AU2015221486B2 - Optimization and individualization of medication selection and dosing - Google Patents

Abstract

The invention provides population models, methods, and algorithms for targeting a dosing regimen or compound selection to an individual patient. The method and algorithms of the invention utilize population models that incorporate genotype information for genes encoding drug metabolizing enzymes for one or more compounds of interest. The methods allow integration of genotype information for one or more genes encoding a drug metabolizing enzyme, particularly a cytochrome P450 gene with patient data. The methods allow integration of genotype information and the effect of one or more compounds on one or more drug metabolizing enzymes. The methods allow iterative feedback of drug metabolizing data obtained from a patient into the process of generating a dosage regimen recommendation for a compound of interest for an individual patient.

Description

OPTIMIZATION AND INDIVIDUALIZATION OF MEDICATION SELECTION

AND DOSING

CROSS REFERENCE TO RELATED APPLICATIONS

[βββΐ] III© present application claims the benefit ofU.S. Provisional Patent Application, Ser. No. 60/740,430, filed Ncmsnber 29,2005 and of U.S. Provisional Patent Application, Ser. No. 60/783,118, filed March 16,2006, the disclosures of which are incorporated herein by reference.

FIELD OF THE INVENTION

[0002] This invention relates to methods for combining a patient’s genetic information, a patient’s non~fceritebie host factors and candidate medication characteristics to optimize and individualize medication dosage and compound selection.

BACKGROUND OF THE INVENTION

[0003] One of the most important but unresolved problems in therapy with potent and often toxic drugs has been too lack of our ability to describe, understand, and quantify toe important mechanistic relationships ami variability between drag doses, concentrations in blood, concentrations of metabolites in other body compartments, and toe therapeutic and toxic drag effects. For toe most part, defining drag action and mter-pstiaat variability has beam limited to simplistic, less informative descriptions of average maximum and mimmurn drag dose requirements that do not permit hue iadwMatiastkm of toesrspy tor each patient [0004] For some dmp over 90% of toe measurable variation in selected pharmacokinetic parameter» has been shown to be heritable. Traditionally in phsnra^okmetic (PK) anaiytii a mtm of concentrations over time m measured. A structural model is defined sad fit to the tea in onfer to obtain estimates of the desired parameters such as clearance mi volume of distribution. Use model is fitted to the individual data by using a least squares algorithm that minimizes toe difference between observed and toe mode! predfetod c&acmmticm. For reasons of simplicity the assumption is made that differences between the observed and predicted concentrations are caused by raaflw error. With tins traditional type of anatyri», a mote is defined tor each subject and toe intovidtttl pwta am te summarised «sons ijKHvMuais. However, irap^dsioa in the snapl® men and sample standard deviation frequently are greeter than expected, white estimates of variability in tite§® parameter» sm not well characterized.

[6005] The FDA is tecogairing the importance of tire genetic contribution to the inter-individual variation in response to therapy. The® has fee® a jagmfisaBt increase in the number of new tog appfications sent to the FDA containing phannacogenetie Mbrmation (Wendy Chou, Ph.D./FBA April 3,2003). Two package inserts reflect this trend. Thioridazine (Mellaril) which is used for neuropsychiatric conditions is amtmindicated in patients who am CYP2D6 poor metabolizes; this wsmtog is specifically stated in two places in the insert Similarly in multiple places hr the package insert for Atoraoxeiin* (Strattera, a medication used for ADHD), the association between genetic polymorphisms in drug metabolism and adverse drug reactions is stated.

[0000] hr certain ethnic groups as many as 10% of tire adolescent population have a CYP2D6 haplotype that is associated with poor metabolism of many antidepressant medications. See Wong et al. (2001) Aia^e%4.MedJSfflM»iCg 29:401-406. Clinical genomic testing of these individuals has clew implications for their treatment and prognosis. In extreme cases, children who were poor metabolizes® and who wee not identified have had tragic outcomes. These negative case reports have included a reported death of a nine-year-old boy who was not recognized to be a poor CYP2D6 metifeoMzer. The treatment of tins child with fluoxetine continued despite the development of multiple symptoms bee«s§® these symptoms were not recognized m being related to his extremely high mn levels of fluoxetine. Sallee et [0007] Adverse tog reactions occur in 28% percent of hospitalized patients aad hr 17% percent of k>3pitafiz©d children. In a report by Phillips in JAMA, 27 top weremost frequently tiled in adverse drag reaction reports. 59 percent (16/27) of these top were metabolized by at taut one enzyme having a poor metabolize? genotype, 37 pwceot (11/27) were metabolized by CYF2D6, aptoiSsally top acting on the ©safel nervous system. 7¾¾ annual cost of the morbidity and mortality associated witii adverse tog reaction is $ 177,000,000 dollars (Year 2000 dollars).

Clearly drug toxicity is a major health issue with 100,000 deaths a year and 2,000,000 persons suffering permanent disability or prolonged hospitalizations as a result of direct medication adverse reactions.

[0008] Although significant inter-individual variability exists in the response to most medications, medication selection and titration is usually empiric rather than individualized. The main reason that physicians do not incorporate genetic and nonheritable host factors responsible for this inter-individual variability into treatment plans is the lack of applicable, easy to use algorithms that translate the patient's characteristics into clinical recommendations. Thus there is a need in the art for a pharmacokinetic dose individualization technique that is informative, cost saving, and effective.

SUMMARY OF THE INVENTION

[0008a] Accordingly, the present invention provides a computer-integrated method of selecting one or more drugs for a patient for treatment of a disease, wherein the method comprises the steps of: (a) providing a disease matrix, which disease matrix includes columns representing a plurality of possible medications for treatment of the disease, and rows representing different factors from the categories of: (1) disease specific evidence based medicine data; (2) drug specific basic pharmacology characteristics; (3) patient specific advanced pharmacology principles; (4) patient specific environmental factors; and (5) patient specific genetic factors; wherein (i) the factors from the category of disease specific evidence based medicine data consist of disease specific efficacy data and disease specific tolerability data; (ii) the factors from the category of drug specific basic pharmacology characteristics consist of: preclinical toxicity variables such as the drug’s therapeutic index; fundamental clinical pharmacokinetic variables such as the drug’s bioavailability and half-life; and drug safety factors such as the risk of teratogenicity; and the risk of life threatening side effects; (iii) the factors from the category of patient specific advanced pharmacology principles include the potential for particular bidirectional pharmacokinetic and pharmacodynamic drug-drug interactions and the potential for particular bidirectional pharmacodynamic drug-disease interactions; (iv) the patient specific environmental factors involve the potential for particular unidirectional pharmacokinetic or pharmacodynamic drug-environment interactions, including ail forms of environmental exposure ranging from food such as grapefruit juice to herbal/vitamin supplements such as St John’s Wort to voluntary toxic exposures such as smoking or alcohol to involuntary toxic exposures such as second hand smoke and pesticides; and (v) the patient specific genetic factors involve the potential for particular unidirectional pharmacokinetic or pharmacodynamic drug-gene interactions, including all forms of genetic variability including DNA variability, mRNA variability, protein alterations and metabolite alterations; and wherein values are assigned to each cell in the matrix ranging from +1 for a favourable quality/result to -1 for an unfavourable quality/result; (b) weighting the values in the disease matrix by way of the weighting values provided in a weighting vector, wherein the weighting vector has one column and the same rows as the disease matrix, and wherein the values in the weighting vector are determined either by a supervised system such as an expert system or by an unsupervised system such as a neural network or artificial intelligence system; (c) providing a patient vector, which patient vector is either a column of the disease matrix or is a 1 by N matrix, where N is the number of distinct factors in the disease matrix, and entering values in the patient vector, determined by the response to a series of YES/NO/UNKNOWN questions for each of the variables considered, where 0 is entered for no, 0.5 is entered for unknown and 1 is entered for yes; and (d) calculating a ranked list or a predictive index of medications from each one of the medications; wherein the ranking score calculated for each one of the medications is the product of the patient vector and the weighted column values for each one of the medications in the disease matrix.

[0008b] Preferably, the output display includes the top 5 factors contributing positively to the ranking score and the lowest 3 factors detracting negatively from the ranking score.

[0009] Preferably, a method for preparing a disease matrix for use in the method comprising the further steps of identifying a universe of possible medications for the disease phenotype; constructing a disease matrix including columns representing each of said possible medications and rows representing different factors from the categories of (1) disease specific evidence based medicine data; (2) drug specific basic pharmacology characteristics; (3) patient specific advanced pharmacology principles; (4) patient specific environmental factors; and (5) patient specific genetic factors; wherein (i) the factors from the category of disease specific evidence based medicine data consist of disease specific efficacy data and disease specific tolerability data; (ii) the factors from the category of drug specific basic pharmacology characteristics consist of: preclinical toxicity variables such as the drug’s therapeutic index; fundamental clinical pharmacokinetic variables such as the drug’s bioavailability and half-life; and drug safety factors such as the risk of teratogenicity; and the risk of life threatening side effects; (iii) the factors from the category of patient specifi c advanced pharmacology principles include the potential for particular bidirectional pharmacokinetic and pharmacodynamic drug-drug interactions and the potential for particular bidirectional pharmacodynamic drug-disease interactions; (iv) the patient specific environmental factors involve the potential for particular unidirectional pharmacokinetic or pharmacodynamic drug-environment interactions including all forms of enviromnental exposure ranging from food such as grapefruit juice to herbal/vitamin supplements such as St John’s Wort to voluntary toxic exposures such as smoking or alcohol to involuntary toxic exposures such as second hand smoke and pesticides; and (v) the patient specific genetic factors involve the potential for particular unidirectional pharmacokinetic or pharmacodynamic drug-gene interactions, including all forms of genetic variability including DNA variability, mRNA variability, protein alterations and metabolite alterations; and obtaining data and assigning values to each cell in the matrix ranging from +1 for a favourable quality/result to -1 for an unfavourable quality/result.

