Quantitative analysis of less soluble form IV in commercial carbamazepine (form III) by diffuse reflectance fourier transform spectroscopy (DRIFTS) and lazy learning algorithm

• Published in: Analytica chimica acta Vol. 550; no. 1; pp. 191 - 198
• Main Authors: Kipouros, K., Kachrimanis, K., Nikolakakis, I., Malamataris, S.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 26.09.2005; Elsevier
• Subjects: Analytical chemistry; Carbamazepine; Chemistry; Crystal polymorphism; Diffuse reflectance; DRIFTS; Exact sciences and technology; Fast PCR; Lazy learning; PLS; Spectrometric and optical methods; Lazy learning; Diffuse reflectance; DRIFTS; Fast PCR; PLS; Crystal polymorphism; Carbamazepine; Performance evaluation; Fourier transform spectroscopy; Multivariate analysis; Modeling; Spectral data; Reflection spectrometry; Learning algorithm; Quantitative analysis; Drug; Diffuse reflection; Bioavailability; Calibration; Non destructive method; PLS regression; Linearity; Commercial form; Principal component analysis; Reflectance; Polymorphism
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2005.06.063

Carbamazepine is a poorly soluble drug, with known bioavailability problems related to its polymorphism, and a form (C-monoclinic or form IV) less soluble than the pharmaceutically acceptable (P-monoclinic or form III) can be formed under various conditions, possible to occur during drug formulation. Therefore, quantitative analysis of form IV in form III is important to the drug formulators. In the present study, a fast and simple non-destructive method was developed for quantification of form IV in form III, by using DRIFTS spectral data subjected to the standard normal variate transformation (row centering and scaling) and to the lazy learning algorithm. Fast principal component (fast PCR) and partial least squares (PLS) regression methods of multivariate calibration were also used, which were compared with lazy learning. The lazy learning algorithm was performing better than the fast PCR and PLS methods (root mean squared error of cross-validation 1.318% versus 3.337 and 3.058%, respectively). Even with a small number of calibration samples it gave satisfactory predictive performance (root mean squared error of prediction <2.0% versus >3.3% of fast PCR and >2.6% of PLS), in the concentration range below 30% (w/w) of form IV. This is attributed to the capability of handling non-linearity in the relation of reflectance and concentration as well as to local modeling using a pre-selected number of nearest neighbor concentrations.