Characterizing A Nasal Spray Formulation From Droplet To API Particle Size - Pharmaceutical Technology

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Characterizing A Nasal Spray Formulation From Droplet To API Particle Size
Regulatory guidance for nasal spray products recognises the effect on drug delivery of the particle size of both the delivered droplets and the suspended active. Here we examine the application of laser diffraction and automated image analysis combined with Raman spectroscopy in this context, highlighting the role these techniques can play in fast and efficient nasal spray characterisation.

Pharmaceutical Technology Europe
Volume 23, Issue 2

Automated particle imaging for API measurement

Figure 3: Overlay of the API CE diameter distribution before and after spraying.
Reliable measurement of the API of a suspension nasal spray formulation relies on the successful differentiation of API particles from any other suspended solids present, such as insoluble excipients for example. Traditionally, manual microscopy is employed for this application, with the operator visually discriminating between particles on the basis of their appearance. However, this approach is both operator dependent and extremely labour intensive, and the results tend to be relatively subjective with low statistical significance, pragmatism limiting the number of particles that can be measured.

Figure 4: Size classification of the API particles before and after spraying.
Using automated particle image analysis, tens of thousands of particles can be characterised in minutes. Individual images of every detected particle are captured, and analysed to generate statistically relevant descriptors of size, shape and transparency. For nasal sprays the formulation is simply dispersed on to a microscope slide prior to measurement. Figure 2 shows example images, captured using automated image analysis, of an API and insoluble excipient present in the nasal spray formulation previously studied, as measured prior to actuation of the device.

Figure 5: The Raman spectra of two individual particles, showing the ability of Raman todifferentiate API and excipients particles. Reference spectra for the API and excipient are shown in the green boxes.
In this case, the active and excipient particles are quite different in terms of their morphological characteristics. Automatic classification of the particle images on the basis of a relevant shape parameter therefore enables identification of the API population and the gathering of data for this discrete group. Figures 3 and 4 show particle size distribution data, specifically for the API, measured before and after spraying. A slight shift in the distribution towards a finer particle size suggests that some shear-induced de-agglomeration occurs within the device during pump actuation.

The authors say…
Although automated image analysis offers significant practical advantage relative to manual microscopy, it shares the limitation of being unable to discriminate between API and excipient particles that are visually identical. The addition of a Raman microprobe and spectrometer to an automated imaging system (Morphologi G3-ID, Malvern Instruments) overcomes this problem, enabling the chemical identification of API particles that are morphologically similar to insoluble excipients. Following acquisition of the images of individual particles, via routine automated imaging, the Raman spectra of selected particles can be measured and correlated with reference spectra to chemically differentiate the population of interest (Figure 5). By using the imaging data in this way to target the acquisition of Raman spectra — so called Morphologically Directed Raman spectroscopy — the time for measurements can be significantly reduced compared with standard Raman mapping methods. In addition, the measurement set up is simplified compared with standard methods, as the position of the particles is automatically determined prior to analysis, removing any operator subjectivity.

Figure 6: Scatterplots for Raman correlation scores of API and excipient particles and examples of associated particle images for the two chemical classes defined by the yellow regions.
To illustrate the benefits of this approach, the nasal spray product previously investigated was analysed. Following dispersion of the sample on to a slide, chemical identification data were gathered for around 9000 particles from a single scan area. Figure 6 shows scatter-plots of the correlation scores for individual particles, referenced to the Raman spectra for the API and excipient. These scatter-plots chemically differentiate populations of API and excipient particles. Of the approximately 9000 particles analysed, around 450 were classified as API, this is consistent with the stated API : excipient ratio of the formulation which is 1:20 by weight.

Figure 7: Particle size distribution of API in a nasal spray based upon Raman chemical classification.
On the basis of this chemical classification it is possible to measure the API particle size distribution (Figure 7). Additionally, it is possible to compare the ingredient-specific particle size and shape distributions for the API and excipient (Figure 8). These data suggest that, in fact, the bulk of the excipient population can be excluded from chemical analysis on the basis of shape alone, by automatic classification using the shape parameter of elongation. Particles with an elongation greater than 0.4 are not API, and so do not need to be chemically identified using Raman. This finding opens up a route to improved measurement times.

Figure 8: Comparison of the particle shape distributions for API and excipient in a nasal spray based upon chemical classification. Shaded area shows particles which can be excluded as being API based upon their elongation alone.
For this product, applying a morphological filter reduces the volume of spectral data which has to be generated, and would ease the analytical burden. In this particular example, classification on the basis of elongation would mean that only around 3000 of the original 9000 particles (approx.) would need to be chemically identified, a time saving of 66%.

Using rapid automated imaging in this way, as a pre-selection tool for chemical analysis of selected particles, is an efficient and powerful way of streamlining measurement towards routine quality control. It also allows for a more objective and statistically sound measurement to compare innovator and generic nasal spray products as required by the FDA draft BE guidance. In addition, of course, the detailed information provided by these techniques allows the insightful investigation of nasal spray products, and indeed other pharmaceuticals, in support of product development goals.


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