Polymorphic Purity Research Articles

Solid-state carbon NMR characterization of the polymorphs of roxifiban

Roxifiban, an experimental antithrombotic prodrug, exists as crystalline forms I and II. A quantitative solid-state nuclear magnetic resonance (NMR) method was developed to characterize the two polymorphs of roxifiban. The differences in the NMR spectra of the polymorphs were utilized in analyses of physical blends of the pure crystalline forms to establish a calibration curve. A detection limit of 9% form II in form I was determined from analysis of a 10% form II blend. Solid-state NMR was a valuable technique to quantify the polymorphic purity of roxifiban where other techniques such as differential scanning calorimetry (DSC) could not be used for this purpose.

Determination of polymorphic forms of ranitidine-HCl by DRIFTS and XRPD.

The identification, characterization and quantification of crystal forms are becoming increasingly important within the pharmaceutical industry. A combination of different physical analytical techniques is usually necessary for this task. In this work solid-state techniques, diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and X-ray powder diffractometry (XRPD) were combined to analyze polymorphic purity of crystalline ranitidine-HCl, an antiulcer drug, H2 receptor antagonists. A series of 12 different mixtures of Form 1 and 2 was prepared by geometric mixing and their DRIFT spectra and XRD powder patterns were obtained and analyzed, either alone or combined together, using Artificial Neural Networks (ANNs). A standard feed-forward network, with back-propagation rule and with multi layer perceptron architecture (MPL) was chosen. A working range of 1.0-100% (w/w) of crystal Form 2 in Form 1 was established with a minimum quantifiable level (MQL) of 5.2% and limit of detection of 1.5% (w/w). The results demonstrate that DRIFTS combined with XRPD may be successfully used to distinguish between the ranitidine-HCl polymorphs and to quantify the composition of binary mixtures of the two.

Determination of polymorphic purity by near infrared spectrometry

Differences in physical and chemical properties between polymorphic compounds (e.g. solubility, dissolution rate, chemical reactivity, resistance to degradation, bioavailability) are highly significant for the pharmaceutical industry, which requires effective methods to control the polymorph of interest in the active principle and also to detect and determine the undesirable form. This paper reports on the characterization and determination of a crystalline and an amorphous polymorphic compound of the antibiotic miokamycin by near infrared spectrometry (NIRS). Based on spectral differences between the two, a new analytical method for determining the crystalline form was developed. The underlying study was approached in both qualitative (classification by binary logistic regression) and quantitative terms (quantitation of the crystalline miokamycin content by multivariate calibration). Logistic regression allows one to accurately classify all samples of amorphous miokamycin and also those contaminated with more than 0.3% of the crystalline form. Low contents (<10%) of the crystalline form were determined by testing various PLS1 calibration models involving different spectral modes and ranges, with relative standard errors (RSEs) in the region of 3.5%. The limit of detection (LOD) thus achieved is 0.37% and coincides with the limit of contamination established by logistic regression. The simplicity, expeditiousness and reliability of the proposed method make it an effective tool for controlling polymorphic purity.

Solid state assay of ranitidine HCl as a bulk drug and as active ingredient in tablets using DRIFT spectroscopy with artificial neural networks.

A new, simple, sensitive and rapid method was developed to analyse the polymorphic purity of crystalline ranitidine-HCI as a bulk drug and from a tablet formulation. Diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy was combined with Artificial Neural Networks (ANNs) as a data modelling tool. A standard feed-forward network, with backpropagation rule and with single hidden layer architecture was chosen. Reduction and transformation of the spectral data enhanced the ANN performance and reduced the complexity of the ANNs model. Spectral intensities from 1738 wavenumbers were reduced into 173 averaged spectral values. These 173 values were used as inputs for the ANN. Following a sensitivity analysis the number of inputs was reduced to 30, or 35, these being the input windows which had most effect on the output of the ANN. For the bulk drug assay, the ANN model had 30 inputs selected from a sensitivity analysis, one hidden layer, and two output neurons, one for the percentage of each ranitidine hydrochloride crystal form. The model could simultaneously distinguish between crystal forms and quantify them enabling the physical purity of the bulk drug to be checked. For the tablet assay, the ANN model had 173 averaged spectral values as the inputs, one hidden layer and five output neurons, two for the percentage of the two ranitidine hydrochloride crystal forms and three more outputs for tablet excipients and additives. The ANN was able to solve the problem of overlapping peaks and it successfully identified and quantified all components in tablet formulation with reasonable accuracy. Some of the advantages over conventional analytical methods include simplicity, speed and good selectivity. The results from DRIFT spectral quantification study show the benefits of the neural network approach in analysing spectral data.