Analytical Techniques: Using Near-Infrared Spectroscopy and Chemometric Modeling to Simultaneously Determine Ibuprofen and Caffeine Concentration in Softgels

Near-infrared spectroscopy (NIR) has become more popular in the pharmaceutical industry in recent years as a rapid analytical tool for identifying raw materials and monitoring manufacturing processes. NIR allows optimal handling of specific and relevant information and is non-destructive and non-invasive, giving it an advantage over conventional pharmacopoeial techniques, such as high-performance liquid chromatography (HPLC), which require additional reagents, supplies, and time for sample preparation¹. 

NIR is based on molecular vibrations generated when incident radiation (commonly from a halogen lamp with a tungsten filament) interacts with matter and stretches or bends the material’s molecular bonds. Stretching refers to increases and decreases in bond length, while bending refers to changes in the angle between molecular bonds². This interaction between radiation and matter produces an analytical response known as a spectrum, which can be detected using a lead sulfide (PbS) detector. 

For NIR spectroscopy, this spectrum is the result of a combination of bands and overtones from fundamental molecular vibrations. These overtones occur at frequencies between 12,500 and 4,000 cm-1, which is roughly two to three times the frequency of fundamental vibrations, which are detected using mid-IR spectroscopy. The hydrogen bonds OH, CH, and NH are band absorptions frequently observed in NIR spectra². 

NIR has been implemented in many stages of the manufacturing process, but little information is available about applying this method to soft gelatin capsules (softgels). A softgel is a solid dosage form consisting of a hydrophilic or lipophilic fill formulation containing one or more APIs hermetically sealed inside a one-piece gelatin shell. Softgels are widely used to improve the bioavailability of poorly water-soluble and poorly permeable APIs, to improve content uniformity of low-dose drug products, and to enhance patient adherence by masking unpleasant scents and tastes^{3, 4}. 

Determining the API content in softgels is commonly done using conventional techniques such as HPLC. However, analyzing two APIs in a softgel increases the complexity, making conventional analysis difficult. 

Mid-IR spectra allow the identification of samples by comparing the sample spectra with standard spectra. However, because NIR spectra are not as structurally selective as mid-IR spectra, chemometric techniques such as multivariate analysis can be applied to the NIR spectra data to extract chemical information and increase analyte selectivity. 

NIR model development 

To develop an NIR quantification method requires two principal steps: calibration and validation. Calibration involves preparing samples for a calibration set with changes to the interesting variable. If the interesting variable is API concentration, a design experiment would be applied to obtain samples with lower and higher API concentrations with reference to a target concentration to develop a calibration curve with an associated equation. The equation can then be used to predict the API concentration in samples not belonging to the calibration set. 

Validation can be internal or external. Internal validation estimates the reproducibility of a developed model and avoids over-interpretation of data. This can be performed using cross-validation techniques such as chemometrics or leave-one-out validation, among others. External validation is performed using external samples that do not belong to the calibration set. 

Using HPLC to determine the concentration of two APIs in soft gelatin capsules requires the development of a different method for each API, which consumes more materials and time than using a single NIR-chemometrics method to predict the concentration of both APIs⁵. The following study describes the development of an NIR-chemometrics method to determine the concentrations of ibuprofen and caffeine in softgels. 

To develop this method, 15 formulation samples were prepared at laboratory scale with different API concentration levels from 0 to 120 percent, with 100 percent being the target concentration for each API, as shown in Table 1. For example, a formulation might contain 50 percent of the target ibuprofen and 100 percent of the target caffeine. The calibration set also included a placebo and capsules from pilot scale to increase the model’s selectivity and sensitivity.

A sample set for validation was prepared at concentration levels of 90, 100, and 110 percent of target concentration for both APIs. These formulations were not used in the calibration set but were instead used to evaluate the predictive power of the models and discriminate between them, with the goal of acquiring the best model with a low root mean square error of prediction (RMSEP) and an R2 value close to 1. 

NIR spectra data were collected using a Buchi NIRFlex N-500 spectrometer with the solid measurement cell and an XL adapter, which allows the measurement of irregular solid samples or direct measurement with a transparent plastic bag, in the wavelength range from 10,000 to 4,000 cm-1. This instrument has a resolution of 8 cm-1 and a polarization interferometer with tellurium dioxide (TeO2) wedges. Because the formulation is a suspension, the spectra data was acquired using the instrument’s transflectance mode. 

