- Cancer Biology & Biomarkers
- Chromatography & Mass Spectrometry
- Contract Research, Clinical Trials and Outsourcing
- Drug Discovery
- Drug Targets
- Flow Cytometry
- Informatics & Lab Automation
- Ingredients, Excipients and Dosages
- Microbiology & RMMs
- NIR, PAT & QbD
- Raman Spectroscopy
- Screening, Assays & High-Content Analysis
- Thermal Processing
Microarrays - Articles and news items
This is the sixth and final article in our series on Rapid Microbiological Methods (RMMs) that have appeared in European Pharmaceutical Review during 2011. In our last article, we reviewed the world of nucleic acid amplification technologies, including PCR-DNA amplification, RNA-based reverse-transcriptase amplification, 16S rRNA typing and gene sequencing for the detection, identification, and in some cases, the enumeration of microorganisms. In our last article of the year, we will explore one of the most exciting areas in microbiological detection and miniaturisation: Micro-Electro-Mechanical Systems, or MEMS.
Imagine, for a moment, a machine so small that the human eye cannot see it and thousands of these machines are manufactured on a single piece of silicon. Imagine a future where gravity and inertia are no longer important, but atomic forces and surface sciences dominate. This is the world of Micro-Electro-Mechanical Systems (MEMS), and the future is now.
MEMS is the integration of mechanical, electrical, fluidic and optical elements, sensors and actuators on common silicon or other solid substrate through microfabrication technology. This is one of the fastest growing segments in the diagnostics and biomedical applications area, particularly for drug discovery and delivery, DNA testing and diagnostics, biotelemetry and genomics. And now, these same technologies are being introduced into the pharmaceutical sector for the rapid detection of contaminants. Examples of MEMS that have already been developed include Lab-On-A-Chip and microfluidics devices, microarrays, biosensors and other nanotechnology platforms.
In order to advance the identification of new drug targets and disease biomarkers, experimental tools for the systems-level analysis of signalling networks are required. Approaches for a targeted analysis of cellular proteomes have improved in recent years. Notably, the reverse phase protein microarray (RPPA) approach offers great advantages due to properties such as high sensitivity and high sample capacity. This review gives an overview of the principle of RPPA and summarises successful applications that illustrate the potential of RPPA for the analysis of clinical samples, systems biology and for drug discovery concepts. Numerous reports demonstrated the power of this approach to produce higher-order information than is currently possible with any other approach while requiring only minute amounts of sample.
Up-to-date, acquired experience on the application of targeted therapeutics revealed that patients benefit from drugs targeting molecules that are overexpressed by tumours. However, the percentage of patients truly benefiting from the targeted treatment depends largely on the type of tumour. In detail, clinical data obtained from the treatment of solid tumours suggests that our current knowledge is not sufficient to decide beforehand which patients will benefit from a certain treatment and which patients do not. This suggests that overexpression of a particular oncogenic protein by a tumour, such as EGFR, HER2, or oestrogen receptor, does not provide dependable information for treatment decisions. Considerable knowledge has been accumulated on the wiring of those pathways that convey information from cell surface receptors and neighbouring cells as well as the nutritional state and related physiological events. An obvious challenge for proteome research is to convert this knowledge into clinically and pharmaceutically relevant information. However, most drugs target proteins and therefore the realisation of personalised treatment concepts requires a systematic large-scale analysis of individual tumours to identify patterns of deregulation characteristic for subgroups of a certain type of cancer. The identification of reliable disease markers could then be translated into new treatment concepts which have been held back due to technological constraints.
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