WO2004033722A2

WO2004033722A2 - Genetic profiling and healthcare management: adme (absorption, distribution, metabolism & elimination) & toxicology related genes and probes

Info

Publication number: WO2004033722A2
Application number: PCT/GB2003/004051
Authority: WO
Inventors: Gareth Wyn Roberts; Keith Grimaldi
Original assignee: Sciona Limited
Priority date: 2002-09-23
Filing date: 2003-09-23
Publication date: 2004-04-22
Also published as: AU2003269159A8; WO2004033722A3; AU2003269159A1; GB0222042D0

Abstract

According to the invention, the number of genes and their configurations (mutations and polymorphisms) needed to be identified in order to provide critical clinical information concerning individual prognosis is considerably less than the 100,000 thought to comprise the human genome. The identification of the identity of the core group of genes enables the invention of a design for genetic profiling technologies which comprises of the identification of the core group of genes and their sequence variants required to provide a broad base of clinical prognostic information ” genostics ”. The invention is meant to radically enhance the ability of clinicians, healthcare professionals and other parties to plan and manage healthcare provision and the targeting of appropriate healthcare resources to those deemed most in need.

Description

GENETIC PROFILING AND HEALTHCARE MANAGEMENT;

ADME (ABSORPTION, DISTRIBUTION, METABOLISM & ELIMINATION) &

TOXICOLOGY PATENT APPLICATION

The invention relates to a method of assessing the most appropriate therapeutic intervention in an individual, patient, group or population suffering from the debilitating consequences of dysfunction, damage or disease of the body and its systems.

People vary enormously in their response to disease and also in their response to therapeutic interventions aimed at ameliorating the disease process and its progression. However, the provision of medical care and medical management is centered around observations and protocols developed in clinical trials on groups or cohorts of patients plan. This group data is used to derive a standardised method of treatment which is subsequently applied on an individual basis (e.g. the comment that drugs are often prescribed on the basis that everyone is an 70kg white male) .

It is standard practice for clinicians to prescribe the same starting dose of a particular drug for a given indication and then adjust the treatment regimen by monitoring the progress of the disease and therapeutic response in individual patients. Observation of actual therapeutic outcome following these adjustments to patients therapy provides, the basis for determining a prognosis for the disease and developing a clinical management plan for patient care. Many algorithms for the treatment of specific disease have been developed in clinical centres of excellence.

The standard practice of clinical management has its disadvantages. In particular it is retro-active in that changes to patient management will occur following the emergence of therapeutic failures, adverse events or other difficulties in undertaking the therapeutic regime. The toxicological effect of any treatment involves four main pathways, Absorption, Distribution, Metabolism and Elimination, better known as ADME. The most important axiom of toxicology is that the dose makes the poison". Therefore variation in genes affecting the Absorption, Distribution, Metabolism and Elimination (ADME) of 'therapeutic' substances, accounts for much of the difference in individuals risk of toxicity.

Drugs interact with the body in many different ways to produce their effect. Some drugs act as false substrates of inhibitors for transport systems (e.g. calcium channels) or enzymes

(acetylcholinesterase) . Most drugs however, produce their effects by acting on receptors, usually located in the cell membrane, which normally respond to endogenous chemicals in the body (Weatherall, Leadingham and Warrell 1996). Drugs that activate receptors and produce a response are called agonists

(e.g cholinomimetics) . Antagonists combine with receptors but do not activate them, thus reducing the probability of the transmitter substance combining with the receptor and so blocking receptor activation. The ability of the drug to interact with the receptor depends on the specificity of the drug for the receptor or target' (Brody, Larner and Minneman 1998) .

In addition to the main categories of agonist and antagonist drugs also have mechanisms of action which include:

• blockade of uptake or transport sites (e.g selective serotonin reuptake inhibitors)

• enzyme inhibition (e.g. angiotensin convertying enzyme inhibitors, acetylcholinesterase inhibitors)

• blockade of ion channels (calcium channel antagonists, anaesthetics)

