Patent Application
20120209535
The present invention relates to methods for evaluating biomarkers. In particular, the invention relates to a method for establishing at least one pattern for at least one pre-defined effector having at least one effect on a biological system, which effect is capable of being determined, to a method of establishing a class of effectors for a pre-defined effect or group of effects, to a method for identifying at least one effect of a pre-defined effector and to a computer program and a computer adapted to carry out these methods.
Biological systems such as individual organisms or populations of organisms will, as a rule, respond to effectors with a change of state, in particular a change in their biochemical properties or biochemical constitution. In this context, an individual organism on which an external or internal effector acts responds, for example, by modified cell activities. This modified activity will then also result in a change in the constitution or quantitative composition of the cellular molecules. In this context, both changes in the transcriptional activities or the protein function and protein turnover as well as metabolic changes may be observed. The latter will, consequently, lead to a change in the qualitative and/or quantitative metabolite constitution of the organism as a result of the effector (modification of the metabolome). Similar changes in the biochemical constitution or properties can be observed in populations of organisms which form a biological system. Such populations of organisms which form a biological system are, for example, microorganisms which form a locally delimitable micro ecosystem.
For many biologically relevant questions it is necessary to assess the influence of effectors on biological systems. In this manner, it is possible better to avoid or to exploit interfering or advantageous influences of effectors. For example, chemicals acting as effectors may have an e.g. toxic effect on a biological system or else a beneficial or healing effect. Almost all effectors ranging from chemicals through physical influences such as radiation to intended or unintended genetic modifications will, ultimately, influence the metabolome. This influence frequently already occurs at a very early stage after the action of the effector so that modifications of the metabolome can be used as an early detection mechanism for particular effects or consequences which an effector may bring about.
Modifications of the metabolome, induced by effectors, will, as a rule, affect not only one metabolite whose status might then be used as what is known as a biomarker. Frequently, a variety of metabolites are affected. Effectors which mediate the same effect need not always modify the same metabolites in this context. However, as a rule, there is a set of key metabolites which is modified by effectors which mediate the same effect. This set of modified key metabolites can currently not always be identified in an efficient manner. Above all, the problem is that most effectors cause not only the characteristic key metabolites, but also individual metabolite modifications which are characteristic only of the individual effector, but are not caused by other effectors which cause the same effect. In addition, there are metabolic modifications which are not related to the effector applied, but are induced by other influences or by variations of the metabolites which are merely caused by the variability due to measurement techniques.
Nevertheless, it would be useful for a very wide range of applications to extract, from a metabolome, the key metabolites which are modified early as a response of a biological system to specific effectors. In this manner, chemicals which are toxic to biological systems might be identified even at an early point in time. Likewise, the therapeutic activity of candidate active substances might be determined at an early point in time and in a reliable manner, and potential side effects might be ruled out. Advantageous or harmful influences of environmental factors for biological systems in general might likewise be identified. Ultimately, diseases might also be identified earlier, and advantageous or disadvantageous effects of modifications of the genetic material might be studied better.
It is an object of the present invention to provide a method by means of which responses of biological systems to specific effectors can efficiently be studied or predicted, or by means of which a deliberate search can be made for particular effectors capable of causing a predetermined response of the biological system.
This object is achieved by methods and computer programs with the features of the independent claims; advantageous refinements of the invention, which may be implemented individually or in any desired combination, are presented in the dependent claims.
The methods comprise the method steps described below. The method steps are preferably carried out in the order presented. In principle, however, it is also possible to carry out individual or several method steps in a different order. Thus, for example, it is also possible to carry out individual or several method steps chronologically in parallel or chronologically overlapping. Furthermore, individual or several method steps or the entire methods may also be carried out repeatedly. For example, method steps a) to j1) which are described hereinbelow may be carried out repeatedly individually or as a whole, for example with a number of repetitions of at least two, a number of repetitions of at least five and especially preferably a number of repetitions of at least 10 or even at least 20. Furthermore, the methods may also comprise additional method steps which are not mentioned in the claims.
In a first aspect, the invention relates to a method for establishing at least one pattern for at least one pre-defined effector having at least one determinable effect on a biological system, comprising the following steps:
By means of this method, it is possible to compile a pattern for a pre-defined effector, for example a novel effector which has not been studied as yet, that is, for example, a set of biomarkers which experience a significant modification when the biological system is exposed to it.
Here and hereinbelow, “providing” may, in principle, be understood as any way of creating the availability of the item to be provided. Providing may, in particular, be carried out in electronic form, for example on a volatile or nonvolatile data memory which can be accessed during the method, so that the item to be provided, in this case the at least one profile of the pre-defined effector, is available. As an alternative or additionally, providing may also involve the use of, for example, a database. However, other types of providing are also possible in principle. Thus, for example, providing may also be carried out manually by a user, for example by manual entry in a computer or in the form of another type of manual provision. Providing may be performed actively, so that the item to be provided is actively supplied to the method, or, alternatively, also passively, so that merely an availability is ensured, for example a retrievability of the data.
