Mexican government [30], which confirms their suitability to measure LC in pharmaceutical injectable solutions. As a conclusion of all these studies, it can be stated that regarding the operational characteristics (cost and time), the NIR methodology is slightly cheaper and faster than the UV one because the samples can be measured directly, without adding sodium hydroxide and without diluting them and without a daily calibration (as required for UV analyses). Besides, as any pharmaceutical industry or laboratory will have a NIR system, the implementation of the PLS–NIR models will no add extra costs. Further, while measuring and executing remote quality control by NIR fiber optics is an established issue within the pharmaceutical PAT portfolio techniques, this is not the case with UV–vis fiber optics (despite recent advances allowed for up to 50 m length probes). Nevertheless, recall that a ‘reference’ method (alternately, certified reference materials) will still be needed to assess the NIR methodologies (and this is why a UV–vis procedure was proposed here). 4. Conclusion The UV and NIR methods presented in this work allow for a simple and fast quantitation of LC in injectable solutions. Both
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