EP2491508A2 - Procédés de production de motifs de référence pour biomarqueurs - Google Patents

Procédés de production de motifs de référence pour biomarqueurs

Info

Publication number
EP2491508A2
EP2491508A2 EP10766047A EP10766047A EP2491508A2 EP 2491508 A2 EP2491508 A2 EP 2491508A2 EP 10766047 A EP10766047 A EP 10766047A EP 10766047 A EP10766047 A EP 10766047A EP 2491508 A2 EP2491508 A2 EP 2491508A2
Authority
EP
European Patent Office
Prior art keywords
effector
pattern
effectors
effect
class
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
EP10766047A
Other languages
German (de)
English (en)
Inventor
Alexandre Prokoudine
Tilmann B. Walk
Jan C. Wiemer
Ralf Looser
Michael Manfred Herold
Bennard Van Ravenzwaay
Werner Mellert
Eric Fabian
Volker Strauss
Hennicke Kamp
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
BASF Plant Science Co GmbH
Original Assignee
BASF Plant Science Co GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by BASF Plant Science Co GmbH filed Critical BASF Plant Science Co GmbH
Priority to EP10766047A priority Critical patent/EP2491508A2/fr
Publication of EP2491508A2 publication Critical patent/EP2491508A2/fr
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection

Definitions

  • the present invention relates to methods for the evaluation of biomarkers.
  • the invention relates to a method for creating at least one pattern for at least one predetermined effector having at least one determinable effect on a biological system, a method for creating an effector class for a given effect or group of effects, a method for creating an effector class to a predetermined Effect or group of effects and a method for identifying at least one effect of a given effector, and a computer program and a computer arranged to carry out these methods.
  • Bio systems such as individual organisms or communities of organisms regularly respond to effectors with a change in state, in particular a change in their biochemical properties or the biochemical nature.
  • An individual organism on which an external or internal effector acts reacts, for example, by altered cell activities. This altered activity then also results in a change in the nature or quantitative composition of the cellular molecules.
  • changes in transcriptional activities or protein function and protein turnover as well as metabolic changes can be observed. The latter result in a change in the qualitative and / or quantitative metabolite nature of the organism as a result of the effector (change in the metabolome).
  • Similar changes in biochemical properties or properties can be observed in communities of organisms that form a biological system.
  • Such communities of organisms that form a biological system are, for example, microorganisms that form a locally delineated microcosm.
  • effectors can act as effectors on a biological system, for example toxic or have a beneficial or curative effect.
  • chemicals can act as effectors on a biological system, for example toxic or have a beneficial or curative effect.
  • Effector-induced changes in the metabolome regularly affect not only a metabolite whose condition could then serve as a so-called biomarker. Often, various metabolites are affected. Effectors that mediate the same effect do not always have to change the same metabolites. However, there is regularly a set of key metabolites that is altered by effectors that have the same effect. This set of altered key metabolites is currently not always identifiable efficiently. The main problem here is that most effectors, apart from the characteristic key metabolites, also cause individual metabolic changes which are only characteristic of the individual effector, but are not caused by other effectors which produce the same effect. In addition, there are metabolic changes that are not related to the applied effector, but are caused by other influences or changes in the metabolites, which are only due to metrological variability.
  • the method steps a) to j1) described below can be carried out individually or in total repeatedly, for example with a number of at least two repetitions, a number of at least five repetitions and particularly preferably a number of at least 10 repetitions or even at least 20 repetitions , Furthermore, the methods may also have additional process steps not listed in the claims.
  • the invention relates to a method for producing at least one pattern for at least one predetermined effector having at least one determinable effect on a biological system, comprising the following steps:
  • provisioning can basically be understood as any effect on the availability of the item to be provided, in particular in an electronic form, for example on a volatile or nonvolatile data memory which can be accessed in the method. so that the item to be provided, here the at least one profile of the given effector, is available Alternatively or additionally, for example, a database can be used to provide it, but other types of provision are also possible in principle manually, for example by manual input into a computer or some other kind of manual deployment. The provision can take place actively, so that the good to be provided is actively supplied to the process, or alternatively also passively, so that only an availability, for example a retrievability of the data, is ensured.
  • a "biological system” is understood to mean a system which comprises one or more organisms. If several organisms are provided, they may in particular be spatially contiguous and have a common metabolism. The organisms may be the same or different.
  • mammals more preferably mammals which can be maintained under controlled conditions, such as dogs, cats, mice or rats, with rats being particularly preferred. Suitable methods for keeping, for example, mammals under controlled conditions are described in WO2007 / 04825.
  • preference may be given to cultivar plants and plants, in particular plants which can be grown under controlled conditions in the greenhouse, such as Arabidopsis thaliana or rice.
  • a “metabolite” is generally understood to mean intermediate products of a metabolic process, in particular a biochemical metabolic process. Metabolism refers to a set of metabolic processes of the biological system. Metabolites according to the invention are small molecules (so-called “small molecule compounds”), such as substrates for enzymes of metabolic pathways, intermediates of such pathways, or their end products. Metabolic pathways are well known in the art and may differ between different species.
  • metabolic pathways are at least the citric acid cycle, the respiratory chain, photosensitization, photorespiration, glycosylase, gluconeogenesis, the hexose monophosphate pathway, the oxidative pentose phosphate pathway, the synthesis and beta-oxidation of fatty acids, the urea cycle Biosynthesis of amino acids, biosynthesis of nucleotides, nucleosides and nucleic acids (including tRNAs, microRNAs (miR A) or mRNAs), protein degradation, nucleotide degradation, biosynthesis or degradation of lipids, polyketides (including flavonoids and isoflavonoids) , isoprenoids (including terpenes, sterols, steroids, carotenoids or xanthophylls), carbohydrates, phenylpropanoids and their derivatives, alkaloids, benzenoids, indoles, indole-sulfur compounds, porphyrins, anthocyanins, hormones
  • metabolites are preferably members of the following molecular groups or classes of molecules: alcohols, alkanes, alkenes, Alkynes, aromatics, ketones, aldehydes, carboxylic acids, esters, amines, imines, amides, cyanides, amino acids, peptides, thiols, thioesters, phosphate esters, sulfate esters, thioethers, suifoxides, ethers or their derivatives, or combinations thereof.
