Multivariate chemometric approach to fiber-optic dissolution testing.

The use of fiber optics in in vitro dissolution testing opens up new possibilities for more powerful data evaluation since an entire UV-Vis spectrum can be collected at each measuring point. This paper illustrates a multivariate chemometric approach to the solution of problems of interfering absorbance of excipients in in vitro dissolution testing. Two different chemometric approaches are tested: multivariate calibration using partial least squares (PLS) regression and curve resolution using multivariate curve resolution alternating least squares (MCR-ALS), generalized rank annihilation (GRAM), and parallel factor analysis (PARAFAC). Multivariate calibration (PLS) can, following the construction of a calibration model from a calibration sample set, give selective and accurate determinations of the active ingredient in dissolution testing despite the presence of interfering absorbance from excipients. Curve resolution (MCR-ALS, GRAM, or PARAFAC) can be applied to dissolution testing data in order to determine the dissolution rate profiles and spectra for the interfering excipients as well as for the active ingredient without any precalibration. The concept of the application of these chemometric methods to fiber-optic dissolution testing data is exemplified by analysis of glibenclamide tablets enclosed in hard gelatin capsules. The results show that, despite highly overlapping spectra and unresolved raw data, it is possible with PLS to obtain an accurate dissolution rate profile of glibenclamide. Applying curve resolution makes it possible to obtain accurate estimates of both dissolution rate profiles and spectra of both the gelatin capsule and the glibenclamide. The application of multivariate chemometric methods to fiber-optic dissolution testing brings a fresh scope for a deeper understanding of in vitro dissolution testing, solving the problem of interfering absorbance of excipients and making it possible to obtain dissolution rate profiles and spectra of these. Obtaining dissolution rate profiles of multiple active pharmaceutical ingredients in tablets consisting of several active compounds is another possibility.

Quantitative impurity profiling by principal component analysis of high-performance liquid chromatography-diode array detection data.

Related organic impurities generally have approximately similar molar absorption coefficients (epsilon) due to their structural similarities. On the assumption that all peaks in an impurity profiling chromatogram have approximately the same maximum molar absorption coefficients (epsilon(max)) and the chromatogram contains one major peak and several much smaller ones, all of which are completely separated, integration of the summed score vectors from the principal component analysis (PCA) decomposition of high-performance liquid chromatography-diode array detection (HPLC-DAD) data will give areas that are quantitatively proportional to the actual content of the compounds. Due to the sequential nature of PCA, the first principal component (PC) will primarily be related to the main compound and all peaks showing a similar spectrum, while the second PC will be related to the impurities with a spectrum different from the main peak. Summing the two score vectors thus makes it possible to take account of different spectra in the score chromatogram, which make the method proposed give better quantitative estimates of the impurities than any single wavelength chromatogram. Multivariate curve resolution alternating least squares (MCR-ALS) is used for comparison. The results are presented for two examples of simulated HPLC-DAD data as well as for three examples of real HPLC-DAD data from impurity profiling. The results show that integration of the score chromatograms can handle differences in the unknown epsilon(max) of the peaks and take account of the different spectra of the impurity peaks, giving quantitative estimates of the content of the impurities that closely correspond to the reference values. The results obtained are also better than integration with the best possible separate wavelength. The method could be a straightforward approach to impurity profiling in order to obtain a good estimate of the content or relative response factors of small chromatographic impurity peaks without knowledge of their molar absorption coefficients and without any precalibration.

Peak purity determination with principal component analysis of high-performance liquid chromatography-diode array detection data.

A method is proposed for the determination of chromatographic peak purity by means of principal component analysis (PCA) of high-performance liquid chromatography with diode array detection (HPLC-DAD) data. The method is exemplified with analysis of binary mixtures of lidocaine and prilocaine with different levels of separation. Lidocaine and prilocaine have very similar spectra and the chromatograms used had substantial peak overlap. The samples analysed contained a constant amount of lidocaine and a minor amount of prilocaine (0.02-2 conc.%) and hence the focus was on determining the purity of the lidocaine peak in the presence of much smaller levels of prilocaine. The peak purity determination was made by examination of relative observation residuals, scores and loadings from the PCA decomposition of DAD data over a chromatographic peak. As a reference method, the functions for peak purity analysis in the chromatographic data system used (Chromeleon) were applied. The PCA method showed good results at the same level as the detection limit of baseline-separated prilocaine, outperforming the methods in Chromeleon by a factor of ten. There is a discussion of the interpretation of the result, with some comparisons with evolving factor analysis (EFA). The main advantage of the PCA method for determination of peak purity over methods like EFA lies in its simplicity, short time of calculation and ease of use.

