Metabolic phenotyping

Abstract

A method of generating models with which to characterise selected aspects of the metabolic phenotype of subjects without dosing a test substance to those subjects or with which to predict, without dosing, the post-dose responses of subjects where those responses are dependent on metabolic phenotype, the method comprising: obtaining pre-dose data relating to a plurality of subjects before dosing with a dosing substance; obtaining post-dose data relating to the plurality of subjects after dosing with the dosing substance; and correlating inter-subject variation in the pre-dose data with inter-subject variation in the post-dose data, and generating a pre-to-post-dose predictive model on the basis of the observed correlation. The models may be used to determine selected aspects of the metabolic phenotype of a subject or to predict, without dosing, the post-dose responses of subjects. This is achieved by analysing data relating to the un-dosed subject in relation to a model describing the correlation of pre-dose and post-dose data relating to a plurality of subjects when dosed with a particular substance which challenges the biochemical transformation or pathway of interest; and generating, according to the predetermined criteria of the model, a numerical measure or classification describing the metabolic phenotype of the un-dosed subject.

1 . A method of generating models with which to characterise selected aspects of a metabolic phenotype of subjects without dosing a test substance to those subjects or with which to predict, without dosing, the post-dose responses of subjects where those responses are dependent on metabolic phenotype, the method comprising: 
 obtaining pre-dose data relating to a plurality of subjects before dosing with a dosing substance;    obtaining post-dose data relating to the plurality of subjects after dosing with the dosing substance; and    correlating inter-subject variation in the pre-dose data with inter-subject variation in the post-dose data, and generating a pre-to-post-dose predictive model on the basis of the observed correlation.    
     
     
         2 . A method according to  claim 1 , wherein the pre- and/or post-dose data are obtained from samples which are biofluids such as urine, blood, blood plasma, blood serum, saliva, sweat, tears, breath or breath condensate.  
     
     
         3 . A method according to  claim 1 , wherein the pre- and/or post-dose data are obtained from samples which are plant tissues, plant fluids or homogenates, plant extracts or plant exudates, including, for example, essential oils.  
     
     
         4 . A method according to  claim 1 , wherein the pre- and/or post-dose data are obtained from samples which are human or animal tissues, fish tissues or oils, tissue extracts, tissue culture extracts, cell culture supernatants or extracts or are of microbial origin.  
     
     
         5 . A method according to  claim 1  wherein the pre- and/or post-dose data comprise data relating to chemical composition or physical parameters.  
     
     
         6 . A method according to  claim 1 , wherein the pre- and/or post-dose samples or subjects are treated prior to analysis (e.g. treated with one or more chemical reagents so as to produce derivative(s) of one or more existing substances) so as to enhance data recovery or to improve sample stability.  
     
     
         7 . A method according to  claim 6  wherein the pre- and/or post-dose data are derived from or are compositional data acquired using nuclear magnetic resonance (NMR) spectroscopy and/or any other chemical analysis techniques such as mass spectroscopy (MS), infrared (IR) spectoscopy, gas chromatography (GC) and high performance liquid chromatography (HPLC) or by using any integrated combination of such techniques e.g. GC-MS.  
     
     
         8 . A method according to  claim 7  wherein the pre- and/or post-dose data are physical data or data derived therefrom.  
     
     
         9 . A method according to  claim 8  wherein, by dosing appropriate substances, a phenotyping model is generated for each of a plurality of biochemical transformations.  
     
     
         10 . A method according to  claim 8  wherein, by dosing appropriate substances, a response prediction model is built for each of a plurality of dosing substances.  
     
     
         11 . A method according to  claim 10  wherein the original pre-dose data set is extended, prior to pattern recognition, by taking ratios and/or other combinations of existing variables.  
     
     
         12 . A method according to  claim 11  wherein, for a group of subjects dosed with any particular substance, a pattern recognition method is used to identify patterns in the variable metabolism of, or the variable reactions to, the dosing substance.  
     
     
         13 . A method according to  claim 8  wherein, for a group of subjects dosed with any particular substance, an unsupervised pattern recognition method is used to identify variation in the pre-dose data that correlates with the variation of interest in the post-dose data.  
     
     
         14 . A method according to  claim 2  wherein, for a group of subjects dosed with any particular substance, a supervised pattern recognition method is used to identify variation in the pre-dose data that correlates with the variation of interest in the post-dose data.  
     
     
         15 . A method according to  claim 3  wherein, for a group of subjects dosed with any particular substance, a data filtering method such as Orthogonal Signal Correction (OSC) is used to remove variation in the pre-dose data that is not correlated with the variation of interest in the post-dose data.  
     