[0010] Preferably, there is computerized methods and/or computer-assisted methods of targeting drug therapy, particularly dosing regimens and compound selection to an individual subject or patient. The methods incorporate genetic and non-heritable factors into drug selection and titration. Preferably, computational algorithms for recommending a dosing regimen for a particular patient utilizing population models, genotype information, and clinical information. The methods allow iterative integration of patient information and clinical data. The methods provide timely, easy to understand, and easy to implement recommendations. Further proactive identification of patients potentially requiring more in depth assessment by a clinical pharmacology specialist.

[0011] Preferably there is a computerized method and/or computer-assisted method of selecting a dosing regimen for a patient the method that includes the steps of: (a) integrating patient data with patient associated genotype information; (b) generating a drag concentration profile for the patient; (c) integrating the drug concentration profile and the target drug concentration profile; and (d) providing a dosing regimen for a first compound likely to result in the target drug concentration profile in the subject. In a more detailed embodiment, the method further includes the steps of (x) providing a biological sample; (y) monitoring a biomarker in the biological sample; and (z) integrating the biomarker value with the drug concentration profile information. Alternatively or in addition, the patient data may comprise patient demographic data and clinical data. Alternatively or in addition, the clinical data may include information regarding a second compound, where the second compound may modulate metabolism of the first compound. Alternatively or in addition, the first compound may be a neuropsychiatric medication. Alternatively or in addition, the method may further comprise the step of determining the genotype of a patient at one or more loci of interest.

[0012] Preferably, there is a computerized method and/or computer-assisted method for selecting a dosing regimen for a patient, where the method includes the steps of: (a) obtaining patient data; (b) obtaining patient associated genotype information; (c) integrating the patient data with the patient associated genotype information; (d) generating a drug concentration profile for the patient; (e) integrating the drug concentration profile and a target drug concentration profile; (f) providing a dosing regimen for the compound likely to result in the target drug concentration profile in the subject; (g) providing a biological sample from the patient; (h) monitoring a biomarker in the biological sample; (i) integrating the biomarker value with the drug concentration profile information; (j) generating a second drug concentration profile for the patient; (k) supplying a second target drug concentration profile; (1) providing a second dosing regiment for the compound likely to result in the second target drug concentration profile. In addition, the method may further include the step of performing the processes of (f) through (1) at least a second time.

Alternatively or in addition, the method may further include the step of selecting a population model for the patient. Alternatively or in addition, the method may further include the step of generating a probability value for a designated response by the patient.

[0013] Preferably, there is a computerized method and/or computer-assisted method of selecting a dosing regimen for a patient, where the method includes the steps of: (a) generating statistical population models of drug interactions for a plurality of genotypes; (b) obtaining patient associated genotype information; and (c) establishing a dosing regimen by applying the genotype information against the population models. In addition, the step of generating population models may include the use of Bayesian algorithms. Alternatively or in addition, the population models of drug interactions may be defined for a combination of genotypes and non-genetic information.

[0014] Preferably, there is a computerized method and/or computer-assisted method for selecting one or more drugs for a patient that includes the steps of: identifying the phenotype; providing a first plurality of possible medications based upon the identified phenotype; and calculating a ranked list or a predictive index of medications from the first plurality of medications based upon, at least in part, patient specific genetic factors, non-heritable patient factors and drug specific factors. In addition, the calculating step may further consider one or more preclinical toxicity variables, one or more pharmacokinetic variables, one or more clinical efficacy variables, one or more clinical toxicity variables, one or more clinical safety issues, and/or one or more ease of use/adherence variables. In addition, in the calculating step, one or more of the following variables could contribute linearly: TI (therapeutic index - the ratio of (50% lethal dose/50% therapeutic dose) = measure of the drug's inherent toxicity); F (Bioavailability = fraction of the dose which reaches the systemic circulation as intact drug); fu (the extent to which a drag is bound in plasma or blood is called the fraction unbound = [unbound drug concentratiofrftotal drug concentration]); f-BIND-T (fraction of drug that is a substrate for a drug-specific efflux transporter "T"); MET-L (drug with linear metabolism); f-MET-E (fraction of drug that is metabolized by drag metabolizing enzyme "E"); PEX (percentage of drag metabolizing enzyme "E" with functional polymorphism "X"); CLcr (creatinine clearance = the volume of blood cleared of creatinine per unit time = (liters/hour)); IDR (rate of idiosyncratic reactions); FORM (formulation); FREQ (frequency of daily drug administration); MAT ED (maternal education level); SES (socio-economic class); and TRANS (method of transportation to/from clinic). Alternatively or in addition, in the calculating step, one or more of the following variables could contribute exponentially: AT A (number of functional non-wild type transporter polymorphisms for the specific patient); MET - NonL (drug with nonlinear metabolism); AEA (number of functional non-wild type drug metabolizing enzyme polymorphisms for the specific patient); MED-IND (concurrent use of medications that induce metabolizing enzymes); MED-INH (concurrent use of medications that inhibit metabolizing enzymes); DIET-IND (concurrent use of dietary supplements that induce metabolizing enzymes); DIET-INH (concurrent use of dietary supplements that inhibit metabolizing enzymes); NNT-EFF (number need to treat = number of patients who need to be treated to reach 1 desired outcome); META-EEF (results from an efficacy meta-analysis of clinical trials involving medications used to treat a neuropsychiatric disorder); NNT-TOX (number need to treat = number of patients who need to be treated to have a 1 toxicity outcome); and META-TOX (results from toxicity meta-analysis of clinical trials involving medications used to treat a neuropsychiatric disorder).

[0015] It is preferred that the calculating step may involve linear algebra computational science to integrate disease specific evidence based medicine data, drug specific basic pharmacology characteristics, patient specific advanced pharmacology principles, and/or patient specific environmental and genetic factors to produce a ranking of potential medications. In addition, or alternatively, the calculating step may assign, for each potential medication, computational values corresponding to a favorability of utilizing the potential m edication for a corresponding plurality of factors. In addition, the plurality of factors may include factors from a pl urality of the following categories: disease specific evidence based medicine data, drug specific basic pharmacology characteristics, patient specific advanced pharmacology principles, patient specific environmental and patient specific genetic factors. Alternatively or in addition, the plurality of computational values may include positive values for favorable factors and negative values for unfavorable factors, and the calculating step involves adding the computational values to determine a score. Alternatively or in addition, the plurality of computational values may include positive values for favorable factors and negative values for unfavorable factors and weights corresponding to the relative importance of such factors, and the calculating step involves adding the weighted computational values to determine a score.

[0016] It is preferred that the computerized method may further comprise a step of generating an adherence score corresponding to a predicted likelihood that the patient will adhere to a scheduled therapy or prescription.

[0017] Preferably there is a computerized method and/or computer-assisted method for selecting a starting dose of a medication for a patient that includes the steps of: for a given medication, determining if the patient is an extensive metabolizer for the medication, an intermediate metabolizer for the medication, or a poor metabolizer for the medication; calculating the starting dose based upon, at least in part, a usual drug dose for a given population (Dpop), the frequency of extensive metabolizers in the given population (fEM), the frequency of intermediate metabolizers in the given population (fivi) and/or the frequency of poor metabolizers in the general population (fpM); and determining a minimal dose adjustment unit for the medication based, at least in part, upon the patient's genetic information. In addition, the step of determining if the patient may be an extensive metabolizer for the medication, an intermediate metabolizer for the medication, or a poor metabolizer for the medication is based, at least in part, upon the patient's genetic information. Alternatively or in addition, (a) the percent of the usual drug dose Dpop for an extensive metabolizer Dem is

where S is the Area Under the Time Concentration Curve for extensive metabolizor subpopulation divided by the Area Under the Time Concentration Curve for intermediate metabolozier subpopulation, and where R is the Area Under the Time Concentration Curve for extensive metabolizer subpopulation divided by the Area Under the Time Concentration Curve for poor metabolizer subpopulation; (b) the percent of the usual drug dose Dpop for a poor metabolizer Dpm is

(c) the percent of the usual drug dose Dpop for an intermediate metabolizer Dim is

Alternatively or in addition, the minimal dose adjustment unit for the medication may be based, at least in part, upon a number of non-functional alleles, Dem, Dim, and/or DPm,· [0018] Preferably, there is a computerized method and/or computer-assisted method for selecting one or more drugs for a patient that includes the steps of: identifying the phenotype; pro viding a first plurality of possible medications based upon the patient's diagnosis; and calculating a ranked list or a predictive index of medications from the first plurality of medications based upon, at least in part, patient specific genetic factors, non-heritable patient factors and drug specific factors. In addition, the calculating step may involve linear algebra computational science to integrate disease specific evidence based medicine data, drug specific basic pharmacology characteristics, patient specific advanced pharmacology principles, and/or patient specific environmental and genetic factors to produce a ranking of potential medications. Alternatively or in addition, the calculating step assigns, for each potential medication, computational values corresponding to a favorability of utilizing the potential medication for a corresponding plurality of factors, where the plurality of factors may include factors from a plurality of the following categories: disease specific evidence based medicine data, drug specific basic pharmacology characteristics, patient specific advanced pharmacology principles, patient specific environmental and patient specific genetic factors. Alternatively or in addition, the plurality of computational values include positive values for favorable factors and negative values for unfavorable factors, and the calculating step involves adding the computational values to determine a score, where the plurality of computational values may include positive values for favorable factors and negative values for unfavorable factors and weights corresponding to the relative importance of such factors, and the calculating step involves adding the weighted computational values to determine a score.

