Combining Spectroscopy with Automated Imaging: A New Analytical Solution to Meet Regulatory Requirements for Inhaled Products - Pharmaceutical Technology

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Combining Spectroscopy with Automated Imaging: A New Analytical Solution to Meet Regulatory Requirements for Inhaled Products
Analytical data for separate components within orally inhaled and nasal drug products are often required by regulators for new drug applications. New systems that combine Raman spectroscopy with automated imaging support the efficient gathering of such data, including information concerning size and shape distributions for individual components within a formulation.


Pharmaceutical Technology
pp. s12-s15

Integrating automated imaging with spectroscopic methods

Automated imaging has evolved into a reliable and relatively quick method for size and shape analysis, prompting significant interest from the pharmaceutical industry for use in applications that have conventionally been approached using microscopy. With effective dispersion units and standard operating procedures, modern imaging systems can provide relevant size and shape data for both wet and dry samples. Images of thousands of individual particles can be rapidly captured to produce number-based distributions of both size and shape descriptors. By applying sophisticated classification, based on size and shape, these systems can:

  • Detect and quantify foreign particulate matter present in a dose
  • Measure API particle size for CMC product-release testing
  • Investigate the degree of agglomeration in a dispersion
  • Validate a laser diffraction particle sizing method
  • Compare the particle size of the emitted dose for bioequivalence testing.

As with microscopy techniques, however, automated imaging can only differentiate morphologically dissimilar particles. Developments made to combine imaging with spectroscopic analysis overcome this issue.

Spectroscopy techniques have broad relevance for organic compounds routinely handled in the pharmaceutical industry. Raman spectroscopy, for example, provides comprehensive pharmaceutical entity identification and is a familiar tool for compositional analysis. More sophisticated imaging systems combine Raman spectroscopy capabilities with imaging technology to exploit the synergistic potential of the two techniques, enabling in-depth characterization of pharmaceutical products based on integrated size, shape, and chemical entity measurement. Such systems complement and extend analytical options for OINDPs because they allow for more detailed investigation of the size-fractionated samples than is afforded by HPLC. By probing the collected samples particle by particle, it is possible to determine the particle size distribution of API on each impactor plate, or the state of dispersion of API. Such information is lost within any analysis that requires dissolution. Importantly for the detailed scoping of OINDP performance in line with regulatory requirements, these new systems can boost the speed, accuracy, and robustness of key analyses, as the following study demonstrates.

Case study: Using integrated size, shape, and chemical identity analysis to characterize DPIs

In an experimental study, the size and shape of particles in a commercially available DPI were measured using an automated imaging system with integrated Raman spectroscopy (Morphologi G3–ID, Malvern Instruments) probe to obtain information about the two APIs present in the formulation. During measurement, samples were automatically dispersed on to a metal-coated microscope slide, and size and shape data were then collected by standard operating procedures. From these data alone, it was impossible to identify the two different APIs because of their morphological similarity, so Raman spectral analysis was applied to chemically differentiate the components and to determine the particle size distribution of each API individually.


Figure 1: A scattergram plot of the correlations scores (lower diagram), developed by referencing against the library spectra (upper diagram), enables the identification of particles as either API 1 or API 2.
For OINDPs, it is the sub-ten micron fraction that is typically of most interest because it is particles in this size range (more narrowly the sub-five micron fraction) that will tend to deposit in the lung. Raman spectral analysis targeted only particles in the 1–10 m size range, which was defined through the application of an appropriate size classification.


Figure 2: Overlaid CED distributions and individual particle images confirm the morphological similarity of the two APIs present in the DPI formulation.
A spectral reference library was created by using Raman point spectra of the pure components to provide a basis for identifying the particles of each API in the sample. The spectrum of each individual particle was correlated with that of a library component, and scored according to the closeness of this correlation; a correlation score close to one indicates a high degree of similarity. Figure 1 shows two discrete particle populations differentiated on the basis of chemical composition, and shows how the system allows the exploration of relationships between particle morphology and chemical parameters. This plot graphically illustrates the relative proportions of each type of particle within the sample, showing that there are far more particles of API 2 than of API 1. The associated data can be manipulated to generate a separate particle size distribution for each of the two different APIs by applying appropriate classification criteria defined in terms of the measured chemical parameters (Figure 2).


Figure 3: Chemically differentiating the particles makes it possible to accurately measure the relative proportions of each API within the DPI formulation. The proportion of the particles in API 2 is much greater than those in API 1.
The overlaid circular equivalent diameter (CED) distributions of the two APIs shown in Figure 2 suggest they are similar in terms of particle size, while images confirm their comparable shape. However, when the relative proportion of APIs in the 1–10 m size range is analyzed, the qualitative observation that there is a greater quantity of API 2 in the sample is confirmed (see Figure 3). The ability to chemically differentiate the samples yields information that the proportion of API 2 in the dose is around ten times higher than that of API 1, which is important information when investigating product performance and the precise composition of the dose delivered to the patient.


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