     
         16 . A method according to  claim 1  when used to identify biomarkers or combinations of biomarkers which provide information on metabolic phenotype or which may be used to predict responses to dosing.  
     
     
         17 . A method of determining selected aspects of the metabolic phenotype of a subject, the method comprising: 
 analysing data relating to an un-dosed subject in relation to a model describing the correlation of pre-dose and post-dose data relating to a plurality of subjects dosed with a particular substance which challenges the biochemical transformation or pathway of interest;    generating, according to a predetermined criteria of the model, a numerical measure or classification describing the metabolic phenotype of the un-dosed subject.    
     
     
         18 . A method according to  claim 17 , wherein data relating to the un-dosed subject is obtained from a biofluid such as urine, blood, blood plasma, blood serum, saliva, sweat, tears, breath or breath condensate or from a plant tissue, plant fluid, plant homogenate, plant extract or plant exudate, including, for example, an essential oil, or from human or animal tissue, fish tissue or oil, or from a tissue extract, tissue culture extract, cell culture supernatant or cell culture extract or from a sample of microbial origin or from any one of the above sample types after treatment to enhance data recovery or sample stability.  
     
     
         19 . A method according to claims  17 , further comprising generating characteristic compositional and/or physical data relating to a subject using nuclear magnetic resonance (NMR) spectroscopy and/or any other techniques or by using any combination of techniques.  
     
     
         20 . A phenotyping method according to  claim 19  when used for the purpose of making a metabolic phenotype-influenced risk assessment and/or for the purpose of targeting the use of special health monitoring regimes and/or for the purpose of targeting the use of precautionary/preventative treatments and/or for the purpose of characterising risk for insurance purposes and/or for the purpose of selecting subjects for any other purpose e.g. for breeding.  
     
     
         21 . A method of predicting a reaction of a subject to a dosing substance, the method comprising: 
 analysing data relating to an un-dosed subject in relation to a model characterising the correlation of pre-dose and post-dose data relating to a plurality of subjects dosed with the particular dosing substance; and    generating, according to the predetermined criteria of the model, a numerical or class prediction for the expected response of the un-dosed subject if it were to be dosed with the dosing substance.    
     
     
         22 . A method according to  claim 21  wherein, according to pre-determined criteria, a maximum or minimum dose of a substance that a subject should receive can be predicted.  
     
     
         23 . A method according to claims  21  wherein, according to pre-determined criteria, an amount of a dosing substance that a subject should receive can be predicted.  
     
     
         24 . A method according to  claim 23  wherein, according to pre-determined criteria, a frequency with which a subject should be dosed with a substance can be predicted.  
     
     
         25 . A method according to  claim 24  wherein, according to pre-determined criteria, a number of doses of a substance that a subject should receive can be predicted.  
     
     
         26 . A method according to  claim 25  wherein, according to pre-determined criteria, an appropriate controlled release formulation for a subject can be selected.  
     
     
         27 . A method according to  claim 26 , wherein data relating to the un-dosed subject is obtained from a biofluid such as urine, blood, blood plasma, blood serum, saliva, sweat, tears, breath or breath condensate or from a plant tissue, plant fluid, plant homogenate, plant extract or plant exudate, including, for example, an essential oil, or from human or animal tissue, fish tissue or oil, or from a tissue extract, tissue culture extract, cell culture supernatant or cell culture extract or from a sample of microbial origin or from any one of the above sample types after treatment to enhance data recovery or sample stability.  
     
     
         28 . A method according to  claim 27 , further comprising generating characteristic compositional and/or physical data relating to a subject using nuclear magnetic resonance (NMR) spectroscopy and/or any other techniques or by using any combination of techniques.  
     
     
         29 . A method of determining selected aspects of a metabolic phenotype of a subject or of predicting a reaction of a subject to a dosing substance, the method comprising analysing data relating to the un-dosed subject with respect to one or more biomarkers which have been previously identified as described in  claim 16 .  
     
     
         30 . A method according to  claim 29  wherein the biomarker(s) react(s) with one or more added reagents to produce a visible change such as a colour change.  
     
     
         31 . A method according to  claim 30  when used to select a group of phenotypically homogenous or similar subjects for a laboratory experiment or clinical trial or for any other purpose.  
     
     
         32 . A method, according to  claim 31 , for rationalising biological variation in experimental data based on pre-dose analysis of biofluids or tissues, where such variation is caused by phenotypic heterogeneity.  
     
     
         33 . A method according to  claim 32  wherein the data is based on physical and/or chemical measurements taken from the subject as a whole.  
     
