Detecting Counterfeit Drugs with FT-NIR

Table of Contents

Introduction
Experimental
Results and Discussion
Conclusion

Introduction

Counterfeit prescription drugs are a growing threat worldwide. In many developing countries, the situation is critical. The World Health Organization (WHO) has published reports and fact sheets stating that up to 1% of medicines in well-developed countries are reported to be counterfeit; whereas in less-developed countries, the levels are 30-40%. According to the WHO, about 100,000 deaths a year in Africa are linked to the counterfeit drug trade. The British think tank, International Policy Network (IPN), estimates that globally 700,000 deaths a year are caused by fake malaria and tuberculosis drugs - comparing the death toll to the equivalent of “4 fully laden jumbo jets crashing everyday.” It’s apparent that criminals are becoming experts at making fake products look like the real thing, endangering the health and safety of consumers to make a quick profit. Furthermore, criminals are making products that may meet pharmacopoeia standards, but are repackaging such generic drug formulations and selling them under leading brand names for a higher price

A suitable technology is needed to quickly, easily, and inexpensively screen medical drugs to detect fake products and poor-quality ingredients. Traditional wet chemical or chromatographic methods cannot be used as fast screening tools as they are time consuming and require expertise in sample preparation and handling. However, near infrared (NIR) spectroscopy is rapid, non-destructive, and can be used to differentiate and quantify organic compounds. With no complex sample preparation, NIR spectroscopy can be extremely inexpensive per sample. NIR spectroscopy can also differentiate between generic formulations that are sold as brand name drugs.

The most impressive example of using NIR spectroscopy as a counterfeit drug screening tool comes from the People’s Republic of China. Researchers at China’s National Institutes for Food and Drug Control (NIFDC) have equipped 200 vans with FT-NIR spectrometers turning them into mobile labs for counterfeit drug testing.

Experimental

For this study, 10 mg tablets of simvastatin (a lipid-lowering medication), from four pharmaceutical companies were analysed. Vendor A provided samples from two manufacturing batches. Vendors B, C, and D provided samples from a single manufacturing batch. Six tablets were taken from each batch and their FT-NIR spectra collected using three QuasIR™ 2000 units (Galaxy Scientific, Nashua, NH, USA) as shown in Figure 1. Diffuse reflectance probes were held upward using versatile probe stands and the tablets were placed on top of the probe tip for measurement. Each tablet was measured once at 8 cm-1 resolution and 32 scans on each instrument.

Figure 1. QuasIR™ 2000

Spectral Sage™ software was used for data collection and Conformity Test methods were developed for each vendor. Figure 2 shows the original spectra of the simvastatin 10 mg tablets. The original spectra were pre-processed with a Savitzky-Golay second derivative using 13 smoothing points. The second derivative spectra of simvastatin 10 mg tablets from different vendors are shown in Figure 3.

Figure 2. Original spectra of simvastatrin 10 mg tablets

Figure 3. Second derivative spectra of simvastatin 10 mg tablets

Results and Discussion

Medicines with the same active ingredient and dosage can be manufactured by different companies with different formulations. Those differences will be reflected in their NIR spectra. In order to distinguish medicines from different vendors, (such as brand name versus generic), a test method for a specified dosage must be developed for a company’s medication. This method can also be used to determine if the medicines to be tested are counterfeit or of poor quality.

The most robust method for verification of sample identity is the conformity index, which uses multiples of the standard deviation to define confidence bands. The average and the standard deviation of the test spectra are calculated at each wavelength, and a threshold is set that is some multiple of the standard deviation away from the average.

In the Spectral Sage™ Conformity Test software package, there are four methods available that can be used to confirm whether the test sample is within the confidence bands. The Max CI method is the most sensitive, and if any single wavelength within a selected spectral range is outside the confidence bands, then the sample fails the verification. This method is not always suitable however, because it may result in false positives. The other three methods therefore require that more than one point is outside the confidence bands. The best method to choose depends on the circumstances. The second method sums the y-axis values for all the data points that are outside the confidence bands, and then divides by the number of data points in the selected range. The test fails if the sum is greater than the sum limit. The third method sums the y-axis values for all the data points that are outside the confidence bands, and then divides by the number of data points that are outside the confidence bands. This test also fails if the sum is greater than the sum limit. The fourth method calculates the percentage of points that are outside the confidence bands, and the test fails if the result is greater than the distance match threshold.

Figure 4. Validation result of conformity test method developed for simvastatin 10 mg tablets from Vendor A

In this study, Conformity Test methods were successfully developed for each vendor. Figure 4 illustrates the conformity test validation result for vendor A. Eighteen spectra of simvastatin 10 mg tablets from batch 01 collected on 3 instruments were used as reference data, while spectra collected from batch 02 and tablets from other vendors were used as test data. The spectra were pre-processed with second derivative (13 points) and vector normalization in three regions: 4250-5000 cm-1, 5600-6900 cm-1 and 7500-9000 cm-1. Regions with water bands were left out to avoid any influence of a change in the moisture in the tablets. In this case, the threshold was set at 7 times the standard deviation. The validation result for a working method should have values below the threshold for spectra collected from medicines that come from the same vendor with the same dosage, and values above the threshold for spectra collected from the same medicines manufactured by other vendors. This is shown in Figure 4.

Conclusion

As the results show, the NIR Conformity Test is a sensitive and reliable method that can be used as a tool for counterfeit drug screening and to differentiate authentic drugs from fake or illegitimate drugs.

This information has been sourced, reviewed and adapted from materials provided by Galaxy Scientific Inc.

For more information on this source, please visit Galaxy Scientific Inc.

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