Reviews in Pharmaceutical & Biomedical Analysis

The metabolome comprises all metabolites in a biological organism, which constitute the end products of its gene expression. Metabolomics consists on the systematic study of the chemical fingerprints resulting from specific cellular processes or, more particularly, the study of an organism’s profile of low molecular weight metabolites. Thus, metabolomics is perhaps the ultimate level of post-genomic analysis as it can reveal changes in metabolite fluxes that are controlled by only minor changes within gene expression. Classical phytochemical approaches often comprised a rather tedious and time consuming process of isolation, dereplication of known substances, followed by structure elucidation and quantification. However, it is important to highlight that, in many situations, the effects of natural products are not due to a single compound, but to a mixture of related and unrelated ones. Thus, metabolomics provides an efficient tool for the quality control and authentication of medicines of natural origin, contributing as well to the characterization of different species. Several combined techniques have been applied in the measurements of intracellular metabolites, whether qualitative or quantitative, which reveal the biochemical status of the organism. This review offers an insight on the methods used in the metabolomics analysis (LC-MS, GC-MS, HPLC-DAD, NMR) of several natural matrices with protective health potential, with special emphasis on the determination of phenolics profiles, once these represent the most abundant and widely spread class of plant natural compounds, additionally exhibiting interesting biological activities.

The aim of the present work was to present the ability of Artificial Neural Networks (ANN) in successfully predicting the response of an Enzyme-linked Immunosorbent assay (ELISA) from the relative input parameters and to further enhance its performance by use of different network architectures, learning algorithms and genetic operators. Representatives of three major categories of ANN topologies were investigated, namely Multilayer Feed-Forward (MLF), Generalized Feed-Forward (GenFF) and Radial Basis Function (RBF) which were trained with back-propagation with momentum and a scaled conjugate gradient learning algorithm. Tuning of the input and hidden layer size was performed by use of a genetic algorithm, while different combinations of genetic operators were used for the optimal GenFF network in order to increase its predictive ability. The major advantage of this approach was the simultaneous data-driven, modeling and optimization process which demands no a priori knowledge of variable correlations and can be employed in setting up new assays.

In recent years mass spectrometry-based proteomics became very important and now it is the leading approach employed in high-throughput analysis. Its relevance increased thanks to availability of genome-sequence database and the development of high sensitivity instruments allows a rapid and automated proteins profiling. The need to analyze complex biological samples at a large-scale level required the development of computational tools to analyze and statistically evaluate data generated from mass spectrometry (MS) experiments. These aspects have stimulated the young emerging field of bioinformatics in proteomics to introduce new software and algorithms to handle large and heterogeneous data sets and to improve the knowledge of discovery process. This review discusses of the most recent progresses in bioinformatics tools useful in mass spectrometry-based proteomics. In particular we will be focusing on software applications applied to proteomics profiling biomarker discovery and cluster analysis. Finally since most known mechanisms leading to biological processes involve different molecules here are reported the most recent methodologies to investigate biological systems through their underlying interactions with particular attention to protein-protein interaction.

This article reviews current methods used in quantitative analysis of pharmaceutical compounds from whole blood matrix by liquid chromatography mass spectrometry. Whole blood matrix reviewed here includes traditional liquid whole blood and dried blood spot (DBS) on a collection paper. A number of bioanalytical methods have been reported over the years for the determination of pharmaceutical compounds from liquid whole blood matrix. These methods have been used to support drug discovery and development as well as therapeutic drug monitoring. Dried blood spot technique was initially developed for newborn screening, later adapted to therapeutic drug monitoring, and now expanding into pharmaceutical drug discovery and development. Sample pretreatment, extraction, chromatography, and mass detection procedures for sample analysis from both liquid whole blood and dried blood spot are summarized in this article. Factors influencing assay performance such as sampling and automation are discussed. Emerging techniques allowing direct analysis of blood samples using mass spectrometry technique are also included.