  • Metabolites can be primary metabolites, ie those that are necessary for the normal (physiological) function of the organism or the organs.
  • metabolites also include secondary metabolites with essentially ecological function, ie metabolites that allow the organism to adapt to the environment.
  • Metabolites in addition to these primary and secondary metabolites, also comprise Wettere, some of which are artificial molecules. These are derived from exogenous molecules, which can be taken up, for example, as active ingredients and then further modified in metabolism.
  • Metobolites may also be peptides, oligopeptides, polypeptides, oligonucleotides and polynucleotides such as RNA or DNA.
  • metabolites have a molecular weight of 50 Da (daltons) to 30,000 Da, more preferably less than 30,000 Da, less than 20,000 Da, less than 15,000 Da, less than 10,000 Da, less than 8,000 Da, less than 7,000 Da, less than 6,000 Da, less than 5,000 Da, less than 4,000 Da, less than 3,000 Da, less than 2,000 Da, less than 1,000 Da, less than 500 Da, less than 300 Da, less than 200 Da, less than 100 Da.
  • a metabolite according to the invention has a molecular weight of about 50 Da to about 1,500 Da.
  • an "effect" can in principle be understood to mean any change in at least one state of the biological system which can be determined.
  • this state may be a biological and / or biochemical and / or chemical state of the biological system.
  • this effect may be reflected in a change in a metabolome of the biological system.
  • An effect in the sense of the present invention may preferably be a change in the cell morphology, the genome, the metabolome (ie the qualitative or quantitative state of the metabolites in an organism or subgroup thereof), the proteome (ie the qualitative or quantitative state of the proteins in a) Organism or subset thereof), the transcriptome (ie the qualitative or quantitative state of the transcripts in an organism or subgroup thereof), organ function, cell, tissue or organicity (toxicity) and / or mental or social condition. It is understood that various effects can occur together within the meaning of the invention.
  • an "effector” is understood to mean a basically arbitrary influence on the biological system, which potentially could have at least one effect, which in principle can be of any kind, on the biological system.
  • this potential effect may be an effect of the type mentioned above, in particular a biochemical and / or biological and / or chemical action, which could be reflected in particular in a change in the metabolism.
  • influences are the exposure of the biological system with one or more chemical substances and / or compounds, such as drugs and / or pesticides and / or a physical effect on the biological system, such as exposure to the biological system with electromagnetic radiation and / or particle radiation.
  • a different duration and / or intensity and / or dose of the influence on the biological system can be imaged in the context of the present invention by suitable effectors.
  • different durations and / or intensities and / or doses of the same influence on the biological system may be considered as different effectors.
  • an effector contains an exposure of the biological system to at least one chemical substance and / or chemical compound and / or to at least one radiation, then for example different durations and / or different intensities and / or different doses of this application may be considered as different effectors become. It can also be a gradation in two or more stages of duration and / or dose and / or intensity.
  • a low dose and a high dose may be given to which the biological system is selectively applied, wherein the application to the low dose and exposure to the high dose are considered to be two different effectors.
  • effector may be used singly or in conjunction with other effectors so that, for example, a group of effectors work together.
  • Preferred effectors in the invention are chemical substances, pharmaceutical Active substances and potential active pharmaceutical ingredients (active substance candidates), pesticides (herbicides, insecticides or fungicides), growth promoters, eg fertilizers, radiation treatments, genetic alterations, eg in the form of random or deliberately created mutations in the genome of an organism or by integration of gene genetic material, and / or changes in environmental conditions (temperature, radiation, food, water balance, gas composition and ambient atmosphere pressure, etc.).
  • a “biomarker” generally refers to a state of a metabolite or of a specific group of metabolites. This state may be dependent, in particular, on boundary conditions and / or parameters, such as, for example, the age of the biological system, time of detection of the condition, in particular a period of time after exposure to the biological system with at least one effector, and optionally further information about the biological system, for example Gender.
  • a biomarker a particular level of a metabolite or a particular group of metabolites may be indicated as a function of a sex of the biological system and / or a time.
  • a biomarker may also designate a change in state of a metabolite or of a specific group of metabolites, for example, again depending on boundary conditions and / or parameters, for example of the abovementioned type.
  • the biomarker itself as a variable variable must be distinguished from its numerical value For example, it can be determined whether a biomarker is significant or not. As discussed below, this determination of whether or not a biomarker is significant can be made, for example, by comparing its numerical value with one or more significance thresholds.
  • a biomarker can in principle be given in any units, for example in absolute units or in relative units, for example as a change from a reference value, in particular a normalization state and / or a state in which the biological system does not interact with the effector and / or the group by effectors and / or any effector.
  • a "profile" of a particular effector or a group of effectors which is also referred to in part as a metabolic profile, means the entirety of the biomarkers which were detected during or after exposure to the biological system with the effector or are detectable. It is therefore a total amount of biomarkers, which are detectable at all, or a subset of this total amount, which is considered for a particular investigation. This entirety is preferably carried out under controlled and standardized conditions during or after exposure to the biological System with the effector or group of effectors detected.
  • this profile may be a metabolome or a subset of the metabolome induced by exposure of the biological system to the at least one effector, or comprise a metabolome or a subset of a metabolome.
  • a profile of metabolites may preferably be determined by methods which allow both the quantitative and qualitative determination of the metabolites in the organism.
  • a sample from the organism can be measured, which contains a representative extract of the metabolites.