     
         34 . A method according to  claim 28  wherein the post-dose data describes a change relative to the pre-dose state e.g. a decrease in blood pressure of a human subject treated with a drug that lowers blood pressure.  
     
     
         35 . A method according to  claim 30  wherein test data that does not conform to the limits of a particular model and/or method can be identified.  
     
     
         36 . A method according to  claim 33  wherein the subject is a animal, in particular a mammal such as a human, a mouse, a rat, a pig, a cow, a bull, a sheep, a horse, a dog or a rabbit or any farmed animal or any animal, such as a race horse, used for the purpose of sport or for breeding.  
     
     
         37 . A method according to  claim 33  wherein the subject is a plant, a fish or any other aquatic organism  
     
     
         38 . A method according to  claim 33  wherein the subject is a biological tissue, a tissue culture, a cell culture or a microbial culture.  
     
     
         39 . A method according to  claim 28  wherein data are obtained from a sample which is representative, or is taken to be representative, of a group of subjects which are considered as a single subject.  
     
     
         40 . A method according to  claim 29  wherein the dosed substance is any substance or mixture or formulation of substances including especially pharmaceutical or medicinal substances or substances in research or development which might potentially become pharmaceutical or medicinal substances, but also including, for example, toxins, pesticides, herbicides, food or feed substances, food or feed additives and fluids of any sort including liquids, gases, vapours and smoke e.g. tobacco smoke.  
     
     
         41 . A method according to  claim 40  whereby the dosed substance is actively or passively dosed in any matrix or medium, by any means or route, including for example, by injection, by eating, by drinking, by inhaling or by smoking, over any time period including a subject's lifetime or any specified part or fraction thereof, such dosing to include that resulting from environmental exposure or pollution or from medical, dental, veterinary or surgical procedures.  
     
     
         42 . A method, according to  claim 41 , for identifying the acetylator phenotype of a subject without dosing a test substance to that subject.  
     
     
         43 . A method, according to  claim 41 , for predicting the response of a subject to dosing with a substance where that response is dependent on acetylator phenotype.  
     
     
         44 . A method according to  claim 2  for predicting the susceptibility of a subject to isoniazid-induced toxicity.  
     
     
         45 . A method according to  claim 2  for predicting the susceptibility of a subject to galactosamine-induced toxicity.  
     
     
         46 . A method according to of  claim 43  for predicting the susceptibility of a subject to paracetamol-induced toxicity.  
     
     
         47 . Apparatus for generating models according to  claim 1 .  
     
     
         48 . Apparatus for response prediction and/or for metabolic phenotyping, the apparatus comprising: 
 one or more models, each model modelling the correlation of pre-dose and post-dose data relating to a plurality of subjects dosed with a particular dosing substance;    a processor for analysing data relating to an un-dosed subject in relation to at least one of the models and thereby determining one or more aspects of the metabolic phenotype of the un-dosed subject or predicting its responses to dosing according to the model(s) employed.    
     
     
         49 . Apparatus, according to  claim 48 , the apparatus being further arranged to generate one or more models according to claims  1 .  
     
     
         50 . Apparatus according to  claim 49 , further comprising one or more analytical instruments or devices to carry out physical and/or chemical analysis, such as NMR spectroscopy, mass spectroscopy, infrared spectroscopy or high performance liquid chromatography.  
     
     
         51 . Apparatus for identifying one or more biomarkers according to  claim 16 .  
     
     
         52 . Apparatus according to  claim 1  for response prediction or metabolic phenotyping which is based on the use of one or more biomarkers which have been previously identified as described in claims  16 .  
     
     
         53 . Apparatus for metabolic phenotyping or for predicting a subject's response(s) to dosing, the apparatus comprising: 
 a test area to receive a sample from the subject under test, said test area incorporating one or more reagents which may react chemically with one or more biomarkers in the sample to produce a change in the visual appearance of the test area, the biomarkers having been previously identified according to  claim 51 , and the resulting visual appearance of the test area being characteristic of metabolic phenotype or predictive of response(s) to dosing.    
     
     
         54 . Apparatus for carrying out the methods claimed in  claim 21  wherein an appropriate dosing regime for a subject can be identified.  
     
     
         55 . Apparatus according to  claim 47 , which is based on the use of antibodies raised against specific biomarkers.  
     
     
         56 . Apparatus according to  claim 55  wherein selected biomarkers are detected and/or quantified by means of enzyme-catalysed reactions using, for instance, enzymes immobilised on a solid support.  
     
     
         57 . Apparatus comprising one or more models generated by a method according to  claim 2 .  
     
     
         58  Apparatus, according to  claim 57 , which is further arranged to identify test data that does not conform to the limits of a particular model.