The primary focus of this review is the discussion of how biosensor-based platforms can be used in conjunction with microbial cells for monitoring, environmental and industrial applications. Two approaches will be comprehensively discussed. The first of these will examine how immunosensors can be used for the sensitive and selective detection of bacterial pathogens in a range of diverse and complex sample matrices. Secondly, we discuss the implementation of free and immobilised microbial cells for facilitating the analysis of chemicals and metabolites in cost-effective devices that, in turn, are directly applicable to environmental monitoring. Further examples, relating to the uses and advantages of microbial fuel cells are also discussed, with particular emphasis on recent and innovative developments.

The search for new in vitro screening tools for early metabolite profiling and identification is becoming a major focus of interest in the pharmaceutical industry. This is motivated by the hope to avoid late failure in drug development and ultimately to launch safer drugs with fewer side effects. Electroanalytical methods alone or coupled on-line with mass spectrometry, can find a niche in this context as they may be readily implemented for the electrically driven synthesis and characterization of xenobiotics oxidized or reduced form(s). Intimately integrated in a dual electrode-enzyme configuration, electroanalysis offers also a mean to study electron transfer at the redox active center of the enzyme in the presence of a substrate and/or an inhibitor.

Benzodiazepines due to their sedative, anti-depressive, muscle relaxant, tranquilizer, hypnotic and anticonvulsant properties, have become common worldwide prescribed medicines in the therapy of anxiety, sleep disorders and convulsive attacks, as relatively safe, with mild side effects. The availability of rapid, sensitive and selective analytical methods is essential for the determination of these drugs in clinical and forensic cases. Benzodiazepines are usually present at trace levels (μgmL-1 or ngmL-1) in a complex biological matrix, and the potentially interfering compounds need to be removed prior to analysis. Therefore, a sample preparation technique is often mandatory both to extract the drugs of interest from the matrix and concentrate them. An extended and comprehensive review on sample preparation giving emphasis on extraction techniques for the chromatographic determination of major benzodiazepines and their metabolites in biological samples is presented providing important physicochemical and bio-pharmacological data to be useful for the development of procedure.

The process of programmed cell death, or apoptosis is characterized by distinct morphological and biochemical mechanisms. Inappropriate apoptosis (too little or too much) is a factor in many human conditions including neurodegenerative diseases, autoimmune disorders and many types of cancer. Although many assays for apoptosis detection have been established so far, precise differentiating apoptosis and necrosis in single cells is still a hallenge. In this study we present the most common analytical techniques for the control of the level of apoptosis.

Since the discovery in the 1950s, cytokinins have been considered to play various important roles in life science. Many analytical procedures, therefore, have been developed for determination of the types, levels and metabolic profiling of endogenous cytokinins. The primary focus of this comprehensive review is on the various analytical methods designed to meet the requirements for cytokinin analyses in complex matrices with special emphasis on gas chromatography (GC), liquid chromatography (LC) and capillary electrophoresis (CE), mostly combined with mass spectrometry (MS). The advantages and drawbacks of the described analytical methods are discussed. As plant tissue contains cytokinins in trace amounts (usually at levels below 30 pmol per gram of fresh weight), the sample pre-treatment steps (extraction, preconcentration and purification) for cytokinins are also reviewed. Finally, the present status and future trends of the analytical approaches are outlined.

Pressurized liquid extraction (PLE) is an innovative sample preparation technique which has been developed as an alternative to conventional extraction methods in many areas, such as environmental, food and pharmaceutical analysis. The aim of the current review is to summarize the application of PLE technique in phytochemical analysis in last decade. The parameters, which may affect the extraction efficiency and selectivity, including the nature of solvent, temperature, pressure, extraction time and sample particle size and so on were explored. In addition, the procedure for method development and the parameters optimization strategies, univariate approach, orthogonal analysis and central composite design, were also discussed. Due to the obvious advantages of high extraction efficiency, short preparation time, little solvent consumption and good reproducibility, PLE undoubtedly have a broad application in phytochemical analysis.