Augmentation of near infrared diffuse reflectance and transmittance spectral data for the development of robust PLSBC models for classifying double blind clinical trial tablets.

The water content of clinical trial tablets can be different between and within different tablet batches, depending on the relative humidity conditions during their production, packaging, storage and analysis. These water variations lead to important spectral variations in the near infrared spectral region which can lead to a wrong identification if the classification model was based on unrepresentative data towards the water content. As model development for clinical trial studies needs to be extremely fast - within one working day - with generally only one batch available, the principle of data augmentation has to be applied to render more robust classification models. Therefore, tablets available for constructing the model are being processed in order to increase or decrease their water content and to make them more representative for tablets to be tested in the future. The inclusion of a deliberate water variation is the most efficient way to develop a model, for which no additional model redevelopment will be required to pass the system suitability tests and to obtain a correct identification.

A new approach in the prediction of the dissolution behavior of suspended particles by means of their particle size distribution.

Though various attempts have been made in literature to model the particle size distribution of an active pharmaceutical ingredient (API) in function of the required release profile of the pharmaceutical product, so far one has not succeeded to develop a universal approach in the correlation of particle size distribution and in vitro dissolution data. In this publication, a new approach is presented on the use of particle size distribution data in the prediction of the in vitro dissolution profile of a suspension formulation. For this purpose, various theoretical experiments were done simply on paper and based on the Noyes-Whitney [A.A. Noyes, W.R. Whitney, J. Am. Chem. Soc. 19 (1897) 930-934] equation, the normalized dissolution profiles of various imaginary size distributions were calculated. For each size distribution, its weighted mean diameters were then calculated. Based on these theoretical data, a model could be developed which scientifically explains the dissolution profile of a suspension in function of its volume-weighted mean particle size (D[4, 3]). The applicability of this correlation model could experimentally be confirmed by evaluation of laser diffraction and in vitro dissolution data as they were obtained for different batches of a suspension formulation. This new approach in the correlation between particle size and dissolution may be an important analytical tool in the engineering of the particle size distribution of drug substance, and more precisely monitoring the D[4, 3] volume-weighted mean diameter may allow one to model the dissolution profile of a suspension formulation and thereby its in vivo release profile.

Implementation of a simple semi-quantitative near-infrared method for the classification of clinical trial tablets.

Near infrared transmission spectroscopy combined with chemometrical methods can be applied for identity confirmation of double-blind clinical trial tablets. Samples of two clinical studies, investigating the dose and placebo effect of an experimental drug, were studied. The identity of the blistered tablets was checked using partial least squares beta classification (PLSBC) applied to their NIR transmission spectra. PLSBC is a new supervised classification approach based on partial least squares (PLS) regression combined with beta-error driven class boundaries. It has the ability to limit the probability for misclassification to a known number and therefore providing the method developer a tool for deciding whether the NIR spectra of the different strengths of tablets are specific enough to obtain a robust classification model. The presented approach has the advantage to be applicable on most commercial available near infrared spectroscopy (NIRS) instrumentation software and it can be applied in a GMP environment since validation according to the ICH Q2A and Q2B guidelines on analytical method validation is fast and relatively easy.

Evaluation of dissolution profiles using principal component analysis.

The performance of principal component analysis (PCA) for the evaluation of dissolution profiles is examined and compared with other methods such as the similarity factor and the calculation of the area under the curve. Both simulated and real data from the pharmaceutical industry are used. The PCA scores plots of the dissolution curves provide information about the between- and within-batch variations. Differences in level or shape can be observed in the first two principal components (PCs). Irrelevant irregularities, which have a strong influence on the similarity factor, are neglected in PC1/PC2. To detect outliers in a set of dissolution curves, PCA was preferred above Hotelling's T2 test. In general, PCA is found to be a useful technique to examine dissolution data visually, but however, it does not contain criteria to decide if batches are similar or not. This can be done by combining PCA with the resampling with replacement or bootstrap method to construct confidence limits.

Identification of pharmaceutical excipients using NIR spectroscopy and SIMCA.

Soft independent modelling of class analogy (SIMCA) is applied to identify near-infrared (NIR) spectra of ten excipients used in the pharmaceutical industry. For each class at least 15 excipient samples were collected for the data base, considering different batches and occasionally various suppliers. Therefore the data of the classes are not always homogeneous. The performance of the original SIMCA method, which is usually described in the literature and also applied by the users, carried out at two confidence levels, 95 and 99%, on original data, SNV (standard normal variate transformation) and second derivative pre-processed data, is discussed. Reasons for the rejection rates are given. No objects were assigned to a wrong class using SIMCA.

The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra.

The effect of data pre-processing (no pre-processing, offset correction, de-trending, standard normal variate transformation (SNV), SNV + de-trending, multiplicative scatter correction, first and second derivative transformation after smoothing) on the identification of ten pharmaceutical excipients is investigated. Four pattern recognition methods are tested in the study, namely the Mahalanobis distance method, the SIMCA residual variance method, the wavelength distance method and a method based on triangular potential functions. The performance of the 32 method combinations is evaluated on the basis of two NIR data sets. The first one, measured in 1994, is used to build the classification models, the second, measured from 1994-1997, is used to assess the quality of the models. The best approach for the given data sets is the wavelength distance method combined with de-trending, a simple baseline correction method. More general recommendations for pre-processing excipient NIR data and for choosing an appropriate classification method are given.