NIR models were elaborated for each API individually. Blending the suspension caused scattering in the spectrum data and significant differences among the acquired spectra. Consequently, different pretreatments were applied individually or in combination to attempt to minimize scattering, baseline drift errors, and background noise and increase the signal-to-noise ratio^{6, 7}. 

Once the spectra data had been collected (Figure 1), chemometrics software was used to apply a multivariate analysis tool such as partial least squares regression (PLS). The ibuprofen model was developed using a spectral range from 10,000 to 4,000 cm-1, and a Savitzky-Golay smoothing and derivative filter was applied (Figure 2a), while a Savitzky-Golay differentiation filter with a first polynomial order and standard normal variate (SNV) normalization was applied to develop the caffeine model (Figure 2b). Next, a partial least squares (PLS) regression was performed. PLS regression is a method used when prediction is the goal and many highly collinear factors are present in the data set.

Figure 3 shows the ibuprofen model, which had an R2 of 0.9993 for calibration, an R2 of 0.9991 for internal validation, and a prediction error of 0.004. The caffeine model resulted in an R2 of 0.9986 for calibration, an R2 of 0.9981 for internal validation, and a prediction error of 0.002, as shown in Figure 4.

These equations were used to predict a pilot-plant sample set. To evaluate the models’ robustness and prediction power, their selectivity, linearity, accuracy, precision, and repeatability were tested.

NIR model external validation 

The ibuprofen and caffeine calibration models were performed with external samples prepared at laboratory scale as well as samples from the final product, taking into account that these models present good linearity with an R2 close to 1 (Table 2). It was important to analyze this internal validation because it allowed for assessment of the appropriate parameters via identification and quantification of error and uncertainty. Thus, an external validation was essential to verify that the calibration models were accurate, precise, and lineal⁸.

References 

1. Ryan Kershner, Mike Kayat, Gary Ritchie, Sigrid Pieters, Chris Heil, Rick Cox, George Reid, and Mark Mabry, “Raman/NIR roundtable,” American Pharmaceutical Review, October 2012, https://www.american pharmaceuticalreview.com/Featured-Articles/ 122433- Raman- NIR- Roundtable/. 

2. Jerome Workman, “Interpretive spectroscopy for near infrared,” Applied Spectroscopy Review, 1996, Vol. 31, No. 3, pages 251-320, doi: 10.1080/05704929608000571. 

3. Rampurna Prasad Gullapalli, “Soft gelatin capsules (Softgels),” Journal of Pharmaceutical Sciences, 2010, Vol. 99, No. 10, doi.org/10.1002/jps.22151. 

4. Procaps, “Softgels,” 2017, www.procapslaboratorios. com/softgels?lang=en-us. 

5. Michal Douša et al., “Esterification of ibuprofen in soft gelatin capsules formulations identification, synthesis and liquid chromatography separation of the degradation products,” Journal of Chromatographic Science, 2017, Vol. 55, No. 8, pages 790-797, 2017, doi: 10.1093/chromsci/ bmx036. 

6. Xiaoliang Wang, Qiang Fu, Jinfang Sheng, Xin Yang, Jianzhong Jia, and Wei Du, “Construction of a universal quantitative model for ibuprofen sustained-release capsules from different manufacturers using near-infrared diffuse reflection spectroscopy,” Vibrational Spectroscopy, 2010, Vol. 53, No. 2, pages 214-217, doi: 10.1016/j. vibspec.2010.03.002. 

7. Marcelo Blanco and Anna Peguero, “Influence of physical factors on the accuracy of calibration models for NIR spectroscopy,” Journal of Pharmaceutical and Biomedical Analysis, 2010, Vol. 52, No. 1, pages 59-65, doi: 10.1016/j.jpba.2009.12.009. 

8. European Medicines Agency, “Guideline on the use of Near Infrared Spectroscopy (NIRS) by the pharmaceutical industry and the data requirements for new submissions and variations,” January 2014, www.ema.europa.eu/ en/use-near-infrared-spectroscopy-nirs-pharmaceutical-industry- data-requirements-new-submissions.