WO2002099452A1 - Procedes d'analyse spectrale et leurs applications dans l'evaluation de la fiabilite - Google Patents

Procedes d'analyse spectrale et leurs applications dans l'evaluation de la fiabilite Download PDF

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WO2002099452A1
WO2002099452A1 PCT/GB2002/002758 GB0202758W WO02099452A1 WO 2002099452 A1 WO2002099452 A1 WO 2002099452A1 GB 0202758 W GB0202758 W GB 0202758W WO 02099452 A1 WO02099452 A1 WO 02099452A1
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sample
statistical property
spectra
spectrum
classifying
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Jeremy Kirk Nicholson
John Christopher Lindon
Timothy Mark David Ebbels
Elaine Holmes
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Metabometrix Limited
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/46—NMR spectroscopy
    • G01R33/465—NMR spectroscopy applied to biological material, e.g. in vitro testing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/46—NMR spectroscopy
    • G01R33/4625—Processing of acquired signals, e.g. elimination of phase errors, baseline fitting, chemometric analysis

Definitions

  • This invention pertains generally to the field of metabonomics, and, more particularly, to chemometric methods for the analysis of chemical, biochemical, and biological data, for example, spectral data, for example, nuclear magnetic resonance (N R) spectra and other types of spectra, and their applications, including, e.g., methods of classification (of spectra, samples, subjects, etc.), methods of identifying biomarkers and/or biomarker combinations; methods of analysis of an applied stimulus or condition; methods of diagnosis; etc.
  • spectral data for example, nuclear magnetic resonance (N R) spectra and other types of spectra
  • N R nuclear magnetic resonance
  • Ranges are often expressed herein as from “about” one particular value, and/or to “about” another particular value. When such a range is expressed, another embodiment includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as approximations, by the use of the antecedent "about,” it will be understood that the particular value forms another embodiment.
  • Biosystems can conveniently be viewed at several levels of bio-molecular organisation based on biochemistry, i.e., genetic and gene expression (genomic and transcriptomic), protein and signalling (proteomic) and metabolic control and regulation (metabonomic). There are also important cellular ionic regulation variations that relate to genetic, proteomic and metabolic activities, and systematic studies on these even at the cellular and sub-cellular level should also be investigated to complete the full description of the bio-molecular organisation of a bio-system.
  • 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.
  • Genomic studies often exploit 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.
  • 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.
  • genomic methods have a high associated operational cost and proteomic methods require investment in expensive capital cost equipment and are labour intensive, but both 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.
  • 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.
  • 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.
  • Gene-based prognosis has yet to become a clinical reality for any major prevalent disease, almost all of which have multi-gene modes of inheritance and significant environmental impact making it difficult to identify the gene panels responsible for susceptibility.
  • genomic and proteomic methods may be useful aids, for example, in drug development, they do suffer from substantial limitations.
  • 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.
  • 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.
  • PR pattern recognition
  • each level of bio-molecular organisation requires a series of analytical bio-technologies appropriate to the recovery of the individual types of bio- molecular data.
  • Genomic, proteomic and metabonomic technologies by definition generate massive data sets which require appropriate multi-variate statistical tools (chemometrics, bio-informatics) for data mining and to extract useful biological information.
  • These data exploration tools also allow the inter-relationships between multivariate data sets from the different technologies to be investigated, they facilitate dimension reduction and extraction of latent properties and allow multidimensional visualization.
  • a pathological condition or a xenobiotic may act at the pharmacological level only and hence may not affect gene regulation or expression directly.
  • significant disease or toxicological effects may be completely unrelated to gene switching.
  • exposure to ethanol in vivo may cause many changes in gene expression but none of these events explains drunkenness.
  • genomic and proteomic methods are likely to be ineffective.
  • all disease or drug-induced pathophysiological perturbations result in disturbances in the ratios and concentrations, binding or fluxes of endogenous biochemicals, either by direct chemical reaction or by binding to key enzymes or nucleic acids that control metabolism. If these disturbances are of sufficient magnitude, effects will result which will affect the efficient functioning of the whole organism.
  • metabolites are in dynamic equilibrium with those inside cells and tissues and, consequently, abnormal cellular processes in tissues of the whole organism following a toxic insult or as a consequence of disease will be reflected in altered biofluid compositions.
  • Biofluids Fluids secreted, excreted, or otherwise derived from an organism
  • biofluids provide a unique window into its biochemical status since the composition of a given biofluid is a consequence of the function of the cells that are intimately concerned with the fluid's manufacture and secretion.
  • the composition of a particular fluid e.g., urine, blood plasma, milk, etc.
  • the composition and condition of an organism's tissues are also indicators of the organism's biochemical status.
  • a xenobiotic is a substance (e.g., compound, composition) which is administered to an organism, or to which the organism is exposed.
  • xenobiotics are chemical, biochemical or biological species (e.g., compounds) which are not normally present in that organism, or are normally present in that organism, but not at the level obtained following administration/ exposure.
  • examples of xenobiotics include drugs, formulated medicines and their components (e.g., vaccines, immunological stimulants, inert carrier vehicles), infectious agents, pesticides, herbicides, substances present in foods (e.g. plant compounds administered to animals), and substances present in the environment.
  • a disease state pertains to a deviation from the normal healthy state of the organism.
  • diseases states include, but are not limited to, bacterial, viral, and parasitic infections; cancer in all its forms; degenerative diseases (e.g., arthritis, multiple sclerosis); trauma (e.g., as a result of injury); organ failure (including diabetes); cardiovascular disease (e.g., atherosclerosis, thrombosis); and, inherited diseases caused by genetic composition (e.g., sickle-cell anaemia).
  • a genetic modification pertains to alteration of the genetic composition of an organism.
  • examples of genetic modifications include, but are not limited to: the incorporation of a gene or genes into an organism from another species; increasing the number of copies of an existing gene or genes in an organism; removal of a gene or genes from an organism; and, rendering a gene or genes in an organism non-functional.
  • Biofluids often exhibit very subtle changes in metabolite profile in response to external stimuli. This is because the body's cellular systems attempt to maintain homeostasis (constancy of internal environment), for example, in the face of cytotoxic challenge. One means of achieving this is to modulate the composition of biofluids.
  • biofluid compositions Even when cellular homeostasis is maintained, subtle responses to disease or toxicity are expressed in altered biofluid composition.
  • dietary, diurnal and hormonal variations may also influence biofluid compositions, and it is clearly important to differentiate these effects if correct biochemical inferences are to be drawn from their analysis.
  • Metabonomics offers a number of distinct advantages (over genomics and proteomics) in a clinical setting: firstly, it can often be performed on standard preparations (e.g., of serum, plasma, urine, etc.), circumventing the need for specialist preparations of cellular RNA and protein required for genomics and proteomics, respectively. Secondly, many of the risk factors already identified (e.g., levels of various lipids in blood) are small molecule metabolites which will contribute to the metabonomic dataset.
  • NMR spectroscopy see, for example, Nicholson et al., 1989
  • intact tissues have been successfully analysed using magic-angle-spinning 1 H NMR spectroscopy (see, for example, Moka et al., 1998; Tomlins et al., 1998).
  • the NMR spectrum of a biofluid provides a metabolic fingerprint or profile of the organism from which the biofluid was obtained, and this metabolic fingerprint or profile is characteristically changed by a disease, toxic process, or genetic modification.
  • NMR spectra may be collected for various states of an organism (e.g., pre- dose and various times post-dose, for one or more xenobiotics, separately or in combination; healthy (control) and diseased animal; unmodified (control) and genetically modified animal).
  • each compound or class of compound produces characteristic changes in the concentrations and patterns of endogenous metabolites in biofluids that provide information on the sites and basic mechanisms of the toxic process.
  • 1 H NMR analysis of biofluids has successfully uncovered novel metabolic markers of organ-specific toxicity in the laboratory rat, and it is in this "exploratory" role that NMR as an analytical biochemistry technique excels.
  • the biomarker information in NMR spectra of biofluids is very subtle, as hundreds of compounds representing many pathways can often be measured simultaneously, and it is this overall metabonomic response to toxic insult that so well characterises the lesion.
  • NMR-based metabonomics over genomics or proteomics
  • Reanalysis of the same sample by 1H NMR spectroscopy results in a typical coefficient of variation for the measurement of peak intensities in a spectrum of less than 5% across the whole range of peaks.
  • the value of each peak intensity will lie in the range 0.95 to 1.05 of the true value.
  • the intrinsic accuracy of NMR provides a distinct advantage when applying pattern recognition techniques.
  • the multivariate nature of the NMR data means that classification of samples is possible using a combination of descriptors even when one descriptor is not sufficient, because of the inherently low analytical variation in the data.
  • biofluids are not chemically stable and for this reason care should be taken in their collection and storage. For example, cell lysis in erythrocytes can easily occur. If a substantial amount of D 2 0 has been added, then it is possible that certain 1 H NMR resonances will be lost by H/D exchange. Freeze-drying of biofluid samples also causes the loss of volatile components such as acetone. Biofluids are also very prone to microbiological contamination, especially fluids, such as urine, which are difficult to collect under sterile conditions. Many biofluids contain significant amounts of active enzymes, either normally or due to a disease state or organ damage, and these enzymes may alter the composition of the biofluid following sampling.
  • Samples should be stored deep frozen to minimise the effects of such contamination.
  • Sodium azide is usually added to urine at the collection point to act as an antimicrobial agent.
  • Metal ions and or chelating agents e.g., EDTA
  • endogenous metal ions e.g., Ca 2+ , Mg 2+ and Zn 2+
  • chelating agents e.g., free amino acids, especially glutamate, cysteine, histidine and aspartate; citrate
  • the analytical problem usually involves the detection of "trace" amounts of analytes in a very complex matrix of potential interferences. It is, therefore, critical to choose a suitable analytical technique for the particular class of analyte of interest in the particular biomatrix which could be, for example, a biofluid or a tissue. High resolution NMR spectroscopy (in particular 1 H NMR) appears to be particularly appropriate.
  • the main advantages of using 1 H NMR spectroscopy in this area are the speed of the method (with spectra being obtained in 5 to 10 minutes), the requirement for minimal sample preparation, and the fact that it provides a non-selective detector for all metabolites in the biofluid regardless of their structural type, provided only that they are present above the detection limit of the NMR experiment and that they contain non- exchangeable hydrogen atoms.
  • the speed advantage is of crucial importance in this area of work as the clinical condition of a patient may require rapid diagnosis, and can change very rapidly and so correspondingly rapid changes must be made to the therapy provided.
  • NMR studies of body fluids should ideally be performed at the highest magnetic field available to obtain maximal dispersion and sensitivity and most 1 H NMR studies have been performed at 400 MHz or greater.
  • NMR spectra of urine is identifiably altered in situations where damage has occurred to the kidney or liver. It has been shown that specific and identifiable changes can be observed which distinguish the organ that is the site of a toxic lesion. Also it is possible to focus in on particular parts of an organ such as the cortex of the kidney and even in favourable cases to very localised parts of the cortex.
  • Pattern recognition (PR) methods can be used to reduce the complexity of data sets, to generate scientific hypotheses and to test hypotheses.
  • PR Pattern recognition
  • the use of pattern recognition algorithms allows the identification, and, with some methods, the interpretation of some non-random behaviour in a complex system which can be obscured by noise or random variations in the parameters defining the system.
  • the number of parameters used can be very large such that visualisation of the regularities, which for the human brain is best in no more than three dimensions, can be difficult.
  • the number of measured descriptors is much greater than three and so simple scatter plots cannot be used to visualise any similarity between samples.
  • Pattern recognition methods have been used widely to characterise many different types of problem ranging for example over linguistics, fingerprinting, chemistry and psychology.
  • pattern recognition is the use of multivariate statistics, both parametric and non-parametric, to analyse spectroscopic data, and hence to classify samples and to predict the value of some dependent variable based on a range of observed measurements.
  • unsupervised One set of methods is termed “unsupervised” and these simply reduce data complexity in a rational way and also produce display plots which can be interpreted by the human eye.
  • supervised whereby a training set of samples with known class or outcome is used to produce a mathematical model and this is then evaluated with independent validation data sets.
  • Unsupervised PR methods are used to analyse data without reference to any other independent knowledge, for example, without regard to the identity or nature of a xenobiotic or its mode of action.
  • Examples of unsupervised pattern recognition methods include principal component analysis (PCA), hierarchical cluster analysis (HCA), and non-linear mapping (NLM).