[0019] Preferably, the method may include a step of generating an adherence score corresponding to a predicted likelihood that the patient will adhere to a scheduled therapy or prescription.

[0020] The present invention provides a computer, a computer system or a computerized tool designed and programmed to perform any or all of the above computer implemented methods. In addition, the computer, computer system or computerized tool may provide a graphical user interface to provide for the collection of appropriate data from users, such as any of the above-discussed factors. Alternatively, or in addition, the computer, computer system or computerized tool may provide a graphical user interface (or any other known computer output, such as a printout) to provide the report, analysis, recommendation or any other output resulting from any of the above-discussed methods.

BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Fig. 1 presents a schematic depiction of the processes involved in a method selecting a dosing regimen for an individual patient.

[0022] Fig. 2 presents risperidone pharmacokinetic profiles for three different dosing regimens for a particular patient. Panel A depicts an exemplary pharmacokinetic model-based simulation of the risperidone concentration time profile. Panel B depicts an exemplary pharmacokinetic model-based simulation of the risperidone concentration time profile after altering the dosing regimen. Panel C depicts an exemplary pharmacokinetic model-based simulation of the risperidone concentration time profile with a third dosing regimen. In each panel a solid line indicates the patient's compound concentration predicted by the methods of the invention in each dosing regimen and the broken line indicates the therapeutic range, in this example arbitrarily chosen to be between 3 and 10 ng/mL. The observed biomarker value is indicated with solid circles or triangles.

[0023] Fig. 3 is an example (very small) segment of a disease matrix for use with an exemplary embodiment of the invention.

[0024] Fig. 4 is a screen shot illustrating a step of an exemplary computer implemented method of the present invention. {0025] Fig. 5 is a scream shot iiustrating another step of m exemplary computer implemented method of it» present imwikm. (0026] Fig. 6 is a screen skit illustrating arsothcr step of sa exemplary confute implemented method of the preseat inventea. {0027] Fig. 7 is a screen shot illustrating another step of m exemplary computer implemented method of the present invention.

[0028] Fig. 8 is a screen shot illustrating another step of an exemplary computer implemented method of the present invention.

[0029] Fig. 9 is a screen shot illustrating moths? step of an exemplary computer implemented method of the present invention. {0030} Fig. 10 is a screes shot illustrating another step of an exemplary compute implemented method of the present invention.

[0031] Fig. 11 is a screen shot illustrating another step of an exemplary computer implemented method of the present invention.

[0032] Fig. 12 is a screen shot iUustating another step of an exemplary compute implemented method of the present invention, [0033] Fig. 13 is a screen shot illustrating another step of an exemplary computer implemented method of the present invention.

[0034] Fig. 14 is a sere® shot illustrating an output report/analysis generated by m exemplary computer implemented method of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0035] Defining and describing the often complex relationships of drag action and intep-patient variability bus historically be very difficult, Developing pharmacokinetic (PK) and ptemacodynaaiic (PD) models of these variables provides a method of defining sad. describing the relationships between drug action and priteat variability, Prather drag or compound actions (effects) are directly related to the drag concentration at the site(s) of action. The» is usually a bettor relationship between the effect of a given drag and its concentration in the blood than between the dose of the (hug given and the effect (§§S€] The invention provides population models for various compounds that incorporate pharmacokinetic and pharmacodynamic models of drag action and i-ntftrpwtiBnt variability· Further the invmtion provides computerized methods and/or computer-assisted methods (including software algorithms) that utilize the one or more population models of the invention to predict a dosing regimen for a particular compound or to predict patient response to a compound, The computerized methods and/or computer-assisted methods (including software algorithms) of the invention generate a pr ediction regarding a subject’s ability to metabolize a compound of interest The computerized methods and/or computer-assisted methods (including software algorithms) of the invention provide for iterative evaluation of a patient’s response to a dosing regimen or compound incorporating slate obtained from monitoring at least one suitable biomark®. Often subjects receive more than one medication. These additional medications may affect the subject’s ability to metabolize a compound of interest Thus, in an embodiment computerized methods and/or compute-assisted methods (including softwa» algori thms) of ft® invention provide a means of integrating information regarding such an additional compound or compounds and the effects of such an additional compound on II» subject’s ability to metabolize a compound of interest 10037] A “compound” comprises, but is not limited to, a drag, medication, agent, therapeutically effective agent, neurapsycMairic medications, Bsurotmmto inhibitors, neurotransmitter receptor modulators, G-protekss, G-protem receptor inhibitors, ACE inhibitors, honnone receptor modulators, alcohols, reverse tnmsoiptes® inhibitors, nucleic add molecules, aldostsroae antagonists, i»lyp@ptktes* peptides, peptidomimetios, glycoproteins, transcription factors» mall molecules, dieraokm® receptors, antisense nucleotide sequences* ehemokme receptor ligands, lipids, antibodies, receptor inhibitors, ligands, stools, steroids, hormones, chamokme receptor agonists, chemokme receptor aatefoisista, agonists, antagonists, ion-channel modulators, diuretics, enzymes, mzym® inhMtes, carbohydrates, deaminases, deaminase inhibitors, hormones, phosphatases, lactones, and vasodilators. A compound may additionally comprise a pharmaceutically acceptable cam» !§#5SJ Neuropsychiatric medications include, but are not limited to, antidepressants, mood elevating agents, norepmephrineHWuptake inhibitors, tertiary mnmm tricyclics, amitriptyline, clomipramine, doxepm, imipramine, secondary ami» tricyclics amoMpine, desipramiae, maprottlins, protriptyline, nortriptyline, selective serotonin-reuptake inhibitors (SSRls), fluoxetine, fluvoxamine, paroxetine, sertraline, eitalopram, eseitalopram, venlafaxine, atypical antidepressants, bupropion, nefazodone, trazodone; noradrenergic and specific serotonergic antidepressants, mirtazapine, monoamine oxidase inhibitors, phenelzine, tranylcypromine, selegiline; antipsychotic agents, tricyclic phenothiazines, ehlorpromazine, triflupomazine, thioridazine, mesoridazin©, fluphenazinc, trifluopersme, thioxanthraies, cMorprothixesne, clopenthixoL, flupenthixol, plflutixol, thiothixene, dibenzepines, loxapine, clozapine, clothiapme, metiapine, zotapine, fluperlapine, olanzapine, butyrophenones, haloperidol, diphenylbutylpiperidines, fluspiriteae, penfluridol, pimozide, haloperidol decanoate, indolones, neuroleptics, anti-anxiety/sedative agents, benzodiazepines, chlordiazepoxide, diazepam, oxazepam, clorazepate, lorazqpam, pmepairt, alprazolam, and faalazepam; mood stabilizing agents, lithium salts, valproic acid; attention deficit hyperactivity disorder agents, dextroamphetamine, mothylphenidsie, pemoline, ami atomoxetine; anticonvulsants, phcnobarbitai, phenytoin, earbamazepins, valproic acid, felbamate, gabapentin, tiapbise, kmotrigin®, iopiramate, zonisamide, oxcaibazepme, levetiraeetam, pregabatin, ©thotok, and peganoae; headache medications, ibuprofen, aspirin/ aoetometaphen/ caffeine, diclofenac, kctoprofen, ketorolac, fluriaprofm, meclofenamatc, naproxen, ergotamke tartrate, dkydroergotamke, ©rgotamiae, acetometaphen/ isometheptene mucate/ tiichtoralphenazone, sumatoptaa succinate, zobnitriptan, rizatripten, naratriptan hydrochloride, ataotriptan, frovatriptan, eletripisn, dtcMenac, fimoprofe», fiurbij«o£ea, kepsprofen, naproxen sodium, amitriptyline d*»pramke, doxepk, imipramine, amtripiyime, fluoxetine, paroxetine, sertraline, venlafaxine, trazodone, bupropion, atenolol, mctoprolol, nadolol, propranolol, timolol, dfitiazem, sasard^jke, mfedipine, tamoM§mk verspumsl, divalproex sodium, gabapentm, valproic add, md topiramate; and dementia medications, tacrine, doneperil, galantamine, gskdhseli^ rivastigmiae, mi ’ memantine.