  • Suitable sample materials include body fluids such as blood, serum, plasma, urine, saliva, feces, tears, secretions, or cerebrospinal fluid, or tissue specimens that can be obtained by biopsy.
  • samples can also be samples from a microcosm or cultured cells.
  • Samples can also be pretreated to obtain, for example, a subcellular fraction (cell nuclei, endoplasmic reticulum, photosystem, peroxisomes, Golgi network, etc.) as the actual sample.
  • the metabolic profiles of such samples may preferably be obtained by mass spectrometric techniques, NMR or other of the methods mentioned below.
  • Mass spectrometric techniques may generally be understood to mean analyzes of samples using mass spectroscopy and / or mass spectrometers, in particular mass separation techniques in which the ions are analyzed with photosensitive detectors. Mass spectrometric techniques and mass spectroscopic techniques can be used equivalently within the scope of the present invention.
  • LC and / or GC are used.
  • the actual quantitative and / or qualitative determination of the metabolites for a profile can then be carried out with suitable measurement or analysis methods.
  • suitable measurement or analysis methods include: mass spectrometry such as GC-MS, LC-MS, direct infusion mass spectrometry, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), capillary electrophoresis mass spectrometry, (CE-MS) , the "high-performance liquid chromatography” coupled mass spectrometry, the Quadrupo!
  • Mass spectrometry sequential mass spectrometry, such as MS-MS or MS-MS-MS, inductively coupled plasma mass spectrometry (ICP-MS), pyrolysis Mass spectrometry (Py-MS, ion mobility mass spectrometry or time of flight mass spectrometry (TOF)) LC-MS and / or GC-MS are particularly preferably used, these methods being described in Nissen, Journal of Chromatography A, 703 , 1995: 37-57, US 4,540,884 or US 5,397,894 The disclosure of these documents is hereby fully incorporated in.
  • NMR nuclear magnetic resonance
  • MRI magnetic resonance irnaging
  • FT-IR Fourier Trasnformations Infrared analysis
  • UV ultraviolet
  • Rl refractive indices
  • fluorescence determinations radiochemical determinations
  • electrochemical determinations electrochemical determinations
  • light scattering LS
  • dispersive Raman spectroscopy flame ionization detectors
  • the aforementioned methods are particularly well suited for determining the conditions of a plurality of metabolites in samples and thus for detecting the values of the characteristics necessary for the profiler division.
  • the methods preferably provide a value for an identity parameter and one or more values for one or more parameters resulting from the physical, chemical or biological properties of the metabolites being measured.
  • values be included in the biomarker profile that allow the determination of the chemical nature of the metabolite, but also a value that can reflect quantitative changes in the metabolite, namely the quantity measured in a given sample.
  • the aforementioned methods are also suitable for high-throughput analyzes, so that different samples can be measured automatically at short intervals and a creation of a large number of profiles that can be compared with one another is possible in a short time.
  • a "pattern" of a specific effector or a group of effectors denotes an amount of biomarkers which have a significant change when the biological system is exposed to a specific effector or a specific group of effectors.
  • the biomarkers in the profile itself or their values may be given in absolute values and / or in relative values and / or in changes, for example rates of change or changes compared to at least one normal value.
  • the change can also be considered in absolute values and / or in relative units, for example compared to at least one reference value and / or a normal state, in particular a state in which no exposure to the effector and / or the group of effectors and / or any effector is done or done.
  • threshold values may be specified here, which are also referred to below as significance pause, for example one or more threshold values for one or more biomarkers, in particular for each biomarker or for each group of biomarkers a significance violation on the basis of which a decision is made Change is significant or not.
  • These threshold values can also be variable and, for example, adjusted iteratively in order to set a sensitivity and / or a selectivity.
  • selectivity is generally understood as the ability or property to systematically select certain elements from a set of possible elements.
  • the selectivity can thus be a measure of the narrowness of the selection.
  • a database is used, then the selectivity can be a measure of the proportion of the selected elements in the total stock of data in the database, in particular in the case of a database search via an index.
  • the selectivity can specify how many biomarkers from the set of biomarkers are selected based on the changes and assigned to the pattern. High selectivity, for example by a high threshold, generally results in a low number of biomarkers in the pattern, low selectivity, for example, by a low threshold, generally results in a high number of biomarkers in the pattern.
  • the term "sensitivity" generally denotes the probability that an actually positive state of affairs will also be recognized by a positive test result.
  • the sensitivity may represent a measure or probability of whether a change in a particular biomarker is actually due to exposure to an effector or group of effectors, or if the change is a random change, a measurement error or noise, or is another interference.
  • the sensitivity may also signify a true positive rate, a sensitivity or hit rate.
  • the sensitivity can indicate the proportion of facts that are correctly identified as positive in the totality of the facts that are actually positive, ie, for example, a proportion of the biomarkers with a change that was correctly considered to be significant in the totality of biomarkers that were due to exposure to the biomarker Effector or the group of effectors should show a significant change.
  • High sensitivity for example by a low threshold, generally results in a high number of biomarkers in the pattern
  • low sensitivity for example by a high threshold
  • the above-described method according to the invention for producing at least one pattern for at least one predetermined effector having at least one determinable effect on a biological system allows rapid convolution of extensive biological data.
  • the most relevant biomarkers from a given profile can be determined quickly and reliably on the basis of the thresholds to be specified.
  • the method can also be easily implemented computer-implemented and can therefore be used in particular with the other method elements in high-throughput analysis.
  • the patterns obtained by the method can be used in the applications disclosed elsewhere in the description and allow a simplified and more efficient analysis of biological data sets.
  • the method according to the invention further comprises the following steps:
  • an existing database can be evaluated.
  • a database with profiles of different effectors can be compiled by means of a large number of measurements.
  • These effectors may, for example, at least partially handle known effectors, ie for example effectors whose effect on the biological system is known, for example their toxic effects.
  • an evaluation can be made by creating profiles for one or more of the effectors.