  • PCA principal components analysis
  • PCA a dimension reduction technique, takes m objects or samples, each described by values in K dimensions (descriptor vectors), and extracts a set of eigenvectors, which are linear combinations of the descriptor vectors.
  • the eigenvectors and eigenvalues are obtained by diagonalisation of the covariance matrix of the data.
  • the eigenvectors can be thought of as a new set of orthogonal plotting axes, called principal components (PCs).
  • PCs principal components
  • the extraction of the systematic variations in the data is accomplished by projection and modelling of variance and covariance structure of the data matrix.
  • the primary axis is a single eigenvector describing the largest variation in the data, and is termed principal component one (PC1 ).
  • PCs ranked by decreasing eigenvalue
  • residual variance signifies how well the model fits the data.
  • the projections of the descriptor vectors onto the PCs are defined as scores, which reveal the relationships between the samples or objects.
  • scores reveal the relationships between the samples or objects.
  • a graphical representation a "scores plot” or eigenvector projection
  • objects or samples having similar descriptor vectors will group together in clusters.
  • Another graphical representation is called a loadings plot, and this connects the PCs to the individual descriptor vectors, and displays both the importance of each descriptor vector to the interpretation of a PC and the relationship among descriptor vectors in that PC.
  • a loading value is simply the cosine of the angle which the original descriptor vector makes with the PC. Descriptor vectors which fall close to the origin in this plot carry little information in the PC, while descriptor vectors distant from the origin (high loading) are important in interpretation.
  • a plot of the first two or three PC scores gives the "best" representation, in terms of information content, of the data set in two or three dimensions, respectively.
  • a plot of the first two principal component scores, PC1 and PC2 provides the maximum information content of the data in two dimensions.
  • Such PC maps can be used to visualise inherent clustering behaviour, for example, for drugs and toxins based on similarity of their metabonomic responses and hence mechanism of action. Of course, the clustering information might be in lower PCs and these have also to be examined.
  • Hierarchical Cluster Analysis permits the grouping of data points which are similar by virtue of being "near" to one another in some multidimensional space.
  • Individual data points may be, for example, the signal intensities for particular assigned peaks in an NMR spectrum.
  • the closest pair of points will have the largest s, For two identical points, s, is 1. Th e similarity matrix is scanned for the closest pair of points. The pair of points are reported with their separation distance, and then the two points are deleted and replaced with a single combined point. The process is then repeated iteratively until only one point remains.
  • a number of different methods may be used to determine how two clusters will be joined, including the nearest neighbour method (also known as the single link method), the furthest neighbour method, and the centroid method (including centroid link, incremental link, median link, group average link, and flexible link variations).
  • the reported connectivities are then plotted as a dendrogram (a tree-like chart which allows visualisation of clustering), showing sample-sample connectivities versus increasing separation distance (or equivalently, versus decreasing similarity).
  • the dendrogram has the property in which the branch lengths are proportional to the distances between the various clusters and hence the length of the branches linking one sample to the next is a measure of their similarity. In this way, similar data points may be identified algorithmically.
  • Non-linear mapping is a simple concept which involves calculation of the distances between all of the points in the original K dimensions. This is followed by construction of a map of points in 2 or 3 dimensions where the sample points are placed in random positions or at values determined by a prior principal components analysis. The least squares criterion is used to move the sample points in the lower dimension map to fit the inter-point distances in the lower dimension space to those in the K dimensional space. Non-linear mapping is therefore an approximation to the true inter- point distances, but points close in K-dimensional space should also be close in 2 or 3 dimensional space (see, for example, Brown et al., 1996; Farrant et al., 1992).
  • a "training set” of NMR metabonomic data is used to construct a statistical model that predicts correctly the "class" of each sample.
  • This training set is then tested with independent data (referred to as a test or validation set) to determine the robustness of the computer-based model.
  • These models are sometimes termed “expert systems,” but may be based on a range of different mathematical procedures.
  • Supervised methods can use a data set with reduced dimensionality (for example, the first few principal components), but typically use unreduced data, with all dimensionality. In all cases the methods allow the quantitative description of the multivariate boundaries that characterise and separate each class, for example, each class of xenobiotic in terms of its metabolic effects.
  • Expert systems may operate to generate a variety of useful outputs, for example, (i) classification of the sample as "normal” or “abnormal” (this is a useful tool in the control of spectrometer automation, e.g., using sequential flow injection NMR spectroscopy); (ii) classification of the target organ for toxicity and site of action within the tissue where in certain cases, mechanism of toxic action may also be classified; and, (iii) identification of the biomarkers of a pathological disease condition or toxic effect for the particular compound under study. For example, a sample can be classified as belonging to a single class of toxicity, to multiple classes of toxicity (more than one target organ), or to no class.
  • supervised pattern recognition methods include the following: soft independent modelling of class analysis (SIMCA) (see, for example, Wold, 1976); partial least squares analysis (PLS) (see, for example, Wold, 1966; Joreskog,
  • SIMCA soft independent modelling of class analysis
  • PLS partial least squares analysis
  • LDA linear descriminant analysis
  • KNN K-nearest neighbour analysis
  • ANN artificial neural networks
  • PNNs probabilistic neural networks
  • Pattern recognition methods have been applied to the analysis of metabonomic data. See, for example, Lindon et al., 2001. A number of spectroscopic techniques have been used to generate the data, including NMR spectroscopy and mass spectrometry. Pattern recognition analysis of such data sets has been successful in some cases.
  • the successful studies include, for example, complex NMR data from biofluids, (see, for example, Anthony et al., 1994; Anthony et al., 1995; Beckwith-Hall et al., 1998; Gartland et al., 1990a; Gartland et al., 1990b; Gartland et al., 1991 ; Holmes et al., 1998a; Holmes et al., 1998b; Holmes et al., 1992; Holmes et al., 1994; Spraul et al., 1999; Tranter et al., 1999) conventional NMR spectra from tissue samples (Somorjai et al., 1995), magic- angle-spinning (MAS) NMR spectra of tissues (Garrod et al., 2001), in vivo NMR spectra (Morvan et al., 1990; Howells et al., 1993; Stoyanova et al., 1995; Kuesel e
  • One aim of the present invention is to provide data analysis methods for the detection of such metabolic variations, as part of a metabonomic approach, which address one or more of the known problems, including those discussed herein.
  • One aspect of the present invention pertains to chemometric methods for the analysis of chemical, biochemical, and biological data, for example, spectral data, for example, nuclear magnetic resonance (NMR) spectra and other types of spectra.
  • spectral data for example, nuclear magnetic resonance (NMR) spectra and other types of spectra.
  • One aspect of the present invention pertains to a method of classifying a spectrum, as described herein.
  • One aspect of the present invention pertains to a method of classifying a sample, as described herein.
  • One aspect of the present invention pertains to a method of classifying a subject as described herein.
  • One aspect of the present invention pertains to a method of identifying a candidate biomarker or biomarker combination, as described herein.
  • One aspect of the present invention pertains to a biomarker or biomarker combination identified by a method as described herein.
  • One aspect of the present invention pertains to a biomarker or biomarker combination identified by a method as described herein, for use in a method of classification.
  • One aspect of the present invention pertains to a method of classification which employs or relies upon one or more biomarkers or biomarker combinations identified by a method as described herein.
  • One aspect of the present invention pertains to use of one or more biomarkers or biomarker combinations identified by a method of classification as described herein.
  • One aspect of the present invention pertains to an assay for use in a method of classification, which assay relies upon one or more biomarkers or biomarker combinations identified by a method as described herein.
  • One aspect of the present invention pertains to use of an assay in a method of classification, which assay relies upon one or more biomarkers or biomarker combinations identified by a method as described herein.
  • One aspect of the present invention pertains to a method of diagnosis employing one or more biomarkers or biomarker combinations identified by a method as described herein.
  • One aspect of the present invention pertains to a method of diagnosis of an applied stimulus or condition, comprising a method of analysis of an applied stimulus, as described herein.
  • One aspect of the present invention pertains to a method of therapeutic monitoring of a subject undergoing therapy, comprising a method of analysis of an applied stimulus or condition, as described herein.
  • One aspect of the present invention pertains to a method of evaluating drug therapy and/or drug efficacy, comprising a method of analysis of an applied stimulus or condition, as described herein.
  • One aspect of the present invention pertains to a method of detecting toxic side-effects of drug, comprising a method of analysis of an applied stimulus or condition, as described herein.
  • One aspect of the present invention pertains to a method of characterising and/or identifying a drug in overdose, comprising a method of analysis of an applied stimulus or condition, as described herein.
  • One aspect of the present invention pertains to a computer system or device, such as a computer or linked computers, operatively configured to implement a method as described herein; and related computer code computer programs, data carriers carrying such code and programs, and the like.
  • Figure 1 is a graph of "binned" signal intensity versus chemical shift ( ⁇ 0-10) for the 95% confidence interval high (A) and low (B) spectra, and a test spectrum (C), as described in Example 1.
  • Figure 2 is a graph of "binned" signal intensity versus chemical shift ( ⁇ 3.78-4.10) for the 95% confidence interval high (A) and low (B) spectra, and a test spectrum (Test 1) (C), as described in Example 1.
  • Figure 3 is a graph of mean signal intensity versus chemical shift ( ⁇ 3.78-4.10) for the 95% confidence interval high (A) and low (B) spectra, and a set of test spectra (C), as described in Example 2.
  • Figure 4 is a graph of standard deviation of signal intensity versus chemical shift ( ⁇ 3.78-4.10) for the 95% confidence interval high (A) and low (B) spectra, and a set of test spectra (C), as described in Example 2.
  • Figure 5 is a graph of relative standard deviation of signal intensity versus chemical shift ( ⁇ 3.78-4.10) for the 95% confidence interval high (A) and low (B) spectra, and a set of test spectra (C), as described in Example 2.
  • Figure 6 is a graph of skew of signal intensity versus chemical shift ( ⁇ 3.78-4.10) for the 95% confidence interval high (A) and low (B) spectra, and a set of test spectra (C), as described in Example 2.
  • Figure 7 is a graph of kurtosis of signal intensity versus chemical shift ( ⁇ 3.78-4.10) for the 95% confidence interval high (A) and low (B) spectra, and a set of test spectra (C), as described in Example 2.
  • the present invention pertains generally to the field of chemometrics, metabonomics, and, more particularly, to methods for the analysis of biological data, particularly spectra.
  • the methods of the present invention are applicable to chemical, biochemical, and biological data, for example, spectra, and especially spectra generated using types of spectroscopy and spectrometry which are useful in chemical and biochemical (i.e., molecular) studies.
  • the methods described herein facilitate more powerful analysis of spectral data.
  • the methods of the present invention make possible the identification of spectral changes associated with an event of interest from a spectral background which is non-specific and/or irrelevant.
  • Spectra often have features (e.g., peaks, noise spikes, baseline artefacts, etc.) which interfere with and/or reduce the power and/or accuracy of subsequent analysis. Some of these features are artefacts of the particular type of spectra, its method of acquisition, adventitious impurities, and the like. However, some of these spectral features are associated with chemical species not accidentally or unintentionally present in the sample under study, but instead reflect, for example, the effects of an applied stimulus.
  • features e.g., peaks, noise spikes, baseline artefacts, etc.
  • a sample from an organism under study may show spectral evidence of a large number of metabolites.
  • these metabolites may be placed in one of three classes:
  • Biomarker combinations may include a time dependence, for example, levels of metabolite A up at 24 hours, and back to normal at 48 hours along with levels of metabolite B down at 24 hours and up at 72 hours.
  • a time dependence for example, levels of metabolite A up at 24 hours, and back to normal at 48 hours along with levels of metabolite B down at 24 hours and up at 72 hours.
  • an increase in taurine together with creatine levels in urine is a general marker for liver damage.
  • toxins which cause lesions in the S3 portion of the renal proximal tubule cause elevations of urinary glucose, amino acids and organic acids with decreases in tricarboxylic acid cycle intermediates.
  • C Metabolites, which appear in the sample and which arise from a xenobiotic itself or its metabolites.
  • paracetamol is seen in urine mainly as paracetamol sulfate and paracetamol glucuronide conjugates. In some cases unchanged paracetamol can also be seen.
  • these metabolites will be present only if the applied stimulus includes a xenobiotic.
  • Interfering signals Such signals often provide little information about the organism's response to an applied stimulus, while dominating and interfering with the metabonomic description of the stimulated organism.
  • biomarkers In contrast, metabolites falling in class A (biomarkers, biomarker combinations) are an indicator the organism's response to an applied stimulus.