Any drug may produce unwanted or unexpected adverse events, these can range from trivial (slight nausea) to fatal (aplastic anaemia) . According to research published in JAMA ( azarou J, Pomeranz BH, Corey PN. 1998. Incidence of adverse drug reactions in hospitalised patients: a meta-analysis of prospective studies. JAMA Apr 15; 279 (15): 1200-5), in 1994, in US, 106,000 deaths were caused by adverse drug reactions, making ADRs the fourth leading cause of death in US. One of the main reasons for adverse events following drug intake is the drug binding to non-specific or non-target receptors in the body (Brody, Larner and Minneman 1998) . Another reason is the interaction of the drug with other drugs given to the patient. This is a particular problem in the elderly who frequently suffer from multiple illnesses requiring many different classes of drugs and providing a real potential for drug interactions (Weatheral, Leadingham and Warrell 1996) . The drug may also produce adverse events over time as the drug is absorbed, distributed, metabolised and excreted e.g. products of metabolising the drug may be reactive themselves and be toxic to the body. Being able to predicting the likelihood of particular individuals suffering from an adverse event and the severity of that event would be important tool for the practitioner.

Another problem the medical practitioner faces, is that certain patients may be particularly susceptible to drug addiction. Examples of drugs with known addictive properties are Amphetamines, Temazepam and Phenobarbitone, although having approved medicinal use e.g. phenobarbitone for epilepsy, they may cause problems of dependency and misuse in individuals. Knowledge of such an individual's susceptibility before .prescribing certain drugs would be an advantage to the medical practitioner.

The core list of genes for the ADME Genostic, would prove of considerable value in aiding decisions concerning the appropriateness and relevance of therapeutic interventions using many drugs . The use of the ADME Genostic would be of considerable utility in determining the likelihood and magnitude of therapeutic response, complications from drug- drug interactions, the potential for adverse events and the difficulties that might arise due to previous, concurrent or future dysfunction, damage or disease of body systems in an individual, patient, group or population. All of these factors are of considerable importance in enabling the selection and monitoring of therapeutic interventions and effective healthcare management . "

In addition, the core list of genes in the ADME genostic would also be of considerable utility in enhancing the analysis of clinical trial data derived from drugs in development.

A list of drugs currently on the market can be found in standard works of reference, in particular the British National Formulary, 2002, the Dental Practioners' Formulary, 2002, Martindale, 2002, Herbal medicines, 2002. Drugs available in the United States can be found in U.S. Pharmacopeia, 2002, and drugs available in Japan can be found in Iryoyaku Ninon Iyakuhinshu, 2002, Ippanyaku Nihon Iyakuhinshu, 2002 and Hokenyaku Jiten, 2002. Drugs available in other countries can be found in the appropriate National Formularies. A list of drugs currently under development worldwide can be found in current journals and textbooks.

In a review entitled, Drug-metabolism research challenges in the new millenium: individual variability in drug therapy and drug safety', it has been stated that: ⁿ with the rapid progress in the understanding of genetic polymorphism and the development of genechip technology, it becomes quite feasible for individuals to be genotyped with respect to critical genes targeted for drug intervention and genes essential for drug transport and metabolism. the

(future) objective is to identify key genetic variations that could impact drug response and drug safety." A.Y.H. Lu, (1998) Drug metabolism and disposition, Vol 26 (12) pl217-1222. There is a wealth of information available on the genetic polymorphisms of enzymes involved in drug metabolism. Genetic variation in genes coding for proteins which act as drug metabolising enzymes, drug transporters, DNA repair enzymes, or drug targets can lead to the production of defective enzymes or altered receptor binding affinities . This can have profound effects on the drug efficacy, drug safety and optimal drug dosage. The genetic variation in these genes has been identified and is included in our ADME core list of genes .

We had previously concluded that a large number of genes would be of value in looking at gentic profil /phenotype interactions for the purposes of managing healthcare and drug administration. These are exemplified below.

Example 1

Core, Genes Relevant for Genetic Profiling of Drug Metabolism,

Efficacy and Safety activities

These genes are elaborated below:

KEY TO ^Λ PROTEIN FUNCTION' COLUMN

E ENZYME

T TRANSPORT & STORAGE

S STRUCTURAL

I IMMUNITY

N NERVOUS TRANSMISSION

G GROWTH S DIFFERENTIATION

In a further novel development we have conducted a further analysis and meta-analysis of the literature which has enabled us to select a subset of the Genostic Drug core to allow the development of a screen for individual persons gene variant profiles the results of which will aid in the prescription of appropriate drugs at appropriate doses . In a novel approach the screen is based on both SNPs and haploptypes combined. Gene based haplotypes consist of a combination of individual SNPs that occur within a defined region of a single gene and in some instances they have been found to have a greater influence on patient response than individual SNPs . An example is the response of asthmatics to albuterol therapy which shows great interindividual variation that is more strongly associated with ADBR2 haplotype than it is with any individual SNP (Drysdale et al . , 2000).

