Current Advances and Future Trends in Characterizing Poorly Water-soluble Drugs Using Spectroscopic, Imaging and Data Analytical Techniques

Jian   X.   Wu; Frans   van den   Berg; Jukka      Rantanen; Thomas      Rades; Mingshi      Yang

doi:10.2174/13816128113199990398

Abstract

A common feature of many new analytical techniques that allows fast and non-destructive analysis of poorly-water-soluble drug is that they generate a large amount of data with a multivariate character within a short time frame, which in turn highlights the need for advanced data analytical methods in extracting information from the complex data set. The current review critically examines how spectroscopy and imaging techniques can be utilized for fast and non-destructive characterization of solid state poorly water-soluble drug formulations. The first part of the present review describes the basics behind many of the currently used methods including Raman, near infrared (NIR), infrared (IR) spectroscopy and X-ray powder diffractometry in characterizing poorly water soluble drugs. Key emphasis was placed on a critical review of the currently used spectral preprocessing methods, and the influence of selected preprocessing on spectral data sets is exemplified. Further the existing uni- and multivariate spectral data analytical methods in analyzing complex spectral data sets are reviewed, covering estimation of spectral peak moments, peak modeling, variations of Principal Component Analysis (PCA), variations of Partial Least Squares (PLS) analysis and Multivariate Curve Resolution (MCR). The second part of the present review discusses hyperspectral imaging, UV imaging, optical microscopy imaging and process imaging methods suitable for characterization of poorly water-soluble solid state drug formulations. Image analytical techniques suitable for analyzing hyperspectral image data set are described. Further, the application of various image analytical techniques leading to the estimation of nucleation and crystal growth rates from polarized light microscopy is described.

Keywords: Spectroscopy, spectral preprocessing, univariate data analysis, multivariate data analysis, hyperspectral imaging, optical microscopy imaging, process imaging, image analysis.