  • a particular metabolite is, or is a candidate as, a biomarker or biomarker combination
  • a biomarker or biomarker combination can often be determined from known data regarding the applied stimulus under study. For example, there may be a large body of public knowledge regarding the metabolism of a particular compound, or of compounds having a particular substructure. Often, a biomarker or biomarker combination, and its associated spectral features, can be readily identified by eye by the skilled artisan from one or more of a range of types of NMR spectra.
  • abnormal with an associated statistical reliability. This may be especially useful, for example, when screening a potential control pool.
  • biomarker or biomarker combination It is also desirable to be able to identify, statistically, a potential (candidate) biomarker or biomarker combination, and assign to it an associated statistical reliability that it is, in fact, a biomarker or biomarker combination.
  • a statistical description of a control population is generated in order to detect (e.g., in a test sample) any departures from control behaviour; such departures are then assigned a statistical reliability.
  • the departure may be described as falling outside a proportion of a suitable control population, for example, falling outside 95% of a control population.
  • one aspect of the present invention pertains to a method of assigning a statistical reliability to a departure from normality, on the basis of a statistical description of a control population.
  • One aspect of the present invention pertains to a method of assigning a statistical reliability to a departure from normality of a subject, on the basis of a statistical description of subjects of a control population.
  • One aspect of the present invention pertains to a method of assigning a statistical reliability to a departure from normality of a test sample, on the basis of a statistical description of samples from a control population.
  • One aspect of the present invention pertains to a method of assigning a statistical reliability to a departure from normality of a test sample from a subject, on the basis of a statistical description of samples from subjects of a control population.
  • One aspect of the present invention pertains to a method of assigning a statistical reliability to a departure from normality of a sample spectrum for a test sample, on the basis of a statistical description of sample spectra for samples from a control population.
  • One aspect of the present invention pertains to a method of assigning a statistical reliability to a departure from normality of a sample spectrum for a test sample from a subject, on the basis of a statistical description of sample spectra for samples from subjects of a control population.
  • One aspect of the present invention pertains to a method of classifying a spectrum, as described herein.
  • One aspect of the present invention pertains to a method of classifying a sample by classifying a spectrum for said sample, wherein said method of classifying a spectrum is as described herein.
  • One aspect of the present invention pertains to a method of classifying a subject by classifying a spectrum for a sample from said subject, wherein said method of classifying a spectrum is as described herein.
  • Subjects, samples, spectra, etc. can be classified (e.g., as normal or abnormal) on the basis of a departure from normality, and in this case, classified with a statistical reliability.
  • classification may be based on a departure from normality described as falling outside a proportion of a suitable control population, for example, falling outside 95% of a control population, and so classification may be described as normal or abnormal with 95% confidence.
  • one aspect of the present invention pertains to a method of classifying with a statistical reliability, on the basis of deviation from a statistical description of a control population.
  • One aspect of the present invention pertains to a method of classifying a subject with a statistical reliability, on the basis of deviation from a statistical description of a control population.
  • One aspect of the present invention pertains to a method of classifying a test sample with a statistical reliability, on the basis of deviation from a statistical description of samples from a control population.
  • One aspect of the present invention pertains to a method of classifying a test sample from a subject with a statistical reliability, on the basis of a statistical description of samples from subjects of a control population.
  • One aspect of the present invention pertains to a method of classifying a sample spectrum for a test sample with a statistical reliability, on the basis of a statistical description of samples from a control population.
  • One aspect of the present invention pertains to a method of classifying a sample spectrum for a test sample from a subject with a statistical reliability, on the basis of a statistical description of samples from subjects of a control population.
  • One aspect of the present invention pertains to a method for classifying a sample spectrum, said method comprising the steps of: (a) calculating at least one statistical property for a set of equivalent spectra for a reference state; and,
  • One aspect of the present invention pertains to a method for classifying a test sample, said method comprising the steps of: (a) calculating at least one statistical property for a set of equivalent spectra for samples representing a reference state; and,
  • test sample classifying said test sample as normal or abnormal, with respect to said reference state, on the basis of a sample spectrum for said test sample and said at least one statistical property.
  • One aspect of the present invention pertains to a method for classifying a test subject, said method comprising the steps of:
  • test subject classifying said test subject as normal or abnormal, with respect to said reference state, on the basis of a sample spectrum for a test sample from said test subject and said at least one statistical property.
  • said at least one statistical property is a plurality of statistical properties.
  • One aspect of the present invention pertains to a method for classifying a sample spectrum, said method comprising the steps of: (a) calculating a statistical property for a set of equivalent spectra for a reference state; and,
  • One aspect of the present invention pertains to a method for classifying a test sample, said method comprising the steps of:
  • test sample classifying said test sample as normal or abnormal, with respect to said reference state, on the basis of a sample spectrum for said test sample and said statistical property.
  • One aspect of the present invention pertains to a method for classifying a test subject, said method comprising the steps of: (a) calculating a statistical property for a set of equivalent spectra for samples from subjects representing a reference state; and, (b) classifying said test subject as normal or abnormal, with respect to said reference state, on the basis of a sample spectrum for a test sample from said test subject and said statistical property.
  • many aspects of the present invention pertain to methods of classifying things, for example, a spectrum, a sample, a subject, etc.
  • the thing is classified, that is, it is associated with an outcome, or, more specifically, it is assigned membership to a particular class (i.e., it is assigned class membership), and is said "to be of,” “to belong to,” “to be a member of,” a particular class.
  • Classification is made (i.e., class membership is assigned) on the basis of particular criteria.
  • normality is one class
  • abnormality is another class
  • predetermined condition is one class
  • the state of normality is a reference state defined by a suitable control or controls, representing that reference state. It is with respect to this reference state (e.g., normality) that classification is made.
  • Suitable controls are usually selected on the basis of the organism (e.g., subject, patient) under study (test subject, study subject, etc.), and the nature of the study (e.g. type of sample, type of spectra, etc.). Usually, controls are selected to represent the state of "normality.” However, controls may be selected another state, if suitable. As described herein, deviations from normality (e.g., higher than normal, lower than normal) in test data, test samples, test subjects, etc. are used in classification, diagnosis, etc. For example, in most cases, control subjects are the same species as the test subject and are chosen to be representative of the equivalent normal (e.g., healthy) organism.
  • a control population is a population of control subjects.
  • control subjects may have characteristics in common (e.g., sex, ethnicity, age group, etc.) with the test subject. If appropriate, control subjects may have characteristics (e.g., age group, etc.) which differ from those of the test subject. For example, it may be desirable to choose healthy 20-year olds of the same sex and ethnicity as the study subject as control subjects.
  • control samples are taken from control subjects.
  • control samples are of the same sample type (e.g., serum), and are collected and handled (e.g., treated, processed, stored) under the same or similar conditions, as the sample under study (e.g., test sample, study sample).
  • sample under study e.g., test sample, study sample.
  • control data e.g., control values
  • control data are obtained from control samples which are taken from control subjects.
  • control data e.g., control data sets, control spectral data, control spectra, etc.
  • control data are of the same type (e.g., 1-D 1 H NMR, etc.), and are collected and handled (e.g., recorded, processed) under the same or similar conditions (e.g., parameters), as the test data.
  • the "reference" state is a pre-determined state defined by a suitable population representative of that pre-determined state.
  • the reference state is that of control, e.g., as defined by one or more control organisms.
  • the reference state is that of pre-dose, e.g., as defined by one or more pre-dose organisms, that is, prior to treatment or therapy, e.g., with a xenobiotic.
  • equivalent spectra e.g., for a reference state.
  • equivalent spectra pertains to spectra for the same state, in this case, the reference state.
  • the equivalent spectra are spectra for a single sample.
  • the equivalent spectra are spectra for a number of samples from a single organism.
  • the equivalent spectra are spectra for one sample from each of a number of organisms of the same type.
  • the equivalent spectra are spectra for a number of samples from each of a number of organisms, all of the same type.
  • the samples are the same type.
  • the set of equivalent spectra comprises at least 10 spectra. In one embodiment, the set comprises at least 20 spectra.
  • the set comprises at least 50 spectra.
  • the set comprises at least 100 spectra.
  • the set comprises at least 200 spectra.
  • the set comprises at least 500 spectra. In one embodiment, the set comprises at least 1000 spectra.
  • Examples of statistical properties include one or more of: mean; standard deviation; relative standard deviation; skewness; and, kurtosis; as well as other well known statistical properties.
  • said at least one statistical property is/are selected from: mean; standard deviation; relative standard deviation; skewness; and, kurtosis.
  • said at least one statistical property is/are selected from: standard deviation; relative standard deviation; skewness; and, kurtosis. In one embodiment, said at least one statistical property is/are selected from: standard deviation and relative standard deviation.
  • said at least one statistical property is mean.
  • said at least one statistical property is standard deviation.
  • said at least one statistical property is relative standard deviation.
  • said at least one statistical property is skewness.
  • said at least one statistical property is kurtosis.
  • confidence intervals e.g., 95% confidence
  • a certain fraction e.g., 95%) of normal variation of the reference state is seen.
  • a certain metabolite indicated by (e.g., signal intensity at) one or more spectral regions, exhibits non-control behaviour at 95% confidence; that is, in only 5% of control organisms is the level of that metabolite seen to be more extreme (further from the mean) than the measured value in the test organism.
  • one aspect of the present invention pertains to a method for classifying a sample spectrum, said method comprising the steps of: (a) calculating at least one statistical property for a set of equivalent spectra for a reference state; and,
  • One aspect of the present invention pertains to a method for classifying a test sample, said method comprising the steps of:
  • One aspect of the present invention pertains to a method for classifying a test subject, said method comprising the steps of:
  • test subject classifying said test subject as normal or abnormal, with respect to said reference state, on the basis of a sample spectrum for a test sample from said test subject and said at least one statistical property, with an associated confidence level.
  • said at least one statistical property is a plurality of statistical properties.
  • One aspect of the present invention pertains to a method for classifying a sample spectrum, said method comprising the steps of:
  • One aspect of the present invention pertains to a method for classifying a test sample, said method comprising the steps of: (a) calculating a statistical property for a set of equivalent spectra for samples representing a reference state; and,
  • test sample (b) classifying said test sample as normal or abnormal, with respect to said reference state, on the basis of a sample spectrum for said test sample and said statistical property, with an associated confidence level.
  • One aspect of the present invention pertains to a method for classifying a test subject, said method comprising the steps of:
  • Each spectrum is, to one degree or another, representative of the composition of the sample from which it was recorded.
  • a sample can be generalised as an n-dimensional object, where the coordinate along each of the axes or dimensions is the concentration of individual chemical or biochemical species.
  • the sample can be represented via its spectrum, also as an n-dimensional object, x, where the coordinate along each of the axes or dimensions (x 1 ( x 2 , x 3 , ... X j ) is an experimental parameter derived from spectral data, for example, the spectral intensity (or equivalent spectroscopic parameter) at each data point.
  • spectral intensity or equivalent spectroscopic parameter
  • each of x 1; x 2 , x 3 , etc. may represent signal intensity at different chemical shifts. It is not necessary to assign spectral features (e.g., peaks, features, lines) at this stage, since the spectrum is treated solely as a statistical object.
  • the spectra may or may not have been subjected to data compression, as described herein.
  • the methods of the present invention are applied to spectra which have been compressed, for example, into buckets, segments, or bins.
  • the spectra may or may not have been normalised.
  • the spectra are normalised, preferably to unit total integrated intensity.
  • An equivalent spectra set, X may be formed from n x equivalent spectra, each of which is denoted X; (where i runs from 1 to n x ) and each of which has descriptors rj (where j ranges from 1 to the total number of descriptors).
  • Each row, i corresponds to an individual spectrum
  • each column, j corresponds to an experimental parameter derived from spectral data, e.g., the signal intensity at a particular value of the spectroscopic parameter.
  • each column represents a set of, e.g., signal intensity values for a particular value of the spectroscopic parameter.
  • the numbers in each column are then treated as a set, and the statistical properties of that set calculated. Examples of statistical properties include, but are not limited to, the mean, standard deviation, relative standard deviation, skewness, and kurtosis.
  • Statistical properties are calculated for the equivalent spectra set, for example, for each column of X, and the results tabulated (e.g., below the respective columns), so that, for each statistical property, there is a new row corresponding to that property.
  • each new row is also a spectrum, and can be considered as a "statistics spectrum.”
  • the set of N numbers (the intensity values in a given column) is sorted in increasing order.
