Near-Infrared Assay and Content Uniformity of Tablets

Near-infrared spectroscopy (NIRS) is an analytical technique based on absorption measured in the near-infrared region of the electromagnetic spectrum that is between the visible and the mid-infrared (IR). The fundamental absorption bands of functional groups occur in the mid-IR and are very strong. Usually, potassium bromide pellets, mulls, or dilutions are required to bring the absorbances within the linear range of the mid-IR detector. The overtone absorptions of these fundamental bands occur in the NIR spectral region and allow direct measurement without sample preparation because of the relative weakness of absorption. The OH, CH, NH, and SH bonds have the strongest overtone absorbances in the NIR region (1).

Figure 1

There is considerable interest in the ability to test solid-dosage form samples more frequently than the 10 per batch specified by the US Pharmacopeia monograph on content uniformity. Interest has increased in using NIR for tablet assay and content-uniformity testing because of concerns of the European Union for better statistically based sampling and the US Food and Drug Administration's initiative on process analytical technology (PAT) for better understanding and monitoring of production (2). NIR can be used as a rapid at-line analysis method to obtain processing feedback in near real time during a tableting campaign. Transmission NIRS through the tablet has been preferred to reflectance NIRS because of heterogeneity within tablets (3, 4). The reflection NIRS technique may be used for coating analysis, but for bulk tablet analysis, the transmission NIRS technique may yield more consistent results.

Figure 2

Laboratory methods for tablet assay and content uniformity are usually time-consuming because they routinely are done by high-performance liquid chromatography (HPLC), which requires lengthy calibration runs, the mixing of buffers, and the procurement and disposal of volatile solvents. Analyzing 10 tablets for content uniformity may take hours, and the results may not be available to tablet-press operators or for batch release for many days or even weeks after the tablets are compressed. Statistical process control (SPC) techniques can be applied while measuring the tablets with NIR in real time during tableting so that assay and content-uniformity problems can be detected before they go beyond acceptable limits.

Figure 3

Experimental

Five batches of tablets (0.25-in. diameter and 100-mg weight) with 0 mg (placebo tablets), 0.1 mg, 0.5 mg, 1.0 mg, and 2.0 mg of chlorpheniramine maleate (CPM) per tablet were formulated and compressed on a tablet press (HT-AP 18 SS-U/I rotary tablet press, Elizabeth Hata International, Inc., North Huntingdon, PA).

Figure 4

The NIR instrument used in the study was XDS MasterLab (FOSS NIRSystems, Laurel, MD), which was capable of automatically measuring multiple tablets after they were positioned in a special tray (see Figure 1). In the insert, a tablet tray is to the left, and to the right is the standards tray containing the wavelength standard (traceable to NIST SRM-2035) for photometric standards. The universal tablet tray used for this study had 20 positions for four different tablet sizes and five positions for the 0.25-in. diameter tablets under test. The tray was loaded twice to scan all 10 tablets. The 10 tablets were scanned in less than 5 min, taking a reference spectrum before scanning each set of five tablets. Spectra were collected in the transmission mode from 800 nm to 1650 nm with 0.5-nm data intervals, and 32 scans were coadded to produce a single spectrum. HPLC analysis was run on each individual calibration and validation tablet after spectra were collected with the NIR instrument. The HPLC reference values and the NIR spectra were used to develop the regression model.

Figure 5

Discussion

Figure 2 shows the raw NIR spectra from the calibration set and a spectrum of pure CPM in green. The pure CPM spectrum was scanned in reflectance and multiplied by a scaling factor to superimpose it over the transmission calibration spectra. By taking the second derivative of the spectra, as shown in Figure 3, the baseline was normalized and the spectral features were enhanced so that the fanning out of the analytical region for CPM was observed at 1138 nm. Figure 4 shows the expanded analytical band demonstrating the linear response from 0.1 mg to 2.0 mg CPM. Smoothing was done on the derivative with a segment of 10 and a gap of zero. A thickness correction was applied as a math pretreatment to correct for tablet thickness and density variance over the region of 1250–1350 nm. The raw spectral variance of the calibration set is large over this region, and the pure CPM has an absorption minimum as seen in Figure 2. Thickness correction is a normalization function offered in Vision software (Foss NIRSystems, Inc.) as a spectral mathematical pretreatment used to correct for path length variance. The integral correction factor was calculated over the range of 1250–1350 nm with a unity-scaling factor.

Figure 6

Integral correction factor =

The correction factor equals the 0.5-nm increment at which the data were collected times the sum of the y-axis values (S'_i) and the y-axis value plus 1 (S'_i+1), divided by 2. Then, the original spectrum was divided by this correction factor throughout the analytical region of 1120–1380 nm.

Figure 8

Partial least squares (PLS) regression was used to develop the prediction model. PLS uses principal component analysis and is a variation of principal component regression (PCR). The correct number of principal components or factors was determined by the Vision software supplied with the instrument by determining where the predicted residual error sum of squares (PRESS) reaches a minimum (5).

Table I: Prediction equation statistics.

