Univariate versus multivariate spectrophotometric data analysis of triamterene and xipamide; a quantitative and qualitative greenly profiled comparative study

Triamterene (TRI) and xipamide (XIP) mixture is used as a binary medication of antihypertension which is considered as a major cause of premature death worldwide. The purpose of this research is the quantitative and qualitative analysis of this binary mixture by green univariate and multivariate spectrophotometric methods. Univariate methods were zero order absorption spectra method (D⁰) and Fourier self-deconvolution (FSD), as TRI was directly determined by D⁰ at 367.0 nm in the range (2.00-10.00 µg/mL), where XIP show no interference. While XIP was determined by FSD at 261.0 nm in the range (2.00-8.00 µg/mL), where TRI show zero crossing. Multivariate methods were Partial Least Squares, Principal Component Regression, Artificial Neural Networks, and Multivariate Curve Resolution-Alternating Least Squares. A training set of 25 mixtures with different quantities of the tested components was used to construct and evaluate them, 3 latent variables were displayed using an experimental design. A set of 18 synthetic mixtures with concentrations ranging from (3.00-7.00 µg/mL) for TRI and (2.00-6.00 µg/mL) for XIP, were used to construct the calibration models. A collection of seven synthetic mixtures with various quantities was applied to build the validation models. All the proposed approaches quantitative analyses were evaluated using recoveries as a percentage, root mean square error of prediction, and standard error of prediction. Strong multivariate statistical tools were presented by these models, and they were used to analyze the combined dosage form available on the Egyptian market. The proposed techniques were evaluated in accordance with ICH recommendations, where they are capable of overcoming challenges including spectral overlaps and collinearity. When the suggested approaches and the published one were statistically compared, there was no discernible difference between them. The green analytical method index and eco-scale tools were applied for assessment of the established models greenness. The suggested techniques can be used in product testing laboratories for standard pharmaceutical analysis of the substances being studied.

Quantitative analyses of cytokine profiles reveal hormone-mediated modulation of cytokine profiles in recurrent spontaneous miscarriage

PURPOSE

Cytokines play important roles in pregnancy complications. Some hormones such as estrogen, progesterone, and dydrogesterone have been shown to alter cytokine profiles. Understanding how cytokine profiles are affected by these hormones is therefore an important step towards immunomodulatory therapies for pregnancy complications. We analyse previously published data on the effects of estrogen, progesterone, and dydrogesterone on cytokine balances in women having recurrent spontaneous miscarriages.

MATERIALS AND METHODS

Levels of eight cytokines (IFN-γ, IL-2, IL-6, IL-10, IL-13, IL-17, IL-23, TNF-α) from n = 22 women presenting unexplained recurrent spontaneous miscarriages were studied. Cytokine values were recorded after in vitro exposure of peripheral blood cells to estrogen, progesterone, and dydrogesterone. We expand on earlier analysis of the dataset by employing different statistical techniques including effect sizes for individual cytokine values, a more powerful statistical test, and adjusting p-values for multiple comparisons. We employ multivariate analysis methods, including to determine the relative magnitude of the effects of the hormone therapies on cytokines. A new statistical method is introduced based on pairwise distances able to accommodate complex relations in cytokine profiles.

RESULTS

We report several statistically significant differences in individual cytokine values between the control group and each hormone treated group, with estrogen affecting the fewest cytokines, and progesterone and dydrogesterone both affecting seven out of eight cytokines. Exposure to estrogen produces no large effects sizes however, while IFN-γ and IL-17 show large effect sizes for both progesterone and dydrogesterone, among other cytokines. Our new method for identifying which collections (i.e. subsets) of cytokines best distinguish contrasting groups identifies IFN-γ, IL-10 and IL-23 as especially noteworthy for both progesterone and dydrogesterone treatments.