The raw genetic profile datasets are processed by a database driven "rules engine" delivering genotype specific drug use advice in a user friendly format.

The rules engine is capable of delivering advice on several hundred drugs currently on the market including those in EXAMPLE 2.

Gene based haplotypes consist of a combination of individual SNPs that occur within a defined region of the chromosome and in some instances they have been found to have a greater influence on patient response than individual SNPs. An example is the response of asthmatics to albuterol therapy which shows great interindividual variation that is more strongly^' associated with ADBR2 haplotype than it is with any individual SMP (Drysdale et al . , 2000).

EXAMPLE 2 LIST OF DRUGS ON WHICH INDIVIDUAL ADVICE CAN BE PROVIDED

5-fluorouracil, Aσelofenac, Acenocoumarol, Acetaminophen, Acrolein, Adrenochrome, Adriamycin, Albuterol, Alfenatil, Alprazolam, Alprenolol, Amiflamine, Aminochrome, Amiodarone, Amitriptyline, Amlodipine, Amonafide, Amphetamine, Alkylating agents, Anticancer, Anti-leukaemia therapy, Anti-tuberculosis, Ap indine, Arte isinin, Ascorbic acid, Atorvastatin, Beta- naphthoflavone, Bilirubin, Budesonide,^' Bufuralol, Buproprion, Caffeine, Cannabis, Captopril, Carbamazepine, Carisoprodol, Carvedilol, Celebrex, Celecoxib, Cerivastatin, Chloramphenicol, Chloropropamide, Chloroquine, Chlorpheniramine, Chlorproguanil, Chlorpromazine, Cimetidine, Cinnarizine, Ciprofloxaσin Cisapride; Cisplatin, Citalopram, Clarithromycin, Cloinidine, Clomipra ine, Clotrimazole, Clozapine, Cocaine, Codeine, Coumarin, Cyclobenzaprine, Cyclophosphamide, .Cyclosporin a, Cytostatic drugs, Dapsone, Debrisoquine, Delavirdine, Deprenyl, Desipramine, Dexamethasone, Dexfenfluramine, Dextromethorphan, Dextroprόpoxyphene, Diazepam, Diclofenac, Diltiazem, Docetal, Dopachrome, Doxorubicin, D-penicillamine, E-3810, Ecstasy (mdma) , Enalapril, Encainide, Encinide, Epirubicin, Erythromycin, Estradiol, Estrone, Ethacrynic acid, Ethanol, Ethoxyresorufin, Etoposide, Felbamate, Felcainide, Flecainide, Fluconazole, Flunarizine, Fluoroquinolones , Fluoxetine, Fluphenazine, Flurbiprof n, Flutamide, Fluvastatin, Fluvoxamine, Furafylline, Gallopamil, Glipizide, Glyburide, Guanoxan, Halofantrine, Haloperidol, Hexobarbital, Hexobarbitone, Hydralazine, Hydrocodone, Ibuprofen, Ifosfa ide, Imipramine, Indomethacin, Indoramin, Insulin, Interferon, Irbesartan, Irinotecan, Isinopril, Isoniazid, Isosafrole, Isotretinoin, Ketamine , Ketoconazole, Labetalol, Lansoprazole, Levadopa, Levomepromazine, Lidocaine , Lisinopril, Lobeline, Loratidine, Lornoxicam, Losartan, Lovastatin, Maprotiline, Mefenamic acid, Meloxicam, Mephenytoin, Met-a-mphetamine, Methadone, Methotrexate, Methoxsalen, Methoxyamphetamine, Methoxyphenamine, Methyl cholanthrene, Metoclopramide, Metoprolol, Mexiletine, Mianserin, Mibefradii, Miconazole, Midazolam, Minaprine, Mirtazapine, Moclobemide, Modafinil, Morphine, Nafcillin, Naproxen, Nelfinavir, Nicardipine, Nicotine, Nilutamide, Noradrenochrome, Norethindrone, Norfloxacin, Norfluoxetine , Nortriptyline, Norverapam.il, -propylamine, Oestrogen / hrt, Olanzapine, Omeprazole, Ondansetron, Oxfloxacin, Oxprenolol, Paclitaxel, Pantoprazole, Paracetamol, Paraxanthine, Paroxetine, Pentamidine, Pentobarbital, Perhexiline, Perphenazine, Phenacetin, Phenelzine, Phenformin, Phenobarbital, Phenobarbitone, Phenothiazines, Phenylbutazone, Phenytoin, Pindolol, Pioglitazone, Piroxicam, Prednisone, Primidone, Probenioid, Procainamide, Progesterone, Proguanil, Propafenone, Propanolol, Propofol, Propranolol, Propylajamaline, Quanoxan, Quinidine, Quinine, Rabeprazole, Raltitrexed, Ranitidine, Rauhimbine, Red-haloperidol, Remoxiride, Retinoic acid, Rifabutin, Rifampicin , Riluzole, Risperidone, Ritonavir, R-mephobarbital, Rofecoxib, Ropivacaine, Rosiglitazone, R-warfarin, Salmeterol, ^■ Secobarbital, Seratrodast, Sertraline, Simvastatin, S-ephenytoin, S-metoprolol, S-naproxen, Sparteine, Succinylcholine, Sulfadiazine, Sulfamethoxazole, Sulfaphenazole, Sulfasalazine , Sulfinpyr zone, , ulphamethizole, Sulphaphenazole, Sulphonamides , Suprofen, S-arfarin, Tacrine, Tamoxifen, Tegafur, Temazepam, Teniposide, Terbinafine, Terfenadine, Testosterone, Theophylline, Thioridazine, Ticlopidine, Tienilic acid, Timolol, Tolbutamide, Tolcapone, To oxetine, Topiramate, Torasemide, Tramadol, Tranylcypromine, Triazolam, Trifluperidol , Trimethadione, Trimethoprim, Trimipramine, Troglitazone, Tropisetron, Valproic acid, Venlafaxine, Verapamil, Warfarin, Zafirlukast, Zidovudine, Zileuton, Zileuton, Zolmitriptan, Zoloft, Zopiclone, Zuclopentixol