Furthermore, a “biological system” is, within the context of the present invention, understood as meaning a system which comprises one or more organisms. If a plurality of organisms are provided, then these may be arranged in particular spatially connected and include a common metabolism. The organisms may be of the same type or else different. Possible nonlimiting examples of biological systems which may be mentioned are mammals, especially preferably mammals which are capable of being kept under controlled conditions, such as, for example, dogs, cats, mice or rats, with rats being especially preferred. Suitable methods for keeping for example mammals under controlled conditions are from WO2007/014825. Others which may be mentioned by preference are cell cultures and plants, in particular plants capable of being grown under controlled conditions in a greenhouse, such as, for example, Arabidopsis thaliana or rice.
Within the context of the present invention, a “metabolite” is understood as meaning in general intermediates of a metabolic process, in particular of a biochemical metabolic process. “Metabolism” refers to all the metabolic pathways of the biological system. Metabolites in the context of the invention are small molecules (known as “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 prior art and may vary between different species. Preferred are metabolic pathways at least of the citric-acid cycle, the respiratory chain, photosynthesis, photorespiration, glycolysis, gluconeogenesis, the hexose monophosphate pathway, the oxidative pentose phosphate pathway, the synthesis and the β-oxidation of fatty acids, the urea cycle, the biosynthesis of amino acids, the biosynthesis of the nucleotides, nucleosides and nucleic acids (including tRNAs, microRNAs (miRNA) or mRNAs), protein degradation, nucleotide degradation, biosynthesis or degradation of lipids, polyketides (including the flavonoids and isoflavonoids), isoprenoids (including the terpenes, sterols, steroids, carotenoids or xanthophylls), of the carbohydrates, of the phenylpropanoids and their derivatives, of the alkaloids, of the benzenoids, of the indoles, of the indole-sulfur compounds, of the porphyrins, of the anthocyanins, of the hormones, of the vitamins, of the cofactors such as prosthetic groups or electron carriers, of the lignins, of the glucosinolates, of the purines or of the pyrimidines. Accordingly, metabolites preferably belong to the following groups or classes of molecules: alcohols, alkanes, alkenes, alkynes, aromatic substances, ketones, aldehydes, carboxylic acids, esters, amines, imines, amides, cyanides, amino acids, peptides, thiols, thiol esters, phosphate esters, sulfate esters, thioethers, sulfoxides, ethers or their derivatives, or combinations of these. Metabolites may be primary metabolites, that is to say those which are required for the normal (physiological) function of the organism or of the organs. However, metabolites also comprise secondary metabolites which have an essentially ecological function, i.e. metabolites which allow the organism to adapt itself to the environment. Besides these primary and secondary metabolites, however, metabolites also comprise other, in some cases artificial, molecules. These are derived from exogenous molecules, which are, for example, taken up as active substances and can then be modified further in the metabolism. Metabolites may furthermore be peptides, oligopeptides, polypeptides, oligonucleotides and polynucleotides such as RNA or DNA. Especially preferably, metabolites have a molecular weight of from 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 8000 da, less than 7000 da, less than 6000 da, less than 5000 da, less than 4000 da, less than 3000 da, less than 2000 da, less than 1000 da, less than 500 da, less than 300 da, less than 200 da, less than 100 da. Preferably, a metabolite in the context of the invention will, however, have a molecular weight of from approximately 50 da to approximately 1500 da.
Within the context of the present invention, an “effect” may, in principle, be understood to mean any change, capable of being determined, of at least one state of the biological system. In particular, this state may be a biological and/or biochemical and/or chemical state of the biological system. For example, this effect may be manifested by a change of a metabolome of the biological system. An effect in the context of the present invention may preferably be a change in cell morphology, in the genome, in the metabolome (in other words, in the qualitative or quantitative state of the metabolites in an organism or a subgroup thereof), in the proteome (in other words, in the qualitative or quantitative state of the proteins in an organism or subgroup thereof), in the transcriptome (in other words, in the qualitative or quantitative state of the transcripts in an organism or subgroup thereof), in the organ function, in the cell, tissue or organ vitality (toxicity) and/or in the psychological or social condition. It is to be understood that different effects may occur together in the context of the invention. Thus, a person skilled in the art is familiar with the fact that changes in cell morphology, in the genome, metabolome, proteome and/or transcriptome can induce changes in organ function, or may even influence the psychological or social condition of an organism. As a rule, it is desirable to detect effects such as organ damage or else psychological damage of organisms at an early point in time. To this end, it is especially preferred for the preceding modification to provide an indicator. With the aid of the modifications of the metabolome, it is, therefore, possible to predict organ dysfunctions or other damage. Naturally, positive effects such as the healing of specific diseases, yield-increasing properties in the cultivation of useful plants, or ecological damage in micro ecosystems may likewise be predicted in advance. Toxicological, pharmacological or bioenvironmental risk stratification of different effectors allows better control of the beneficial use or dealings with these effectors and maximal avoidance of harmful use or dealings with them.