  • this also comprises the following step:
  • a comparison with the at least one further effector of the database for example with at least one known further effector, can be performed.
  • a search for similar effectors can be performed.
  • a comparison of certain objects can generally be subsumed under various methods.
  • a comparison of two patents For example, it can be determined whether the patterns include the same biomarkers.
  • the result of this comparison may be, for example, in a characteristic quantity which indicates the degree of agreement. For example, this may be a percentage, for example, 100 percent if the first pattern looks at the same biomarkers as the second pattern.
  • a correspondence indication or a similar indication can also be used to characterize the degree of correspondence.
  • the values of the patterns can also be compared or at least included in the comparison.
  • one or more similarity thresholds can also simply be predefined, so that, for example, a percentage deviation of the two values which lies above a predefined threshold is rated as non-conformity and a percentage deviation of the values from one another below the threshold as one Accordance. Other comparison methods are conceivable.
  • this also comprises the following step:
  • This variant of the method thus relates to the actual comparison of the effects of the given effector, which then actually have to be determined or otherwise known, with the known effect of the further effector.
  • effects can be categorized. For example, in this way digital effects can be simply indicated, such as the effect "is toxic to the liver.”
  • the effects can additionally be quantified, for example in degrees such as “highly toxic to animals", “average liver toxicity” or “weakly febertoxic”. Other quantifications can be found.
  • a quantitative statement can also be made by quantifying the degree of correspondence in a simple digital statement "Effect agrees” or "Effect does not agree”. Again, this can be done with known mathematical methods, for example, by assigning numerical values to the degrees, so that, for example, again percentages or other quantitative information can be used as a statement for a degree of agreement.
  • the starting point is the objectively determinable effects. If the effects are the same, but the paiters are not, the pattern determination has not yielded the desired result and may need to be improved. On the other hand, if the effects are the same, the pattern comparison and the comparison of the effects will give the same result, and the determination and comparison of the patterns will be a successful way of comparing the effects of different effectors or predicting, for example, the effects of unknown new effectors.
  • this also comprises the following step:
  • step f) if in step f) an at least partial coincidence of the pattern is detected, perform the following step:
  • This variant of the method represents a refinement of the pattern generation by a corresponding refinement of the algorithm.
  • this variant of the method describes the case that, although an at least partial agreement of the previously generated pattern (for example, according to the above description, a one hundred percent match, a match above a predetermined threshold or a match by at least a predetermined threshold), but that for these effectors, according to the determined pattern at least a partial match of the effects (for example, again should have at least a predetermined degree or more than a predetermined degree), actually no effect match or only a small effect match (for example, below a predetermined threshold) determine eats. In other words, this may include the case where the patterns are the same but the effects are not.
  • this also comprises the following step:
  • step j) if in step f) no match of the patterns is detected, perform the following step: j1) if a match is found in method step h): change in the significance threshold, in particular lowering of the significance threshold, and repetition of at least method steps b) and c).
  • step j1) if a match is found in method step h): change in the significance threshold, in particular lowering of the significance threshold, and repetition of at least method steps b) and c).
  • the pattern generation can be refined by adjusting the thresholds.
  • a method is particularly preferred wherein the method steps j) and j1) are carried out repeatedly with a stepwise reduction of the significance threshold.
  • the method described above in one of the described embodiments can be used in particular to group effectors according to their effect.
  • the invention relates to a method for creating an effector class for a given action or group of effects.
  • the method is based on the above-described method for creating at least one pattern and contains this method as a core module.
  • step E) assigning the effectors determined in step D) to the effector class.
  • a method for creating a pattern in one of the embodiments described above is preferably used.
  • other methods for creating patterns can also be used, or it is possible to fall back on already known patterns.
  • certain patterns of effectors of a certain effect from the literature are now known in part because, for example, it is known that certain effectors have an influence on certain metabolites.
  • an "effectors class” is understood to mean an amount of effectors which have the same known effect on the biological system or at least a similar effect on the biological system. output
  • the point for the creation of an effector class is therefore a specific effect on the biological system or a group of effects, which is summarized.
  • the effectors clsese may be an amount of effectors which have at least one specific effect on the growth and / or functioning of the biological system, for example a specific toxic effect and / or a specific curative effect.
  • an effector class can initially be configured as an empty set and can be subsequently supplemented, for example, so that it preferably comprises at least one effector, in particular a plurality of effectors.
  • An effector class can, as will be explained in more detail below, also be provisionally created, wherein, for example, at least one effector is first assigned to the effector class, for which it is assumed that it has the specific effect or group of effects. Subsequently, the effectors class, as explained in more detail below, can be supplemented by one or more further effectors, for example iteratively.
  • the effector class can be chemical compounds which mediate certain effects, eg organ, tissue or cell toxicity, possibly according to a specific molecular mechanism of action ("mode of action"). For the toxicological risk stratification, it is helpful to know the exact mechanism of action for compounds.
  • the effect mediated by an effector class may also be a pharmacological effect.
  • an early classification of an active ingredient in the further pharmacological classification helps further and allows early risk stratification so that unsuitable drug candidates can be sorted out well in advance of clinical trials.
  • the effect mediated by an effector class may also be or include a herbicidal, fungicidal or insecticidal action or any combination of these effects.
  • an early classification of an active ingredient in the weather classification helps to early risk stratification, so that inappropriate drug candidates can be sorted in good time before the start of further studies.
  • Genetic changes as effectors can also form effector classes.
  • yield or pest resistance-enhancing genetic changes can be combined into one effector class, etc.
  • the method according to the invention preferably allows the compilation and combination of effectors into an effector class based on the individual patterns of the individual effectors.
  • the effectors of an effector cage here preferably have substantially identical patterns. Based on these considerations, the inventive method for creating an effector class allows said creation of the effectors class. Preferred is a process wherein all or at least one of steps B) to E) are carried out repeatedly.