CONCLUSIONS

While some statistically significant differences in cytokine levels after exposure to estrogen are found, these have small effect sizes and are unlikely to be clinically relevant. Progesterone and dydrogesterone both induce statistically significant and large effect-size differences in cytokine levels, hence therapy with these two progestogens is more likely to be clinically relevant. Univariate and multivariate methods for identifying cytokine importances provide insight into which groups of cytokines are most affected and in what ways by therapies.

multiSyncPy: A Python package for assessing multivariate coordination dynamics

In order to support the burgeoning field of research into intra- and interpersonal synchrony, we present an open-source software package: multiSyncPy. Multivariate synchrony goes beyond the bivariate case and can be useful for quantifying how groups, teams, and families coordinate their behaviors, or estimating the degree to which multiple modalities from an individual become synchronized. Our package includes state-of-the-art multivariate methods including symbolic entropy, multidimensional recurrence quantification analysis, coherence (with an additional sum-normalized modification), the cluster-phase 'Rho' metric, and a statistical test based on the Kuramoto order parameter. We also include functions for two surrogation techniques to compare the observed coordination dynamics with chance levels and a windowing function to examine time-varying coordination for most of the measures. Taken together, our collation and presentation of these methods make the study of interpersonal synchronization and coordination dynamics applicable to larger, more complex and often more ecologically valid study designs. In this work, we summarize the relevant theoretical background and present illustrative practical examples, lessons learned, as well as guidance for the usage of our package - using synthetic as well as empirical data. Furthermore, we provide a discussion of our work and software and outline interesting further directions and perspectives. multiSyncPy is freely available under the LGPL license at: https://github.com/cslab-hub/multiSyncPy , and also available at the Python package index.

Screening of neoplastic diseases by statistical analysis of urine fluorescence spectroscopic data. Application of multivariate techniques for enhancing classification

The composition of human fluids is modified during the course of neoplastic diseases. Urine analysis offers the advantage of being a noninvasive method for which samples are easily and routinely collected from patients. In this work, urine fluorescence spectra recorded upon excitation at 405 nm were obtained from healthy volunteers and individuals with different oncologic pathologies. A large number of indexes, i.e., parameters obtained from spectral data which assist spectral features characterization, were developed to classify healthy and pathological populations. The discrimination ability of simple predictive indexes, obtained from spectra pretreated with different normalization procedures and by taking their derivatives, was statistically assessed. In addition, multivariate methods, such as principal component analysis and multivariate curve resolution by alternating least squares, were used to develop more elaborate indexes for distinguishing between healthy and pathological populations. All indexes were systematically evaluated on a statistical basis by in lab-developed routines capable of detecting outliers, judging the normal distribution of the indexes, evaluating variance homogeneity, testing the difference between the means of healthy and pathological populations, as well as performing a receiver operator curve analysis to assess the classification power of each index. Those indexes with the best performances were further combined to perform a linear discriminant analysis, which yielded a powerful classification algorithm with an area under the receiver operator curve of 0.986, a sensitivity of 97.7%, a specificity of 100%, and an overall accuracy of 98.8%. The present study shows that the statistical analysis of urine fluorescence data with a proper combination of multivariate techniques bears a high potential to develop massive screening tests for the early detection of oncologic pathologies.

Biochemical changes of macrophages and U87MG cells occurring as a result of the exposure to iron oxide nanoparticles detected with the Raman microspectroscopy

The core size of iron oxide nanoparticles (IONPs) is a crucial factor defining not only their magnetic properties but also toxicological profile and biocompatibility. On the other hand, particular IONPs may induce different biological response depending on the dose, exposure time, but mainly depending on the examined system. New light on this problem may be shed by the information concerning biomolecular anomalies appearing in various cell lines in response to the action of IONPs with different core diameters and this was accomplished in the present study. Using Raman microscopy we studied the abnormalities in the accumulation of proteins, lipids and organic matter within the nucleus, cytoplasm and cellular membrane of macrophages, HEK293T and U87MG cell line occurring as a result of 24-hour long exposure to PEG-coated magnetite IONPs. The examined nanoparticles had 5, 10 and 30 nm cores and were administered in doses 5 and 25 μg Fe/ml. The obtained results showed significant anomalies in biochemical composition of macrophages and the U87MG cells, but not the HEK293T cells, occurring as a result of exposure to all of the examined nanoparticles. However, IONPs with 10 nm core diminished the accumulation of biomolecules in cells only when they were administered at a larger dose. The Raman spectra recorded for the macrophages subjected to 30 nm IONPs and for the U87MG cells exposed to 5 and 10 nm showed the presence of additional bands in the wavenumber range 1700-2400 cm^-1, probably resulting from the appearance of Fe adducts within cells. Our results indicate, moreover, that smaller IONPs may be effectively internalized into the U87MG cells, which points at their diagnostic/therapeutic potential in the case of glioblastoma multiforme.