Figures 5 and 6 are plots of the PLS factor loadings and weights around the 1138-nm absorption band for CPM. The loadings and weights appear spectra-like and are not noisy, indicating good modeling attributes for the factors chosen. Figure 7 is a plot of the PRESS leading to a model with eight factors. The model chosen used only six of these factors, trading decreased error for robustness (6). The PRESS for six factors was 0.0095. The resulting model had a multiple correlation coefficient (R² ) value of 0.9998 and a standard error of calibration (SEC) of 0.0119. The one-left-out cross-validation demonstrates good predictability with a standard error of cross-validation of 0.0148.

Table II: Repeatability results for 0.1 mg and 0.5 mg tablets of chlorpheniramine maleate (CPM).

Table I contains the model statistics for the CPM prediction equation. Table II is the repeatability results for five tablets measured 10 times each of nominal 0.1 mg CPM and 10 tablets of 0.5 mg CPM. The same tablet placed in the same tray position was scanned 10 times. Data for tablet tray position (number 1) are shown, and only the combined statistical results are shown for the other four tablets from each dosage level. The average precision for the nominal 0.1-mg level was 0.0039. The precision for the nominal 0.5-mg level was 0.0055. The bias was 0.0018 for the lower-level CPM and 0.0057 for the higher level. Table III contains the results from scanning the ten 0.1 mg CPM tablets for content uniformity. Table IV contains the results from scanning the ten 0.5-mg CPM tablets for content uniformity. The Vision software has a convenient routine analysis method for calculating content uniformity automatically. Figures 8 and 9 are X control charts for the 0.1-mg and 0.5-mg CPM content uniformity tests. These charts are for SPC, plotting target label claim and ±15% control limits. The HPLC results showed that some of the nominal 0.5-mg CPM tablets were as high as 0.53 mg CPM.

Table III: Content uniformity test results for ten 0.1-mg chlorpheniramine maleate tablets.

Table IV: Content uniformity test results for ten 0.5-mg chlorpheniramine maleate (CPM) tablets.

Figure 10 shows the NIR predicted CPM concentrations versus the HPLC results for each tablet in the calibration set. One tablet was left out of the calibration set at each level for prediction-model validation. Figure 11 shows the NIR predictions of the validation set versus the HPLC results for CPM on each tablet.

Figure 8

Better precision and accuracy can be achieved with a training set designed with smaller increments around the target label claim. Tablets from on-line press processing can be scanned and sent to the laboratory for HPLC analysis and selected for calibration samples to cover the range using a few extra pilot-batch samples needed to extend the range to ±15% of label claim.

Figure 9

Conclusion

Near-infrared assay and content uniformity of tablets provides a fast, accurate means of monitoring tablets for production that is in step with the US Food and Drug Administration's process analytical technology initiative. The data show promising results that could relieve laboratory workload of high-performance liquid chromatography analysis and bring analysis closer to "real time" for process monitoring. Ten tablets can be analyzed in <5 min. The software provided with the instrument is used for data collection and developing prediction models. The software also provides dedicated routine analysis methods for content-uniformity analysis yielding results in percent label claim and percent relative standard deviation as well as pass–fail indication.

Figure 10

The average repeatability result for five different tablets measured 10 times of nominal 0.1-mg chlorpheniramine maleate was 0.0039 with a bias of 0.0018. The repeatability result for 5 tablets of 0.5 mg chlorpheniramine maleate was 0.0055 with a bias of 0.0057. Better precision and accuracy can be achieved with a training set designed with smaller increments around the target label claim.

Figure 11

Robert Mattes* is an applications scientist at FOSS NIRSystems, Inc., 7703 Montpelier Rd., Laurel, MD 20723 tel. 301.680.7251, fax 301.236.0134, rmattes@foss-nirsystems.com and Denise Root is a marketing manager at FOSS NIRSystems. Om Anand, Maria Gerald Rajan, Namrata R. Trivedi, and Wen Qu are graduate students, and Yingxu Peng, PhD, and Yichun Sun, PhD, are postdoctorate fellows, Department of Pharmaceutical Sciences, University of Tennessee, Memphis, TN.

*To whom all correspondence should be addressed.

Submitted: Nov. 11, 2006. Accepted: Jan. 17, 2007.

Key words: content uniformity, near-infrared, process analytical technology

References

1. Handbook of Near-Infrared Analysis, D.A. Burns and E.W. Ciuczak, Eds.(Marcel Dekker, Inc., New York, NY, 2001).

2. US Food and Drug Administration, Guidance for Industry: PAT—A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance, (FDA,Rockville, MD, Sept. 2004).

3. P.J. Larkin, E. Fruhling, and C. Longfellow, "Comparison of Fourier Transform (FT) and Grating Based NIR Spectrometers for Content Uniformity of Pharmaceutical Solid Dosage Forms," Am. Pharm. Review. 9 (6), 102–109 (2006).

4. Q. Ji et al., "Rapid Content Uniformity Determination of Low-Dose TCH 346 Tablets by NIR," Am. Pharm. Review 9 (5), 20–26 (2006).

5. R. Kramer, Chemometric Techniques for Quantitative Analysis, (Marcel Dekker, Inc., New York, NY, 1998).

6. K.R. Beebe, R.J. Pell, and M.B. Seasholtz, Chemometrics: A Practical Guide, (John Wiley & Sons, Hoboken, NJ, 1998).