[ΘΘ39] By “drag” is intended a chemical entity, biological product, or combination of chemical entities or biological products administered to a person to teat, prevent, or control a disease or condition. The term “drug” may include, without limitation, spate that am approved for sale as pharmaceutical products by government regulatory agencies such as foe U.S. Food and Drag Admimstratfon, European Medicines Evaluation Agency, agents that do not require approval by a government regulatory agency, food additives or supplements including agents commonly characterized as vitamins, natural products, and completely or incompletely characterized, mixtures of chemical entities including natural agents or purified or partially purified natural products. It is understood that ft© methods of the invention are suitable for use with any of the drugs or compounds in the 2005 Physicians Desk Reference, Thomson Healthcare 59&.edL, herein incorporated by reference in its entirety.

[0949] The computerized methods and/or computer-assisted methods (including software algorithms) of the invention utilize subject or patient associated genotype information- The term “genotype** refers to the alleles present in genomic DNA from a subject or patient where m aUete can be defined by foe particular nucleotide(s) present in a nucleic add sequence at a particular sites(s). Often a genotype is foe nueleotkteCs) prescat at a single polymorphic site known to vary in the human population. By “genotype ΜοΜηβΙϊοη” is intended information pertaining to variances or alterations in the genetic structure of a gem or locus of interest Genotype information may indicate the presence or absence of a predetermined allele. A ‘loci of interest” may be a gene, allele, o? polymorphism of interest Genes or lod of interest include genes that meed# a) medication specific metabolizing enzymes, b) medication specific transportera, c) medication specific receptors, d) enzymes, transporters or receptors affecting other drop that intact with fo® snedrctafioii in question or e) body functions fork affect that activities of the medication in question. In an embodiment of foe invention lod of interest include» but are not limited to, five cytochrome P450 genes, foe serotonin transporter pan, foe tis^inaa® transporter g«se, and the dopaniBie receptor genes. The five cytochrome P450 g«»§ saa encode CYP2D6, CYP1A2, CYP209, CYP2C9 «id CYP2E1. Alleles of particular asteit include, but are not limited to, the CYPLA2*1A or 1A2*3 allele, the CYP2C59*1A, 2C19*1B, or 2C19*2A allele, sad the CYP2D6*1A, 206*2,2D6*2N, 206*3,2D6*4, 2D6*5,206*6,2D6*7,2D6*8,206*10,2D6*12, or 206*17 allele, The serotonin receptor genes encode serotonin recepton ΙΑ, IB, ID, 2A, or 2C and die dopamine receptor genes encode dopamine receptor* Dl, D2, D3, D4,05, and 06. The serotonin transported gene is ate an important part of the genotype. Additional genes, alleles, polymorphisms, and loci of interest are presented in Tables 1 and 2.

[3041}

p§43J Is m saatjodkaeat of the invention, file computerized methods asd/or . coa^sr-ewiled method$ (iadodkf software dgmtihsm) are litilized to select & dosing regimen for a patient in need of a llcuκφsychiatric medication. Am&jorgme in the neuropsychiatric panel is CYP2D6. Substrates of CYF2D6 typically are weak bases with the «tioais binding site located amy from the carbon atom to be oxidized.

In particulars substrates of CYP2D6 include amitriptyline, mmiptyUm, Moperidol, and dasipramine. Some individuals have altered CYP2D6 geae sequences that result in synthesis of enzymes devoid of catalytic activity or in enzymes with diminished, catalytic activity. These individuals metabolize SSRIs and tricyclic antidepressants (TCAs) poorly. DupUcadon/muitipUcation of the functional CYP2D6 gme also has been observed and results in ultrarapid metabolism of SSSIs and other dregs. Individuals without inactivating polymorphisms, deletions, or duplications have die phenotype of an extensive drag metabolizer arid are designated as CYE2D6*1. The CYP2D6*3 and *4 alleles account for marly 70% of the total deficiencies that result in the poor metabolizes phenotype. The polymorphism responsible for CYP2D6*3 (2549A>del) produces a frame-shift in the rriRNA. A polymorphism involved with the CY?2D6*4 allele (1846G>A) disrupts mKNA splicing. These changes produce truncated forms of CYP2D6 devoid of catalytic activity. Other poor metabolizers are CYP2D6*5, *10, and *17. CYP2D6*5 is due to complete gem deletion. The polymorphisms in CYF2D6* 10 and * 17 produce amino acid substitutions in ft© CYP2D6 enzyme which have decreased enzyme activity. All of these polymorphisms are autosomal co-dominant traits. Only mtimduals who are homozygous or who are compound heterozygous for these polymorphisms are poor metabolizers. Individuals who are heterozygous, with one normal gen® aid one polymorphic gene, will have metabolism intermediate between foe extensive (normal) and poor metabolizers. Individuals who are heterozygous for duplkation/mtti%ficatioit alleles are ultra-rapid metabolizers.

[0044] CYP1A2 metabolizes many aromatic and heterocyclic anilines mrtm-tmg clozapine gmd rmipranilrne. The CYP1A2 * IF allele can result in a product with higher indudbility or increased activity. (See Sachs® et aL (1999) Br, J, Chik Phamaacol. 47:445-449). CYP2C19 dso metabolizes mmy substrate including infipramine, citalopram, ami diazepam. The CYP2C19 *2A, *2B, *3, *4, *5A, *5B, *6, *7, and ':'8 alleles encode products with little or no activity. See Theanu et aL (1999) I. PharmacoL Bm Thfir.J290:635-640.

[0045] CYP 1A1 can be associated with toxic or allergic reactions by extrahepatic generation of motive metabolites. CYP3A4 metabolizes a variety of substrata including alprazolam. CYP1B1 cm be associated with toxic or allergic reactions by extra-hepatic generation of reactive metabolites and also metabolize* steroid hormones (c.g., 17-isiradiol). Substrates for CYP2A6 and CYP2B6 iaelucie valproic add .and bupropion, respectively. Substrates fra· CYP2Q? mclud® Tylenol and antabus© (disulfbram). Substrates for CYP2E1 include phenytom and carbamazepine. Decreases in activity in rase or more of foe cytochrome P450 enzymes can impact om or more of foe other cytochrome P430 enzymes.

[9044] Methods of detaffibiiig genotype information are known in foe art Genotype information obtained by any method of ddemsining genotype known in the art may be employed in the practice of foe invention. Any means of detennming genotype known in the art may be used in foe m ethods of the invention.

[0947] Generally genomic DNA is used to determine genotype, although mRNA aatlysii has been used as a screening method in some case». Routine, commercially available methods can be used to extract genomic DNA flora a blood or tissue sample such as foe QIAamp@ Tissue Kit (Qiagen, Chatswortfa, CA), Wizard@ Genomic DNApurificationldt (Promega) and foe A.S.A.P.TM Genomic DNA isolation lat (Boehrmger Mannheim, Indianapolis, IN).

[9949] Typically before foe genotype is drtaaBined, enzymatic amplification of foe DNA segment containing the loci of interest is performed, A common type of enzymatic amplification is foe polymerase chain reaction (PCR). Known methods of PCR include, but arc not limited to, methods using paired primers, nested primers, single specific prime», degenerate prim», gene-specific primers, vector-specific prim», partially-mismatohed prime», and foe like. Known methods of PCR include, but are not limited to, methods using DNA polymerases from extremophiSes, engineered DNA polymerases, and long-range PCR. It is recognized that it is preferable to use high fidelity PCR reaction conditions in foe methods of foe invention. See also lanis et al., eds. (1990) PCR Protocols: A Guide to Methods and Applications (Academic Press, New York); Innis and Gelfand, eds. (1993) PCR Strategies (Academic Press, New York); Ibbm sad Grifimd, Λ (1999) PCR Msfoods

Dieffafostoh, C. «ad Dvekskr, G., Cold Spring Harbor laboratory Press, 1995. Long mage»PCS amplification method^ melody methods such as those described in file TaKaRa LA PCR guide, Takara Shnzo Co., Lid.

[0049] When using RNA m a source of template, iww transcriptase can be used to synthesize complementary DMA (cDNA) strands. Ligase chain reaction, strand displacement amplification, self-sustained sequence replication or nucleic add sequence-based amplification also can be used to obtain isolated nucleic acids. See, for example, Lewis (1992) Genetic Engineering News 12(9):1; GoatelK et ai. (1990) Proc. Natl. Acad. Set USA 87:1874-1878; and Weiss (1991) Science 254:12921293.

[0050] Methods of determining genotype include, but are not limited to, direct nucleotide sequencing, dye primer sequencing, allele specific hybridization, allele specific restriction, digests, mismatch cleavage reactions, MS-PCR, allele-specific PCR, and commercially available kite such as those for die detection of cytochrome P450 vaiiants (TAG-ITTM kits are available from Tm Bioscienees Corporation (Toronto, Ontario). See, Stoncking et ai, 1991, Am. J. Hmn. Genet, 48:370-382; Prince et al, 2001, Genome Res. 11(1):152-162; and Myakishev et al, 2001, Genome 11(1):163-169.