Within the scope of the present invention an “effector” is understood as meaning, in principle, any influence on the biological system that might potentially have at least one effect, which may, in principle, be of any type, on the biological system. This potential effect may, in particular, be an effect of the abovementioned type, in particular a biochemical and/or biological and/or chemical effect, which might, in particular, be manifested by a change in the metabolism. Examples of such influences are exposure of the biological system to one or more chemical substances and/or compounds such as, for example, medicaments and/or pesticides, and/or physical action on the biological system, for example exposure of the biological system to electromagnetic radiation and/or particle radiation. A different duration and/or intensity and/or dose of the effect on the biological system may also be visualized by suitable effectors within the scope of the present invention. For example, different durations and/or intensities and/or doses of one and the same influence on the biological system may be considered to be different effectors. If, for example, an effector includes 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 exposure may be considered to be different effectors. In this context, the duration and/or dose and/or intensity may also be graded into two or more levels. For example, when exposing the biological system to at least one chemical substance and/or chemical compound and/or to at least one radiation, a low dose and a high dose to which the biological system is exposed, as desired, will be predetermined, with exposure to the low dose and exposure to the high dose being considered to be two different effectors.
An effector may be used individually or in cooperation with other effectors so that for example a group of effectors acts together. Preferred effectors within the scope of the invention are chemical substances, pharmaceutical active substances and potential pharmaceutical active substances (candidate active substances), pesticides (herbicides, insecticides or fungicides), growth promoters, for example fertilizers, radiation treatments, modifications of the genetic material, for example in the form of random or deliberately generated mutations in the genome of an organism or by integration of genetic material via recombinant methods, and/or changes in environmental conditions (temperature, radiation, nutrition, water balance; gas composition and pressure of the surrounding atmosphere and the like).
Within the scope of the present invention, 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 constraints and/or parameters, for example age of the biological system, time of recording of the state, in particular a period after exposing the biological system to at least one effector, and optionally further information on the biological system, for example a sex. Thus, for example, a specific level of a metabolite or a particular group of metabolites may be specified as a biomarker as a function of a sex of the biological system and/or a point in time. As an alternative or in addition, a biomarker may also describe a state change of a metabolite or a specific group of metabolites, for example again as a function of constraints and/or parameters, for example of the abovementioned type. A distinction must be made between the biomarker itself, as a variable quantity, and its numerical value, with the aid of which, for example, it is possible to determine whether a biomarker is significant or not. As explained in greater detail hereinbelow, this determination of whether a biomarker is significant or not may, for example, be carried out by comparing its numerical value with one or more significance thresholds. A biomarker may, in principle, be expressed in any unit, for example in absolute units or in relative units, for example as a change relative to a reference value, in particular a normal state and/or a state in which the biological system is not exposed to the effector and/or the group of effectors and/or any effector in the first place.
A “profile” of a specific effector or group of effectors, in some cases also referred to as metabolic profile, is understood as meaning, in the context of the present invention, the totality of biomarkers which are recorded or have been recorded or can be recorded during or after exposure of the biological system to the effector. It therefore takes the form of a total set of biomarkers which can also be recorded, or of a subset of this total set which is taken into account for a specific study. This totality is preferably recorded under controlled and standardized conditions during or after exposure of the biological system to the effector or the group of effectors. For example, this profile may be a metabolome, or subset of the metabolome, caused by exposing the biological system to the at least one effector, or may comprise a metabolome or subset of a metabolome. A profile of metabolites may preferably be determined by methods which allow both quantitative and qualitative determination of the metabolites in the organism. To this end, a sample from the organism, which sample comprises a representative extract of the metabolites, may be analyzed. Suitable sample materials include bodily fluids such as blood, serum, plasma, urine, saliva, fecal matter, tear fluid, secretions or liquor, or tissue samples obtained via biopsy. Naturally, samples may also be samples from a micro ecosystem or from cultured cells. Samples may also be pretreated, for example to obtain a subcellular fraction (nuclei, endoplasmic reticulum, photosystem, peroxisomes, Golgi apparatus and the like) as the actual sample. The metabolic profiles of such samples can be obtained preferably by mass spectrometry techniques, NMR or other of the methods mentioned hereinbelow. Mass spectrometry techniques may generally be understood as meaning analyses of samples using mass spectroscopes and/or mass spectrometers, in particular mass separation techniques in which the ions are analyzed by photosensitive detectors. Mass spectrometry techniques and mass spectroscopy techniques can be used equally in the context of the present invention.
Before the actual qualitative and/or quantitative determination of the metabolites the latter may initially be separated further, which facilitates said determination, especially in the case of samples with a complex composition. To this end, it is possible to employ separation methods which are well known in the prior art. These are, preferably, chromatography-based techniques such as “liquid chromatography (LC)”, “high performance liquid chromatography (HPLC)”, gas chromatography (GC), thin-layer chromatography, size-exclusion chromatography or affinity chromatography. However, it is most preferred to employ LC and/or GC.