Spatial covariance analysis of FDG-PET and HMPAO-SPECT for the differential diagnosis of dementia with Lewy bodies and Alzheimer's disease

We investigated diagnostic characteristics of spatial covariance analysis (SCA) of FDG-PET and HMPAO-SPECT scans in the differential diagnosis of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD), in comparison with visual ratings and region of interest (ROI) analysis. Sixty-seven patients (DLB 29, AD 38) had both HMPAO-SPECT and FDG-PET scans. Spatial covariance patterns were used to separate AD and DLB in an initial derivation group (DLB n=15, AD n=19), before being forward applied to an independent group (DLB n=14, AD n=19). Visual ratings were by consensus, with ROI analysis utilising medial occipital/medial temporal uptake ratios. SCA of HMPAO-SPECT performed poorly (AUC 0.59±0.10), whilst SCA of FDG-PET (AUC 0.83±0.07) was significantly better. For FDG-PET, SCA showed similar diagnostic performance to ROI analysis (AUC 0.84±0.08) and visual rating (AUC 0.82±0.08). In contrast to ROI analysis, there was little concordance between SCA and visual ratings of FDG-PET scans. We conclude that SCA of FDG-PET outperforms that of HMPAO-SPECT. SCA of FDG-PET also performed similarly to the other analytical approaches, despite the limitations of a relatively small SCA derivation group. Compared to visual rating, SCA of FDG-PET relies on different sources of group variance to separate DLB from AD.

Meta-Analysis Methods of Diagnostic Test Accuracy Studies

Meta-analytic techniques are used to combine the results of different studies that have evaluated the accuracy of diagnostic tests. In this article, we present univariate and multivariate meta-analysis methods for a single test and we provide an extensive description of methods for meta-analysis and comparison of multiple diagnostic tests. We close with a practical example of a meta-analysis that aimed to determine whether Rheumatoid Factor identifies patients with Rheumatoid Arthritis.

A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets

Canonical Correlation Analysis (CCA) and its regularised versions have been widely used in the neuroimaging community to uncover multivariate associations between two data modalities (e.g., brain imaging and behaviour). However, these methods have inherent limitations: (1) statistical inferences about the associations are often not robust; (2) the associations within each data modality are not modelled; (3) missing values need to be imputed or removed. Group Factor Analysis (GFA) is a hierarchical model that addresses the first two limitations by providing Bayesian inference and modelling modality-specific associations. Here, we propose an extension of GFA that handles missing data, and highlight that GFA can be used as a predictive model. We applied GFA to synthetic and real data consisting of brain connectivity and non-imaging measures from the Human Connectome Project (HCP). In synthetic data, GFA uncovered the underlying shared and specific factors and predicted correctly the non-observed data modalities in complete and incomplete data sets. In the HCP data, we identified four relevant shared factors, capturing associations between mood, alcohol and drug use, cognition, demographics and psychopathological measures and the default mode, frontoparietal control, dorsal and ventral networks and insula, as well as two factors describing associations within brain connectivity. In addition, GFA predicted a set of non-imaging measures from brain connectivity. These findings were consistent in complete and incomplete data sets, and replicated previous findings in the literature. GFA is a promising tool that can be used to uncover associations between and within multiple data modalities in benchmark datasets (such as, HCP), and easily extended to more complex models to solve more challenging tasks.

Dataset of functional connectivity during cognitive control for an adult lifespan sample

We provide functional connectivity matrices generated during functional magnetic resonance imaging (fMRI) during different tasks of cognitive control in healthy aging adults. These data can be used to replicate the primary results from the related manuscript: Reconfiguration and dedifferentiation of functional networks during cognitive control across the adult lifespan (Rieck et al., 2021). One-hundred-forty-four participants (ages 20-86) were scanned on a Siemens 3T MRI scanner while they were completing tasks to measure functional activity during inhibition, initiation, shifting, and working memory. Estimates of functional connectivity (quantified with timeseries correlations) between different brain regions were computed using three different brain atlases: Schaefer 100 parcel 17 network atlas (Schaefer et al., 2018; Yeo et al., 2011), Power 229 node 10 network atlas (Power et al., 2011), and Schaefer 200 parcel 17 network atlas (Schaefer et al., 2018; Yeo et al., 2011). The resulting functional connectivity correlation matrices are provided as text files with this article. Cov-STATIS (Abdi et al., 2012; a multi-table multivariate statistical technique; https://github.com/HerveAbdi/DistatisR) was used to examine similarity between functional connectivity during the different domains of cognitive control. The effect of aging on these functional connectivity patterns was also examined by computing measures of "task differentiation" and "network segregation." This dataset also provides supplemental analyses from the related manuscript (Rieck et al., 2021) to replicate the primary age findings with additional brain atlases. Cognitive neuroscience researchers can benefit from these data by further investigating the age effects on functional connectivity during tasks of cognitive control, in addition to examining the impact of different brain atlases on functional connectivity estimates. These data can also be used for the development of other multi-table and network-based statistical methods in functional neuroimaging.