  • expert knowledge is used in step A).
  • the knowledge of an expert for example, a toxicologist, be used to specify at least one effector, which is known to have a predetermined effect, for example, a predetermined toxic effect.
  • the possibility of putting together effector classes can also be used to make predictions about at least one effect of at least one new, at least not yet completely known, effector and / or to determine at least one effect of an effector.
  • one or more classes of effectors may be used, which have been invented according to the method for creating an effector class for a given effect or group of effects according to one or more of the embodiments described above.
  • effector classes obtained in other ways can be used.
  • certain types of effectors are known from the literature, since, for example, the effects of numerous effectors are cataloged, so that effectors of the same effect can be grouped.
  • the invention also relates to a method for identifying at least one effect of a given effector, comprising the following steps:
  • An identification of at least one effect can generally mean the determination of a result that the predetermined effector has at least one specific, specifically indicated effect.
  • the at least one effect can also be identified in such a way, which is likewise to be understood as the identification of at least one effect, that a comparison with at least one other ren effector is performed and, accordingly, the effect of the given effector
  • the predetermined effector has at least one identical, similar or dissimilar effect to the at least one further effector with which the comparison is carried out.
  • step iii) if a match or a similarity is found in step iii), the known effect of the effector class is equated with the effect to be determined of the given effector.
  • the effector in question the effect of which is to be determined
  • the effector class used for comparison the effect of which is known.
  • at least a preliminary assessment of the effect of this effector can be obtained quickly. As a result, considerable research costs can be saved and, for example, animal experiments can be reduced to a minimum.
  • the method described above can also be carried out without the use of an effector class, whereby methods using an effector class and methods without using an effector class can also be combined.
  • the path via at least one effector class is not selected, or at least not exclusively, then it is also possible, for example, to resort directly to the patterns determined.
  • the invention also relates to a method for identifying at least one effect of a given effector, comprising the following steps: Firstly, at least one pattern of the given effector, in particular according to one of the above-described inventive method for creating a pattern;
  • step IV equating the effect of the given effector with the known effect.
  • this method can basically also be combined with the method in which the path over the at least effector class is selected.
  • effects of effectors can be quickly and reliably predicted by comparison with known effectors, whether grouped by effectors or individually, at least for the time being.
  • the methods described above may be implemented in whole or in part by means of a computer or may be performed at least partially using a computer.
  • one or more of the following method steps may be performed using a computer: a), b), c), d), e), f), g), h), i), i1), j), j1) , A), B), C), D), E), i), ü), iü), I), II), III), IV), V), VI).
  • the invention therefore further comprises a computer program with program code for carrying out the method according to one of the preceding method claims, when the program is executed in a computer.
  • the computer program may be arranged to perform or at least assist one or more or all of the method steps.
  • all of method steps a) to c), all method steps A) to E), all method steps i) to iii) or all of method steps l) to VI) can be performed using at least one computer or computer network or using the computer program become.
  • the computer program can in particular be designed as a handeibares product.
  • the computer program according to the invention is preferably stored on a machine-readable carrier.
  • the invention additionally comprises a computer, set up for carrying out a method according to one of the preceding method claims.
  • the computer may generally comprise at least one data processing device and / or a computer network.
  • the invention also relates to a data carrier on which a data structure is stored which, after loading into a working and / or main memory of a computer or computer network, carries out the method according to one of the preceding method claims.
  • the proposed methods, the computer program, the computer and the data carrier have numerous advantages over methods and devices known from the prior art which have already been partially listed above.
  • the data for example raw data with measured values to biomarkers of a multiplicity of different effectors, can thus be evaluated and categorized efficiently, and new types of representation and / or representation can be used (for example in the form of the patterns and / or the effector classes) find and / or reduce data volumes significantly.
  • new types of representation and / or representation can be used (for example in the form of the patterns and / or the effector classes) find and / or reduce data volumes significantly.
  • the experimental effort and time for screening a variety of new effectors can be significantly reduced.
  • Figures 1A-1B an embodiment of a method according to the invention for determining a
  • FIGS. 2A-2D an embodiment of a method according to the invention for creating an effectors class
  • FIGS. 3A-3B show a first embodiment of a method for identifying at least one effect of a given effector
  • FIGS. 4A-4B show a second embodiment of a method for identifying an effect of a given effector
  • FIGS. 5A and 5B show an application example of the method according to FIGS. 4A and 4B for comparing effects of two chemically similar substances.
  • FIGS. 1A and 1B show an exemplary embodiment of a method according to the invention for producing at least one pattern for at least one predetermined effector having at least one determinable effect on a biological system.
  • FIG. 1 shows a schematic flow chart of this method
  • FIG. 1B shows a screen display of an exemplary screen display. Both figures will be described together below.
  • Reference numeral 112 denotes the method step of comparing at least one value of at least one biomarker of the profile with at least one significance threshold for determining whether the biomarker is significant
  • the reference numeral 14 refers to the summary of significant biomarkers of the profile into a pattern.
  • FIG. 1B Shown here is a possible representation of a database 116 which is ready for the method according to the invention. is made and from the means of an appropriate software, which implements the proposed method, an evaluation can be made.
  • FIG. 1B shows in the column of a table headed "metabolite” various metabolites 118 whose values or their changes are monitored during the exposure of the biological system, in this case a rat, with various effectors 120.
  • the metabolites 1 18 can be optionally selected by appropriate markers in a list of possible metabolites ("Select" column).
  • select a list of possible metabolites
  • different biomarkers 122 are detected and are indicated in the rows behind the metaboles 118.
  • the absolute values or changes of a particular metabolite 1 18 can be detected for male (m) and female (f) test subjects (e.g., rats).
  • biomarkers 22 may be detected for loading the test subjects at a low dose (I) and for a high dose (h).