The table below shows a selection of the genes comprising the ADME panel together with some examples of common drugs, the effectiveness of which is influenced by the gene variant:

EXAMPLE 3

A SELECTION OF GENES FROH THE PANEL AND THE DRUGS THEY

METABOLIZE

The selected genes and gene variants can be readily detected in a sample of DNA use of a set of specific gene probes and their detection using a number of well known SNP detection technologies (such as mass tag detection, DNA probe/chip arrays etc) .

EXAMPLE 4

A SELECTION OF GENES FROM THE PANEL WITH RELEVANT PROBE

SEQUENCES

The genes and gene variants chosen can then be analysed in order to generate individual profiles which can be linked to specific advice concerning optimal drug of choice for a particular disease or condition and the most appropriate dosages for that drug in a given patient (described in Example 4) .

The generation of genetic profiling data and its analysis alongside clinical information derived from patients presents considerable challenges for data handling and analysis . The volume of information, number of information categories and the variable nature of the information (e.g. dimensional or categorical) ensure that the operation of a database combining genetic and clinical information to generate a prognostic outcome is a complex task.

However, the complexity can be dealt with using existing analytical approaches. Association analysis between genetic polymorphisms can be dealt with by using standard statistical techniques (analysis of variance, meta-analysis etc) with appropriate corrections for multiple testing. The thresholds for statistical significance will be derived from scientific convention (e.g. significance at the 5% level following Bonnferoni correction) . The data concerning genotype/phenotype relationships between the core group of genes and clinical signs and symptoms and therapeutic interventions will form a central component of the database.

The creation of a database containing and elaborating on such genotype/phenotype relationships will become an important tool for the practice of molecular medicine and the development of healthcare management. In order to derive benefit from such a database it must be capable (following interrogation using a patients profile of genetic variation derived from the core group of genes) of analysing the profile and providing a meaningful output to the healthcare professional which will provide guidance on the prognosis, healthcare management and therapeutic interventions appropriate to the patient.

