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Introduction

In pharmaceutical tablet production, one of the key measurements of product qualityis the standard active content uniformity test. This provides ameasure of uniformity of the blendfrom the assay of a number oftablets (typically 10) taken fromthe tablet press. The test usuallyinvolves dissolving the tablet forHPLC and only the active contentis measured. However, the tabletcomprises a number of otheringredients and it is known thatthese ingredients can impactimportant product properties suchas dissolution (a major regulatoryconcern at the moment), stability,bio-availability and variousprocess-quality parameters such as hardness. It is also known thatsome properties can depend onmicroscopic size and distribution

of the ingredients, both activesand excipients.

Modern tablet presses are capableof producing up to 10,000 tabletsper minute, yet the uniformity ofcontent measurement describedabove is the common techniqueused to provide information onthe quality of the blend. Oftenvery little is known about the distribution of ingredients withintablets and this is known to have a significant influence on quality.With costs of poor quality runninginto billions of dollars worldwide,manufacturing is under scrutiny;increasing pressures to reduceexpensive rework costs in production. It is considered thatimproved understanding of spatialdistribution of the ingredientscould prove pivotal in providing a better overall understanding ofthe blending/tabletting process.

In both formulation developmentareas and the process analyticalsupport functions involved introubleshooting poor batches itisxtremely useful to be able toidentify anomalous distributionsof ingredients. NIR microscopyand imaging has already been successfully employed in somemajor pharmaceutical companiesto provide invaluable informationto help solve manufacturing problems. These successes, combined with the opportunitiesfor technology advances, indicatethe technique may even have afuture as a production control tool.

This note describes the applicationof the Spectrum™ Spotlight 350FT-IR imaging system to indicateits potential for troubleshootingreal tablet manufacturing issueswhich are currently of concern toboth industry nd regulators alike.

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Analysis of Solid Dosage FormsUsing the Spectrum Spotlight 350FT-NIR Imaging System

The Spectrum Spotlight 350 imagingsystem was used for the examplesshown here. Samples were placed onmicroscope slides and all data collection performed using theSpotlight software.

Typical spectral resolution was 16 cm-1 number of scans per pixelwas 4 or 8. Tablets can be placeddirectly on microscope slides, or ifpowder blends are to be studied, thepowders may be poured into specialcups and the surface levelled flatprior to analysis.

An example display showing thekind of image which is generated isshown in Figure 1. This shows thetotal NIR diffuse reflectance image ofca. 5 x 5 mm area of an indigestionrelief tablet. Here the sample com-prises hydroxides of aluminium andmagnesium, sucrose and a number ofother minor ingredients. At everypixel in the image one can view theNIR spectrum collected at that pixel,as shown in Figure 2.

Method

The imaging measurement involvescollecting NIR diffuse reflectancespectra over a predefined samplearea where the area is divided into a number of square pixels of either6.25 x 6.25 or 25 x 25 µm. The sample moves below a linear detectorarray, generating images up to 80pixels per second. At each pixel aspectrum in the range 7800-3600 cm-1

is generated. By examining the spectral variation between pixels one obtains information on the spatial distribution of ingredients.Due to the highly scattering nature of the samples, the spatial resolutionof the technique is not well-defined1

but aggregates of typically 60-150 µm(observed in problem batches) can be readily discerned.

In many instances, isolated bandheights due to the component ofinterest may be plotted to show thevariation in concentration across thearea. In order to improve the contrastin the image, some spectral pre-processing of the image may beperformed, for example convertingthe spectra to the second derivativeprior to plotting the peak heights. A number of image enhancementmethods are possible, including principal components-based andsupervised learning approaches. The area of image analysis is one ofconsiderable activity at the moment.

For diffuse reflectance imaging ofsolid dosage forms, the spectralregion of 7500-3600 cm-1 is considered as the most useful as itcombines an information-rich regionof the vibrational spectrum withfewer direct specular reflectance distortions when compared with themid-infrared region. Samples shouldideally be flat over the region examined. It is also advisable toplane samples with a microtomeprior to analysis if they have highlycurved surfaces. In addition this isalso primarily a surface technique,the penetration depth being somewhat sample dependent – sotablet coatings could interfere withthe measurement.

Figure 1: Total NIR reflectance Image.

Figure 2: NIR reflectance spectrum from single 25 µm pixel.