The actual qualitative and/or quantitative determination of the metabolites for a profile may then be carried out using suitable measurement or analytical methods. These preferably include: mass spectrometry such as GC-MS, LC-MS, “direct infusion” mass spectrometry, Fourier transformation ion cyclotrone resonance mass spectrometry (FT-ICR-MS), capillary electrophoresis mass spectrometry, (CE-MS), mass spectrometry coupled with “high-performance liquid chromatography”, quadrupole mass spectrometry, sequential mass spectrometries 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). It is especially preferable to use LC-MS and/or GC-MS. These methods are described in Nissen, Journal of Chromatography A, 703, 1995: 37-57, U.S. Pat. No. 4,540,884 oder U.S. Pat. No. 5,397,894. The disclosure of these documents is herewith incorporated in its entirety. Alternatives to mass spectrometry which may be employed are: “nuclear magnetic resonance” (NMR), “magnetic resonance imaging” (MRI), Fourier transformation infrared analyses (FT-IR), ultraviolet (UV) spectroscopy, refraction indices (RI), fluorescence determinations, radiochemical determinations, electrochemical determinations, “light scattering” (LS), dispersive Raman spectroscopy or flame-ionization detectors (FID).
The methods mentioned hereinabove are particularly suitable for determining the states of a multiplicity of metabolites in samples and therefore for recording the values of the characteristics required for compiling the profile. The methods preferably provide a value for an identity parameter and one or more values for one or more parameters induced by the physical, chemical or biological properties of the measured metabolites. The biomarker profile can therefore include not only values which allow the chemical nature of the metabolites to be determined, but also a value capable of reflecting quantitative changes in the metabolites, in other words the amount measured in a particular sample. The methods mentioned hereinabove are also suitable for high-throughput analyses, so that different samples can be measured in an automated manner at short time intervals, and it is possible to compile a multiplicity of profiles, capable of being compared with each other, within a short time.
A “pattern” of a particular effector or group of effectors within the scope of the present invention is intended to mean a set of biomarkers which exhibit a significant change when the biological system is exposed to a particular effector or a particular group of effectors. For example, the biomarkers may be specified in the profile itself, or their values in absolute values and/or in relative values and/or in changes, for example rates of changes or changes in comparison with at least one normal value. The change may also be viewed in absolute values and/or in relative units, for example in comparison with at least one reference value and/or a normal state, in particular a state in which there is not, or has not been, any exposure to the effector and/or group of effectors and/or any effector.
What must be regarded as significant in this context depends on the individual case and can be preselected for example by a user and/or an evaluation device, and/or adjusted manually. In this context, for example, it is possible to pre-define one or more threshold values, also referred to hereinbelow as significance thresholds, for example one or more threshold values for one or more biomarkers and, in particular, for each biomarker or for each group of biomarkers a significance threshold, with the aid of which it is possible to decide whether a change is significant or not. These threshold values may also be variable in this context and, for example, adapted iteratively in order to adjust the sensitivity and/or selectivity.
Within the scope of the present invention, “selectivity” is generally understood as meaning the ability or property of systematically selecting specific elements from a total number of possible elements. The selectivity may therefore represent a measure of the narrowness of the selection. If, as is also possible within the scope of the present invention, a database is used, then the selectivity may be a measure of proportion of the selected elements in the total content of the data of the database, in particular for a database search by means of an index. Thus, for example, the selectivity may specify the number of biomarkers which are selected from the total number of biomarkers with the aid of the changes and assigned to the pattern. A high selectivity, for example owing to a high threshold value, generally leads to a lower number of biomarkers in the pattern, and a low selectivity, for example owing to a low threshold value, generally leads to a higher number of biomarkers in the pattern.
Within the scope of the present invention, “sensitivity” generally means the likelihood that an event which is genuinely positive will indeed be identified by a positive test result. For example, the sensitivity may represent a measure or a likelihood of whether a change of a particular biomarker can indeed be attributed to exposure to an effector or group of effectors, or whether the change is a random change, a measurement error or a noise or any other interfering influence. For example, the sensitivity may also describe a true-positive rate, a sensitivity or a hit ratio. The sensitivity may in particular be the proportion of events which are correctly identified as positive out of the total number of the events which are indeed positive, i.e. for example a proportion of the biomarkers with a change which has correctly been considered to be significant out of the total number of biomarkers which should exhibit a significant change due to exposure to the effector or group of effectors. A high sensitivity, for example owing to a low threshold value, generally leads to a high number of biomarkers in the pattern, while a low sensitivity, for example owing to a high threshold value, generally leads to a low number of biomarkers in the pattern.
The method according to the invention described hereinabove for establishing at least one pattern for at least one pre-defined effector having at least one determinable effect on a biological system permits the rapid convolution of extensive biological data. With the aid of the threshold values to be pre-defined, the most relevant biomarkers can be ascertained rapidly and reliably from a pre-defined profile. The method can also be carried out readily by computer implementation and may therefore be used in particular with the other method elements in high-throughput analysis. The patterns obtained by the method may be used in the applications discussed elsewhere in the description, and they allow a simplified and more efficient analysis of biological data sets.
In a preferred embodiment, the method according to the invention furthermore comprises the following steps:
In this manner, for example, an already existing database may be evaluated. By means of a multiplicity of measurements, for example, a database with profiles of different effectors can be collected. At least some of these effectors may, for example, be known effectors, in other words for example effectors whose effects on the biological system are known, for example their toxic effects. Within this database, an evaluation may be carried out by establishing profiles for one or more of the effectors.