Ab initio study of the biogenic amino acids

Ten amino acids have been subjected to the quantum chemical calculations using the ab initio MO-LCAO-SCF calculations. When the geometry optimization started form the X-ray structure confirming the zwitterionic form, the ab initio calculations in vacuo result in the amino acid (canonical) form with the hydrogen atom attached not to the amine but to the carboxylate group. At the optimum geometry, a number of properties were evaluated: dipole moment, dipole polarizability, molecular surface, molecular volume, HOMO, LUMO, ionization energy, and electron affinity using the ΔSCF approach and their values corrected for electron correlation by the 2nd order perturbation theory (MP2). Also, the Mulliken electronegativity and Pearson hardness were evaluated. These properties have been mutually correlated by employing the statistical multivariate methods: the cluster analysis, the probabilistic neural network classifier, the principal component analysis, and the Pearson pair correlation. In addition, the molecular electrostatic potential mapped on the isovalue surface of charge density has been drawn. After the full vibrational analysis, thermodynamic properties at 300 K were evaluated: internal energy, entropy, and the free energy.

Reconfiguration and dedifferentiation of functional networks during cognitive control across the adult lifespan

Healthy aging is accompanied by reduced cognitive control and widespread alterations in the underlying brain networks; but the extent to which large-scale functional networks in older age show reduced specificity across different domains of cognitive control is unclear. Here we use cov-STATIS (a multi-table multivariate technique) to examine similarity of functional connectivity during different domains of cognitive control-inhibition, initiation, shifting, and working memory-across the adult lifespan. We report two major findings: (1) Functional connectivity patterns during initiation, inhibition, and shifting were more similar in older ages, particularly for control and default networks, a pattern consistent with dedifferentiation of the neural correlates associated with cognitive control; and (2) Networks exhibited age-related reconfiguration such that frontal, default, and dorsal attention networks were more integrated whereas sub-networks of somato-motor system were more segregated in older age. Together these findings offer new evidence for dedifferentiation and reconfiguration of functional connectivity underlying different aspects of cognitive control in normal aging.

Organ Metallome Processed with Chemometric Methods Enable the Determination of Elements that May Serve as Markers of Exposure to Iron Oxide Nanoparticles in Male Rats

The systemic influence of iron oxide nanoparticles on the elemental homeostasis of key organs was examined in male rats. In tissues taken at different intervals from nanoparticles injection, the dynamics of elemental changes was analyzed. The organ metallome was studied using total reflection X-ray fluorescence. The obtained data were processed with advanced cluster and discriminant analyses-to classify the tissues according to their organs of origin and to distinguish accurately the nanoparticle-treated and normal rats. Additionally, in the case of liver and heart, it was possible to determine the elements of highest significance for different treatments, which may serve as markers of exposure to iron oxide nanoparticles.

Risk Stratification Strategies for Colorectal Cancer Screening: From Logistic Regression to Artificial Intelligence

Risk stratification is a system by which clinically meaningful separation of risk is achieved in a group of otherwise similar persons. Although parametric logistic regression dominates risk prediction, use of nonparametric and semiparametric methods, including artificial neural networks, is increasing. These statistical-learning and machine-learning methods, along with simple rules, are collectively referred to as "artificial intelligence" (AI). AI requires knowledge of study validity, understanding of model metrics, and determination of whether and to what extent the model can and should be applied to the patient or population under consideration. Further investigation is needed, especially in model validation and impact assessment.