[0051] Additional methods of determining genotype include, but are not limited to, methods involving contacting a nucldc add sequence corresponding to one of fee loci of interest or a product of such a locus wife a probe. The probe is able to distinguish a particular form of the pm or fee gem product, or fee presence of a particular variance or variances for example by differential binding or hybridization. Thus, exemplary probes include nucleic add hybridization probes, peptide nucleic acid probes. nucleotide-containing probes feat also contain at least one nucleotide analog, and antibodies, such as monoclonal antibodies, and otter probe. Those skilled in fee art are familiar wife fee preparation of psobas wife particular specificities. One of skill in fee art will recognize feat a variety of variables can be adjusted to optimize fee discrimination between variant forms of a gene including changes in salt concentration, pH, temperature, and addition of various agents feet affect fee differential affinity ofbase pairing (see Amabel et al., eds. (1995) Current Protocols in Molecular Biology, (Grew® Publishing end Wiley-Intadmse, New York).

[0052] The exemplary computerized methods and/or computer-assisted methods (including software algorithms) of fee invention may employ fee following rationale. The phannacokfedic characteristics of a compound, particularly a netwopsycMahic drag, affect fee initial dose of a compound more than fee compound’s pharmacodynamic properties. A compound's phmrnmokmaik· profile is a dynamic summation of its absorption, distributions mefetoMsaa, and excretion. Genetic differences in drag mefeboBzing enzymes (DME) tot affect enzyme activity mid feus drag metabolism constitute a major component of most compounds’ pharmacokinetic variability. DMEs include, but are not limited to, a) medication specific metabolising enzymes., b) medication specific transporters, c) medication specific receptors, d) enzymes, transporters or receptors affecting other drags feat interact wife fee medication in question or e) body functions feat affect tot activities of the medication in question. Most compounds’ absorption, distribution, and excretion characteristics are independent of fee genetic variability in DME activity. Specific DME polymorphisms affect fee metabolism of most compounds in a reproducible, predictable, uniform manner. Typically a detectable polymorphism in a specific DME will either have no affect or will reduce enzyme activity. Thus, the subject will have either: 1. two functional alleles (a wild-type, normal, or extensive metabolizar); 2. one functional allele (an intermediate metabolizes;); or 3. no functional alleles (a poor metabolize).

Additionally for certain geo», such as CYP2D6, multiple copies of to gene nay be present. In such instances, to presence of more than two functional alleles for a particular gene correlates wife .an ultaspid metabolize state. f §ftS3] Frequently more than one DMEs working either in series or in parallel metabolize a particular eompowid. The effect of genetic variability for each DME cmbedetemiiMsliiid^<tob^tyarMls>MBl«a»d, The invention provide® methods of combining or integrating the genetic variability effect for each DME or DMEs tot funetkm sequentially or concummtiy. The methods of to invention utilize Bayesian

Imputation phamiacokmetic modeling sad analysis to integrate and predict the effects of multiple DMEs on metabolism of a particular compound. l#t54| Also, the concurrent use of more than om compound cm »ff©et fe® activity of a subject's DMEs. Again, the effect of genetic variability for each DME can bo determined independently for each compound The computerized methods and/or computer-assisted methods {including software algorithms) of the invention utilize Bayesian population pltasacoMiieic modeling and analysis to intqpute and predict the effects of multiple compounds on one or more DMEs. 11» methods of the invention allow the integration of information about the genetic variability of one or more DMEs Mid one or more compounds to generate an area under the time concentration curve (AUC) value. The AUC value reflects the amount of a particular compound accessible to a patient and is the clinically important variable. i0®55j The AUC value is determined by drug dose and patient specific phamiamta‘««ties- Prior to this invention, medical practise utilized a “one size fits all” approach teat kept the drug dose constant In the “one size fits all” approach, variability in pharmacokinetics among patients leads to variability in AUC that results in intespatient clinical variability such as side effects or variable efficacy levels. Thus the methods of the invention provide a means of selecting compound dosing regimens that provide patients with simitar AUC values. The methods of the invention integrate the number of genetic variation* to be included, the population frequency for each genetic variation, and AUC data for each genetic variation. The methods of the invention transforms a heterogenous population into multiple homogenous subpoputations. Such homogenous sufepoputatiom, suitable dosing regimens, and suitable compounds can be described in a population profile of the invention fiiSfl By “dosing regimen" is intended a combination of factors including “dosage level” and “frequency of administration”. An optimized, dosing regimen provides a therapeutically reasonable beta*» between pharmacological effectiveness and deleterious effects. A “frequency of adriiiaistratio»” refers to how often in a specified time period a treatment is adatinstand, ©.g., once, twice, or dire» times per day, every other day, every other week, etc. For a compound or compounds of interest, a frequency of ahaim^stifia is eta»» to adaeve a pbarmacologfcaily effective average or peak serum level without excessive deleterious effects. Thus, it is desirable to maintain ύic serum level of the drag within a therapeutic window of concentrations for a high percentage of time.

[0057] The exemplary software program of the invention employs Bayesian methods. The Bayesian methods allow fewer drug mmuremeuis for individual FK parameter estimation, sample sizes (e.g. one sample), and random samples, Therapeutic drug monitoring data, when applied appropriately, cm also be used Id detect and quantify clinically relevant drug-chug intemotrom. Time methods are more informative, cost-saving, and reliable than methods relying on simply reporting results as below, within or above a published range.

Determining a predictive index called the “simplicity index”

Definitions:

The following abbreviations and definitions will be used in the construction of the simplicity index - the variables are grouped by common themes:

Preclinical Toxicity variables 1. TD50 ** called “50% therapeutic dose” =» the dose of the medication that results in 50% of the animals Med achieving the desired therapeutic outcome 2. LD50 » called “50% lethal dose” * file dose of the medication that results in 50% of tire animals tested dying 3. TI = called therapeutic index ” the ratio of LD50/TD50 » a measure of the drug’s inherent toxicity

Pharmacokinetic variables 4. F * Bioavaikbility »fraction of the dose which reaches the systemic circulation as intact drug 5. fu = The extent to which a drag is bound in plasma or blood is called the fraction unbound - [unbound drag concentratk>n]/[totol drag concentration] 6. f-BIND-T » friction of drug that is a substrate for a drag-specific efflux transporter “T” 7. ΡΤΧ» percentage of transporter‘T*’ with functional polymorphism 8. ΑΤΑ » number of functional noa-wild type transporter polymorphisms for Λ® specific patient 9. MET-NonL* drag wife noa-fiMK kmM>oK«i 10. MET ~ L =* drag wife finear metabolism 11. i-MET-E * fiaciion of drug fits! is metabolized by drag mefetxfeziag enzyme “E” 12. PEX« parceategs of drug metabolizing aozyme “Ew wife functional polymorphism “X” 13. AEA » number of functional non-wild type drag metabolizing enzyme polymorphisms for the specific patient

14. AUC = Total area under the plasma drug concentration-time curve = mg*hour/L

15. CL - clearance * foe volume of blood cleared of drag per unit time * (Bters/hour), CL53 dose/AUC 16. CLsr®* creatinine clearance “ the volume of blood cleared of creatinine per unit time585 (Mters/hour) 17. MED-IND = concurrent use of medications that induce metabolizing enzymes 18. MED-INH = concurrent use of medications that inhibit metabolizing enzymes 19. DIBT-IND *> concurrent use of dietary sa^lenients that induce metabolizing ismymm 20. DIET-INK * concurrent me of dietary supplements that inhibit metabolizing enzymes

Clinical efficacy variables 21. NNT- EFF * number need to treat »the number of patients who need to be treated to reach 1 desired outcome 22. OR « odds ratio * a measure of foe degree of association; for «sample, the odds of reaching A® desired outcome among fee treated cnees compared wife the odds of not reaching fee desired outcome among fee controls 23. META-EPF - results from an efficacy meta-analysis of clinical trial* involving medications used to treat a nemropsychiatric dtsoKkr Clinical toxicity variables 24. NNT- TOX = numb*» need to treat - the nm*«Ofpsdi«Edgwiio need to be tested to .lave 1 toxicity outcome 25. OR * odds ratio * a measure of the degree of association; for example, the odds of reaching the drug toxicity among the treated cases compared with the odds of not reaching drag toxicity among the controls 26. META-TOX ® results from a toxicity meta-analysis of clinical trials involving medications used to treat a neuropsychiatria disorder

Clinical Safety issues 27. IDll = rate of idiosyncratic reactions

Ease of Use/'Adherence variables 28. FORM * formulation 29. FREQ = frequency of daily drag administration 30. MAT ED * maternal education level 31. SES = socio-economic class 32. TRANS = method of transportation to/firom clinic [§§§§1 An algoriftm can be vmd to rank the most appropriate medications for an individual patient The design of the algorithm requires the initial identification of the phenotype, which provides a preliminary identification of the universe of possible medications. At the next stop of the algorithm, fiw results of ft® target gone analyses can be sequentially entered. The algorithm (hat produces the predictive index (called ft® “simplicity index”) combines the above factors using to following principles: 1. Each factor contributes differentially based on weighting ami scaling variables detemtod during to validation process.