In another preferred embodiment of the method according to the invention, it also comprises the following step:
In this manner, for example, a comparison may be carried out for the pre-defined effector with the at least one further effector of the database, for example with at least one further effector which is already known. Thus, for example, a search may be carried out for similar effectors.
In a comparison of specific objects, various methods may generally be subsumable within the scope of the present invention. A comparison of two patterns may, for example, be carried out as to whether the patterns comprise the same biomarkers.
The result of this comparison may, for example, consist in a characteristic quantity which describes the degree of the match. This may be, for example, a percentage, for example 100 percent, if the first pattern consists of the same biomarkers as the second pattern. Correlation information or similar information may also be used in order to characterize the degree of the match.
Furthermore, as an alternative or in addition, it is also possible to compare the values of the patterns or at least to include the values in the comparison. Thus, it is possible to check not only whether the patterns comprise the same biomarkers, but also whether and optionally to what extent the values of these matching biomarkers coincide. Again, this can be done for example by using a correlation function or similar mathematical methods. Furthermore, it is also possible to simply pre-define one or more similarity thresholds so that for example a percentage deviation of the two values from each other which is above a pre-defined threshold is considered to be a non-match, and a percentage deviation of the values from each other which is below the threshold is considered to be a match. Other comparison methods may also be envisaged.
In another preferred embodiment of the method according to the invention, it also comprises the following steps:
This method variant thus relates to the actual comparison of the effects of the pre-defined effector, which must then also actually be determined or known in another way, with the known effect of the further effector.
For a comparison of the effects, in turn, it is possible to employ a variety of methods. Thus, for example, it is possible to categorize effects. In this way, for example, effects may readily be specified digitally, for example the effect is “hepatotoxic”. The effects may additionally be quantified further, for example in graded information such as “highly hepatotoxic”, “averagely hepatotoxic” or “weakly hepatotoxic”. Other quantifications may also be found. Thus, it is possible in turn to give a quantitative expression by quantifying the degree of the match instead of a simple digital expression “effect matches” or “effect does not match”. Again, this may be done, in turn, using known mathematical methods, for example by assigning numerical values to the graded information so that, for example, percentages or other quantitative information may in turn be used as an expression for the degree of the match.
If purely digital expressions of the type “effect matches” or “effect does not match” (for example “both effectors are hepatotoxic” or “one of the effectors is hepatotoxic, while the other one is not”) are given, then further evaluation is comparatively simple. If, however, intermediate values are allowed which categorize the degree of the match, then the operation may be carried out in turn with a threshold value method. Thus, for example, the degree of the match of the effects may be compared with one or more threshold values. If the degree of the match exceeds the threshold value, it can be assumed that the effects match, while a non-match may be assumed if the degree of the matches lies below the threshold value.
After this method has been carried out, there are several options. The starting point is found, as a rule, by the objectively determinable effects. If the effects match, while the patterns do not, then the pattern determination has not delivered the desired result and must optionally be improved. If, however, the effects match then the pattern comparison and the comparison of the effects deliver the same result, and the determination and comparison of the patterns therefore represent a successful way of comparing effects of different effectors or, for example, of predicting effects of unknown new effectors.
Accordingly, in another preferred embodiment of the method according to the invention, it also comprises the following step:
This method variant represents an improvement in the pattern generation by correspondingly refining the algorithm. In other words, this method variant describes the case in which, although there is an at least partial match of the previously generated patterns (for example according to the description above a one hundred percent match, a match above a pre-defined threshold or a match by at least a pre-defined threshold), in fact no effect match or merely a minor effect match (for example below a pre-defined threshold) can be found for these effectors which, according to the ascertained patterns, should have an at least partial match of the effects (for example again by at least one pre-defined degree or by more than a pre-defined degree). In other words, this may comprise the case that, while the patterns match, the effects do not.
This is an indicator that the patterns have been selected incorrectly. For example, this may be attributable to biomarkers, whose values exhibit more of a random match, having been erroneously selected for the pattern even though these biomarkers do not represent suitable indicators for the suitable effect. This may for example be the case when the above-described threshold values in the pattern generation, in particular in method steps b) and c), have been selected unduly low for all the biomarkers, for a few biomarkers or for individual biomarkers. Correspondingly, according to the proposed method variant, a full or partial repetition of the pattern generation may be carried out. For example it is possible to automatically or manually increase all, a few or individual significance thresholds, so as to exclude from the patterns biomarkers which do not represent a suitable indicator for the effect.
In a preferred method variant, this can be carried out in particular in such a way that method steps i) and i1) are carried out repeatedly with a stepwise increase of the significance threshold.
Furthermore, the case may arise that no match of the patterns is found, even though a match of the effects can be found. This may mean in particular that biomarkers which would in fact have been suitable indicators of the effect have been excluded from the pattern generation by the threshold value method in method steps b) and c), for example because the threshold values were set too high.
In another preferred embodiment of the method according to the invention, it also comprises the following step:
This in turn means that the pattern generation may be refined by adapting the threshold values. In this case, a method which is especially preferred is one in which method steps j) and j1) are carried out repeatedly with a stepwise reduction of the significance threshold.