  • the value N*f is calculated, and from the sorted set the N*f-th member identified, or where N * f is not a whole number, the value of the theoretical N * f- th member calculated, for example, by extrapolation of the adjacent members.
  • simple (e.g., linear, normal, etc.) and complex methods of extrapolation are well known in the art.
  • N 10
  • f 0.9 (i.e., 90%)
  • N 10
  • N*f 9
  • d(6) 0.9
  • 10% of the numbers are less than or equal to 3
  • 90% of the numbers are less than or equal to 6
  • the 80% confidence interval is defined as 3 ⁇ s 6.
  • the confidence interval is characterised by a "low” and “high” threshold (L and H, respectively) defined by the cumulative distribution.
  • the specified fraction of members fall within the range from L to H; more specifically, the specified fraction of members are more than L and less than or equal to H.
  • 95% of the members will fall in the range from L to H, more specifically, 95% of the members will be more than L and less than or equal to H, that is, L ⁇ s H.
  • confidence levels e.g., predetermined confidence levels
  • a particular confidence level is selected (e.g., 95%) and the relevant low and high thresholds calculated from the cumulative distribution function, for the values (e.g., spectral intensity, x, j ) at each descriptor j.
  • the low thresholds, L j are tabulated, e.g., below the columns; graphically, this gives a "low threshold (95%)” spectrum.
  • the high thresholds, H j are tabulated, e.g., below the columns; graphically this give a "high threshold (95%)” spectrum.
  • classification may be made on the basis of the extent to which the sample spectrum falls within a confidence interval spectrum, for example, on the basis of whether or not the sample spectrum falls wholly, partially, or not at all, within a confidence interval spectrum.
  • control organisms For example, once a statistical description has been prepared for data from a (preferably large) group of control organisms, individual control organisms (whether a part of the control group, or outside the control group) can be compared to determine how similar or dissimilar they are from the control pool. In this way, highly variant, and therefore "abnormal" organisms can be identified, and their data removed from the pool.
  • test organisms e.g., which have been subjected to an applied stimulus
  • data from test organisms can be compared to help classify them as "normal” or "abnormal,” with respect to the control organisms.
  • a 95% confidence interval spectrum for the mean is calculated, and a sample spectrum is classified on the basis of the extent to which it falls within that confidence interval spectrum.
  • Corresponding classifications may be made on the basis of confidence interval spectra for other statistical properties, and the corresponding property for the sample spectrum.
  • one aspect of the present invention pertains to a method for classifying a sample spectrum for a test sample, said method comprising the steps of:
  • One aspect of the present invention pertains to a method for classifying a test sample, said method comprising the steps of:
  • test sample classifying said test sample as normal or abnormal, with respect to said reference state, on the basis of a sample spectrum for said test sample and said at least one statistical property, with an associated confidence level, specifically on the basis of the extent to which said sample spectrum falls within said confidence interval spectrum.
  • One aspect of the present invention pertains to a method for classifying a test subject, said method comprising the steps of:
  • said at least one statistical property is a plurality of statistical properties.
  • said at least one confidence interval spectrum is a plurality of confidence interval spectra.
  • One aspect of the present invention pertains to a method for classifying a sample spectrum for a test sample, said method comprising the steps of: (a) calculating a statistical property for a set of equivalent spectra for a reference state; including calculating a confidence interval spectrum associated with a predetermined confidence level for said statistical property; and,
  • One aspect of the present invention pertains to a method for classifying a test sample, said method comprising the steps of:
  • test sample as normal or abnormal, with respect to said reference state, on the basis of a sample spectrum for said test sample and said statistical property, with an associated confidence level, specifically on the basis of the extent to which said sample spectrum falls within said confidence interval spectrum.
  • test subject classifying said test subject as normal or abnormal, with respect to said reference state, on the basis of a sample spectrum for a test sample from said test subject and said statistical property, with an associated confidence level, specifically on the basis of the extent to which said sample spectrum falls within said confidence interval spectrum.
  • separate pools can be compared.
  • a set of sample spectra may be compared to a set of equivalent spectra for a reference state, using, for example, the methods described below.
  • the set of sample spectra may be, for example, spectra for a single sample, spectra for a set of samples (e.g., one spectrum per sample, several spectra per sample), spectra for a set of samples from a set or organisms (e.g., one spectrum per sample, one sample per organism, etc.).
  • the set of sample spectra may be, for example, for a test group, another control group, etc.
  • additional statistics are calculated for the equivalent spectra set X, for example, by using a "bootstrap" method.
  • a number of sub-sets of X denoted X ss>k , where k ranges from 1 to the number of sub-sets, n ss , may be selected (e.g., at random).
  • the number of spectra in each sub-set is the same.
  • n ss the number of sub-sets, n ss . is as large as is practical (e.g., as limited by the size of the set X, and available computing power). Many methods for selecting the subset are known, including, for example, “sampling without replacement” and “sampling with replacement.”
  • Statistics e.g., mean, standard deviation, relative standard deviation, skewness, and kurtosis
  • M a new set of n ss mean spectra
  • std ⁇ a new set of n ss standard deviation spectra
  • rstd k a new set of n ss relative standard deviation spectra
  • SK a new set of n ss skewness spectra, sk ⁇ denoted SK
  • Confidence limits e.g., H and L
  • a cumulative distribution function for each of these new sets e.g., M, STD, RSTD, SK, KT.
  • a 95% confidence limit may be chosen, and the H and L values for each set calculated (e.g., H m and L m ; H sld and L std ; H rstd and L rsld ; H sk and L sk ; H kt and L kt ).
  • Statistics spectra e.g., mean, standard deviation, relative standard deviation, skewness, and kurtosis
  • These statistics spectra are used to assess the set of sample spectra with respect to the set of equivalent spectra for a reference state, to determine, for example, how similar or dissimilar they are. For example, does the sample set mean fall within the confidence interval for the reference sub-set mean. In this way, a test pool of organisms can be qualified as "normal” or "abnormal.”
  • This approach is useful, for example, when assessing a new pool of control organisms to determine whether or not it conforms with the existing pool of control organisms.
  • one aspect of the present invention pertains to a method of classifying a set of sample spectra, said method comprising the steps of: (a) calculating a statistical property (e.g., mean) for each of a plurality of subsets (e.g., setl , set2, etc.) of a set of equivalent spectra for a reference state, to yield a set of statistical properties (e.g., meanl , mean2, etc.); including calculating at least one confidence interval spectrum associated with a predetermined confidence level for said statistical property (e.g., H and L for each of meanl , mean2, etc.);
  • a statistical property e.g., mean
  • one aspect of the present invention pertains to a method of classifying a set of sample spectra, said method comprising the steps of:
  • said at least one statistical property is a plurality of statistical properties.
  • said at least one confidence interval spectrum is a plurality of confidence interval spectra. These methods may be employed in a corresponding method of classifying a sample, a set of samples, a subject, or a set of subjects, on the basis of an appropriate set of sample spectra.
  • One aspect of the present invention pertains to a method of classifying a sample or a set of samples, comprising a method of classifying a set of sample spectra as described herein, wherein said set of sample spectra are for said sample or said set of samples.
  • One aspect of the present invention pertains to a method of classifying a subject or a set of subjects, comprising a method of classifying a set of sample spectra as described herein, wherein said set of sample spectra are for a sample or a set of samples, wherein said sample or set of samples are from said subject or said set of subjects.
  • these methods may be employed in a corresponding method of classifying a sample, for example, on the basis of a set of sample spectra for said sample.
  • these methods may be employed in a corresponding method of classifying a set of samples, for example, on the basis of a set of sample spectra comprising a sample spectrum for each sample (i.e., one spectrum per sample); on the basis of a set of sample spectra comprising a plurality of sample spectra for each sample (i.e., many spectra per sample).
  • these methods may be employed in a corresponding method of classifying a subject, for example, on the basis of a set of sample spectra for a sample from said subject (i.e., many spectra per sample, one sample); on the basis of a set of sample spectra comprising a sample spectrum for each of a plurality of samples from said subject (i.e., one spectrum per sample, many samples); on the basis of a set of sample spectra comprising a plurality of sample spectra for each of a plurality of samples from said subject (i.e., many spectra per sample, many samples).
  • these methods may be employed in a corresponding method of classifying a set of subjects, for example, on the basis of a set of sample spectra comprising a sample spectrum for a sample from each of said subjects (i.e., one spectrum per sample, one sample per subject); on the basis of a set of sample spectra comprising a set of sample spectra for a sample from each of said subjects (i.e., many spectra per sample, one sample per subject); on the basis of a set of sample spectra comprising a sample spectrum for each of set of samples from each of said subjects (i.e., one spectrum per sample, many samples per subject); on the basis of a set of sample spectra comprising a plurality of sample spectra for each of set of samples from each of said subjects (i.e., many spectra per sample, many samples per subject).
  • the set of sample spectra and each of the sub-sets of the set of equivalent spectra are the same size.
  • the size of the sub-sets is 10 or more. In one embodiment, the size of the sub-sets is 20 or more. In one embodiment, the size of the sub-sets is 50 or more. In one embodiment, the size of the sub-sets is 100 or more.
  • the number of sub-sets is 10 or more. In one embodiment, the number of sub-sets is 50 or more. In one embodiment, the number of sub-sets is 100 or more. In one embodiment, the number of sub-sets is 200 or more. In one embodiment, the number of sub-sets is 1000 or more.
  • a spectrum may be classified as normal or abnormal on the basis of the extent to which it falls within a confidence interval spectrum, for example, on the basis of whether or not it falls wholly, partially, or not at all, within a confidence interval spectrum.
  • all cases may be classified as one of two mutually exclusive classes "falls within” or “falls outside.” Classification as "falls within” is equivalent to classification as “not falls outside” and classification as “falls outside” is equivalent to classification as "not falls within.”
  • said "extent to which falls within” and said "extent to which falls outside” is determined by whether or not a predetermined fraction of data points fall outside a corresponding confidence interval spectrum.
  • a spectrum may be classified as abnormal if even one data point or only a few data points (e.g., more than a predetermined fraction of data points) fall outside the confidence interval spectrum.
  • the requirements for uniformity and conformity are set, for example, by the predetermined fraction, which is selected according to the particular circumstances.
  • the spectrum is classified as abnormal or normal on the basis of whether or not more than a predetermined fraction of data points of said sample spectrum fall outside said confidence interval spectrum.
  • the spectrum is classified as abnormal on the basis that more than a predetermined fraction of data points of said sample spectrum fall outside said confidence interval spectrum.
  • one or a very few data points falling outside the confidence interval may indicate candidates for biomarkers and biomarker combinations.
  • conformity requirements may be relaxed. For example, if a candidate control organism is rare or expensive, it may be desirable to relax the conformity criteria; in contrast, if a candidate control organism can be readily and cheaply replaced, then it may be desirable to tighten the conformity criteria.
  • conformity is not required because the expected changes in the test group are very large, it may be desirable to relax the conformity criteria; in contrast, if conformity is crucial in order to detect small or subtle changes in the test group, then it may be desirable to tighten the conformity criteria.
  • the downstream consequences may also play a role is deciding the conformity requirement. For example, the uniformity of the controls will affect the confidence with which a test sample is classified as "abnormal," which may ultimately determine, for example, whether or not a particular therapy or surgery is performed. In such cases, strict conformity may be desirable.
  • examples of such predetermined fractions include 0.1%, 0.5%, 1%, 2%, 3%, 5%, 8%, 10%, 15%, 20%, and 25%.
  • descriptor, j Since the statistical description of the data is arranged by descriptor, j, it is possible to identify those descriptors which are responsible for the spectrum (e.g., and consequently, the sample, the organism) being classified as abnormal. For example, for a test organism subjected to an applied stimulus, peaks at these notable descriptors are strong candidates as biomarkers or biomarker combinations for the applied stimulus in question. These notable descriptors identify windows or spectral regions (e.g., NMR chemical shift ranges) of particular interest.
  • the cumulative distribution statistics, and in particular the confidence interval spectrum may be used to classify a particular descriptor (e.g., a particular experimental parameter derived from spectral data), such as a window or spectral region (e.g., in which one or more peaks fall) as a biomarker or biomarker combination, or at least as a candidate as one, with a certain degree of confidence.
  • a particular descriptor e.g., a particular experimental parameter derived from spectral data
  • a window or spectral region e.g., in which one or more peaks fall
  • data from test organisms subjected to a particular applied stimulus may reveal one or more signals which fall outside the 95% confidence interval for control organisms.