Rapid detection of adulteration of minced beef using Vis/NIR reflectance spectroscopy with multivariate methods

High economic returns induce the continuous occurrence of meat adulteration. In this study, visible/near-infrared (Vis/NIR) reflectance spectroscopy with multivariate methods was used for the rapid detection of adulteration in minced beef. First, the reflectance spectra of different adulterated minced beef samples were measured at 350-2500 nm. Standardization and Savitzky-Golay (SG) smoothing were applied to reduce spectral interference and noise. Then, support vector machine (SVM), random forest (RF), partial least squares regression (PLSR), and deep convolutional neural network (DCNN) were adopted for adulteration type identification and level prediction. Moreover, principal component analysis (PCA), locally linear embedding (LLE), subwindow permutation analysis (SPA), and competitive adaptive reweighted sampling (CARS) were performed to eliminate redundant information. SG smoothing performed better on interference reduction. DCNN and PCA identified adulteration type with the accuracy above 99%. In adulteration level prediction, the RF with spectra of important wavelengths selected by CARS provided optimal performance for beef adulterated with pork, and coefficient of determination of prediction (R²_P) and the root mean square error of prediction (RMSEP) were 0.973 and 2.145. The best prediction for beef adulterated with beef heart was obtained using PLSR and CARS with R²_P of 0.960 and RMSEP of 2.758. Accordingly, Vis/NIR reflectance spectroscopy coupled with multivariate methods can provide the rapid and accurate detection of adulterated minced beef.

Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets

Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.

On the spectroscopic investigation of lipstick stains: Forensic trace evidence

In forensic science, lipsticks are considered as crucial trace evidence because it helps in the linking of the criminal with the crime scene. In the present work, twenty-five lipstick samples are characterized and discriminated by using ATR-FTIR spectroscopy coupled with multivariate statistical methods. The utilized approach is non-destructive, fast, and provides reproducible results. It is observed from the FTIR spectra that lipstick contains various aliphatic and aromatic compounds e.g. Propyl ester of Hexanoic acid, Silicates, etc. Further, the discrimination power is calculated by using three approaches i.e. visual examination, cluster analysis (HCA and k-means) and factor analysis method. The multivariate method combined with t-statistics delivered a higher value of discriminating power i.e. 100% which is an improvement on the 99.00% discrimination power of visual comparison method. The developed method is validated by analyzing five duplicate samples and predicted them to their respective brands significantly. This study establishes a method which provides proof of concept discrimination of the lipstick samples. In the future, it can be quite possible to create an FTIR database of more lipstick samples for the identification of unknown/suspected lipstick samples.

Multicomponent spectrometric analysis of drugs and their preparations

Pharmaceutical preparations may contain a single ingredient or multi-ingredients as well as excipients. In multicomponent systems, specific analytical methods are required to determine the concentrations of individual components in the presence of interfering substances. Ultraviolet and visible spectrometric methods have widely been developed for the analysis of drugs in mixtures and pharmaceutical preparations. These methods are based on ultraviolet and visible multicomponent analysis and chemometrics (multivariate data analysis). The commonly used chemometric methods include principal component analysis (PCA); regression involving classical least squares (CLS), partial least squares (PLS), inverse least squares (ILS), principal component regression (PCR), multiple linear regression (MLR), artificial neural networks (ANNs); soft independent modeling of class anthology (SIMCA), PLS-discriminant analysis (DA); and functional data analysis (FDA). In this chapter, the applications of multicomponent ultraviolet and visible, derivative, infrared and mass spectrometric and spectrofluorimetric methods to the analysis of multi-ingredient pharmaceutical preparations, biological samples and the kinetics of drug degradation have been reviewed. Chemometric methods provide an efficient solution to calibration problems in the analysis of spectral data for the simultaneous determination of drugs in multicomponent systems. These methods facilitate the assessment of product quality and enhance the efficiency of quality control systems.

Chemical composition and surfactant characteristics of marine foams investigated by means of UV-vis, FTIR and FTNIR spectroscopy

In this study, we collected the ultraviolet-visible (UV-vis), Fourier transform infrared (FTIR) and Fourier transform near-infrared (FTNIR) spectra of marine foams from different sites and foams produced by marine living organisms (i.e. algae and molluscs) to retrieve information about their molecular and structural composition. UV-vis spectra gave information concerning the lipid and pigment contents of foams. FTIR spectroscopy gave a more detailed qualitative information regarding carbohydrates, lipids and proteins in addition with information about the mineral contents of foams. FTNIR spectra confirmed the presence of carbohydrates, lipids and proteins in foams. Then, due to the higher content of structural information of FTIR spectroscopy with respect to FTNIR and UV-vis, we join the FTIR spectra of marine foams to those of humic substance from marine sediments and to the spectra of foams obtained by living organisms. We submitted this resulting FTIR spectral dataset to statistical multivariate methods to investigate specific aspects of foams such as structural similarity among foams and in addition, contributions from the organic matter of living organisms. Cluster analysis (CA) evidenced several cases (i.e. clusters) of marine foams having high structural similarity with foams from vegetal and animal samples and with humic substance extracted from sediments. These results suggested that all the living organisms of the marine environment can give contributions to the chemical composition of foams. Moreover, as CA also evidenced cases of structural differences within foam samples, we applied two-dimensional correlation analysis (2DCORR) to the FTIR spectra of marine foams to investigate the molecular characteristics which caused these structural differences. Asynchronous spectra of two-dimensional correlation analysis showed that the structural heterogeneity among foam samples depended reasonably on the presence and on the qualitative difference of electrostatic (hydrogen bonds) and nonpolar (van der Waals and π-π) interactions involving carbohydrate proteins and lipids present. The presence and relevance of these interactions agree with the supramolecular and surfactant characteristics of marine organic matter described in the scientific literature.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA.