The generation of such an output can be achieved using machine learning algorithms.

The genetic algorithm (Goldberg 1989, Fogarty and Ireson 1994) has been shown to provide a general process for achieving good results for search in large noisy domains. Starting from a population of randomly generated points in a search space, and given an .evaluation of each of those points, the genetic algorithm is designed to converge the population to an optimum point in the search space. Processes of data selection, crossover, mutation and replacement of old members of the dataset achieve this with new members of more value. The effective use of the genetic algorithm process is a representation of the search space, -which is responsive to the heuristics, embodied in the genetic operators.

The user must also supply an evaluation function identifying the degree to which the point in space approaches an optimum ( ^weighting' ) such that the selection operator for propagation through the dataset can choose them. The genetic algorithm can be used to find predictively meaningful categories that is :

intervals of continuous attribute values sets of nominal attribute values combinations of attributes

Together these attributes can create a simple Bayesian classifier for aspects of healthcare management.

Additional techniques (e.g. Bahadur-Lazarsfeld expansion) enable second order approximation of dependencies between predictive attributes . This allows the full complexity of the individual's genetic variation profile and the specifics of their clinical, psychological and social state to be assessed in order to produce an output concerning their prognosis, healthcare management and the possibilities for therapeutic intervention.

The data can them be presented as a series of recommendations to the practitioner based on advice for specific genotype and drug combinations Example 4.

EXAMPLE 5

ANALYSIS OF DATABASE TO ASCERTAIN GENOTYPE/PHENOTYPE

RELATIONSHIPS

The relevant genotypes can be collated and linked to specific recommendations in a systematic way.

CYP2C19

Genotype code ALLELEl ALLELE2 Phenotype

1 *1 *1 EM Extended etaboliser

2 *1 * *22 I IMM Intermediate

3 *1 *3 IM

4 *2 * *22 P PMM Poor

5 *2 *3 PM

6 *3 *3 PM Drug Indication Phenotype Advice

H.pylori Increased cure with dual

Omeprazole ulcer PM therapy

Increased cure with dual

IM therapy

EM Triple therapy advised

H.pylori Increased cure with dual Rabeprazole ulcer PM therapy

Increased cure with dual

Pantoprazole IM therapy

60% cure with dual, may Lansoprazole EM prefer triple

Try lower dose, observe carefully for adverse

Mephenytoin Epilepsy PM effects

Try lower dose, observe carefully for adverse

IM effects EM Normal dosage conditions

lower dose, 60% of normal, monitor closely for side

Amytryptiline Depression PM effects lower dose, 80% of normal, monitor closely for side

IM effects EM 100% dose

Fluoxetine Depression PM Consider lower dose

IM Consider lower dose EM Normal dosage

Sertraline Depression PM Consider lower dose IM Consider lower dose EM Normal dosage

The presentation of this data can be further optimised and enhanced for use by the patients healthcare worker by distilling its content into a series of simple text boxes containing the relevant advice.

EXAMPLE 6

PRESENTATION OF ADVICE CAN BE OPTIMISED TO AID THE USE OF IT

BY THE HEALTHCARE WORKER.

Query Results

WARFARIN

Assembly of such data will allow the merging of accepted treatment algorithms with the polymorphic variation underlying specific aspects of genomic functionality. This will produce new algorithms that will provide a prognostic indication for individual patients and, coupled with the expertise of their responsible clinician, allow the appropriate healthcare decisions to be made in a pro-active way.

The identification of genetic variation in the core list of genes and its application to healthcare management will have significant beneficial effects on the way in which clinicians will be able to formulate plans for healthcare management.

This will be seen in at least two ways . The first by enabling the targeting of resources at appropriate individuals and the second by enabling an objective risk assessment of the optimum configuration for different types of therapeutic intervention (e.g drugs, surgery, radiotherapy, occupational therapy) and the identification of those patients at significant risk of suffering adverse events from therapeutic intervention.

REFERENCES

A.Y.H. Lu, (1998) Drug metabolism and disposition, Vol 26 (12) P1217-1222.

British National Formulary Number 43. British Medical Association and Royal Pharmaceutical Society of Great Britain (March 2002) .

