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

The benefits of nasal and pulmonary drug delivery continue to prompt the development of all types of orally inhaled and nasal drug products (OINDPs), including dry powder inhalers, metered dose inhalers, nebulizers, and nasal sprays. The drug-delivery formulation can be a solution, but is more often a solid suspension or a powder blend that may incorporate multiple APIs and/or excipients. This mixture of ingredients can complicate the gathering of component-specific analytical data, such as the particle-size distribution of an individual API, which directly influences the success of drug delivery. Such information is also typically required by regulatory agencies to support new drug applications (NDAs), abbreviated new drug applications (ANDAs), and to prove bioequivalence.

For suspension-based nasal sprays, FDA requires the particle size of API to be measured before and after actuation when demonstrating bioequivalence (1). Similarly, dry-powder inhaler (DPI) development, whether for an innovator or generic product, necessitates probing the degree of agglomeration of a powder blend to assess dispersion and ensure an appropriate level of formulation understanding. Such requirements also extend into the manufacturing environment to ensure the effective control of parameters such as particle size, which define drug-delivery performance (2).

This article examines the regulatory requirements for API-specific particle-size information, with a focus on new technology that combines Raman spectroscopy with automated imaging. New, automated systems can rapidly generate data for particle size and shape, and enable chemical identification of different components in a formulation.

Regulatory requirements for OINDPs

The widely recognized correlation between the in vivo deposition behavior of OINDP drug particles and their size has resulted in extensive regulatory requirements for particle-size information and, more specifically, particle-size data for any present APIs. In addition, regulators emphasize the importance of detecting foreign particles in OINDP formulations, which is an application that similarly calls for reliable material differentiation. Examining guidance in these areas helps to identify analytical techniques that can help with regulatory submissions, and sets into context the benefits that can be achieved with newer technologies relative to traditional microscopy.

For nasal sprays, the chemistry, manufacturing, and controls (CMC) guidance pertinent to both NDAs and ANDAs, highlights the need to ensure that for suspension-based products particle size data are submitted to "...provide information and data on the presence of large particles, changes in morphology of the drug substance particles, extent of agglomerates, and crystal growth" (3).

Furthermore, for both suspension- and solution-nasal sprays there is a regulatory requirement to develop appropriate acceptance criteria for particulate matter that may come from the constituents of the formulation, or from the device–container and associated closure components. CMC guidance for metered-dose inhalers (MDIs) and DPIs also emphasizes the need to apply techniques (conventionally microscopy) to detect large particles and agglomerates that may define the morphology of either API or excipient, and to detect the presence of foreign particulate matter (2). In addition, the guidance highlights the need to identify and control any morphological changes that occur with time, for formulations where the crystalline form of API has an impact on the properties of the drug product, such as bioavailability, performance, and stability.

Particle size data are widely used to demonstrate bioequivalence in an OINDP, since this can be difficult and relatively costly to do using in vivo testing. Guidance relating to the demonstration of bioequivalence in a suspension-based nasal spray specifically suggests in vitro measurement of the particle size of the API before and after actuation (4). Such data confirm that the delivered particle size is as per the specification defined, ensuring an optimal deposition profile that is unaffected by the drug-delivery process.

Accessing component specific data

The preceding examples highlight the importance of differentiating particles within a formulation to gather information for discrete populations, such as for API, excipient, or foreign matter. This need extends right through from the earliest stages of formulation, where data support a knowledge-led approach to development, and then into manufacturing and quality control to ensure consistent production. Techniques routinely applied within this context include cascade impaction and microscopy.

A multistage cascade impactor divides a sample into a series of sized fractions on the basis of particle inertia. These fractions are then analyzed individually, usually to obtain a value for the mass median aerodynamic diameter (MMAD) of API alone (5). High-performance liquid chromatography (HPLC) is the most frequently applied chemical identification technique used to calculate API mass during this analysis; however, the sample-preparation approach used to perform this action means that specific information relating to particle size and shape is lost.

Issues that can be addressed via visual differentiation of the dose, as seen in many examples of foreign-particle detection, are still widely tackled using conventional microscopy methods. As with cascade impaction, microscopy techniques also share the same drawbacks as manual analysis; they are manually intensive and potentially inaccurate because of operator variability. In addition, microscopy techniques rely on manual interrogation of the sample and can be subjective, which compromises data quality. Finally, microscopy also cannot provide useful information where particles are morphologically indistinguishable.


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