Raman imaging of drug delivery systems

This review article includes an introduction to the principals of Raman spectroscopy, an outline of the experimental systems used for Raman imaging and the associated important considerations and limitations of this method. Common spectral analysis methods are briefly described and examples of interesting published studies which utilised Raman imaging of pharmaceutical and biomedical devices are discussed, along with summary tables of the literature at this point in time.

Evaluating the efficiency of spectral resolution of univariate methods manipulating ratio spectra and comparing to multivariate methods: an application to ternary mixture in common cold preparation

Simple, accurate, and selective methods have been developed and validated for simultaneous determination of a ternary mixture of Chlorpheniramine maleate (CPM), Pseudoephedrine HCl (PSE) and Ibuprofen (IBF), in tablet dosage form. Four univariate methods manipulating ratio spectra were applied, method A is the double divisor-ratio difference spectrophotometric method (DD-RD). Method B is double divisor-derivative ratio spectrophotometric method (DD-RD). Method C is derivative ratio spectrum-zero crossing method (DRZC), while method D is mean centering of ratio spectra (MCR). Two multivariate methods were also developed and validated, methods E and F are Principal Component Regression (PCR) and Partial Least Squares (PLSs). The proposed methods have the advantage of simultaneous determination of the mentioned drugs without prior separation steps. They were successfully applied to laboratory-prepared mixtures and to commercial pharmaceutical preparation without any interference from additives. The proposed methods were validated according to the ICH guidelines. The obtained results were statistically compared with the official methods where no significant difference was observed regarding both accuracy and precision.

Administration costs of intravenous biologic drugs for rheumatoid arthritis

Background

Cost-effectiveness studies explicitly reporting infusion times, drug-specific administration costs for infusions or real-payer intravenous drug cost are few in number. Yet, administration costs for infusions are needed in the health economic evaluations assessing intravenously-administered drugs.

Objectives

To estimate the drug-specific administration and total cost of biologic intravenous rheumatoid arthritis (RA) drugs in the adult population and to compare the obtained costs with published cost estimates.

Methods

Cost price data for the infusions and drugs were systematically collected from the 2011 Finnish price lists. All Finnish hospitals with available price lists were included. Drug administration and total costs (administration cost + drug price) per infusion were analysed separately from the public health care payer’s perspective. Further adjustments for drug brand, dose, and hospital type were done using regression methods in order to improve the comparability between drugs. Annual expected drug administration and total costs were estimated. A literature search not limited to RA was performed to obtain the per infusion administration cost estimates used in publications. The published costs were converted to Finnish values using base-year purchasing power parities and indexing to the year 2011.

Results

Information from 19 (95%) health districts was obtained (107 analysable prices out of 176 observations). The average drug administration cost for infliximab, rituximab, abatacept, and tocilizumab infusion in RA were €355.91; €561.21; €334.00; and €293.96, respectively. The regression-adjusted (dose, hospital type; using semi-log ordinary least squares) mean administration costs for infliximab and rituximab infusions in RA were €289.12 (95% CI €222.61–375.48) and €542.28 (95% CI €307.23–957.09). The respective expected annual drug administration costs were €2312.96 for infliximab during the first year, €1879.28 for infliximab during the forthcoming years, and €1843.75 for rituximab. The obtained average administration costs per infusion were higher (1.8–3.3 times depending on the drug) than the previously published purchasing power adjusted and indexed average administration costs for infusions in RA.

Conclusions

The administration costs of RA infusions vary between drugs, and more effort should be made to find realistic drug-specific estimates for cost-effectiveness evaluations. The frequent assumption of intravenous drug administration costs equalling outpatient visit cost can underestimate the costs.