Alexey L. Pomerantsev

Process analytical technology: a critical view of the chemometricians

The role of chemometrics in process analytical technology (PAT) solutions development is presented in the review on the basis of publications from 1993 to 2011. Main areas of application, stages of implementation, instruments, and chemometric methods used for the PAT implementations are reviewed. Generally speaking, PAT is considered to be an approach applicable not only in pharmaceutical industry but also in any production area such as food industry and biotechnology. PAT is claimed to be a new flexible manufacturing concept that accounts for variability and adapts the process to fit it. Copyright

Quality control of packed raw materials in pharmaceutical industry.

The possibility of routine testing of pharmaceutical substances directly in warehouses is of great importance for manufactures, especially taking into account the demands of PAT. The application of NIR instruments with remote fiber optic probe makes these measurements simple and rapid. On the other hand carrying out measurements through closed polyethylene bags is a real challenge. To make the whole procedure reliable we propose the special trichotomy classification procedure. The approach is illustrated by a real-world example.

Concept and role of extreme objects in PCA/SIMCA

For the construction of a reliable decision area in the soft independent modeling by class analogy (SIMCA) method, it is necessary to analyze calibration data revealing the objects of special types such as extremes and outliers. For this purpose, a thorough statistical analysis of the scores and orthogonal distances is necessary. The distance values should be considered as any data acquired in the experiment, and their distributions are estimated by a data‐driven method, such as a method of moments or similar. The scaled chi‐squared distribution seems to be the first candidate among the others in such an assessment. This provides the possibility of constructing a two‐level decision area, with the extreme and outlier thresholds, both in case of regular data set and in the presence of outliers. We suggest the application of classical principal component analysis (PCA) with further use of enhanced robust estimators both for the scaling factor and for the number of degrees of freedom. A special diagnostic tool called extreme plot is proposed for the analyses of calibration objects. Extreme objects play an important role in data analysis. These objects are a mandatory attribute of any data set. The advocated dual data‐driven PCA/SIMCA (DD‐SIMCA) approach has demonstrated a proper performance in the analysis of simulated and real‐world data for both regular and contaminated cases. DD‐SIMCA has also been compared with robust principal component analysis, which is a fully robust method. Copyright

Noninvasive detection of counterfeited ampoules of dexamethasone using NIR with confirmation by HPLC-DAD-MS and CE-UV methods

Application of near-infrared (NIR) measurements together with chemometric data processing is widely used for counterfeit drug detection. The most difficult counterfeits to detect are the “high quality fakes”, which have the proper composition but are produced in violation of technological regulations by underground manufacturers. This study uses such forgeries and addresses important issues. The first is the possibility of applying the NIR/chemometric approach to the detection of injectable formulations of drugs (in this case dexamethasone), which are aqueous solutions with low concentration of active ingredients, directly in the closed ampoules. The second issue is the comparison of NIR/chemometric conclusions with detailed chemical analysis.

Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing: Trends in chemometrics…

In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.

Confidence intervals for nonlinear regression extrapolation

Abstract The various methods of confidence intervals construction for nonlinear regression are considered. The new method named by a method of associated simulation (the AS-method) is proposed. Using computerized simulation, it is shown on the example that only two methods, the bootstrap and the AS-method, give a satisfactory accuracy. The advantage of the AS-method is the speed. In comparison with the bootstrap, the prize is at least 10 000 times. This method may be applied when regression parameters estimates are obtained by the maximum likelihood method. It was proposed to use the AS-method when extrapolation of complicated physico-chemical model is performed to predict the behavior of the model in the area far from observation.

Prediction of the aging of polymer materials

Abstract The article deals with prediction of aging of multiplex polymer systems under conditions where the direct measurements of required properties are either impossible or difficult. To receive the reliable forecast, it is necessary to use physically reasonable models of processes. Solution of such problem and brief mathematical background is described in the article.

Quantitative risk assessment in classification of drugs with identical API content

When combating counterfeits it is equally important to recognize fakes and to avoid misclassification of genuine samples. This study presents a general approach to the problem using a newly-developed method called Data Driven Soft Independent Modeling of Class Analogy. The possibility to collect representative data for both training and validation is of great importance in classification modeling. When fakes are not available, we propose to compose the test set using the legitimate drugs analogs, manufactured by various producers. These analogs should have the identical API and a similar composition of excipients. The approach shows satisfactory results both in revealing counterfeits and in accounting for the future variability of the target class drugs. The presented case studies demonstrate that theoretically predicted misclassification errors can be successfully employed for the science-based risk assessment in drug identification.

Chemometric aided NIR portable instrument for rapid assessment of medicine quality.

The progress in instrumentation technology has led to miniaturization of NIR instruments. Fast systems that contain no moving parts were developed to be used in the field, warehouses, drugstores, etc. At the same time, in general these portable/handheld spectrometers have a lower spectral resolution and a narrower spectral region than stationary ones. Vendors of portable instruments supply their equipment with special software for spectra processing, which aims at simplifying the analysts work to the highest degree possible. Often such software is not fully capable of solving complex problems. In application to a real-world problem of counterfeit drug detection we demonstrate that even impaired spectral data do carry information sufficient for drug authentication. The chemometrics aided approach helps to extract this information and thus to extend the applicability of miniaturized NIR instruments. MicroPhazir-RX NIR spectrometer is used as an example of a portable instrument. The data driven soft independent modeling of class analogy (DD-SIMCA) method is employed for data processing. A representative set of tablets of a calcium channel blocker from 6 different manufacturers is used to illustrate the proposed approach. It is shown that the DD-SIMCA approach yields a better result than the basic method provided by the instrument vendor.

On the type II error in SIMCA method

A novel method for theoretical calculation of the type II (β) error in soft independent modeling by class analogy is proposed. It can be used to compare tentatively predicted and empirically observed results of classification. Such an approach can better characterize model quality and thus improve its validation. Method efficiency is demonstrated on the famous Fisher Iris dataset and on a real‐world example of quality control of packed raw materials in pharmaceutical industry. Copyright