Electricity: measuring and testing – Particle precession resonance – Using a nuclear resonance spectrometer system
Reexamination Certificate
2001-12-20
2004-01-27
Gutierrez, Diego (Department: 2859)
Electricity: measuring and testing
Particle precession resonance
Using a nuclear resonance spectrometer system
Reexamination Certificate
active
06683455
ABSTRACT:
TECHNICAL FIELD
This invention pertains generally to the field of chemometrics, metabonomics, and, more particularly, to methods for the analysis of chemical, biochemical, and biological data, for example, spectra, for example, nuclear magnetic resonance (NMR) and other types of spectra.
BACKGROUND
Significant progress has been made in developing methods to determine and quantify the biochemical processes occurring in living systems. Such methods are valuable in the diagnosis, prognosis and treatment of disease, the development of drugs, as well as for improving therapeutic regimes for current drugs.
Diseases of the human or animal body (such as cancers, degenerative diseases, autoimmune diseases and the like) have an underlying basis in alterations in the expression of certain genes. The expressed gene products, proteins, mediate effects such as abnormal cell growth, cell death or inflammation. Some of these effects are caused directly by protein—protein interactions; other are caused by proteins acting on small molecules (e.g. “second messengers”) which trigger effects including further gene expression.
Likewise, disease states caused by external agents such as viruses and bacteria provoke a multitude of complex responses in infected host.
In a similar manner, the treatment of disease through the administration of drugs can result in a wide range of desired effects and unwanted side effects in a patient.
At the genetic level, methods for examining gene expression in response to these types of events are often referred to as “genomic methods,” and are concerned with the detection and quantification of the expression of an organism's genes, collectively referred to as its “genome,” usually by detecting and/or quantifying genetic molecules, such as DNA and RNA. Genomic studies often exploit a new generation of proprietary “gene chips,” which are small disposable devices encoded with an array of genes that respond to extracted mRNAs produced by cells (see, for example, Klenk et al., 1997). Many genes can be placed on a chip array and patterns of gene expression, or changes therein, can be monitored rapidly, although at some considerable cost.
However, the biological consequences of gene expression, or altered gene expression following perturbation, are extremely complex. This has led to the development of “proteomic methods” which are concerned with the semi-quantitative measurement of the production of cellular proteins of an organism, collectively referred to as its “proteome” (see, for example, Geisow, 1998). Proteomic measurements utilise a variety of technologies, but all involve a protein separation method, e.g., 2D gel-electrophoresis, allied to a chemical characterisation method, usually, some form of mass spectrometry.
In recent years, it has been appreciated that the reaction of human and animal subjects to disease and treatments for them can vary according to the genomic makeup of an individual. This has led to the development of the field of “pharmacogenomics.” A fuller understanding of how an individual's own genome reacts to a particular disease will allow the development of new therapies, as well as the refinement of existing ones.
At present, genomic and proteomic methods, which are both expensive and labour intensive, have the potential to be powerful tools for studying biological response. The choice of method is still uncertain since careful studies have sometimes shown a low correlation between the pattern of gene expression and the pattern of protein expression, probably due to sampling for the two technologies at inappropriate time points (see, e.g., Gygi et al., 1999). Even in combination, genomic and proteomic methods still do not provide the range of information needed for understanding integrated cellular function in a living system, since they do not take account of the dynamic metabolic status of the whole organism.
For example, genomic and proteomic studies may implicate a particular gene or protein in a disease or a xenobiotic response because the level of expression is altered, but the change in gene or protein level may be transitory or may be counteracted downstream and as a result there may be no effect at the cellular and/or biochemical level. Conversely, sampling tissue for genomic and proteomic studies at inappropriate time points may result in a relevant gene or protein being overlooked,
Nonetheless, recent advances in genomics and proteomics now permit the rapid identification of new potential targets for drug development. With a new target in hand, and with the aid of combinatorial chemistry and high throughput screening, the pharmaceutical industry is capable of rapidly generating and screening thousands of new candidate compounds each week.
However, in practice, only a few of these candidate compounds will be taken further, for example, into pre-clinical and clinical development. It is therefore critical to identify those candidate compounds with the most promise, and this is usually judged by efficacy and toxicology, before selection for clinical studies. However, these selection processes are imperfect and many drugs fail in clinical trials due to lack of efficacy and/or toxicological effects. It is also possible that other drugs may fait overall because they are only effective in a subgroup of patients who have an unrecognised pharmacogenomic response. There is a great need to find new ways of reducing this compound “attrition” or losses of drugs late in the development process, for example, through the development and application of analytical technologies designed to maximise efficiency of compound selection and to minimise attrition rates.
While genomic and proteomic methods may be useful aids in compound selection, they do suffer from substantial limitations. For example, while genomic and proteomic methods may ultimately give profound insights, into toxicological mechanisms and provide new surrogate biomarkers of disease, at present it is very difficult to relate genomic and proteomic findings to classical cellular or biochemical indices or endpoints. One simple reason for this is that with current technology and approach, the correlation of the time-response to drug exposure is difficult. Further difficulties arise with in vitro cell-based studies. These difficulties are particularly important for the many known cases where the metabolism of the compound is a prerequisite for a toxic effect and especially true where the target organ is not the site of primary metabolism. This is particularly true for pro-drugs, where some aspect of in situ chemical (e.g., enzymatic) modification is required for activity.
A new “metabonomic” approach has been proposed which is aimed at augmenting and complementing the information provided by genomics and proteomics. “Metabonomics” is conventionally defined as “the quantitative measurement of the multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification” (see, for example, Nicholson et al., 1999). This concept has arisen primarily from the application of
1
H NMR spectroscopy to study the metabolic composition of biofluids, cells, and tissues and from studies utilising pattern recognition (PR), expert systems and other chemoinformatic tools to interpret and classify complex NMR-generated metabolic data sets. Metabonomic methods have the potential, ultimately, to determine the entire dynamic metabolic make-up of an organism.
A pathological condition or a xenobiotic may act at the pharmacological level only and hence may not affect gene regulation or expression directly. Alternatively significant disease or toxicological effects may be completely unrelated to gene switching. For example, exposure to ethanol in vivo may switch on many genes but none of these gene expression events explains drunkenness. In cases such as these, genomic and proteomic methods are likely to be ineffective. However, all disease or drug-induced pathophysiological perturbations result in disturbances in the ratios and concentrations, binding or fluxes of endogenous
Ebbels Timothy Mark David
Holmes Elaine
Lindon John Christopher
Nicholson Jeremy Kirk
Gutierrez Diego
Metabometrix Limited
Morrison & Foerster / LLP
Vargas Dixomara
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