The above-described method in one of the configurations described may be used in particular to group effectors according to their effect. In another aspect, therefore, the invention relates to a method of establishing a class of effectors for a pre-defined effect or group of effects. The method is based on the method presented above for establishing at least one pattern and comprises this method as a key component. The method comprises the following steps:
In method step B), it is preferred to use a method of establishing a pattern in one of the configurations described above. In principle, however, other methods of establishing patterns may also be used, or already known patterns may be employed. For example, specific patterns of effectors of a particular effect are by now known from the literature in some cases, since, for example, it is known that specific effectors have an effect on specific metabolites.
A “class of effectors” within the scope of the present invention is understood as meaning a set of effectors which have the same known effect on the biological system or at least a similar effect on the biological system. The starting point for establishing a class of effectors is therefore a particular effect on the biological system, or a combined group of effects. The class of effectors may be a set of effectors which have at least one specific effect on the growth and/or functionality of the biological system, for example a specific toxic effect and/or a specific curative effect. A class of effectors may, in principle, first be configured as an empty set and then for example be added to later so that it preferably comprises at least one effector, in particular a plurality of effectors. As will be explained in greater detail below, a class of effectors may optionally also be established beforehand, for example firstly by at least one effector suspected of having the specific effect or the group of effects being assigned to the class of effectors. Thereafter, it is possible to supplement the class of effectors with one or more further effectors, as explained in greater detail hereinbelow, for example iteratively.
According to the possible effects, the class of effectors may comprise chemical compounds which mediate specific effects, for example organ toxicity, tissue toxicity or cell toxicity, optionally according to a specific molecular mode of action. For a toxicological risk stratification, it is helpful to know the precise mode of action for compounds. The effect mediated by a class of effectors may also be a pharmacological effect. Again, early categorization of an active substance is helpful for further pharmacological classification and allows early risk stratification so that unsuitable candidate active substances can promptly be eliminated before the start of clinical studies. Likewise, the effect mediated by a class of effectors may be, or comprise, a herbicidal, fungicidal or insecticidal effect or any combination of these effects. Again, early categorization of an active substance is helpful in the further classification and makes possible early risk stratification so that unsuitable candidate active substances can promptly be eliminated before the start of further studies. Genetic modifications as effectors may likewise form classes of effectors. Thus, for example, yield-increasing genetic modifications, or genetic modifications which increase pest resistance, may be combined in in each case one class of effectors. The method according to the invention preferably makes it possible to compile and collate effectors to form a class of effectors on the basis of the individual patterns of the individual effectors. The effectors of a class of effectors here preferably have essentially identical patterns. On the basis of these considerations, the method according to the invention for establishing a class of effectors makes possible said establishing of the class of effectors.
A method in which all or at least one of steps B) to E) is/are carried out repeatedly is preferred.
In a preferred embodiment of the method according to the invention, expert knowledge is employed in step A). For example, it is possible to employ the knowledge of an expert, for example a toxicologist, to pre-define at least one effector which is known to have a pre-defined effect, for example a pre-defined toxic effect.
The possibility of collating classes of effectors may furthermore be used to make predictions about at least one effect of at least one new, at least as yet not fully known effector and/or in order to determine at least one effect of an effector. In this context, it is possible in particular to use one or more classes of effectors which may have been established by the method of establishing a class of effectors for a pre-defined effect or group of effects according to one or more of the configurations described hereinabove. In principle it is also possible, as an alternative or in addition, however, to use classes of effectors obtained in other ways. Thus, particular classes of effectors are in turn known from the literature since, for example, the effects of many effectors are catalogued so that effectors with the same effect can be grouped.
Accordingly, in another aspect, the invention also relates to a method for identifying at least one effect of a pre-defined effector, comprising the following steps:
Identification of at least one effect may, in this context, generally be understood as ascertaining a result that the pre-defined effector has at least one particular, specifically indicated effect. As an alternative or in addition, the at least one effect may also be identified, which is likewise to be understood by identifying at least one effect, by carrying out a comparison with at least one other effector and, accordingly,
Thus, for example, it may be ascertained that the pre-defined effector has at least one equal, similar or dissimilar effect to the at least one further effector with which the comparison is carried out.
In a preferred embodiment of the above method according to the invention, if a match or a similarity is found in step iii), the known effect of the class of effectors is equated with the effect to be ascertained of the pre-defined effector. This means that the effector in question, whose effect is to be ascertained, may in particular have the same effect as the class of effectors employed for the comparison, whose effect is in fact known. In this way, it is efficiently possible to quickly obtain at least one provisional estimate of the effect of this effector while reducing laboratory experiments to one unknown effector. This allows considerable research costs savings and it also makes it possible to reduce for example animal experiments to a minimum.
The method described hereinabove may, in principle, also be carried out without using a class of effectors, and methods using a class of effectors and methods without using a class of effectors may also be combined. If the route via at least one class of effectors is not selected, or at least not exclusively selected, it is possible also to resort directly to for example the ascertained patterns. Here, in particular, it is possible again to employ one or more patterns which have been obtained by means of the method of establishing at least one pattern according to one or more of the configurations mentioned hereinabove. Alternatively or as an addition, however, it is also possible in turn to employ one or more patterns which have been obtained in another way or which are known for example from the literature.
