NIR-Based Chemical Imaging As An Anticounterfeiting Tool
A look at how the technology works and how is was applied to study antimalarial tablets.
By Jean-Claude Wolff & John K. Warrack, GlaxoSmithKline, and Linda Kidder & E.Neil Lewis, Malvern Instruments
Counterfeit drugs pose a significant and fastgrowing threat to public health and to the pharmaceutical industry. For an individual patient, treatment with a counterfeit drug is at best ineffective, and, at worst, lethal. For pharmaceutical manufacturers, both reputation and revenues are at stake.
Near infrared (NIR) chemical imaging-as distinct from NIR spectroscopy-is a powerful analytical technique that is being used increasingly in the fight against counterfeit drugs. Capable of simultaneously analyzing a number of tablets or capsules, NIR imaging can be automated to provide rapid “genuine/ fake” detection. At higher magnifications, it delivers detailed information about the abundance and location of chemical species. Here we consider the challenges posed by counterfeit pharmaceuticals and the unique attributes of NIR that make it such an important tool in dealing with them.
Counterfeit drugs can take many forms: they may contain no active pharmaceutical ingredient (API); they may have the wrong API; or they may be made using the correct API but in the wrong concentration or form. While intuitively it might appear that those containing the correct API should present the least danger, this is not necessarily the case.
Tablets, for example, are engineered in a highly sophisticated manner in order to ensure optimal in vivo breakdown and dose delivery. Even a counterfeit tablet containing API at the correct concentration may fail to replicate the right delivery action, releasing API at an incorrect rate, at the wrong time or in the wrong place, thus compromising efficacy. Initial detection of counterfeits is often through their packaging, with covert markers and holograms now widely used to identify the genuine product.
Once a counterfeit is uncovered, the potential for harm is assessed in the laboratory. Traditionally the analytical techniques applied are analogous to those used for QA/ QC of the genuine item. They include chromatographic assays which are used to determine what is in the product and at what concentration, and laborious dissolution testing. In practice, the analytical burden associated with counterfeit detection can be high. NIR imaging is a relatively new technique that is generating significant interest within the pharmaceutical industry for this and other applications. Unlike conventional NIR spectroscopy, which provides averaged compositional data, NIR imaging gathers spectra for thousands of pixels across the face of a sample, producing spatially resolved data.
At low magnifications, true NIR imaging systems based on two-dimensional detectors (as opposed to linear mapping systems that acquire data one line at a time) have large fields of view, allowing simultaneous analysis of, say, a complete blister pack or a composite selection of tablets from different sources. This ability to have both real and suspect product in the same field of view means that the identification of counterfeit product can be made without building complex calibration models. Short analysis times of just a few minutes, no requirement for sample preparation, easy automation and minimal manual input, all add to the attractiveness of the technique for rapid and effective counterfeit detection.
Understanding NIR Imaging
Conventional NIR spectroscopy relies on the absorption of light in the wavelength range 700 to 2500 nm to detect the presence of different chemical bonds, and hence species. NIR imaging combines this capability with the spatial resolution capabilities of chemical imaging, not only detecting the presence of chemical species but also pinpointing their location. In place of a single spectrum, NIR imaging simultaneously generates tens of thousands of spectra, each relating to a specific area of the sample. Powerful statistical analysis tools extract pertinent information from the resulting dataset.
In the instrumentation, quartz halogen lamps offer a safe and easily configurable source of illumination, while a liquid crystal tunable filter provides a simple mechanism for wavelength discrimination, maintaining excellent image quality. Spectra are captured using a two-dimensional array, a critical component in a true NIR imaging analyzer. This array eliminates any need to move samples during analysis, accelerating measurement and, importantly, permitting the development of systems that have no moving parts.
Simple optics allow easy configuration of the system for the study of a small area, an individual particle or granule, for example, or a larger region, such as a composite sample of tens of tablets. NIR imaging systems can collect more than 80,000 spectra in minutes, with user-friendly software processing raw data into usable information. Image generation allows a qualitative overview of a sample, which can be useful for making rapid comparisons.
If simple information is required, for example, conformation that a sample is genuine, then this process can be further accelerated by focusing on just a few key wavelengths, slashing data collection and processing times. More detailed quantitative analysis generates objective answers to more challenging questions. Both approaches are important for counterfeit detection, as the following study illustrates.
Investigating A Sample of Anti-Malarial Tablets
Thirty anti-malarial tablets, all white cylinders scored on one side and debossed with a trade name on the other, were imaged using the Sapphire near infrared chemical imaging system. Of these, 10 were the genuine product, while seven of the counterfeits contained paracetamol (acetaminophen) and 13 an unknown, substitute API.
The tablets were analyzed without any sample preparation using a magnification of 40 microns/pixel, with an analysis time of around three minutes for each sample. Initially, a principal component analysis (PCA) was used to extract information from the resulting dataset. PCA is an unsupervised multivariate analysis that essentially extracts distinguishing features from a dataset regardless of their origin (physical or chemical) . It is a useful initial screening tool as can be seen from Figure 1. In this figure, the second principal component score image for each sample is “stitched” into an array, so that all of the samples can be viewed in one image, even though the data for each of these samples was acquired separately.