  • biomarker 122 in the column associated with the metabolite threonine in column ml7 indicates the absolute value or change of the metabolite threonine when a male test subject (m) is administered with a low dose (I) when measured 7 days after exposure to the test subject the effector 120, for example a substance with the name "Compound 1" or a substance with the name "Compound 2".
  • Each metabolite 1 18 is thus associated with a plurality of biomarkers 22.
  • FIG. 1B shows profiles 124 or parts of these profiles 124 relative to the two effectors 120 Compound 1 and Compound 2, the profile for compound 1 being designated by reference number 126 and the profile for compound 2 by reference number 28 by way of example.
  • FIG. 1B initially shows the step of providing profiles 124, in this case optionally for a plurality of effectors 120, in this case by means of a database 16 and a corresponding possibility for display, grouping and / or Evaluation of the biomarker 22 contained in this database 16 by means of a computer program.
  • FIG. 1B also shows, by way of example, the method step 1 2 mentioned in FIG. 1A of the comparison of the values of the biomarkers 122 with corresponding significance thresholds.
  • significance thresholds may also be influenced by a user.
  • biomarkers 122 are marked which have a significantly increased value, ie, for example, an increase in the value of these biomarkers 122 above a significance threshold.
  • significantly reduced biomarkers 122 may alternatively or additionally be highlighted, for example by means of a different color.
  • the column headed "direction" may indicate the major direction of change of the biomarkers 122 for each of the metabolites 118.
  • the pattern for compound 2 in the exemplary embodiment illustrated in FIG. 1B includes, for example, the biomarkers threonine m! 7 , Threonine ml14, threonine m! 28, threonine mh7, threonine mh14, threonine mh28, glycine ml7, glycine ml14, etc., ie the values of the table 128 assigned to pattern 128 in FIG. 1B, but not those in the fields of this table registered numerical values.
  • an example of a given effector 120 can be used to create a pate.
  • this creation can also be carried out iteratively, as described above, for example by an interactive adaptation of threshold values.
  • the pattern is designated symbolically by the reference numeral 130 in FIG. In the following figures, this symbolic reference numeral 130 is no longer shown, so that with respect to this reference numeral, for example, reference can be made to FIG. 1B.
  • FIGS. 2A-2D show an exemplary embodiment of a method according to the invention for producing an effector class for a given effect or group of effects.
  • FIG. 2A shows a schematic flow chart of an exemplary embodiment of the method according to the invention
  • FIG. 2B shows an iterative method variant
  • FIGS. 2C and 2D in turn show screen illustrations. gene of an embodiment of the method in different stages using a database 1 16. The figures will be explained together again in the following.
  • the reference numeral 210 denotes a method step in which at least one effector 120 is preset, which is presumably to be assigned to the effector class to be created. This effector 120 is assigned to the protagonist orenkanke to be created.
  • the method step 210 is followed by a method step 212, in which at least one pattern of the at least one effector of the effector class is created.
  • a creation is not to be understood as the creation of a new object but the updating of the at least one pattern.
  • a database 1 16 is provided, wherein in the database 1 16 a plurality of further effectors 120 profiles 124 are stored.
  • the database 16 is searched for effectors 120 with identical or similar profiles as those profiles of the effectors 120 already assigned to the effectors class. This can be done either by a direct comparison of the profiles 124 or using patterns.
  • at least one profile can be created in the database 6 for each, several or at least one further effector 120, for example a profile with the same biomarkers 122, which also has the at least one pattern of the effectors of the effectors class created or updated in method step 212 , On the basis of this pattern comparison, it can be determined whether the at least one further effector has an identical or similar profile 124. If, in method step 2, 6 such effectors 120 are determined which have the same or similar profiles as the effectors 120 which are already assigned to the effectors class, these effectors 120 can be assigned to the effectors class in a method step 218.
  • the method described in FIG. 2A can be carried out in particular iteratively. This is shown in FIG. 2B.
  • method steps 210 and 212 first of all, for example based on at least one effector 120, one or more effectors 120 are predefined, which are presumably assigned to the effector class to be determined.
  • the protagonistorentail is designated in Figure 2B by the reference numeral 220.
  • this effector class 220 which can initially be regarded as provisional effector class 220, one or more patterns are then determined in method step 212.
  • a database 116 with further effectors 120 is specified, and this database 16 is searched for effectors 120 with identical or similar profiles 124, for example with the same or similar pattern 130. If this search is successful, then this at least one further effector 120, which may have been determined in this way, is assigned to the effector class 220 in method step 218. The method may then be redone, as indicated in FIG. 2B, to identify further effectors 220 to be associated with the effector class 220.
  • FIGS. 2C and 2D This method will be explained further by way of example with reference to FIGS. 2C and 2D. For example, in FIG. 2C, a screen display is again shown, which explains the method steps 2 0 and 212.
  • This representation is a representation of a part of a database 116.
  • an effectors class 220 is to be selected by way of example, which contains effectors 120 which have an effect of the type of peroxisome proliferation.
  • a provisional effector class 220 is first formed, which is based, for example, on expert knowledge and / or literature references.
  • the expert knowledge consists, for example, that the effectors 120 of the type mecoprop-p, fenofibrate and dibutyl phthaiate have the said effect.
  • these effectors 120 are assigned to the intermediate effector class 220.
  • profiles 124 are given to these effectors.
  • These profiles 124 which are shown in FIG. 2C by way of example and optionally in excerpts, in turn comprise a plurality of biomarkers 122.
  • biomarkers 122 For example, analogously to the representation in FIG.
  • these biomarkers 122 are biomarkers which are characterized by the gender of the test subject , the amount of the dose and / or the time after exposure of the test subjects with the effector 120, each for different metabolites 118.
  • the metabolites 1 18 are, in contrast to the representation in Figure B, indicated only by numbers.
  • biomarkers 122 are shaded gray, the values of which have a significant change, it being possible, for example, to refer to the description of FIG. 1B. From this comparison of the biomarkers 122 or their values with corresponding significance thresholds, in turn, a pattern can be created.