2. The following variables contribute linearly to to final ranking score: Ή, F, fe, f-BIND-T, MET - L, f-MET-E, PEX, CL* IDR, FORM, FREQ, MAT ED, SES, TRANS 3. The following variables contribute e%ponmtial.ty to to final raakisg

score: ΑΤΑ, MET - NonL, AEA, MED-1MD» MED-INH, DIET-IMD, DIET-INH, NNT“ EFF, META-EEF, NNT- TOX, META-TOX

The algorithm produce a rank list of medications based on the above patient specific genetic factors, non-hsritabie patient factors and drag specific factors. An exemplary software tool for determining such a predictive index, called fire “simplicity index,” is described in detail below.

The following abbreviations and definitions will be used in 11½ determination of the initial starting dose:

AbbfgviatleMit

Dw * the perceived usual drag dosage for the general population Extensive metabolizers EM ® «.tensive metabolaer f em =* frequency of «tensive metabolizers in the general population

Dem 31 Drag dosage for extensive metabolize subpopulatkm AUCem - Area Under the Time Concentration Curve for «tensive metabolizer subpopulation

Intermediate metabolizers M - iniennediats metabolizer f m 38 frequency of mtenaediate metabolizers in the general population

Dm =» Drag dosage for intermediate metabolizer sul^opuktion AUCim * Are® Undear foe Time Concentration Curve for ktetmediate metabolizer subpopulation

Poor metabolizers PM « poor nsstabolizer f km ® frequency of poor metabolizes in foe general population

Djm - Drag dosage for poor metabolizes stfopopulation

AUCm * Ares Under foe Time Concentration Curve for poor metafeolizM subpopulation (0959] The following sestk® d*wsib«s how the dosing for foe mom homogeneous subgroups is determined; the dosing results are expressed as a fraction, of the clinician's usual heterogeneous whole group dosages.

[0666] For any one specific polymorphic DME (assuming all other relevant polymorphic DME have normal activity), foe usual drug dose seen in a population is the weighted summation of foe drug dosages in each genetic different subpopuiatioE expressed in equation 1: (SeeKiiclfoeBMr Acta Psyetefo Scaad 21X11:104:173-192 BUT note authors made mistake in non-nvmh&amp;ed equation between Equations 1 and 2, page 178): (Equation il

5§t€3J Assuming ih® goal is to maintain foe same AUC for all three subpopuktioBS of patients, the following subpopulation dosing relationships hold:

(6062] By substituting equations 2 and 3 into equation 1, and then rearrangieg foe equation to solve for foe percent dose adjustment needed for each subgroup relative to foe population dose:

(Equation (Equation (Equation

Equations 4, 5» and 6 show how ft® dosing for ft® more temegeneous subgroup Is determined and how the dosing results are expressed, as a fraction of the dinitiaa’s usual h*terog<emem whole gm·up dosages. (00631 The cumulative effect of various genetic or environmentally basal alterations in DME activity will result in interpatient variability m subsequent drug dosing requirmnents. If the variability is large ©sough, thea ‘tone size fits all” dosing approach can cause noticeable toxicity in some patients and lack of efficacy in others.

In this situation, clinicians alter their drag prescribing or drag dosing behavior. We d«»fin« the smallest clinically relevant dosing change used by clinicians to compensate for thi« intarpattent variability as the “minimal dose adjustment unit*’ (MDA unit).

[0064J The MDA unit for naiiopsycMatric drags is 20%. This means that a clinician will alter their dosing of nearapsychiatric medications in response to specific information if the dosing change is 20% or greater. Perturbations that either singly or in combination suggest a < 20% change in dosing of neuropsycMatric medications are usually ignored. (0065] MDA units are additive - so that a patient with on© MDA unit from a genetic polymorphism aid one MDA unit from a drug interaction needs a 40% reduction in dose. . (0066] Example: The approach in the previous section leads to individualized initial drag dose recommendations for each of ft© 3 subgroups (extensive, poor and intermediate metabolizes»). Each subgroup represents a specific number of functional alleles for the specific DME (extensive metabolises» have 2 functional, intermediate metabolize*» have 1 functional and poor meteboiizers lave 0 functional). The resultant dosing recommcraktioas are expressed m percentages of Site clinician’s usual starting dose. It is possible to investigate the effect of increasing numbers of non-frmctional alleles using these new dating recommendations. For example, if DRx% is the doting recommendation for subgroup X expressed as a percentage of the clinician’s usual stating dose then the following are true:

Effect of claim 1 non-functional allele * (DRem% * DR jm%)/DR em%

Effect of 2 non-functional allele * (DR em% - DR |^|%)/DR km%

Below is a spreadsheet (Table 3) that examines this for CYP2D6, CYP2C19 and CYP2C9. The summary table below demonstrates·, a. it is apparent that each additional nonfunctional allele altos dosing recommendation by at least 20% b. there is a “genetic dose” - “dosing reduction” relationship that appeals constant across these 3 CYP450 genes. This approach can be used to solidify the importance of subsequent DM genes and to quantify their effect in MDA units. c. 2D6 and 2C19 have 1 MDA unit per non-functional allele d. 2C9 has 2 MDA units per non-functional allele. This implies that drug metabolized through 2C9 have vciy large variability in dosage requirements. This confirms the clinical impression about these drugs (warfarin, phenytoin).

[0067]

Table 3

[0068]

Table 4 Relationship between non-functional alleles and dose reduction

Determining final dosage requirements [0069] For some drugs, there is vary little pharmacokinetic genetic variability but rather clinically relevant pharmacodynamic genetic variability most likely at the drug’s receptor. For these medications, the impact of genetic testing will be reflected in the final dosage requirements instead of the initial dosage requirements.

[0070] Studies that demonstrate this gcmeiic-pharmacodynarmc effect will be captured in the software tint encodes the calculations used to derive the simplicity index described earlier. This invention will incorporate this information and report not only the rank simplicity index of tire potential drug candidates but also those candidates that would require a higher than expected dosing requirement to achieve the desire effect.

Population models [0071] The purpose of population pharmacokinetic modeling is to describe the statistical distribution of pharmacokinetic parameters in the population under study and to identify potential sources of intra- and inter-individual variability among patients. Population modeling is a powerful tool to study if and to what extent, demographic parameters (e.g. age, weight, and gender), pathophysiologic conditions (e.g. as reflected by creatinine clearance) and pharmacogenetic variability can influence the dose-concentration relationship. A population pharmacokinetic analysis is robust, can handle sparse data (such as therapeutic drug monitoring data) and is designed to generate a full description of the drug’s PK behavior in the population. A “population model” of the invention provides a description of the statistical distribution of at least one pharmacokinetic parameter in a given population and identifies at least on potential source of variability among patients with regards to a particular compound or agent. A population model of the invention may further provide mean parameter estimates with their dispersion, between subject variability and residual variability, within subject variability, model misspecification and measurement error for a particular compound.

[0072] An embodiment of the invention provides several novel population models for predicting a medication concentration-time profile and for selecting a dosing regimen based on a user-entered target range (see examples). Hie computerized methods and/or computer-assisted methods (including software , algorithms) of the invention employ population models such as, but not limited to, the novel population models of the invention and externally developed population models. In an embodiment, such externally developed population models are adjusted or rearranged in such a manner that they can be programmed into the software of the invention.

[0073] In various embodiments, the computerized methods and/or computer-assisted methods (including software algorithms) of the invention comprise the step of monitoring a biomarker. By “biomarker” is intended any molecule on species present in a patient that is indicative of the concentration or specific activity of an exogenous compound m the subject Biomarkers include, but are not limited to, a compound, a metabolite of die compound, an active metabolite of the compound, a molecule induced or altered by administration of fee compound of interest, and a molecule that exhibits an altered cytological, cellular, or subcelluiar location concentration profile in after exposure to a compound of interest. Methods of monitoring biomsrkers are known in the art and include, but are not limited to, therapeutic drug monitoring. Any method of monitoring a biomarker suitable for the indicated biomarker known in fee art is useful in fee practice of the invention.

[0074] Exemplary computerized methods and/or computer-assisted methods (including software algorithms) of fee invention use data generated by therapeutic drug monitoring (TDM). TDM is fee process of measuring one or more concentrations of a given drug or its active metabolites) in biological sample such as, but not limited to, blood (or in plasma or serum) with the purpose to optimize the patient’s dosing regimen. The invention encompasses any means of measuring one or more concentrations of a given drug or its active metabolite(s) in a biological sample known in the art By “biological sample” is intended a sample collected from a subject including, but not limited to, tissues, cells, mucosa, fluid, scrapings, hairs, cell lysates, blood, plasma, serum, and secretions. Biological samples such as blow! samples can be obtained by any method known to one skilled in fee art.

[0075] The following examples are offered by way of illustration and not limitation.

EXPERIMENTAL

Example 1. Optimization of [0076] An 11 -year-old boy wife autism was started on risperidone (Risperdal®) therapy, at 0.5 mg two times a day, The patient’s pressured speech and labile mood did not improve wife time. The lack of efficacy could be due to insufficient coverage or to non-compliance The patient’s dosing regimen, was analyzed by the methods of this invention.