  • the underlying metabolite(s) responsible for this peak or peak region may then be classified as a biomarker or biomarker combination, or at least as a candidate as one, for that applied stimulus, with 95% confidence.
  • one aspect of the present invention pertains to a method of identifying a candidate biomarker or biomarker combination, said method comprising the steps of: (a) calculating a statistical property (e.g., mean) for a set of equivalent spectra for a reference state; including calculating at least one confidence interval spectrum associated with a predetermined confidence level for said statistical property (e.g., H and L for mean);
  • a statistical property e.g., mean
  • H and L for mean
  • One aspect of the present invention pertains to a method of identifying a candidate biomarker or biomarker combination, said method comprising the steps of:
  • one aspect of the present invention pertains to a method of identifying a candidate biomarker or biomarker combination, said method comprising the steps of:
  • One aspect of the present invention pertains to a method of identifying a candidate biomarker or biomarker combination, said method comprising the steps of:
  • said at least one statistical property is a plurality of statistical properties.
  • said at least one confidence interval spectrum is a plurality of confidence interval spectra.
  • biomarkers and/or biomarker combinations may be further examined using conventional methods to confirm that they are, in fact, biomarkers and/or biomarker combinations.
  • identity of the chemical species may be confirmed, for example, using complementary spectroscopic and analytic techniques.
  • Relevant metabolic pathways which involve the chemical species may be examined to determine their role, for example, in the applied stimulus under study.
  • Biomarkers and/or biomarker combinations may also be identified using pools of data.
  • one aspect of the present invention pertains to a method of identifying a candidate biomarker or biomarker combination, said method comprising the steps of:
  • One aspect of the present invention pertains to a method of identifying a candidate biomarker or biomarker combination, said method comprising the steps of: (a) calculating a statistical property (e.g., mean) for each of a plurality of subsets (e.g., setl, set2, etc.) of a set of equivalent spectra for a reference state, to yield a set of statistical properties (e.g., meanl , mean2, etc.); including calculating at least one confidence interval spectrum associated with a predetermined confidence level for said statistical property (e.g., H and L for each of meanl , mean2, etc.);
  • a statistical property e.g., mean
  • one aspect of the present invention pertains to a method of identifying a candidate biomarker or biomarker combination, said method comprising the steps of: (a) calculating at least one statistical property (e.g., mean, std) for each of a plurality of sub-sets (e.g., setl , set2, etc.) of a set of equivalent spectra for a reference state, to yield a set of statistical properties (e.g., meanl , mean2, etc., stdl , std2, etc.); including calculating at least one confidence interval spectrum associated with a predetermined confidence level for at least one of said at least one statistical property (e.g., H and L for each of meanl , mean2, etc., stdl, std2, etc.);
  • at least one statistical property e.g., mean, std
  • One aspect of the present invention pertains to a method of identifying a candidate biomarker or biomarker combination, said method comprising the steps of:
  • said at least one statistical property is a plurality of statistical properties.
  • said at least one confidence interval spectrum is a plurality of confidence interval spectra.
  • one aspect of the present invention pertains to a method of identifying a candidate biomarker or biomarker combination for an applied stimulus or condition, as described herein. Additional aspects of the invention involve the biomarker or biomarker combination so identified.
  • one aspect of the present invention pertains to a biomarker or biomarker combination identified by a method as described herein.
  • One aspect of the present invention pertains to a biomarker or biomarker combination identified by a method as described herein, for use in a method of classification.
  • One aspect of the present invention pertains to a method of classification which employs or relies upon one or more biomarkers or biomarker combinations identified by a method as described herein.
  • One aspect of the present invention pertains to use of one or more biomarkers or biomarker combinations identified by a method of classification as described herein.
  • One aspect of the present invention pertains to an assay for use in a method of classification, which assay relies upon one or more biomarkers or biomarker combinations identified by a method as described herein.
  • One aspect of the present invention pertains to use of an assay in a method of classification, which assay relies upon one or more biomarkers or biomarker combinations identified by a method as described herein.
  • One aspect of the present invention pertains to a method of diagnosis employing one or more biomarkers or biomarker combinations identified by a method as described herein. Analysis of an Applied Stimulus or Condition
  • the methods described herein including, for example, methods of classifying a spectrum, method of classifying a sample, methods of classifying a subject, methods of identifying a candidate biomarker or biomarker combination, may be employed in a method of study or analysis of an applied stimulus or condition.
  • an analysis or study may involve collecting NMR spectra for blood serum samples from a number of healthy control subjects (control pool) and from a number of subjects diagnosed with a particular condition, for example, osteoporosis (disease pool).
  • the spectra for the control pool may be taken as representative of normality, and may be used as an equivalent set for the reference state (of healthy subjects).
  • spectra from the disease pool may then be subjected to the methods described herein, for example, a method of classifying a spectrum, a sample, or a subject, in an effort to determine if, and preferably to confirm that, the test subject is "abnormal.”
  • the underlying spectrum may be examined to identify the spectral region(s) which give rise to classification as abnormal, and the chemical species associated with the spectral region(s) identified as candidate biomarkers or biomarker combinations, preferably with an associated confidence level.
  • sets of spectra from the from the disease pool may similarly examined, to permit similar results.
  • one aspect of the present invention pertains to a method of analysis of an applied stimulus (e.g., a condition), which method employs: a method of classifying a spectrum, a method of classifying a sample, a method of classifying a subject, or a method of identifying a candidate biomarker or biomarker combination; as described herein; wherein said sample spectrum or spectra are for a sample from an organism which has been subjected to said applied stimulus (e.g., in which a condition is present); and, wherein said set of equivalent spectra for a reference state comprises one or more control spectra for each of one or more samples from each of one or more organisms which have not been subjected to said applied stimulus (e.g., in which a condition is absent).
  • an applied stimulus e.g., a condition
  • Such methods may be used, for example, as a method of diagnosis; a method of therapeutic monitoring; a method of evaluating drug therapy and/or drug efficacy; a method of detecting toxic side-effects of drug; a method of characterising and/or identifying a drug in overdose.
  • one aspect of the present invention pertains to a method of diagnosis of an applied stimulus or condition, comprising a method of analysis of an applied stimulus or condition, as described herein.
  • One aspect of the present invention pertains to a method of therapeutic monitoring of a subject undergoing therapy, comprising a method of analysis of an applied stimulus or condition, as described herein.
  • One aspect of the present invention pertains to a method of evaluating drug therapy and/or drug efficacy, comprising a method of analysis of an applied stimulus or condition, as described herein.
  • One aspect of the present invention pertains to a method of detecting toxic side-effects of drug, comprising a method of analysis of an applied stimulus or condition, as described herein.
  • One aspect of the present invention pertains to a method of characterising and/or identifying a drug in overdose, comprising a method of analysis of an applied stimulus or condition, as described herein.
  • the study may be in respect of an applied stimulus.
  • applied stimulus pertains to a stimulus under study which is applied to, or is present in, an organism(s) under study, and is not applied to, and is absent in, a control organism(s).
  • condition The applied stimulus may be referred to as a "condition,” which condition is present in study organism(s), but absent in control organism(s)
  • condition relates to a state which is, in at least one respect, distinct from the state of normality, as determined by a suitable control population.
  • a condition may be pathological (e.g., a disease) or physiological (e.g., phenotype, genotype, fasting, water load, exercise, hormonal cycles, e.g., oestrus, etc.).
  • pathological e.g., a disease
  • physiological e.g., phenotype, genotype, fasting, water load, exercise, hormonal cycles, e.g., oestrus, etc.
  • condition is the state of "at risk of a condition, "predisposition towards a” condition, and the like, again as compared to the state of normality, as determined by a suitable control population.
  • osteoporosis, at risk of osteoporosis, and predisposition towards osteoporosis are all conditions (and are also conditions associated with osteoporosis).
  • condition is the state of "at risk of,” “predisposition towards,” and the like
  • a method of diagnosis may be considered to be a method of prognosis.
  • the phrases "at risk of,” “predisposition towards,” and the like indicate a probability of being classified/diagnosed (or being able to be classified/diagnosed) with the predetermined condition which is greater (e.g., 1.5x, 2x, 5x, 10x, etc.) than for the corresponding control.
  • a time period e.g., within the next 5 years, 10 years, 20 years, etc.
  • a subject who is 2x more likely to be diagnosed with the predetermined condition within the next 5 years, as compared to a suitable control is "at risk of that condition.
  • the degree of a condition for example, the progress or phase of a disease, or a recovery therefrom.
  • the degree of a condition may refer to how temporally advanced the condition is.
  • Another example of a degree of a condition relates to its maximum severity, e.g., a disease can be classified as mild, moderate or severe).
  • Yet another example of a degree of a condition relates to the nature of the condition (e.g., anatomical site, extent of tissue involvement, etc.).
  • applied stimuli include, but are not limited to, a xenobiotic, a disease state, and a genetic modification.
  • xenobiotic refers to a substance (e.g., compound, composition) which is administered to an organism, or to which the organism is exposed.
  • xenobiotics are chemical, biochemical or biological species (e.g., compounds) which are not normally present in that organism, or are normally present in that organism, but not at the level obtained following administration.
  • examples of xenobiotics include drugs, formulated medicines and their components (e.g., vaccines, immunological stimulants, inert carrier vehicles), infectious agents, pesticides, herbicides, substances present in foods (e.g. plant compounds administered to animals), and substances present in the environment.
  • disease state pertains to a deviation from the normal healthy state of the organism.
  • diseases states include, but are not limited to, bacterial, viral, and parasitic infections; cancer in all its forms; degenerative diseases (e.g., arthritis, multiple sclerosis); trauma (e.g., as a result of injury); organ failure (including diabetes); cardiovascular disease (e.g., atherosclerosis, thrombosis); and, inherited diseases caused by genetic composition (e.g., sickle-cell anaemia).
  • genetic modification pertains to alteration of the genetic composition of an organism.
  • examples of genetic modifications include, but are not limited to: the incorporation of a gene or genes into an organism from another species; increasing the number of copies of an existing gene or genes in an organism; removal of a gene or genes from an organism; and, rendering a gene or genes in an organism non-functional.
  • sample e.g., a particular sample under study (“study sample”), a test sample, etc.
  • a sample may be in any suitable form.
  • the sample may be in any form which is compatible with the particular type of spectroscopy, and therefore may be, as appropriate, homogeneous or heterogeneous, comprising one or a combination of, for example, a gas, a liquid, a liquid crystal, a gel, and a solid.
  • Samples which originate from an organism may be in vivo; that is, not removed from or separated from the organism.
  • said sample is an in vivo sample.
  • the sample may be circulating blood, which is "probed” in situ, in vivo, for example, using NMR methods.
  • Samples which originate from an organism may be ex vivo; that is, removed from or separated from the organism (e.g., an ex vivo blood sample, an ex vivo urine sample).
  • said sample is an ex vivo sample.
  • said sample is an ex vivo blood or blood-derived sample. In one embodiment, said sample is an ex vivo blood sample. In one embodiment, said sample is an ex vivo plasma sample. In one embodiment, said sample is an ex vivo serum sample. In one embodiment, said sample is an ex vivo urine sample.
  • said sample is removed from or separated from an/said organism, and is not returned to said organism (e.g., an ex vivo blood sample, an ex vivo urine sample).
  • said sample is removed from or separated from an/said organism, and is returned to said organism (i.e., "in transit") (e.g., as with dialysis methods).
  • said sample is an ex vivo in transit sample.
  • samples include: a whole organism (living or dead, e.g., a living human); a part or parts of an organism (e.g., a tissue sample, an organ); a pathological tissue such as a tumour; a tissue homogenate (e.g. a liver microsome fraction); an extract prepared from a organism or a part of an organism (e.g., a tissue sample extract, such as perchloric acid extract); an infusion prepared from a organism or a part of an organism (e.g., tea, Chinese traditional herbal medicines); an in vitro tissue such as a spheroid; a suspension of a particular cell type (e.g.
  • hepatocytes an excretion, secretion, or emission from an organism (especially a fluid); material which is administered and collected (e.g., dialysis fluid); material which develops as a function of pathology (e.g., a cyst, blisters); and, supernatant from a cell culture.
  • fluid samples include, for example, blood plasma, blood serum, whole blood, urine, (gall bladder) bile, cerebrospinal fluid, milk, saliva, mucus, sweat, gastric juice, pancreatic juice, seminal fluid, prostatic fluid, seminal vesicle fluid, seminal plasma, amniotic fluid, foetal fluid, follicular fluid, synovial fluid, aqueous humour, ascite fluid, cystic fluid, blister fluid, and cell suspensions; and extracts thereof.