Brody T.M. , Larner J. , Minneman K.P. Human Pharmacology Molecular to Clinical. 3rd Ed. Mosby, 1998.

Dental Practitioners' Formulary, 2000-2002 Edition. British Medical Association and Royal Pharmaceutical Society of Great Britain (2002) .

Drysdale, C. M. , McGraw, D. W., Stack, C. B., Stephens, J. C, Judson, R. S., Nandabalan, K. , Arnold, K. , Ruano, G. , and Liggett, S. B. 2000. Proc Natl Acad Sci 97, 10483-10488.

Fogarty T.C. and Ireson N.S. (1994) Evolving Bayseian classifiers for credit control - a comparison with other machine -learning methods . IMA Journal of Mathematics Applied in Business and Industry 5, 63-75. Gilles PN. 1999. Single nucleotide polymorphic discrimination by an electronic dot blot assay on semiconductor microchips. Nature Biotechnology 17 (4): 365-370.

Goldberg D.E. (1989) Genetic algorithms in search optimisation and machine learning. Addison-Wesley.

Goodman and Gillman. The Pharmacological Basis of Therapeutics, 9th Ed. McGraw-Hill, New York 1996.

Lazarou J, Pomeranz BH, Corey PN. 1998. Incidence of adverse drug reactions in hospitalised patients: a meta-analysis of prospective studies. JAMA Apr 15; 279 (15): 1200-5

Poste G. 1998. Molecular medicine and information-based targetting of healthcare. Nature Biotechnology. 16 (Supplement) : 19-21.

Rieder MJ, Taylor SL, Clark AG and Nickerson) . Sequence variation in the human angiotensin converting enzyme. Nature Genetics 22, 62 - 9, 1999.

Weatherall Dj , Ledingham JGG and Warrel DA. Eds Oxford Textbook of Medicine 3rd Edition. Oxford Medical Publications 1996.

Claims

A set of nucleotide probes for detecting relevant variants (mutations and polymorphisms), e.g. nucleotide substitutions (missense, nonsense, splicing and regulatory) , small deletions, small insertions, small insertion deletions, gross insertions, gross deletions, duplications, complex rearrangements and repeat variations in a target group of genes which relate to adverse events; said probes being complementary to DNA and RNA sequences of said group of genes; characterised in that said group is a core group of genes consisting of substantially all of the following:

2. A set of probes, said probes being antibodies or antibody fragments which interact with specific expressed proteins encoded by gene sequences of a group of genes, said probes being for detecting relevant variants (mutations and polymorphisms) , e.g. nucleotide substitutions (missense, nonsense, splicing and regulatory) , small deletions, small insertions, small insertion deletions, gross insertions, gross deletions, duplications, complex rearrangements and repeat variations in a target group of genes; characterised in that said group is a core group of genes consisting of substantially all of the genes defined in claim 1.

3. A set according to claim 1 or 2 in which a minority of said probes for listed genes are absent.

4. A set according to claim 1 or 2 in which a limited number of additional probes are present together with substantially all of the probes for the listed genes .

5. A set according to claim 1 or 2 in which a limited number of probes are replaced by probes for non-listed genes.

6. A set of probes for a core group of genes according to any of claims 1 to 5 in which each gene to be probed is substantially similar (greater than 85% homologous) in sequence to the respective member of the core list of genes .

7. A set according to any of claims 1 to 6 consisting of probes for members of a sub-group of the core group.

8. A set according to any preceding claim in which said probes are in the form of an array and are spatially arranged at known locations on a substrate.

9. A set according to any preceding claim wherein said probes are on a ^"substrate which forms part of or consists of one or more chip plate (s), for use in a chip assay for detection of said gene variants .

10. A set according to any preceding claim in which said probes are mass, electrostatic or fluorescence tagged probes .

11. A set according to claim 8 or 9 in which said substrate is a semiconductor microchip.

12. A set according to any preceding claim for use in a biological assay for detection of said gene variants.

13. A set according to any preceding claim for use in the measurement of differential gene expression levels.

14. A medical device including a set according to any preceding claim for use in an assay for detection of said gene variants.

15. A medical device including a set according to any of claims 1 to 13 for use in an array for detection of differential gene expression levels.

