Jon Gabrielsson

Diagnostic properties of metabolic perturbations in rheumatoid arthritis

IntroductionThe aim of this study was to assess the feasibility of diagnosing early rheumatoid arthritis (RA) by measuring selected metabolic biomarkers.MethodsWe compared the metabolic profile of patients with RA with that of healthy controls and patients with psoriatic arthritis (PsoA). The metabolites were measured using two different chromatography-mass spectrometry platforms, thereby giving a broad overview of serum metabolites. The metabolic profiles of patient and control groups were compared using multivariate statistical analysis. The findings were validated in a follow-up study of RA patients and healthy volunteers.ResultsRA patients were diagnosed with a sensitivity of 93% and a specificity of 70% in a validation study using detection of 52 metabolites. Patients with RA or PsoA could be distinguished with a sensitivity of 90% and a specificity of 94%. Glyceric acid, D-ribofuranose and hypoxanthine were increased in RA patients, whereas histidine, threonic acid, methionine, cholesterol, asparagine and threonine were all decreased compared with healthy controls.ConclusionsMetabolite profiling (metabolomics) is a potentially useful technique for diagnosing RA. The predictive value was without regard to the presence of antibodies against cyclic citrullinated peptides.

Recent Developments in Multivariate Calibration

This review covers the area of multivariate calibration; from pre-processing of data prior to modeling and applications of regression methods for calibration and prediction. The importance of pre-treatment of data is highlighted with many of the recently developed methods together with traditional methods. Several articles provide comparisons between different pre-processing methods. Methods for data from coupled chromatographic methods, which have found increasing use and where data pre-processing is a prerequisite for multivariate modeling, are also included. Many of the novel chemometric methods deal with model complexity and interpretation. A diverse set of applications are also presented and references are also given to early papers, making it possible to acquire a deeper knowledge of methods of interest.

Multivariate Methods in the Development of a New Tablet Formulation

Abstract The overall objective of this article is to use an efficient approach to find a suitable tablet formulation for direct compression. By using traditional approaches to statistical experimental design in tablet formulation, the number of experiments quickly grows when many descriptive variables or many excipients are included. To facilitate the screening process, a multivariate design, which allows a systematical evaluation of a large number of excipients with a limited number of experiments, was implemented. Formulations with acceptable values for disintegration time and crushing strength were obtained with some of the formulations in the present study. The multivariate experimental design strategy yielded PLS models that will be used to identify a region of interest for the optimization. The strategy is general and can be applied in many different areas of pharmaceutical research and development.

Robustness Testing of a Tablet Formulation Using Multivariate Design

ABSTRACT A total of 45 experiments were carried out to evaluate the robustness of two similar tablet formulations—a product of two strengths—with respect to normal batch-to-batch variation of the excipients and the active pharmaceutical ingredient. The formulations consist of 10 ingredients. Because of the differing amounts of active pharmaceutical ingredients, the two formulations also differ in the amounts of two of the diluents and one of the binders. The excipients and active pharmaceutical ingredient were characterized in terms of multiple variables, and principal properties were calculated with principal component analysis. A Plackett and Burman design was applied to the principal properties. The relationships between the design factors and two responses, mean disintegration time and mean crushing strength, were evaluated by using regression methods. Both formulations were found to be robust under controlled conditions.

Multivariate methods in developing an evolutionary strategy for tablet formulation.

The aim of this study was to develop a new strategy for choosing excipients in tablet formulation. Multivariate techniques such as principal component analysis (PCA) and experimental design were combined in a multivariate design for screening experiments. Of a total 87 investigated excipients, the initial screening experiments contained 5 lubricants, 9 binders, and 5 disintegrants, and 35 experiments were carried out. Considering a reduced factorial design was used, the resulting PCA and partial least squares (PLS) models offered good insight into the possibilities of tablet formulation. It also offered solutions to the problems and clearly gave directions for optimum formulations. Further, it offered several alternatives for achieving quality formulations. Additional experiments conducted to validate and verify the usefulness of the model were successful, resulting in several tablets of good quality. The conclusion is that a multivariate strategy in tablet formulation is efficient and can be used to reduce the number of experiments drastically. Combining multivariate characterization, physicochemical properties, experimental design, multivariate design, and PLS would lead to an evolutionary strategy for tablet formulation. Since it includes a learning strategy that continuously incorporates data for new compounds and from conducted experiments, this would be an even more powerful tool than expert systems.

Multivariate Methods in the Development of a New Tablet Formulation : Excipient Mixtures and Principal Properties

ABSTRACT A tablet formulation for direct compression has previously been studied using multivariate design. An optimization study of one of the most important tablet properties, disintegration time, revealed that excipients with Principal Properties (PPs) that were predicted as suitable by the model were not represented within the studied material. The feasibility of using mixtures of excipients in the multivariate approach to tablet formulation to solve this problem has been investigated in the present study. By mixing different excipients of the same excipient class, it should be possible to obtain mixtures with the predicted PPs, which in turn should give a formulation with the desired properties. In order to investigate the utility of this approach, separate mixture designs were applied to both binders and fillers (diluents). As reported here, the Partial Least Squares Projections to Latent Structures (PLS) model developed in the previously published screening study has been validated in the sense that the interesting region of the PP space identified in it has been shown to contain excipients, pure or mixed, that give the formulation suitable properties. Formulations with suitable properties were found with the mixture experiments. The local models also offer several alternatives for the composition of the formulation that yield the desired disintegration time.

Multivariate methods in the development of a new tablet formulation: optimization and validation.

In a previous study of the development of a tablet formulation approximately 100 excipients were characterized in screening experiments using multivariate design. Acceptable values for important responses were obtained with some of the formulations. The relationships between the properties of the excipients and the responses were evaluated using PLS. In this study additional experiments were performed in order to validate models obtained from the screening study and to find a formulation of suitable composition with desired tablet properties. A formulation with the desired disintegration time was found with the additional experiments and the agreement between observed and predicted values was fair for the tablets that did disintegrate. A limitation of this study was that tablets from four experiments did not disintegrate within the set time limit. The lack of agreement between observed and predicted values of these four experiments was probably due to the nature of one of the factors in the design. Considering the reduced experimental design the results are still encouraging.

Evaluation of Preprocessing Methods

This chapter describes a strategy based on orthogonal projections to latent structures (OPLS) methodology for evaluation of different preprocessing techniques applied to spectroscopic data. Preprocessing multivariate data always involves a risk of removing variation, which contains information that is related to the problem at hand. The O2PLS (bidirectional OPLS) modeling provides an easy overview of the original data in comparison to the different preprocessed data sets. The model-based analysis of the different types and amounts of variation associated with each type of preprocessing method provides the user with a number of options, for example, to verify the desired effect of pretreatment. It is also possible to diagnose the unwanted variation and thus establish a good knowledge base for deciding the appropriate method of pretreatment. Ultimately, applying OPLS methodology before multivariate modeling will aid in deciding whether preprocessed data should be used and, if so, which method should be applied. The approach is general and should be applicable to other types of data.