  • fluid samples include, for example, blood plasma, blood serum, whole blood, urine, (gall bladder) bile, cerebrospinal fluid, milk, saliva, mucus, sweat, gastric juice, pancreatic juice, seminal fluid, prostatic fluid, seminal vesicle fluid, seminal plasma, amniotic fluid, foetal fluid, follicular fluid, synovial fluid, aqueous humour, ascite fluid, cystic fluid, blister fluid, and cell suspensions;
  • tissue samples include liver, kidney, prostate, brain, gut, blood, blood cells, skeletal muscle, heart muscle, lymphoid, bone, cartilage, and reproductive tissues.
  • samples include air (e.g., exhaust), water (e.g., seawater, groundwater, wastewater, e.g., from factories), liquids from the food industry (e.g. juices, wines, beers, other alcoholic drinks, tea, milk), solid-like food samples (e.g. chocolate, pastes, fruit peel, fruit and vegetable flesh such as banana, leaves, meats, whether cooked or raw, etc.).
  • air e.g., exhaust
  • water e.g., seawater, groundwater, wastewater, e.g., from factories
  • liquids from the food industry e.g. juices, wines, beers, other alcoholic drinks, tea, milk
  • solid-like food samples e.g. chocolate, pastes, fruit peel, fruit and vegetable flesh such as banana, leaves, meats, whether cooked or raw, etc.
  • the organism in general, may be a prokaryote (e.g., bacteria) or a eukaryote (e.g., protoctista, fungi, plants, animals).
  • a prokaryote e.g., bacteria
  • a eukaryote e.g., protoctista, fungi, plants, animals.
  • the organism may be an alga or a protozoan.
  • the organism may be a plant, an angiosperm, a dicotyledon, a monocotyledon, a gymnosperm, a conifer, a ginkgo, a cycad, a fern, a horsetail, a clubmoss, a liverwort, or a moss.
  • the organism may be a chordate, an invertebrate, an echinoderm (e.g., starfish, sea urchins, brittlestars), an arthropod, an annelid (segmented worms) (e.g., earthworms, lugworms, leeches), a mollusk (cephalopods (e.g., squids, octopi), pelecypods (e.g., oysters, mussels, clams), gastropods (e.g., snails, slugs)), a nematode (round worms), a platyhelminthes (flatworms) (e.g., planarians, flukes, tapeworms), a cnidaria (e.g., jelly fish, sea anemones, corals), or a porifera (e.g., sponges).
  • a nematode round worms
  • the organism may be an arthropod, an insect (e.g., beetles, butterflies, moths), a chilopoda (centipedes), a diplopoda (millipedes), a crustacean (e.g., shrimps, crabs, lobsters), or an arachnid (e.g., spiders, scorpions, mites).
  • an insect e.g., beetles, butterflies, moths
  • a chilopoda centipedes
  • a diplopoda millipedes
  • crustacean e.g., shrimps, crabs, lobsters
  • an arachnid e.g., spiders, scorpions, mites.
  • the organism may be a chordate, a vertebrate, a mammal, a bird, a reptile (e.g., snakes, lizards, crocodiles), an amphibian (e.g., frogs, toads), a bony fish (e.g., salmon, plaice, eel, lungfish), a cartilaginous fish (e.g., sharks, rays), or a jawless fish (e.g., lampreys, hagfish).
  • a reptile e.g., snakes, lizards, crocodiles
  • an amphibian e.g., frogs, toads
  • a bony fish e.g., salmon, plaice, eel, lungfish
  • cartilaginous fish e.g., sharks, rays
  • jawless fish e.g., lampreys, hagfish
  • the organism may be a mammal, a placental mammal, a marsupial (e.g., kangaroo, wombat), a monotreme (e.g., duckbilled platypus), a rodent (e.g., a guinea pig, a hamster, a rat, a mouse), murine (e.g., a mouse), avian (e.g., a bird), canine (e.g., a dog), feline (e.g., a cat), equine (e.g., a horse), porcine (e.g., a pig), ovine (e.g., a sheep), bovine (e.g., a cow), a primate, simian (e.g., a monkey or ape), a monkey (e.g., marmoset, baboon), an ape (e.g., gorilla, chimpanzee,
  • the organism may be any of its forms, for example, a spore, a seed, an egg, a larva, a pupa, or a foetus.
  • the subject e.g., a human
  • the subject may be characterised by one or more criteria, for example, sex, age (e.g., 40 years or more, 50 years or more, 60 years or more, etc.), ethnicity, medical history, lifestyle (e.g., smoker, non-smoker), hormonal status (e.g., pre- menopausal, post-menopausal), etc.
  • population refers to a group of organisms (e.g., subjects, patients). If desired, a population (e.g., of humans) may be selected according to one or more of the criteria listed above.
  • spectra used in the methods described herein are spectra obtained following acquisition, including the normal pre-processing associated with the particular type of spectrum (e.g., data processing, compression, baseline correction, signal averaging, Fourier transformation, etc.).
  • the spectra are, or comprise, NMR spectra or NMR spectral data.
  • a spectrum which comprises NMR spectral data may be, for example, spectrum derived from NMR spectral data, e.g., by data processing, compression, baseline correction, signal averaging, Fourier transformation, etc.
  • a spectrum which comprises NMR spectral data may be, for example, a composite spectrum.
  • the sample spectrum is, or comprises, a sample NMR spectrum or sample NMR spectral data; and, the set of equivalent spectra is a set of spectra, each of which is, or comprises, an NMR spectrum or NMR spectral data.
  • the set of sample spectra is a set of spectra, each of which is, or comprises, a sample NMR spectrum or sample NMR spectral data; and, the set of equivalent spectra is a set of spectra, each of which is, or comprises, an NMR spectrum or NMR spectral data.
  • spectroscopy examples include, but are not limited to, the following: spectroscopies of all regions of the electromagnetic spectrum, including, for example, microwave spectroscopy; far infrared spectroscopy; infrared spectroscopy; Raman and resonance Raman spectroscopy; visible spectroscopy; ultraviolet spectroscopy; far ultraviolet (or vacuum ultraviolet) spectroscopy; x-ray spectroscopy; optical rotatory dispersion, circular dichroism (e.g., ultraviolet, visible and infrared); Mossbauer spectroscopy; atomic absorption and emission spectroscopy; ultraviolet fluorescence and phosphorescence spectroscopy; magnetic resonance, including nuclear magnetic resonance (NMR), electron paramagnetic resonance (EPR), and MRI (magnetic resonance imaging); and mass spectrometry, including variations of ionization methods (including electron impact, chemical ionisation, thermospray, electrospray, matrix assisted laser de
  • composite spectrum refers to a spectrum (or data vector) which comprises spectral data (e.g., NMR spectral data, e.g., an NMR spectrum) as well as at least one other datum or data vector.
  • spectral data e.g., NMR spectral data, e.g., an NMR spectrum
  • Examples of other data vectors include, e.g., one or more other NMR spectral data, e.g., NMR spectra, e.g., obtained for the same sample using a different NMR technique; other types of spectra, e.g., mass spectra, numerical representations of images, etc.; obtained for the another sample, of the same sample type (e.g., blood, urine, tissue, tissue extract), but obtained from the subject at a different timepoint; obtained for another sample of different sample type (e.g., blood, urine, tissue, tissue extract) for the same subject; and the like.
  • NMR spectral data e.g., NMR spectra, e.g., obtained for the same sample using a different NMR technique
  • other types of spectra e.g., mass spectra, numerical representations of images, etc.
  • obtained for the another sample of the same sample type (e.g., blood, urine, tissue, tissue extract)
  • Clinical parameters which are suitable for use in composite methods include, but are not limited to, the following:
  • the principal nucleus studied in biomedical NMR spectroscopy is the proton or 1 H nucleus. This is the most sensitive of all naturally occurring nuclei.
  • the chemical shift range is about 10 ppm for organic molecules.
  • 13 C NMR spectroscopy using either the naturally abundant 1.1 % 13 C nuclei or employing isotopic enrichment is useful for identifying metabolites.
  • the 3 C chemical shift range is about 200 ppm.
  • Other nuclei find special application. These include 15 N (in natural abundance or enriched), 19 F for studies of drug metabolism, and 31 P for studies of endogenous phosphate biochemistry either in vitro or in vivo.
  • the FID can be multiplied by a mathematical function to improve the signal-to-noise ratio or reduce the peak line widths. The expert operator has choice over such parameters.
  • the FID is then often filled by a number of zeros and then subjected to Fourier transformation. After this conversion from time-dependent data to frequency dependent data, it is necessary to phase the spectrum so that all peaks appear upright - this is done using two parameters by visual inspection on screen (now automatic routines are available with reasonable success). At this point the spectrum baseline can be curved. To remedy this, one defines points in the spectrum where no peaks appear and these are taken to be baseline.
  • An NMR spectrum consists of a series of digital data points with a y value (relating to signal strength) as a function of equally spaced x-values (frequency). These data point values run over the whole of the spectrum. Individual peaks in the spectrum are identified by the spectroscopist or automatically by software and the area under each peak is determined either by integration (summation of the y values of all points over the peak) or by curve fitting. A peak can be a single resonance or a multiplet of resonances corresponding to a single type of nucleus in a particular chemical environment (e.g., the two protons ortho to the carboxyl group in benzoic acid). Integration is also possible of the three dimensional peak volumes in 2-dimensional NMR spectra.
  • the intensity of a peak in an NMR spectrum is proportional to the number of nuclei giving rise to that peak (if the experiment is conducted under conditions where each successive accumulated free induction decay (FID) is taken starting at equilibrium). Also, the relative intensity of peaks from different analytes in the same sample is proportional to the concentration of that analyte (again if equilibrium prevails at the start of each scan).
  • NMR spectral intensity refers to some measure related to the NMR peak area, and may be absolute or relative.
  • NMR spectral intensity may be, for example, a combination of a plurality of NMR spectral intensities, e.g., a linear combination of a plurality of NMR spectral intensities.
  • NMR NMR spectral intensity
  • NMR spectroscopic techniques can be classified according to the number of frequency axes and these include D-, 2D-, and 3D-NMR.
  • 1 D spectra include, for example, single pulse; water-peak eliminated either by saturation or non-excitation; spin-echo, such as CPMG (i.e., edited on the basis of spin-spin relaxation); diffusion-edited, selective excitation of specific spectra regions.
  • 2D spectra include for example J-resolved (JRES); 1 H-1 H correlation methods, such as NOESY, COSY, TOCSY and variants thereof; heteronuclear correlation including direct detection methods, such as HETCOR, and inverse-detected methods, such as 1H-13C HMQC, HSQC, HMBC.
  • JRES J-resolved
  • 1 H-1 H correlation methods such as NOESY, COSY, TOCSY and variants thereof
  • heteronuclear correlation including direct detection methods such as HETCOR
  • inverse-detected methods such as 1H-13C HMQC, HSQC, HMBC.
  • 3D spectra include many variants, all of which are combinations of 2D methods, e.g. HMQC-
  • Preferred nuclei include 1 H and 13 C.
  • Preferred techniques for use in the present invention include water-peak eliminated, spin-echo such as CPMG, diffusion edited, JRES, COSY, TOCSY, HMQC, HSQC, and HMBC.
  • NMR analysis (especially of biofluids) is carried out at as high a field strength as is practical, according to availability (very high field machines are not widespread), cost (a
  • 600 MHz instrument costs about £500,000 but a shielded 800 MHz instrument can cost more than £,500,000, depending on the nature of accessory equipment purchased), and ability to accommodate the physical size of the instrument.
  • the 1 H observation frequency is from about 200 MHz to about 900 MHz, more typically from about 400 MHz to about 900 MHz, yet more typically from about 500 MHz to about 750 MHz.
  • 1 H observation frequencies of 500 and 600 MHz may be particularly preferred. Instruments with the following 1 H observation frequencies are/were commercially available: 200, 250, 270 (discontinued), 300, 360 (discontinued), 400, 500, 600, 700, 750, 800, and 900 MHz.
  • NMR spectra can be measured in solid, liquid, liquid crystal or gas states over a range of temperatures from 120 K to 420 K and outside this range with specialised equipment.
  • NMR analysis of biofluids is performed in the liquid state with a sample temperature of from about 274 K to about 328 K, but more typically from about 283 K to about 321 K.