Without data processing, the spectral variation can be dominatedby baseline effects due to local sam-ple morphology. Simple second deriv-ative spectral processing effectivelyremoves the gross baseline effectsmaking it easier to select featuresdue to real chemical differences.Figure 3 shows two such spectrafrom different parts of the deriva-tised image where the individualpeaks due to the various componentsare now easily observed.

Once the bands are established, single frequency images can be readily constructed in the Spotlightsoftware to show the distributions ofthe individual ingredients, as shownin Figures 4a and 4b.

Using a 25 µm pixel size, the imageabove contained some 37,000 spectra. Scanning at two scans perpixel, this corresponded to a measurement time of approximately15 minutes. A 1 x 1 µm image couldbe scanned using similar conditionsin one to two minutes.

Figure 3: Second derivative processed spectra.

Figure 4a: Single frequency image at 7255 cm-1.

Figure 4b: Single frequency image at 6960 cm-1.

Results

This technique has provided furtherinsight into the blending process in a number implementations2. In thisexample, a product exhibited poorcontent uniformity results as measured by HPLC, but there was noobvious reason for this. NIR imagingwas used to investigate blends fromgood batches and problem batches.Samples were taken from the top,middle and bottom of the blender forthe good and poor batches. Chemicalimages were generated for each sample and the images compared(Figure 5). Of particular interest werethe images for the active ingredientdistribution, where a clear differencein distribution of the active component was observed. In additionit was noted that the original activeparticle size before blending wasapproximately 11 µm whereas theaggregates were observed to be

between 100-400 µm. This could bean example of "overmixing" wherethe like particles attract each other to form aggregates. In such samplesthe active domain size is moreimportant than the original particlesize in determining quality of thefinal blend2.

This, and similar results for poorblends have demonstrated the potential of this approach forimproving both the quality of theblending process and provide additional quality-related informationon distribution of a number of ingredients within the blend.

In a typical process, the NIR spectraof all the major raw materials areknown. This simplifies both thedata interpretation and the techniqueused to collect the data. Prior knowledge of the component spectraenables easier optimization of the

data processing parameters. Forexample, if the distribution of theactive is of primary interest, simplecurve fitting of the active spectrumto those in the image can yield thespatial distribution variation or if anactive absorption band can be easilyisolated from those of the otheringredients, simple peak height/areaimaging may suffice.

The potential as a production control tool is also stimulating anumber of developments. For example for a particular process the ability of the product to flow into an exit from the tablet press may be related to the distribution of the lubricant, magnesium stearate.A simple method to image the magnesium stearate absorption maybe sufficient to help monitor possibleaggregation of this component.

Figure 5. Chemical images of active distribution (red). Upper row: Good blend uniformity. Lower row: Poor blend uniformity. Mean active aggregate size (upper row) approximately 200 µm.

Conclusion

There is little doubt the next decadewill see significant evolution in themanufacturing of solid dosage forms.Focus is likely to shift more towardsprocess understanding rather thanend-product testing. Current methodsto characterize the quality of a blendwill be challenged and regulatorypressure will encourage more adoption of new technologies to better understand processes. Thiswill drive both improved processefficiencies and product quality.Imaging technologies will be a keyfactor in understanding blends, andFT-NIR imaging plays a central roleproviding new information andapplication success stories. We havesuccessfully used NIR imaging toprovide invaluable information tohelp resolve a number of existingmanufacturing problems which havedemonstrated fast instrument paybacktimes. These successes are laying thefoundations for future productioncontrol technologies.

In addition, it is expected that thetechnology will be applied to otherdelivery systems such as transdermalpatches, to study new coatings techniques such as dry powder electrostatic coating, counterfeitstudies, and even tablet stabilitystudies. Today, we are still discoveringthe potential applications of this revolutionary technology.

References

1. “Comparing Near-IR and mid-IRmicroscopic reflectance FT-IRImaging;" Spragg, Hoult, Sellors”;presented at FACCS conference2002.

2. “NIR Microscopy ofPharmaceutical Dosage Forms”;Clarke, Hammond; Eur. Pharma.Rev.; Issue 1, 2003, p41.

© 2003 PerkinElmer, Inc. All rights reserved. PerkinElmer is a registered trademark and Spectrum is a trademark of PerkinElmer, Inc. All trademarks depicted are the property of their respective holders or owners. PerkinElmer reserves the right to change this document at any time and disclaims liability for editorial, pictorial, or typographical errors.

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