[0077] The patient demographic data (age, sex, weight) and the risperidone dose and times of administration were entered into the program. A population model was selected. The population model selected was a Risperidone model based on data of pediatric psychiatry patients. As risperidone is metabolized by CYF2D6, there are 3 models: one for extensive metabolizers (EM model), one for intermediate metabolizers (IM model) and one for poor metabolizers (PM model).

[0078] The genotype of the patient was determined and found to be CYP2D6 *1/*1. This genotype fit the extensive metabolizer (EM model). The patient’s data and the genotype were analyzed by an algorithm of the invention and a drug concentration profile for the patient was generated. An exemplary pharmacokinetic model-based simulation of the risperidone concentration time profile based on this patient’s data is shown in Fig. 2a. The average concentration was predicted to be around ~2 ng/mL. This information is integrated witii a target drug concentration profile or therapeutic value. The therapeutic value for risperidone ranges between 3 and 10 ng/mL. Comparison of the drug concentration profile for the patient and the target drug concentration profile indicated that if the patient were adherent, the dose may be too low. The algorithm generated two recommendations: the dose can be increased and a biomarker should be monitored.

[0079] The risperidone dose was increased to 1 mg given twice a day (morning and evening). In addition, a biomarker evaluation was performed. Drug levels were ordered and therapeutic drug monitoring were performed. The pre-dose level and two post dose levels (lh after dose) and (4h after dose) were measured. These date were entered in the software program. The software program performed a Bayesian recalculation based on the a priori information from the model in combination with the new patient specific information (i.e. the drug levels). Exemplary results of this Bayesian update are shown in Fig. 2b. The concentrations were not within the target range for the major part of the dosing interval. Depending on patient’s response this would allow for further increasing the dose. The pharmacokinetic simulation also indicated that this patient has a rather rapid elimination of file drug form the body. The software program generated several recommendations. In order to maintain the target concentration more frequent dosing lias to be considered. Based on the Bayes pharmacokinetic estimates for this patient and given the chosen target range the dosing regimen that best meets the criteria would be 1.5 mg dosed every S hours. An exemplary model-based profile and subsequent Bayesian individualization process are shown in Fig. 2c.

[0080] The above-described methods according the present invention can be implemented on a computer system such as a persona! computer, a client/server system, a local area network, or the like. Th® computer system may be portable including but not limited to a laptop computer or hand-held computer. Further the computer may be a general purpose system capable of executing a variety of commercially available software products, or may be designed specifically to run only the drug identification and selection algorithms that are the subject of this invention. The computer system may include a display unit, a main processing unit, and one or more input/output devices. The one or more input/output device may include a touchscreen, a keyboard, a mouse, and a printer. The device may include a variety of external communication interfaces such as universal serial bus (USB), wireless, including but not limited to infrared and RF protocols, serial ports and parallel ports. The display unit may be any typical display device, such as a cathode-ray tube, liquid crystal display, or the like.

[Θ081] The main processing unit may further include essential processing unit (CPU) in memory, and a persistent storage device that are interconnected together. The CPU may control the operation of the computer and may execute one or more software applications that implement the steps of an embodiment of the present invention. The software applications may be stored permanently in the persistent storage device that stores the software applications even when the power is off and then loaded into the memory when the CPU is ready to execute the particular software application. The persistent storage device may be a hard disk drive, an optimal drive, a tape drive or the like. The memory may include a random access memory (RAM), a read only memory (ROM), or the like. toaElExSmEildEinte^&amp;QEJtefil {0082] As introduced above m algorithm used to construct the drug predictive index (“simplicity index”) utilizes an initial identification of the disease phenotype (e.g. epilepsy, depression, etc.), which provides a prelimmaiy identification of the universe of possible medications for that condition. An exemplary software tool for producing the simplicity index uses linear algebra computational science to integrate disease specific evidence based medicine data, drug specific basic pharmacology characteristics, patient specific advanced pharmacology principles, and patient specific environmental and genetic factors to produce a ranking of potential medications for an individual patient based on these factors. There are separate algorithms for each disease phenotype but the algorithms can be run simultaneously. Further, in the exemplary embodiment, there are fores components used to produce foe final ranking score: a disease matrix, a patient vector and a weighting vector. Each of the five factors and three components will be defined below followed by an example with a sample output. The output contains both the drug predictive index and an adherence score.

Definitions:

Disease specific evidence based medicine data [0083] Disease specific evidence based medicine data consists of disease specific efficacy and tolerability data for potentially effective medications. This disease specific efficacy and tolerability data may exist for age or disease subgroups; each age or disease subgroup is considered separately. For example in epilepsy, evidence based data exists for five age groups (neonates, infants, children, adults, and elderly adults) along with four disease subgroups (partial onset seizures, generalized tonic clonic seizures, absence seizures, and myoclonic seizures). In this example, there would be a maximum of 20 separate evidence based data sets covering all age-seizure type combinations.

[0084] The first step in foe evidence based approach is to identify all relevant scientific information about the efficacy and tolerability of any potential therapeutic modality (medical, surgical or dietary). Articles are identified through multiple methods including, but not limited to, electronic literature searches of the medical literature, hand searches of major medical journals, the Cochrane library of randomized controlled trials, and the reference lists of all studies Identified from the electronic literature searches. These articles may include, but are not limited to, randomized control trials, nonrandomized controlled trials, case series, case reports, and expert opinions. Supplementary data is found in package Inserts of individual drugs.

[0085] The data in each article is evaluated for drug specific efficacy and tolerability data. The analysis is performed using the grading system used by foe national scientific organization associated with that specialty. If there is no national scientific organization associated with foe specialty Am foe default grading system is the American Academy of Neurology evaluation system. Ate foe evidence teed analysis is complete, the efficacy and tolerability data for each potential drag (stratified by age and disease subgroup) is summarized according to the following Table 5 using a scale from 1+ to -1.

[0086]

Table 5: Drag scoring system for efficacy and tolerability data

Drug Specific Basic Pharmacology Characteristics [0087] Drug specific basic pharmacology characteristics are evaluated in form categories: Preclintcal toxicity, fundamental clinical pharmacokinetic variables and drug safety. An example in the preclinical toxicity category is a drag’s therapeutic index. This is defined as the ratio of LD50/TD50 where TD50 is foe doss of foe medication that results in 50% of the animals tested achieving foe desired therapeutic outcome while LD50 is foe dose of foe medication that results in 50% of the animals tested dying. Fundamental clinical pharmacokinetic variables include, but are not limited to, i) a drug’s bioavailability (fraction of the dose which reaches the systemic circulation as intact drug), ii) the fraction of the drug circulating unbound (defined by the extent to which a drug is bound in plasma or blood = [unbound drug concentration]/[total thug concentration)), in) the type of metabolism the drug undergoes (whether linear or nonlinear), iv) the type of elimination the drug undergoes (e.g. percentage of drug renally excreted or hepatically metabolized) and v) the drug’s half-life.

Drug safety includes, but is not limited to, the risk of life threatening side effects (idiosyncratic reactions) and the risk of teratogenicity. For each drug under consideration, each variable in the three categories is scored on a scale from +1 (most favorable) to -1 (most unfavorable).

Patient Specific Advanced Pharmacology Factors [0088] Patient specific advanced pharmacology factors include i) bidirectional pharmacokinetic or pharmacodynamic drug-drug interactions and ii) bidirectional pharmacodynamic drug-disease interactions. A pharmacokinetic drag-drug interaction is considered potentially clinically significant if there is a documented interaction that shows one drag either induces or inhibits the activity of a specific enzyme associated with the metabolism of the other drug by > 20%, Only concomitant medications actually being taken at the time of tire analysis are considered in the analysis. For drag-disease interactions, the word “diseases” refers to all forms of altered health ranging from single organ dysfunction (e.g. renal failure) to whole body illness (e.g. systemic lupus erythematosus). The potential for drag-drug or drug-disease interactions is evaluated on a scale form +1 (most favorable) to -1 (most unfavorable).

[0089] To clarify using an example: In a specific patient, assume drug A is being evaluated for use in disease D. The patient is currently taking oral contraceptives, a Matin for hypercholesterolemia and is overweight To evaluate the “Patient specific advanced pharmacology factors” for drug A for this patient there are 8 potential drag-drag interactions and 4 potential drag-disease interactions to evaluate: i) pharmacokinetic effect of drug A on oral contraceptives, ii) pharmacokinetic effect of oral contraceptives on drag A, ill) pharmacokinetic effect of drag A on statin medications, iv) pharmacokinetic effect of statin medication on drag A, v>viil) the same four combinations mentioned previously but «camming the pharmacodynamic interactions between drags, ix) pharmacodynamic effect of drug A on hypercholesterolemia, x) pharmacodynamic effect of hypercholesterolemia on drug A, xi) pharmacodynamic effect of drug A on weight, xii) pharmacodynamic effect of weight on drug A. If Drug A has i) a clinically significant negative effect on statin pharmacokinetics and ii) causes weight gain then Drug A would receive a score of-1 for these two assessments and a score of 0 for the remaining 10 evaluations. This approach is repeated for each drug under consideration (e.g. drugs B, C,.. .etc).