In a further aspect, therefore, the invention also relates to a method for identifying at least one effect of a pre-defined effector, comprising the following steps:
As mentioned hereinabove, this method may in principle also be combined with the method in which the route via the at least one class of effectors is chosen. In both cases, effects of effectors can at least provisionally be predicted rapidly and reliably by comparison with known effectors, whether they now be grouped according to classes of effectors, or be individual.
The methods described hereinabove may be carried out fully or in part by means of a computer, or else they may be carried out at least in part using a computer. In particular, it is possible for one or more of the following method steps to be carried out by using a computer: a), b), c), d), e), f), g), h), i), i1), j), j1), A), B), C), D), E), i), ii), iii), I), II), Ill), IV), V), VI).
The invention therefore furthermore comprises a computer program having a program code for implementing the method according to any of the preceding method claims when the program is run in a computer. The computer program may be adapted to carry out, or at least assist, one or more or all of the method steps. In particular, all of method steps a) to c), all of method steps A) to E), all of method steps i) to iii) or all of method steps I) to VI) may be carried out by using at least one computer or computer network or using the computer program.
The computer program may in particular be configured as a saleable product. The computer program according to the invention is preferably stored on a machine-readable medium.
The invention furthermore comprises a computer, adapted to carry out a method according to any of the preceding method claims. The computer may generally comprise at least one dataprocessing device and/or one computer network.
Finally, the invention also relates to a data medium on which a data structure is stored, which carries out the method according to any of the preceding method claims, after loading in a working and/or main memory of a computer or computer network.
The proposed methods, the computer program, the computer and the data medium have many advantages over methods and devices known from the prior art, some of which have already been mentioned hereinabove. Thus, it is possible in particular to ascertain relationships and make predictions in very confusing experimental data sets. The data, for example raw data with measurement values for biomarkers of a multiplicity of different effectors, can be evaluated and categorized efficiently in this manner, and new types of presentation and/or representation may be found (for example in the form of patterns and/or classes of effectors), and/or data sets can be reduced considerably. Furthermore, owing to the possibility of predicting effects of previously unknown or only insufficiently known effectors, the experimental workload and the time for screening a multiplicity of new effectors can be reduced considerably.
Other possible details and features of the invention may be found in the following description of preferred exemplary embodiments. Some of the exemplary embodiments are represented schematically in the figures. Reference numbers which are the same in different figures refer to elements which are the same or functionally the same or correspond to one another in their function. The invention is not restricted to the exemplary embodiments.
In detail:
show an exemplary embodiment of a method according to the invention for determining a pattern of an effector;
show an exemplary embodiment of a method according to the invention of establishing a class of effectors;
show a first exemplary embodiment of a method for identifying at least one effect of a pre-defined effector;
show a second exemplary embodiment of a method for identifying an effect of a pre-defined effector; and
show a use example of the method according to
In
This method is represented by way of example in
Various biomarkers 122 are recorded for each metabolite 118 and are specified in the rows following the metabolites 118. For example, the absolute values or changes of a particular metabolite 118 may be recorded for male (m) and female (f) test subjects (for example rats). Furthermore, biomarkers 122 may be recorded for exposure of the test subjects to a low dose (l) and for exposure to a high dose (h). Furthermore, the absolute values or changes of the metabolites 118 may be recorded after a pre-defined duration, for example after a duration in days, for example after 7 days (7), after 14 days (14) or after 28 days (28). Thus, for example, the biomarker 122 in the row allocated to the metabolite threonine in column ml7 denotes the absolute value or the change of the metabolite threonine when a male test subject (m) is exposed to a low dose (l) for measurement 7 days after exposure of the test subject to the effector 120, for example a substance with the designation “compound 1” or a substance with the designation “compound 2”. Each metabolite 118, therefore, has assigned to it a multiplicity of biomarkers 122. The total number of biomarkers 122 of a particular effector 120 is also referred to as profile 124.
Thus,
Furthermore, on the other hand,
By means of this evaluation, the method step denoted hereinabove by the reference number 114 of the method according to
In this manner, it is thus possible for example to establish a pattern for a pre-defined effector 120. This establishing may optionally also be carried out iteratively as described above, for example by interactive adaptation of threshold values. The pattern is symbolically denoted in
In
In
In a further method step which is likewise represented in
In a method step denoted by reference number 216 in
The method described in
One or more patterns are then determined in method step 212 for this class of effectors 220, which can initially be considered to be a provisional class of effectors 220.
In method steps 214 and 216, a database 116 with further effectors 120 is, in turn, specified, and a search is made in this database 116 for effectors 120 with the same or similar profiles 124, for example with identical or similar patterns 130. If this search is successful, then this at least one further effector 120 that has possibly been ascertained in this manner is assigned to the class of effectors 220 in method step 218. The method may then, as indicated in
This method will be illustrated in greater detail by way of example with the aid of
In this example, it is desired to establish, by way of example, a class of effectors 220, which comprises effectors 120 which have an effect of the peroxisome proliferation type.