  • An example of a typical temperature is about 300 K.
  • LDL low density lipoprotein
  • biofluid samples are diluted with solvent prior to NMR analysis. This is done for a variety of reasons, including: to lessen solution viscosity, to control the pH of the solution, and to allow addition of reagents and reference materials.
  • An example of a typical dilution solvent is a solution of 0.9% by weight of sodium chloride in D 2 0.
  • the D 2 0 lessens the overall concentration of H 2 0 and eases the technical requirements in the suppression of the solvent water NMR resonance, necessary for optimum detection of metabolite NMR signals.
  • the deuterium nuclei of the D 2 0 also provides an NMR signal for locking the magnetic field enabling the exact co-registration of successive scans.
  • the dilution ratio is from about 1 :50 to about 5:1 by volume, but more typically from about 1 :20 to about 1 :1 by volume.
  • An example of a typical dilution ratio is 3:7 by volume (e.g., 150 ⁇ L sample, 350 ⁇ L solvent), typical for conventional 5 mm NMR tubes and for flow-injection NMR spectroscopy.
  • Typical sample volumes for NMR analysis are from about 50 ⁇ L (e.g., for microprobes) to about 2 mL.
  • An example of a typical sample volume is about 500 ⁇ L.
  • NMR peak positions are measured relative to that of a known standard compound usually added directly to the sample.
  • a known standard compound usually added directly to the sample.
  • TSP partially deuterated form of TSP
  • 3-trimethylsilyl-[2,2,3,3- 2 H 4 ]- propionate sodium salt For biofluids containing high levels of proteins, this substance is not suitable since it binds to proteins and shows a broadened NMR line.
  • Added formate anion e.g., as a salt can be used in such cases as for blood plasma.
  • NMR spectra are typically acquired, and subsequently, handled in digitised form.
  • Conventional methods of spectral pre-processing of (digital) spectra are well known, and include, where applicable, signal averaging, Fourier transformation (and other transformation methods), phase correction, baseline correction, smoothing, and the like (see, for example, Lindon et al., 1980).
  • a typical 1 H NMR spectrum is recorded as signal intensity versus chemical shift ( ⁇ ) which ranges from about ⁇ 0 to ⁇ 10.
  • ⁇ signal intensity versus chemical shift
  • the spectrum in digital form comprises about 10,000 to 100,000 data points.
  • it is often desirable to compress this data for example, by a factor of about 10 to 100, to about 1000 data points.
  • the chemical shift axis, ⁇ is "segmented" into “buckets” or "bins" of a specific length.
  • ⁇ For a 1-D 1 H NMR spectrum which spans the range from ⁇ 0 to ⁇ 10, using a bucket length, ⁇ , of 0.04 yields 250 buckets, for example, ⁇ 10.0- 9.96, ⁇ 9.96-9.92, ⁇ 9.92-9.88, etc., usually reported by their midpoint, for example, ⁇ 9.98, ⁇ 9.94, ⁇ 9.90, etc.
  • the signal intensity within a given bucket may be averaged or integrated, and the resulting value reported. In this way, a spectrum with, for example, 100,000 original data points can be compressed to an equivalent spectrum with, for example, 250 data points.
  • a similar approach can be applied to 2-D spectra, 3-D spectra, and the like.
  • the "bucket” approach may be extended to a "patch.”
  • the "bucket” approach may be extended to a "volume.” For example, a 2-D 1 H NMR spectrum which spans the range from ⁇ 0 to ⁇ 10 on both axes, using a patch of ⁇ 0.1 x ⁇ 0.1 yields 10,000 patches. In this way, a spectrum with perhaps 10 8 original data points can be compressed to an equivalent spectrum of 10 4 data points.
  • the equivalent spectrum may be referred to as "a spectral data set,” “a data set comprising spectral data,” etc., comprising experimental parameters derived from spectral data.
  • spectral regions carry no real diagnostic information, or carry conflicting biochemical information, and it is often useful to remove these "redundant" regions before performing detailed analysis.
  • the data points are deleted.
  • the data in the redundant regions are replaced with zero values.
  • Certain metabolites exhibit a strong degree of physiological variation (e.g., diurnal variation, dietary-related variation) that is unrelated to any pathophysiological process. Such variation may undesirably complicate analysis, and mask more relevant details. Therefore, it may be useful to delete the spectral regions associated with such compounds. However, it is often possible to isolate these effects in later (e.g., pattern recognition) analysis.
  • Xenobiotics e.g., drugs
  • their metabolites often give rise to large signals which do not directly correlate to the conditions (e.g., pathologies) which are induced by the xenobiotic. Therefore, it is often useful to delete the spectral regions associated with such compounds.
  • NMR data is handled as a data matrix.
  • each row in the matrix corresponds to an individual sample (often referred to as a "data vector"), and the entries in the columns are, for example, spectral intensity of a particular data point, at a particular ⁇ or ⁇ (often referred to as "descriptors").
  • Multivariate projection methods such as principal component analysis (PCA) and partial least squares analysis (PLS), are so-called scaling sensitive methods.
  • PCA principal component analysis
  • PLS partial least squares analysis
  • Scaling and weighting may be used to place the data in the correct metric, based on knowledge and experience of the studied system, and therefore reveal patterns already inherently present in the data.
  • missing data for example, gaps in column values
  • missing data may replaced or "filled” with, for example, the mean value of a column ("mean fill”); a random value (“random fill”); or a value based on a principal component analysis ("principal component fill”).
  • mean fill mean value of a column
  • random fill random value
  • principal component fill a value based on a principal component analysis
  • Translation of the descriptor coordinate axes can be useful. Examples of such translation include normalisation and mean centering.
  • Normalisation may be used to remove sample-to-sample variation. Many normalisation approaches are possible, and they can often be applied at any of several points in the analysis. Usually, normalisation is applied after redundant spectral regions have been removed. In one approach, each spectrum is normalised (scaled) by a factor of 1/A, where A is the sum of the absolute values of all of the descriptors for that spectrum. In this way, each data vector has the same length, specifically, 1. For example, if the sum of the absolute values of intensities for each bucket in a particular spectrum is 1067, then the intensity for each bucket for this particular spectrum is scaled by 1/1067.
  • Mean centering may be used to simplify interpretation. Usually, for each descriptor, the average value of that descriptor for all samples is subtracted. In this way, the mean of a descriptor coincides with the origin, and all descriptors are "centred” at zero. For example, if the average intensity at ⁇ 10.0-9.96, for all spectra, is 1.2 units, then the intensity at ⁇ 10.0-9.96, for all spectra, is reduced by 1.2 units.
  • unit variance scaling data can be scaled to equal variance.
  • the value of each descriptor is scaled by 1/StDev, where StDev is the standard deviation for that descriptor for all samples. For example, if the standard deviation at ⁇ 10.0-9.96, for all spectra, is 2.5 units, then the intensity at ⁇ 10.0-9.96, for all spectra, is scaled by 1/2.5 or 0.4.
  • Unit variance scaling may be used to reduce the impact of "noisy" data. For example, some metabolites in biofluids show a strong degree of physiological variation (e.g., diurnal variation, dietary-related variation) that is unrelated to any pathophysiological process. Without unit variance scaling, these noisy metabolites may dominate subsequent analysis.
  • Pareto scaling is, in some sense, intermediate between mean centering and unit variance scaling. In effect, smaller peaks in the spectra can influence the model to a higher degree than for the mean centered case. Also, the loadings are, in general, more interpretable than for unit variance based models.
  • the value of each descriptor is scaled by 1/sqrt(StDev), where StDev is the standard deviation for that descriptor for all samples. In this way, each descriptor has a variance numerically equal to its initial standard deviation.
  • the pareto scaling may be performed, for example, on raw data or mean centered data.
  • Logarithmic scaling may be used to assist interpretation when data have a positive skew and/or when data spans a large range, e.g., several orders of magnitude. Usually, for each descriptor, the value is replaced by the logarithm of that value. For example, the intensity at ⁇ 10.0-9.96 is replaced the logarithm of the intensity at ⁇ 10.0-9.96, for all spectra.
  • each descriptor is divided by the range of that descriptor for all samples. In this way, all descriptors have the same range, that is, 1. For example, if, at ⁇ 10.0-9.96, for all spectra, the largest value is 87 units and the smallest value is 1 , then the range is 86 units, and the intensity at ⁇ 10.0-9.96, for all spectra, is divided by 86 units.
  • this method is sensitive to presence of outlier points.
  • each data vector is mean centred and unit variance scaled. This technique is a very useful because each descriptor is then weighted equally and, in the case of NMR descriptors, large and small peaks are treated with equal emphasis. This can be important for metabolites present at very low, but still detectable, levels.
  • the variance weight of a single parameter is calculated as the ratio of the inter-class variances to the sum of the intra- class variances.
  • a large value means that this variable is discriminating between the classes. For example, if the samples are known to fall into two classes (e.g., a training set), it is possible to examine the mean and variance of each descriptor. If a descriptor has very different mean values and a small variance, then it will be good at separating the classes.
  • Feature weighting is a more general description of variance weighting, where not only the mean and standard deviation of each descriptor is calculated, but other well known weighting factors, such as the Fisher weight, are used.
  • Spurious or irregular data in spectra are preferably identified and removed.
  • Common reasons for irregular data include poor phase correction, poor baseline correction, poor chemical shift referencing, poor water suppression, bacterial contamination, shifts in the pH of the biofluid, toxin- or disease-induced biochemical response, and idiosyncratic response to xenobiotics.
  • Outliers are identified in different ways depending on the method of analysis used. For example, when using principal component analysis (PCA), small numbers of samples lying far from the rest of the replicate group can be identified by eye as outliers.
  • PCA principal component analysis
  • a more objective means of identification for PCA is to use the Hotelling's T Test which is the multivariate version of the well known Student's T test used in univariate statistics. For any given sample, the T2 value can be calculated and this is compared with a standard value within which a chosen fraction (e.g., 95%) of the samples would normally lie. Samples with T2 values substantially outside this limit can then be flagged as outliers.
  • SIMCA SIMCA
  • PNNs a similar method is used.
  • a confidence level (e.g., 95%) is selected and the region of multivariate space corresponding to confidence values above this limit is determined.
  • This region can be displayed graphically in several different ways (for example by plotting the critical T2 ellipse on a PCA scores plot). Any samples falling outside the high confidence region are flagged as potential outliers. Naturally, such samples are investigated in detail to determine the causes of their outlying nature before removing them from the model.
  • the methods of the present invention, or parts thereof, may be conveniently performed electronically, for example, using a suitably programmed computer system.
  • One aspect of the present invention pertains to a computer system or device, such as a computer or linked computers, operatively configured to implement a method of the present invention, as described herein.
  • One aspect of the present invention pertains to computer code suitable for implementing a method of the present invention, as described herein, on a suitable computer system.
  • One aspect of the present invention pertains to a computer program comprising computer program means adapted to perform a method according to the present invention, as described herein, when said program is run on a computer.
  • One aspect of the present invention pertains to a computer program, as described above, embodied on a computer readable medium.
  • One aspect of the present invention pertains to a data carrier which carries computer code suitable for implementing a method of the present invention, as described herein, on a suitable computer.
  • Computers may be linked, for example, internally (e.g., on the same circuit board, on different circuit boards which are part of the same unit), by cabling (e.g., networking, ethernet, internet), using wireless technology (e.g., radio, microwave, satellite link, cellphone), etc., or by a combination thereof.
  • cabling e.g., networking, ethernet, internet
  • wireless technology e.g., radio, microwave, satellite link, cellphone
  • Examples of data carriers and computer readable media include chip media (e.g., ROM, RAM, flash memory (e.g., Memory StickTM, Compact FlashTM, SmartmediaTM), magnetic disk media (e.g., floppy disks, hard drives), optical disk media (e.g., compact disks (CDs), digital versatile disks (DVDs), magneto-optical (MO) disks), and magnetic tape media.
  • chip media e.g., ROM, RAM, flash memory (e.g., Memory StickTM, Compact FlashTM, SmartmediaTM
  • magnetic disk media e.g., floppy disks, hard drives
  • optical disk media e.g., compact disks (CDs), digital versatile disks (DVDs), magneto-optical (MO) disks
  • magnetic tape media e.g., magnetic tape, and magnetic tape media.
  • the methods described herein can be used in the analysis of chemical, biochemical, and biological data.
  • the methods described herein provide powerful means for the diagnosis and prognosis of disease, for assisting medical practitioners in providing optimum therapy for disease, and for understanding the benefits and side-effects of xenobiotic compounds thereby aiding the drug development process.