16. A method for use in assessing the genomic profile of a patient or individual, the method comprising testing for and detecting the presence or absence of DNA or RNA encoding the relevant structural variants (as defined in claim 1) in a target group of genes by hybridising a nucleic acid-containing sample from said patient or individual to a set according to any of claims 1 and 3 to 13 and relating the probe hybridisation pattern to said variations .

17. A method for use in assessing the the genomic

. profile of a patient or individual, the method comprising testing for and detecting the presence or absence of DNA or RNA encoding the relevant structural variants (as defined in claim 2) in a target group of genes by interacting an expressed-protein-containing sample from said patient or individual with a set of probes according to any of claims 2 to 13 and relating the probe interaction pattern to said variations .

18. Use of a set or device according to any of claims 1 to 13 for the prognosis and management of patients suffering from or at risk of adverse events .

19. Use of a set or device according to any of claims 1 to 13 for predicting likely therapeutic response and adverse events following therapeutic intervention.

20. Use of a set or device according to any of claims 1 to 13 for predicting likely therapeutic response and adverse events following the intake of a specific drug.

21. Use of a set or device according to any of claims 1 to 13 for predicting likely patterns of symptom clusters (symptom profiles) in disease and the likelihood of subsequent, contingent, disease or symptoms.

22. Use of a set or device according to any of claims 1 to 13 for general health screening, occupational health purposes, healthcare planning on a population basis and other healthcare management utilisations.

23. Use of a set or device according to any of claims 1 to 13 for the development of new strategies of therapeutic intervention and in clinical. trials .

24. Use of a set or device according to any of claims 1 to 13 for construction of and generation of algorithms ^• for patient and healthcare management.

25. Use of a set or device according to any of claims 1 to 13 for modelling or assessing the impact of diseases or healthcare management strategies on individuals, groups, patient cohorts or populations

26. Use of a set or device according to any of claims 1 to 13 for modelling, assessing or exploring the theoretical impact of diseases and healthcare management strategies on individuals, groups, patient cohorts or populations.

27. Use of a set or device according to any of claims 1 to 13 for predicting optimum configuration/management of thereapeutic intervention.

28. A method according to claim 16 or 17 in which the identification of gene variants is indicative of a higher risk of experiencing adverse events for the patient or individual ..

29. A method for generating a model to assess whether a patient or individual or population or group is or are likely to experience adverse events, which method comprises : i) obtaining DNA or RNA or protein samples from patients or individuals diagnosed as suffering from adverse events; ii) obtaining DNA or RNA or protein samples from a control group of subjects diagnosed as not suffering from the adverse events; iii) analysing the samples obtained in i) and ii) to identify the polymorphic variations encoded in the core group of genes as defined in any of claims 1 to 7 ; iv) calculating the frequencies of these alleles in the samples from i) and ii) ; v) comparing the frequencies of these alleles in i) and ii) ; vi) performing a statistical analysis on the results from v) in order to generate a model for assessing the risk of experiencing adverse events.

30. A method for assessing whether a given subject will be at risk of developing symptoms, which comprises comparing said subject's genotype with a model generated by the method of claim 29.

31. A method according to any of claims 16, 17, 29 and 30 wherein at least one step is computer-controlled.

32. An assay suitable for use in a method according to any of claims 16, 17, 29 and 30; said assay comprising means for determining the presence or absence of relevant polymorphic variants of the core group of genes as defined in any of claims 1 to 7 in a biological sample.

33. A formatted. assay technique (kit) for use in assessing the risk of a patient or individual experiencing adverse events; said kit comprising: i) means for testing for the presence or absence or DNA or RNA encoding relevant polymorphic variants of the core group of genes as defined in claim 1 or 3 to 7 in a sample of human DNA; ii) reagents for use in the detection process iii) readout indicating the probability of a patient or individual experiencing adverse events .

34. A formatted assay technique (kit) for use in assessing the risk of a patient or individual experiencing adverse events; said kit comprising: i) means for testing for the presence or absence of proteins encoded by the core group of genes and/or relevant polymorphic variants of the core group of genes as defined in any of claims 2 to 7 in an expressed-protein-containing human sample; ii) reagents for use in the detection process iii) readout indicating- the probability of a patient or individual experiencing adverse events .

35. A set of probes according to claim 26, wherein the probes are selected from the group consisting of oligonucleotides and polynucleotides .