The use of proteomics for the assessment of clinical samples in research

Proteomics, the analysis of expressed proteins, has been an important developing area of research for the past two decades [Anderson, NG, Anderson, NL. Twenty years of two-dimensional electrophoresis: past, present and future. Electrophoresis 1996;17:443-53]. Advances in technology have led to a rapid increase in applications to a wide range of samples; from initial experiments using cell lines, more complex tissues and biological fluids are now being assessed to establish changes in protein expression. A primary aim of clinical proteomics is the identification of biomarkers for diagnosis and therapeutic intervention of disease, by comparing the proteomic profiles of control and disease, and differing physiological states. This expansion into clinical samples has not been without difficulties owing to the complexity and dynamic range in plasma and human tissues including tissue biopsies. The most widely used techniques for analysis of clinical samples are surface-enhanced laser desorption/ionisation mass spectrometry (SELDI-MS) and 2-dimensional gel electrophoresis (2-DE) coupled to matrix-assisted laser desorption ionisation [Person, MD, Monks, TJ, Lau, SS. An integrated approach to identifying chemically induced posttranslational modifications using comparative MALDI-MS and targeted HPLC-ESI-MS/MS. Chem. Res. Toxicol. 2003;16:598-608]-mass spectroscopy (MALDI-MS). This review aims to summarise the findings of studies that have used proteomic research methods to analyse samples from clinical studies and to assess the impact that proteomic techniques have had in assessing clinical samples. © 2004 The Canadian Society of Clinical Chemists. All rights reserved.

Data visualization during the early stages of drug discovery

Multidimensional compound optimization is a new paradigm in the drug discovery process, yielding efficiencies during early stages and reducing attrition in the later stages of drug development. The success of this strategy relies heavily on understanding this multidimensional data and extracting useful information from it. This paper demonstrates how principled visualization algorithms can be used to understand and explore a large data set created in the early stages of drug discovery. The experiments presented are performed on a real-world data set comprising biological activity data and some whole-molecular physicochemical properties. Data visualization is a popular way of presenting complex data in a simpler form. We have applied powerful principled visualization methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), to help the domain experts (screening scientists, chemists, biologists, etc.) understand and draw meaningful decisions. We also benchmark these principled methods against relatively better known visualization approaches, principal component analysis (PCA), Sammon's mapping, and self-organizing maps (SOMs), to demonstrate their enhanced power to help the user visualize the large multidimensional data sets one has to deal with during the early stages of the drug discovery process. The results reported clearly show that the GTM and HGTM algorithms allow the user to cluster active compounds for different targets and understand them better than the benchmarks. An interactive software tool supporting these visualization algorithms was provided to the domain experts. The tool facilitates the domain experts by exploration of the projection obtained from the visualization algorithms providing facilities such as parallel coordinate plots, magnification factors, directional curvatures, and integration with industry standard software. © 2006 American Chemical Society.

Guiding local regression using visualisation

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.

Transglutaminases as tanning agents for the leather industry

This study was aimed at determining whether the protein crosslinking enzymes, transglutaminases, had the potential to be used as tanning agents, using native bovine hide and purified soluble rat tail collagen as real and model substrates, respectively. We demonstrate that transglutaminases (TGs) were capable of covalently crosslinking collagen molecules together such that on average every collagen molecule contained at least one epsilon(gamma-glutamyl)lysine crosslink. However, transglutaminase-mediated crosslinking did not affect the denaturation temperature of either native bovine hide or soluble rat tail collagens when used in isolation or together with other proteins and bifunctional diamines as crosslinking facilitators. In an initial study into the effect of TG-mediated crosslinking on the tensile strength of chrome-tanned bovine hide, such crosslinking led to a 30 per cent decrease in tensile strength. Despite a change in the gel melting point mediated by epsilon(gamma-glutamyl)lysine crosslinking, the use of transglutaminases as alternative tanning agents seems unlikely given the present data.

Some aspects of the analytical chemistry of malathion

A thorough investigation of the recommended colorimetric method for the determination of malathion (an organophosphorus pesticide) has led to the identification of the major cause of all the problems with which the method suffers. The method, which involves the extraction of the copper (II) complex or the hydrolysis product of malathion from aqueous solution into immiscible organic solvents, has many drawbacks. For example, the colour of the organic extract fades very quickly and a slight increase in the contact time of the hydrolysis product and the copper reagent within the aqueous solution, results in a decrease in the ab-solute absorbance. Also, the presence of any reducing agents can be a significant source of error. In the present work, it has been shown that the basic cause of all these problems is the ability of copper (II) ion to be reduced to copper (I) ion. It has further been shown that these problems can be resolved by re-placing copper (II) by bismuth (III). This has led to the development of a modified colorimetric method for the determination. of malathion, which has distinct advantages over all other existing methods in terms of reagents required, ease in application, avoidance of interferences and stability of colour for extended periods of time. The modified colorimetric method described above has been further improved by making use of a ligand exchange reaction involving dithizone. The resulting final organic extract in this case is bright orange in colour, the absorbance of which can be measured even with simple photometers. The usefulness of the modified colorimetric method has been demonstrated by determining malathion in technical products, and in aqueous solution containing the compound down to sub ppm levels. The scope and applicability of atomic absorption spectrophotometry has been extended by demonstrating for the first time that the technique can be used for the indirect determination of malathion. Almost all of the work described above has been accepted for publication by international journals and considerable interest in the work has been shown by chemists working in the field of pesticide analysis and research.

Evaluation of disinfecting procedures for aseptic transfer in hospital pharmacy departments

Current practice in National Health Service (NHS) hospitals employs 70% Industrial Methylated Spirit spray for surface disinfection of components required in Grade A pharmaceutical environments. This study seeks to investigate other agents and procedures that may provide more effective sanitisation. Several methods are available to test the efficacy of disinfectants against vegetative organisms. However, no methods currently available test the efficacy of disinfectants against spores on the hard surfaces encountered in the pharmacy aseptic processing environment. Therefore, a method has been developed to test the efficacy of disinfectants against spores, modified from British Standard 13697 and Association of Analytical Chemists standards. The testing procedure was used to evaluate alternative biocides and disinfection methods for transferring components into hospital pharmacy cleanrooms, and to determine which combinations of biocide and application method have the greatest efficacy against spores of Bacillus subtilis subspecies subtilis 168, Bacillus subtilis American Type Culture Collection (ATCC) 6633, and Bacillus pumilis ATCC 27142. Stainless steel carrier test plates were used to represent the hard surfaces in hospital pharmacy cleanrooms. Plates were inoculated with 10(7)-10(8) colony-forming units per milliliter (CFU/mL) and treated with the various biocide formulations, using different disinfection methods. Sporicidal activity was calculated as log reduction in CFU. Of the biocides tested, 6% hydrogen peroxide and a quaternary ammonium compound/chlorine dioxide combination were most effective compared to a Quat/biguanide, amphoteric surfactant, 70% v/v ethanol in deionised water and isopropyl alcohol in water for injection. Of the different application methods tested, spraying followed by wiping was the most effective, followed closely by wiping alone. Spraying alone was least effective.

Guiding local regression using visualisation

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.

Remote-controlled experiments with cloud chemistry

Developing cleaner chemical processes often involves sophisticated flow-chemistry equipment that is not available in many economically developing countries. For reactions where it is the data that are important rather than the physical product, the networking of chemists across the internet to allow remote experimentation offers a viable solution to this problem.