  • this pattern may include biomarkers 122 that have a significant change in the same direction in all three profiles 124 of the three effectors 120 of the preliminary effectors class 220.
  • biomarkers 122 since all effectors 120 for the metabolite metabolite 45 have a significant change in the biomarker 122 named fh7 and the biomarkers labeled fh14, these biomarkers 122 are preferably associated with the pattern 130. In this way, for the provisional effector class 220 a pattern can be compiled.
  • effector class 220 it is then possible to search in the database 116 for further effectors 120, which are likewise to be assigned to the effector class 220.
  • FIG. 2D in a representation analogous to FIG. 2C.
  • biomarkers 122 are plotted against a plurality of metabolites 118. These biomarkers are in turn assigned to effectors 120.
  • the effectores Bezafibrate, Clofibrate, Dicamba and Dichlorprop-p as well as their associated profiles 124 are listed as further effectors 120.
  • further effectors 120 Based on a comparison of the patterns 130, which are not explicitly marked in FIG. 2D, as in FIG. 2C, further effectors 120 can be determined which have identical or similar profiles 124, in particular which have identical or similar patterns 130. In this way, further effectors 120 can be determined, for example groupwise or iteratively, which are to be assigned to the effector class 220.
  • FIGS. 3A and 3B show a first exemplary embodiment of a method according to the invention, by means of which at least one effect of a predetermined effector 120 can be determined. Possible effects in FIG. 3B are designated by the reference number 310.
  • FIG. 3A once again shows a schematic sequence of a basic form of the exemplary embodiment of the proposed method, whereas FIG. 3B shows in tabular form an example in which a database is searched for effects 310 on the effector 120 of the diethylhexylphthalate type.
  • At least one effector class for at least one known effect 310 is first created in method step 312.
  • an effect 310 can be specified, for which, for example, according to the method described with reference to FIGS. 2A-2D, an effector class is determined.
  • method step 314 at least one pattern 130 of the given effector 120 is created, whose effect 310 is to be determined.
  • method step 316 a comparison of the pattern 130 of the predetermined effector 120 determined in method step 314, the effect of which is to be determined, with the at least one pattern 130 of the effectors 120 combined in the at least one effector class 220 takes place.
  • FIG 3A abstract described method with a concrete wisdomschenspiei be deposited. In the representation shown there, which in turn shows a screen representation of a computer-assisted implementation of the method described in FIG. 3A, one or more effects of the diethylhexylphthalate-type effector 120 are to be determined.
  • a plurality of effects 310 are plotted in the first column of the table shown in FIG. 3B.
  • These effects 310 are exemplarily provided with more or less characteristic designations in the illustration.
  • the term “liver_oxidative_stress” mjd_hd ⁇ roup_ef_putative_06122007 may indicate a particular type of oxidative stress on the liver
  • the other terms in the first column of the table in Figure 3B set forth other types of effects 310, which are not discussed in detail here
  • an effector class or, if appropriate, several effector classes were determined beforehand, for example based on the method described in FIGS 2A to 2D,
  • an effector class 220 can be stored for each effect 310. With an associated pattern 130 this effector class 220.
  • Pearson correlation coefficients 318 basically indicate to what extent the patterns of the effect classes 220, which were determined, for example, by means of the iterative method described in FIG. 2B, are reliable. In a fully reliable pattern 130, the Pearson correlation coefficient 318 would be exactly at +1, that is, at the right end in the third column in Figure 3B, and the uncertainty interval would be 0.
  • other types of correlations or correlation coefficients can also be used. For example, alternatively or additionally, Spearman correlation coefficients can be used.
  • the correlation of the pattern of the predetermined effector 120 determined in step 314, the effect of which is to be determined, is plotted numerically in the second column and in the third column in the form of points, respectively with uncertainty interval for each effect 310 or effector class 220 ,
  • Indicated here is the Pearson correlation coefficient r numerical (in the second column) as well as a plot on a scale from -1 (left end) to +1 (right end) in the third column.
  • This Pearson correlation coefficient 320 thus indicates the degree of coincidence of the pattern 130 of the effector 120 whose effect is to be determined with the pattern 130 of the effector class 220 in each case.
  • this Pearson correlation coefficient 320 in the third column in FIG. 3B should be at the right end of the scale, ie at +1.
  • FIGS. 4A and 4B show an alternative method to FIGS. 3A and 3B for identifying at least one effect of a given effector. Again, FIG. 4A shows a schematic flow chart of this method.
  • method step 410 in FIG. 4A designates a method step in which at least one pattern 130 of the predetermined effector 120, the effect of which is to be identified, is created. For example, this can again take place by means of the method described in FIGS. 1A and 1B.
  • the reference numeral 412 denotes a method step in which a database 116 is provided, in which 120 profiles 124 are stored for a plurality of further effectors. Also in this regard, reference may be made to the above embodiments.
  • Reference numeral 414 denotes a method step in which the database 1 16 searches for effectors 120 having a pattern 130 similar or identical to the pattern 130 created in step 410. This can again be done, for example, by means of a comparison with a correlation method. In this regard, reference may again be made, for example, to the description of FIG. 3B; a similar correlation method may, for example, also be used in method step 414 for comparing the patterns 130.
  • step 416 a check is made as to whether the effectors 120 determined in step 414 (assuming that at least one such effector 120 has been detected-which need not necessarily be the case) have at least one known effect. This can be done, for example, in that the effectors 120 determined in step 414 have already been assigned to an effector class 220 and / or in which expert knowledge about the detected effectors 120 is utilized.
  • Method step 418 represents a conditional method step. Namely, if a known effect is detected in method step 416, the effect of the given effector having the known effect (analogously also several known effects can be identified). This then identifies at least one effect of the effector 120 in question. Otherwise, that is, if no known effect is detected in step 416, the method in Figure 4 was without result.