  • the methods described herein can be applied in a non-medical setting, such as in post mortem examinations, forensic science, and the analysis of complex chemical mixtures other than mammalian cells or biofluids.
  • the technique can be used to identify subjects suffering from cerebral edema immediately on arrival in the acute emergency department of a hospital.
  • cerebral edema will be a problem: as a result, it may not be possible to intervene until clinical symptoms of cerebral edema become evident, which may be too late to save the patient.
  • patients arriving at acute emergency departments can be screened for internal bleeding and organ rupture, to facilitate early surgical intervention.
  • the methods described herein can be used to identify a clinically silent disease (e.g., low bone mineral density (e.g., osteoporosis); infection with Helicobacter Pylori) prior to the onset of clinical symptoms (e.g., fracture; development of ulcers).
  • a clinically silent disease e.g., low bone mineral density (e.g., osteoporosis); infection with Helicobacter Pylori
  • onset of clinical symptoms e.g., fracture; development of ulcers.
  • Diagnosis identification of disease
  • the methods described herein can be used to replace treadmill exercise tests, echiocardiograms, electrocardiograms, and invasive angiography as the collective method for the identification of coronary heart disease. Since the current tests for coronary heart disease are slow, expensive, and invasive (with associated morbidity and mortality), the methods described herein offer significant advantages.
  • Differential diagnosis e.g., classification of disease, severity of disease, etc., for example, the ability to distinguish patients with coronary artery disease affecting 1 ,2, or all 3 coronary arteries (see example below); the ability to distinguish disease at different anatomical sites, e.g., in the left coronary artery versus the circumflex artery, or in the carotid arteries as opposed to the coronary arteries.
  • a condition e.g., coronary heart disease, osteoporosis
  • an acute event e.g., heart attack, bone fracture
  • Drugs may exist to help prevent the acute event (e.g., statins for heart disease, bisphosphonates for osteoporosis), but often they cannot be efficiently targeted at the population level.
  • the requirements for a test to be useful for population screening are that they must be cheap and non-invasive.
  • the methods described herein are ideally suited to population screening. Screens for multiple diseases with a single blood sample (e.g., osteoporosis, heart disease, and cancer) further improve the cost/benefit ratio for screening.
  • Prognosis prediction of future outcome
  • a sample can be used to assess the risk of myocardial infarction among sufferers of angina, permitting a more aggressive therapeutic strategy to be applied to those at greatest risk of progressing to a heart attack.
  • the methods described herein can be used for population screening (as for diagnosis) but in this case to screen for the risk of developing a particular disease.
  • Such an approach will be useful where an effective prophylaxis is known but must be applied prior to the development of the disease in order to be effective.
  • bisphosphonates are effective at preventing bone loss in osteoporosis but they do not increase pathologically low bone mineral density. Ideally, therefore, these drugs are applied prior to any bone loss occurring. This can only be done with a technique which facilitates prediction of future disease (prognosis).
  • the methods described herein can be used to identify those people at high risk of losing bone mineral density in the future, so that prophylaxis may begin prior to disease inception.
  • Antenatal screening for a wide range of disease susceptibilities.
  • the methods described herein can be used to analyse blood or tissue drawn from a pre-term fetus (e.g., during chorionic vilus sampling or amniocentesis) for the purposes of antenatal screening. Aids to Therapeutic Intervention
  • Therapeutic monitoring e.g., to monitor the progress of treatment. For example, by making serial diagnostic tests, it will be possible to determine whether and to what extent the subject is returning to normal following initiation of a therapeutic regimen.
  • Patient compliance e.g., monitoring patient compliance with therapy.
  • Patient compliance is often very poor, particularly with therapies that have significant side- effects. Patients often claim to comply with the therapeutic regimen, but this may not always be the case.
  • the methods described herein permit the patient compliance to be monitored, both by directly measuring the drug concentration and also by examining biological consequences of the drug.
  • the methods described herein offer significant advantages over existing methods of monitoring compliance (such as measuring plasma concentrations of the drug) since the patient may take the drug just prior to the investigation, while having failed to comply for previous weeks or months.
  • Toxicology including sophisticated monitoring of any adverse reactions suffered, e.g., on a patient-by-patient basis. This will facilitate investigation of idiosyncratic toxicity. Some patients may suffer real, clinically significant side-effects from a therapy which were not seen in the majority. Application of the methods described herein facilitate rapid identification of these rare, idiosyncratic toxicities so that the therapy can be discontinued or modified as appropriate. Such an approach allows the therapy to be tailored to the individual metabolism of each patient.
  • the methods described herein could be re-applied to the untreated metabonomic fingerprint to identify pattern elements which predict future responses to statins.
  • the clinician would know whether or other patients should be treated with statins, without having to wait weeks or months to assess the outcome.
  • the methods described herein can be used to identify the metabolic consequences of a range of actions on a subject (who may be either dead or alive at the time of the investigation).
  • the methods described herein can be applied to identify metabolic consequences of asphyxiation, poisoning, sexual arousal, or fear.
  • the methods described herein can also be used to identify (known or novel) genotypes and/or phenotypes, and to determine an organism's phenotype or genotype. This may assist with the choice of a suitable treatment or facilitate assessment of its relevance in a drug development process.
  • the generation of metabonomic data in panels of individuals with disease states, infected states, or undergoing treatment may indicate response profiles of groups of individuals which can be differentiated into two or more subgroups, indicating that an allelic genetic basis for response to the disease, state, or treatment exists. For example, a particular phenotype may not be susceptible to treatment with a certain drug, while another phenotype may be susceptible to treatment.
  • one phenotype might show toxicity because of a failure to metabolise and hence excrete a drug, which drug might be safe in another phenotype as it does not exhibit this effect.
  • metabonomic methods can be used to determine the acetylator status of an organism: there are two phenotypes, corresponding to "fast" and "slow” acetylation of drug metabolites. Phenotyping can be achieved on the basis of the urine alone (i.e., without dosing a xenobiotic), or on the basis of urine following dosing with a xenobiotic which has the potential for acetylation (e.g., galactosamine). Similar methods can also be used to determine other differences, such as other enzymatic polymorphisms, for example, cytochrome P450 polymorphism.
  • the methods described herein may also be used in studies of the biochemical consequences of genetic modification, for example, in "knock-out animals” where one or more genes have been removed or made non-functional; in “knock-in” animals where one or more genes have been incorporated from the same or a different species; and in animals where the number of copies of a gene has been increased, as in the model which results in the over-expression of the beta amyloid protein in mice brains as a model for Alzheimer's disease). Genes can be transferred between bacterial, plant and animal species.
  • genomic, proteomic, and metabonomic data sets into comprehensive "bionomic" systems may permit an holistic evaluation of perturbed in vivo function.
  • the methods described herein may be used as an alternative or adjunct to other methods, e.g., the various genomic, pharmacogenomic, and proteomic methods.
  • 900 1 H NMR spectra were collected for Han-Wistar and Sprague-Dawley rat urines using a 600 MHz Bruker DRX600 NMR spectrometer.
  • the rats comprised several different groups, each of which was a control group in a different toxicology study. Animals were examined to ensure they were healthy before being included, and were housed in metabolism cages with a standard day/night cycle and standard diet, with urine collected either once or twice daily.
  • Figure 1 illustrates the 95% confidence interval high (A) and low (B) spectra, and the Test 1 spectrum (C), for a wide range of chemical shift ( ⁇ 0-10).
  • the data for a narrow range of chemical shift ( ⁇ 3.78- 4.10) for Test 1 are also illustrated in Figure 2.
  • Test 1 spectrum departs from 95% of the control population in the chemical shift range ⁇ 3.78-3.94. Metabolites in this window may be candidates as biomarkers or part of a biomarker combination.
  • Example 2
  • 1 H NMR spectra for a first (control) pool of 450 Han-Wistar rats were collected, as described in Example 1. 100 sub-sets of spectra (sampling with replacement) were selected, each sub-set having 45 spectra. Statistics (mean, standard deviation, relative standard deviation, skew, and kurtosis) were calculated for each sub-set ("bootstrap"). 95% confidence intervals (L and H) were then calculated for each of the resulting sets of statistics. Results for 9 spectral regions (e.g., ⁇ 4.12-4.08 reported as ⁇ 4.10) are shown in Table 2.
  • test population clearly departs from 95% of the control population, in respect of mean, standard deviation, relative standard deviation at various points in the chemical shift range shown.
  • metabolites in the corresponding windows may be candidates as biomarkers or part of a biomarker combination, for example, for the differences between the two populations.

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Abstract

L'invention concerne des procédés de chimiométrie destinés à l'analyse de données chimiques, biochimiques et biologiques, par exemple, des données spectrales, à savoir, un spectre à résonance magnétique nucléaire et d'autres types de spectres, et leurs applications. Plus spécifiquement, cette invention a trait à un procédé de classification d'un spectre d'échantillon consistant (a) à calculer au moins une propriété statistique pour une série de spectres équivalents pour un état de référence, et (b) à classifier ledit spectre d'échantillon comme normal ou anormal, en fonction de l'état de référence, sur la base d'au moins une propriété statistique. Cette invention concerne aussi des procédés correspondants de classification (de spectres, d'échantillons, de sujets, etc.), des procédés d'identification de biomarqueurs et/ou de combinaisons de biomarqueurs, des procédés d'analyse d'un stimulus appliqué ou d'une condition, des procédés de diagnostic, etc.
PCT/GB2002/002758 2001-06-04 2002-05-31 Procedes d'analyse spectrale et leurs applications dans l'evaluation de la fiabilite WO2002099452A1 (fr)

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US8068987B2 (en) 2001-08-13 2011-11-29 Bg Medicine, Inc. Method and system for profiling biological systems
WO2008088343A1 (fr) * 2007-01-19 2008-07-24 Biophysical Corporation Procedes de fourniture de service d'evaluation d'etat de sante individualise
US9459203B2 (en) 2014-09-29 2016-10-04 Zyomed, Corp. Systems and methods for generating and using projector curve sets for universal calibration for noninvasive blood glucose and other measurements
US9459201B2 (en) 2014-09-29 2016-10-04 Zyomed Corp. Systems and methods for noninvasive blood glucose and other analyte detection and measurement using collision computing
US9442065B2 (en) 2014-09-29 2016-09-13 Zyomed Corp. Systems and methods for synthesis of zyotons for use in collision computing for noninvasive blood glucose and other measurements
US9448165B2 (en) 2014-09-29 2016-09-20 Zyomed Corp. Systems and methods for control of illumination or radiation collection for blood glucose and other analyte detection and measurement using collision computing
US9448164B2 (en) 2014-09-29 2016-09-20 Zyomed Corp. Systems and methods for noninvasive blood glucose and other analyte detection and measurement using collision computing
US9453794B2 (en) 2014-09-29 2016-09-27 Zyomed Corp. Systems and methods for blood glucose and other analyte detection and measurement using collision computing
US9610018B2 (en) 2014-09-29 2017-04-04 Zyomed Corp. Systems and methods for measurement of heart rate and other heart-related characteristics from photoplethysmographic (PPG) signals using collision computing
US9459202B2 (en) 2014-09-29 2016-10-04 Zyomed Corp. Systems and methods for collision computing for detection and noninvasive measurement of blood glucose and other substances and events
CN107003328A (zh) * 2014-12-18 2017-08-01 皇家飞利浦有限公司 用于确定生物样本中纤维蛋白原浓度的方法
CN105738303A (zh) * 2016-02-04 2016-07-06 云南中烟工业有限责任公司 基于全谱段分子光谱的卷烟烟气质量趋势分析方法
CN105606552A (zh) * 2016-02-04 2016-05-25 云南中烟工业有限责任公司 基于全谱段分子光谱的卷烟烟丝质量趋势分析方法
US9554738B1 (en) 2016-03-30 2017-01-31 Zyomed Corp. Spectroscopic tomography systems and methods for noninvasive detection and measurement of analytes using collision computing
CN110146657A (zh) * 2019-06-10 2019-08-20 中国烟草总公司郑州烟草研究院 一种评价卷烟落头倾向的方法
CN113687010A (zh) * 2021-08-16 2021-11-23 大连海洋大学 准确区分紫海胆-中间球海胆和紫海胆上市性腺的方法
CN113687010B (zh) * 2021-08-16 2023-09-15 大连海洋大学 准确区分紫海胆-中间球海胆和紫海胆上市性腺的方法

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