For this peroxisome proliferation effect, a provisional class of effectors 220 is initially formed which is based for example on expert knowledge and/or literature information. The expert knowledge consists for example in that the effectors 120 of the mecoprop-p, fenofibrate and dibutyl phthalate type have the abovementioned effect. These effectors 120 are therefore assigned to the provisional class of effectors 220, as shown in
Furthermore, biomarkers 122 whose values exhibit a significant change, in turn, are shown against a gray background in
With this provisional class of effectors 220, a search can then be made in the database 116 for further effectors 120 which are likewise to be assigned to the class of effectors 220. This is represented by way of example in
In the method represented in
In method step 314, at least one pattern 130 is established for the pre-defined effector 120, whose effect 310 is to be ascertained.
In method step 316, finally, a comparison is made between the pattern 130 ascertained in method step 314 for the pre-defined effector 120, whose effect is to be ascertained, and the at least one pattern 130 of the effectors 120 combined in the at least one class of effectors 220.
With the aid of
To this end, a multiplicity of effects 310 are entered in the first column of the table shown in
Preferably, a class of effectors or optionally a plurality of classes of effectors have beforehand been determined for each of these effects 310, for example with the aid of the method described in
Furthermore, the second and third columns of the table according to
Furthermore, the correlation of the pattern determined in step 314 for the pre-defined effector 120 whose effect is to be determined is entered in
In the specific embodiment in
On the other hand, there is a comparatively low match for the other classes of effectors 220. Accordingly, it can be ruled out with a high likelihood that the effector diethylhexyl phthalate has the same effect as these classes of effectors 220.
As a result of the method according to
Method step 410 in
Reference number 412 denotes a method step in which a database 116 is provided in which profiles 124 are stored for a multiplicity of further effectors 120. In respect of this, again, reference can be made to the exemplary embodiments above.
Reference number 414 denotes a method step in which a search is made in the database 116 for effectors 120 with patterns similar or identical to the pattern 130 established in step 410. This may for example again be done by means of a comparison using a correlation method. In this respect, reference may for example again be made to the description of
In method step 416 a check is carried out as to whether the effectors 120 ascertained in step 414 (assuming that at least one such effector 120 has been ascertained—which need not necessarily be the case) have at least one known effect. This may for example be done by the effectors 120 ascertained in step 414 already having been assigned to a class of effectors 220 and/or by again using expert knowledge about the effectors 120 which have been ascertained.
Method step 418 represents a conditional method step. Specifically, if a known effect has been found in method step 416, then the effect of the pre-defined effector is equated with the known effect (likewise, a plurality of known effects may be identified). At least one effect of the effector 120 in question is thereby identified. Otherwise, that is to say when no known effect is found in step 416, the method in
Furthermore, comparison results of the pattern 130 of the pre-defined effector diethylhexyl phthalate with the respective pattern 130 of the respective effector 120 are represented in the third column of the table shown in
As the next-closest pattern, with a Pearson correlation coefficient r=0.713, an effector 120 denoted here as “treatment 294” was ascertained in the table according to
Method steps 416 and 418 are not represented in
In general, for example in the method in
The efficacy of the method described in
2-Acetylaminofluorene is known to be an effector 120 which has the following effects 310:
For the chemically similar substance 4-acetylaminofluorene, on the other hand, it is known that this effector 120 has the following effects 310:
Despite the chemical similarity, very different effects 310 of these effectors 120 can therefore be observed in practice. The question is whether these different effects can be identified by means of a method according to the present invention.
Accordingly, in a representation similar to
Correspondingly, the effector 2-acetylaminofluorene itself in turn naturally occupies the first position of the ranking in the left-hand table in
In the table excerpt in
These results impressively show that the method described with the aid of
110 Provision of at least one profile of the pre-defined effector
112 Comparison of at least one value of at least one biomarker of the profile with at least one significance threshold
114 Combination of significant biomarkers of the profile to form a pattern
116 Database
118 Metabolites
120 Effectors
122 Biomarkers
124 Profile
126 Profile for compound 1
128 Profile for compound 2
130 Pattern
210 Specification of at least one effector
212 Establishing or updating of at least one pattern of the at least one effector
214 Provision of a database with profiles for further effectors
216 Search for effectors with the same similar profiles
218 Assignment of the ascertained effectors to the class of effectors
220 Class of effectors
310 Effect
312 Establishing at least one class of effectors for at least one known effect
314 Establishing at least one pattern of the pre-defined effector
316 Comparison of the pattern from step 314 with pattern from step 312
318 Pearson correlation coefficient for patterns of the classes of effectors
320 Pearson correlation coefficient for pattern of the effector
410 Establishing at least one pattern of the pre-defined effector
412 Provision of a database in which profiles are stored for a multiplicity of further effectors
414 Searching in the database for effectors with a pattern similar or identical to the pattern established in step 410
416 Checking whether the effectors ascertained in step 414 have at least one known effect
418 If a known effect is found in step 416, equating the effect of the pre-defined effector with the known effect
| Number | Date | Country | Kind |
|---|---|---|---|
| 09173689.2 | Oct 2009 | EP | regional |
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/EP2010/065450 | 10/14/2010 | WO | 00 | 4/20/2012 |
| Number | Date | Country | |
|---|---|---|---|
| 61255876 | Oct 2009 | US |