  • this method is exemplified by the example of the effector Diethylhexyphthalat. A pattern 130 is created for this effector 120, and this pattern 130 is compared with known patterns 130 of a plurality of other effectors 120 in a database 116.
  • FIG. 4B shows a pictorial representation of a result of this pattern comparison, since the effector diethylhexyl phthalate in question is also present in the database 6, and this too is again listed in the illustration according to FIG. 4B itself.
  • comparison results of the pattern 130 of the predetermined effector diethylhexyl phthalate with the respective pattern 130 of the respective effector 120 are shown in the third column of the table shown in FIG. 4B for each effector 120.
  • these comparisons are made by way of example by means of a Pearson correlation.
  • the Pearson correlation coefficient r is indicated here in each case again the Pearson correlation coefficient r.
  • matches can be found and ranking can be made as a prioritization by degree of agreement.
  • the highest degree of correspondence (rank 1) with a Pearson correlation coefficient r equal to 1 naturally provides diethylhexyl phthalate itself, since the pattern 130 of this effector 120 naturally coincides perfectly with its own pattern 130.
  • treatment 294 an effector 120 has been identified in the table of Figure 4B, referred to herein as "treatment 294." It can thus be expected that the effector diehtylhexyl phthalate in question will be the same or at least has a similar effect as the effector "treatment 294".
  • the process steps 416 and 418 are not shown in FIG. 4B.
  • the effector "treatment 294" has a known effect 310, for example due to expert knowledge or due to a known assignment of this effector to an effector class 220 having at least one
  • r is equal to 0.713, which may be, for example, above a predetermined match threshold
  • one or more matching thresholds can be predetermined.
  • matching thresholds can be selected, for example, more or less arbitrarily and can be set, for example, above 0.5 r, preferably of r> 0.6, and more preferably of r> 0.7.
  • An iterative adaptation of this matching threshold is also possible, for example if it is determined by additional tests that this threshold was chosen too low, that is to say that the effector 120 in question was erroneously assigned an effect 310 which it does not have in reality.
  • FIGS. 4A and 4B The effectiveness of the method described in FIGS. 4A and 4B shall be clarified further with reference to FIGS. 5A and 5B.
  • two chemically similar substances are investigated, namely:
  • 2-Acetylaminofluorenes is known to be an effector 120, having the following effects 310: strong liver enzyme inducer,
  • FIG. 5A a comparison of the metabolic profiles 124 of these effectors 2-acetyl (aminofluorene and 4-acetylaminofluorene with various other effectors 120) is accordingly shown by means of the method described above, for example analogously to FIG. In this way, patterns 130 can be determined for each of these effectors 120.
  • Figure 5B in analogy to Figure 4B, a Pattem comparison of the pattern 130 determined with reference to Figure 5A is shown 4B shows a ranking on the basis of the Pearson correlation coefficients, in which the left-hand table in FIG. 5B shows a comparison of the pattern 130 for the effector 2-acetylaminofluores with the remaining effector 120 in FIG. 5A, and the right-hand table shows a comparison of the pattern 130 of the effector 4-acetylaminofluorene with the pattern of the other effectors 120 in FIG. 5A.
  • the effectors similar to these questionable effectors 2-acetylaminofluorenes and 4-acetylamino-like effecters follow in the sequence of their similarity.
  • the lower table excerpt in FIG. 5B it can be seen that in the left table, in which 2-acetylaminofluorenes are compared with the other effectors 120 in FIG.
  • Profiles are stored 414 Search in database for effectors with pattern similar or identical to the pattern created in step 410
  • step 416 If a known effect is detected in step 416, equating the effect of the given effector with the known effect

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Abstract

La présente invention concerne des procédés d'évaluation de biomarqueurs, et en particulier un procédé de production d'au moins un motif pour au moins un effecteur prédéfini ayant au moins une action biologique, pouvant être déterminée, sur un système biologique, un procédé permettant d'établir une classe d'effecteurs pour une action ou un groupe d'actions prédéfinis et un procédé d'identification d'au moins une action d'un effecteur prédéfini. La présente invention concerne encore un programme informatique et un ordinateur qui sont conçus pour la mise en oeuvre de ces procédés.
EP10766047A 2009-10-21 2010-10-14 Procédés de production de motifs de référence pour biomarqueurs Ceased EP2491508A2 (fr)

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US5397894A (en) 1993-05-28 1995-03-14 Varian Associates, Inc. Method of high mass resolution scanning of an ion trap mass spectrometer
KR20020064298A (ko) * 1999-10-13 2002-08-07 시쿼넘, 인코포레이티드 다형성 유전 마커를 동정하기 위한 데이타베이스 및 이의제조 방법
WO2002103320A2 (fr) * 2001-06-18 2002-12-27 Rosetta Inpharmatics, Inc. Diagnostic et prévision du cancer du sein chez des patients
JP2008518598A (ja) * 2004-10-29 2008-06-05 ノバルティス アクチエンゲゼルシャフト 薬剤の毒性評価
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WO2007048074A1 (fr) * 2005-10-21 2007-04-26 Genenews Inc. Procede et appareil permettant de mettre des niveaux de produits de biomarqueurs en correlation avec une maladie
JP5421767B2 (ja) * 2006-04-10 2014-02-19 ウィスコンシン・アルムニ・リサーチ・ファウンデーション ヒト胚性幹細胞を使用して薬学的化合物および他の化学物質の毒性を評価するための試薬および方法
JP5083320B2 (ja) * 2007-08-22 2012-11-28 富士通株式会社 化合物の物性予測装置、物性予測方法およびその方法を実施するためのプログラム
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EP2128791B1 (fr) * 2008-05-30 2018-08-01 Thermo Fisher Scientific (Bremen) GmbH Procédé de traitement de données spectrométriques

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CA2777417A1 (fr) 2011-04-28
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