The US Food and Drug Administration's process analytical technology (PAT) initiative reflects the need for improved quality control in the pharmaceutical industry. As the complexity of pharmaceutical products increases, quality control becomes more costly and difficult. New analytical instrumentation should be developed to address the quality control problems associated with more complex pharmaceutical products. Dielectric spectroscopy, one of many process analytical technologies, has been used for several decades. As any other technology, it is continuously evolving. New instrumentation, algorithms, and materials broaden the applicability and capabilities of this method. Selecting the optimal architecture for a sensing system intended for each specific application requires a good understanding of main principles and engineering trade-offs common for this field. This article is written primarily for field practitioners who need to make a judicious choice of the best measurement technique for their application. Main principles and design features of dielectric sensors are discussed in the framework of pharmaceutical applications.

The range of potential applications of dielectric spectroscopy is quite broad. Virtually any physical process change leads to changes in dielectric properties of samples. Process variability is a primary concern for the pharmaceutical industry (1). Exposure to mechanical and thermal stress can cause a change in the physical properties of pharmaceuticals. Such variations are important to control because physical properties generally determine the efficacy of the drug. For instance, tablet coatings control the rate of drug delivery within the body of a patient (2–5) and influence the bioavailability of the drug. In addition, coatings protect the active ingredient from chemically harsh environments in the body (6). Similarly, API content in a given sample determines the potency of the drug. The drying process of pharmaceuticals is critical because 70% of global granulated pharmaceutical product is made using wet granulation (7). In wet granulation, liquid binding gels are used to facilitate bonding between active ingredients. At this stage, it is important to measure moisture because specific moisture levels are required for the formation of the correct-size granules. Further, in high-shear wet granulation methods, incorrect moisture levels can lead to a process-induced transformation (PIT) (8). In these transformations, the properties of the active ingredients can change, resulting in reduced efficacy.

Overview of sensing methods

NIR spectroscopy is widely used to measure API content, distribution of contaminants, moisture content, and polymorphism determination (9). Similarly, Raman spectroscopy has demonstrated success in measuring pharmaceutical reaction times, polymorphism, and differences between solid-state forms (8).

Digital imaging methods are used to monitor particle size in pharmaceutical powders (10). Optical methods have been used for inspection of tablet coatings (6), measurement of constituent concentration of pharmaceutical powder mixtures (11), and quantitative analyses of ascorbic acid in pharmaceuticals (11, 12). For example, laser induced breakdown spectroscopy (LIBS) is used to measure coating thickness of tablets (13).

Previous work using dielectric spectroscopy. Dielectric spectroscopy is a promising method to study physical properties of pharmaceutical solutions, colloids, microcapsules, gels, and emulsions. Craig provides a thorough review of these applications (14), which are summarized in this section.

Pharmaceutical solutions are important because the solubility of a drug in the solvent directly influences its rate of disassociation in the body, which in turn impacts bioavailability. Dielectric properties such as the static dielectric constant of solutions has been used to study the solubility of cosolvent systems, reaction rates of pharmaceutical solutions, and drug stability (14).

in which D is the diffusion coefficient of counterions with a particle radius of a, and f_c is the loss peak frequency. The inverse-square relationship between relaxation frequency and particle radius was confirmed in a subsequent study (16).