Simultaneous determination of albumin and immunoglobulin G with fluorescence spectroscopy and multivariate calibration.

A method is proposed for the simultaneous determination of albumin and immunoglobulin G (IgG1) with fluorescence spectroscopy and multivariate calibration with partial least squares regression (PLS). The influence of some instrumental parameters were investigated with two experimental designs comprising 19 and 11 experiments, respectively. The investigated parameters were excitation and emission slit, detection voltage and scan rate. When a suitable instrumental setting had been found, a minor calibration and test set were analysed and evaluated. Thereafter, a larger calibration of albumin and IgG1 was made out of 26 samples (0-42mugml(-1) albumin and 0-12.7mugml(-1) IgG1). This calibration was validated with a test set consisting of 14 samples in the same concentration range. The precision of the method was estimated by analysing two test set samples for six times each. The scan modes tested were emission scan and synchronous scan Delta60nm. The results showed that the method could be used for determination of albumin and IgG1 (albumin, root mean square error of prediction (RMSEP) <2, relative standard error of prediction (RSEP) <6% and IgG1, RMSEP <1, RSEP <8%) in spite of the overlapping fluorescence of the two compounds. The estimated precision was relative standard deviation (R.S.D.) <1.7%. The method was finally applied for the analysis of some sample fractions from an albumin standard used in affinity chromatography.

Use of control sample for estimation of prediction error in multivariate determination of lidocaine solutions with non-column chromatographic diode array UV spectroscopy.

The aim of this study was to investigate the ability of a control sample, of known content and identity, to diagnose and correct errors in the predictions when the same multivariate calibration model was used for analysis of new samples over time. A calibration set consisting of 16 samples with a known content of lidocaine was analysed and two external test sets, A and B, were used for the validation. Test set A contained 15 samples with different concentrations of lidocaine and test set B contained three samples with different lidocaine content, which were analysed six times in order to obtain a measure of repeatability. The multivariate calibration was done with PLS regression on UV spectra collected between 245 and 290 nm. A representative UV spectrum was exported from the collected DAD files by two methods, average spectrum over the whole file and average spectrum over the sample plug. Test set A was analysed further on another three occasions together with a control sample. The results showed that the control sample could be used to give a diagnosis and estimate of the prediction error. Moreover, the measured prediction error of the control sample could also be used to correct the predictions, thereby reducing the prediction error. Finally, some practical considerations regarding use of the proposed DAD method with a control sample are presented. The procedure suggested could lead to an efficient analytical approach where the same calibration model could be used over time without recalibration, which may be attractive in industrial quality control or screening analysis in pharmaceutical research.

Rapid determination of lidocaine solutions with non-column chromatographic diode array UV spectroscopy and multivariate calibration.

A new method for the rapid determination of pharmaceutical solutions is proposed. A conventional HPLC system with a Diode Array Detector (DAD) was used with no chromatographic column connected. As eluent, purified water (Milli Q) was used. The pump and autosampler of the HPLC system were mainly utilised as an automatic and convenient way of introducing the sample into the DAD. The method was tested on the local anaesthetic compound lidocaine. The UV spectrum (245-290 nm) from the samples analysed in the detector was used for multivariate calibration for the determination of lidocaine solutions. The content was determined with PLS regression. The effect on the predictive ability of three factors: flow, data-collection rate and rise time as well as two ways of exporting a representative UV spectrum from the DAD file collected was investigated by means of an experimental design comprising 11 experiments. For each experiment, 14 solutions containing a known content of lidocaine were analysed (0.02-0.2 mg ml(-1)). From these 14 samples two calibration sets and two test sets were made and as the response in the experimental design the Root Mean Square Error of Prediction (RMSEP) values from the predictions of the two test sets were used. When the factor setting giving the lowest RMSEP was found, this setting was used when analysing a new calibration set of 12 lidocaine samples (0.1-0.2 mg ml(-1)). This calibration model was validated by two external test sets, A and B, analysed on separate occasions for the evaluation of repeatability (test set A) and determination over time (test set B). For comparison, the reference method, liquid chromatography, was also used for analysis of the ten samples in test set B. This comparison of the two methods was done twice on different occasions. The results show that in respect of accuracy, precision and repeatability the new method is comparable to the reference method. The main advantages compared with liquid chromatography are the much shorter time of analysis (<30 s) as well as the automatic and simple analytical procedure and the low consumption of organic solvents.