Patient Specific Environmental Factors [0090] Patient specific environmental factors involve unidirectional, pharmacokinetic or pharmacodynamic, drug-environment interactions. Unidirectional refers to the effect of the environmental agent on the drug. A pharmacokinetic drug-environment interaction is considered potentially clinically significant if there is a documented interaction that shows the environmental agent either induces or inhibits the activity of a specific enzyme associated with the metabolism of the drug by > 20%. A pharmacodynamic drug-environment interaction is considered potentially clinically significant if there is a documented interaction that shows the environmental factor alters (either positively or negatively) the action of the drug by > 20%. Only environmental factors occurring at the time of the analysis am considered in the analysis. For drug-environment interactions, the word “environment” refers to all forms of exposure ranging from food (grapefruit juke) to herbal/vitamin supplements (e.g. St Johns wort) to voluntary toxic exposures (e.g. smoking or alcohol) to involuntary toxic exposures (second hand smoke, pesticides). The potential for drug environment interactions is evaluated on a scale from +1 (most favorable) to -1 (most unfavorable).

Patient Specific Genetic Factors [0091] Patient specific genetic factors involve unidirectional, pharmacokinetic or pharmacodynamic, drug-gene interactions. Unidirectional refers to the effect of the genetic variation on the pharmacokinetic or pharmacodynamic action of the drag. A pharmacokinetic drug-gene interaction is considered potentially clinically significant if there is a documented interaction that shows the genetic factor either increases or reduces the activity of a specific enzyme associated with die metabolism of the drug by > 20%. A pharmacodynamic drug-gene interaction is considered potentially clinically significant if there is a documented interaction that shows the genetic factor alters (either positively or negatively) die action of the drug by > 20%. For drug-gene interactions, the word “gene” refers to all forms of genetic variability including DNA variability, mRNA variability, protein alterations or metabolite alterations. The potential for drug-gene interactions is evaluated on a scale from +1 (most favorable) to -1 (most unfavorable).

Disease matrix [0092] An example (very snail) segment of a disease matrix is provided in Fig. 3. The disease matrix includes column headings for distinct treatment modalities (e.g. medication, therapy, surgery, dietary plan, etc.) while the rows are distinct factors from the five categories listed above: disease specific evidence based medicine data, drug specific basic pharmacology characteristics, patient specific advanced pharmacology principles, patient specific environmental and patient specific genetic factors. The value in each cell in the matrix ranges from +1 (favorable quality/result) to -1 (unfavorable quality/result).

[0093] Referring to the example disease matrix segment in Fig. 3, the first column 10 lists the specific factor to be evaluated for a list of specific treatments and/or drugs; column 12 provides the category for the specific factor; and columns 14-20 provide the specific disease matrix values that file specific factor associates with a specific drug or treatment. For example, the factor of Row 8, “Pharmacokinetics (metabolism),” is listed in the “Basic pharmacology” category and has a wide variance of matrix values or scores depending upon the proposed drug or treatment: carbamazepine has a -0.5 matrix value; phenobarbital has a 1.0 matrix value; phenytoin has a -1.0 matrix value; and topiramate has a 1.0 matrix value. As another example, the factor of Row 23, “Patient is a CYP2C9 poor metabolizer,” is listed in the “Genetic factors” category and also has a variance of matrix scores depending upon the proposed drug or treatment carbamazepine 1ms a -0.3 matrix value; phenobarbital has a -1.0 matrix value; phenytoin has a -1.0 matrix value; and topiramate has a 0,0 matrix value.

Patient vector column (matrix) [0094] A patient vector is constructed for each individual patient, In the exemplary embodiment, the patient vector is a column (not shown in Fig, 3) of the disease matrix. Optionally, the patient vector may be a 1 by N matrix, where N is the number of distinct factors for that particular disease algorithm taken from toe five categories listed above: disease specific evidence based medicine data, drug specific basic pharmacology characteristics, patient specific advanced pharmacology principles, patient specific environmental and patient specific genetic factors. The items in the patient vector are determined by the response to a series of YES/NO/UNKNOWN questions for each of the variables considered. The questions are yes/no questions and the matrix enters a 0 (for no), 0.5 (for unknown) or a 1 (for yes). ,

Weighting vector [0095] A weighting vector is constructed for each disease matrix. In the exemplary embodiment, the weighting vector is a column (not shown in Fig. 3) of the disease matrix. Optionally, the weighting vector is a 1 by N matrix, where N is the number of distinct factors for dial particular disease algorithm taken from the five categories listed above: disease specific evidence based medicine data, drag specific basic pharmacology characteristics, patient specific advanced pharmacology principles, patient specific environmental and patient specific genetic factors. The values in the weighting vector are determined by either a supervised system (e.g. expert system) or an unsupervised system (e.g. neural network or an artificial intelligence system). The weighting is usually different for the different factors in the disease algorithm. For example, referring back to Fig. 3, Row 2, “Child with partial seizures starting therapy** has a weight of claim 1000, Row 13, “The patient has migranes/headaches” has a weight of claim 150, and Row 23, “Patient is a CYP2C9 poor metabolizer” has a weight of250.

Algorithm Output [6096] The mam output of the algorithm is a ranking of all potential therapies (medications, surgeries or diet) for that specific disease ranging from most likely to be successful (highest score) to least likely to be successful (lowest score). Each drug’s score is the product of the patient vector, toe weighting vector and toe particular drug’s column value in the disease matrix. The dosing for the drug is determined by toe algorithm described above, in the exemplary embodiment, to® output display includes the top 5 factors contributing and the lowest 3 factor detracting from toe score are included for evaluation. Above the ranking is an adherence score reflecting toe likelihood toe patient will adhere to the proposed treatment regimen. The determination and interpretation of this number is described in toe Adherence score section.

Adherence score [0097] The adherence score is determined in a similar fashion to toe simplicity index: toe score is the product of an “adherence matrix”, a patient vector and a weighting vector. For each disease, potential adherence problems are assessed using a series of approximately 10 yes/no /unknown questions, If all questions are answered unknown then the adherence score will be 50% implying a 50% chance the patient will adhere to the treatment regimens. The more questions that are answered “no”, the higher toe adherence score and the greater the chance the patient will adhere to the prescribed treatment regimen. The more questions answered “yes”, the lower the adherence score and the greater toe chance toe patient will not adhere to the prescribed treatment regimen.

[0098] Patient Example: · History: The patient is a 7 year old male presenting with frequent storing episodes lasting 30-60 seconds associated with unresponsiveness, facial twitching and extreme tiredness afterwards.

He develops a funny taste in his mouth in toe few minutes before the events occur. He has had about 10 of these in toe past year with 3 in toe last month. Hie patient does not have depression, ADHD or anxiety but does have frequent migraines. Hie patient is currently taking erythromycin for an infection but takes no chronic medications. There is no family history of epilepsy, The patient loves to drink grapefruit juice. The family has insurance, no transpcetsfro» problems and no identifiable stressors,. • Physical examination: Normal in detail except the patient is very overweight • Lab tests: EEG shows normal background and focal discharges in the temporal lobe. MRI of the brain is normal. Fharmacogenetic testing shows a CYP2C9 polymorphism that makes him a pot»· metabolism for drags metabolized by CYP2C9, • Diagnosis: Newly diagnosed idiopathic partial epilepsy characterized by partial onset seizures. • Need: Determine the best antiepileptic medications for this specific patient [0099] Step 1: As can be seen if Fig. 4, after logging onto algorithm program - select disease -- a screen will be provided in which the physician will select in field 22 that the patient’s diagnosis is Epilepsy, but in field 24 that the patient’s diagnosis is not depression.

[0100] Step 2: As can be seen if Fig. 5, a next step - enter age, gender and puberty status - another screen will be provided in which foe physician selects in field 26 that the patient is between 2 and 18 years old, in field 28 that the patient is male and in field 30 that the patient is pre-pubertal.

[0101] Step 3: As can be seen in Fig. 6, a next step - select type of epilepsy and whether starting or on medications - another screen will be provided in which the physician selects in field 32 that the patient is a child with partial seizures and no previous treatment Fields 34-50 are not selected.

[0102] Step 4: As can be seen in Fig. 7, a next step - enter comorbid conditions - another screen will be provided in which the physician selects in field 52 that hie patient is overweight and in field 54 that the patient has migraines or headaches. Fields 56-62 are not selected.

[0103] Step 5: As can be seen in Fig. 8» a next step — enter EEG and MR! test resuits - another screen will be provided in which the physician selects in field 64 that the patient’s EEG is abnormal with epileptiform discharges and in field 66 that hie patient’s MRI/CT shows normal cortical structure.

[0104] Step 6: As can be seen in Fig. 9» a next step - enter concomitant medications - another screen will be provided in which the physician selects in field 68 that the patient is taking an antibiotic, antiviral, antifungal, antiparasitic or anti-IB medications. Fields 70-88 ate not selected.

[0105] Step 7: As can be seen in Fig. 10, a next step - the enter concomitant medications step is continued and another screen will be provided for the physician to identify specific antibiotic, antiviral, antifungal, antiparasitic or anti-TB medications that the patient is taking. In this example, fire physician selects in field 104 that the patient is taking erythromycin. Fields 90-102 and 106-114 are not selected.

[0106] Step 8: As can be seen in Fig. 11, a next step - enter environmental factors - another screen will be provided in which hie physician selects in field 118 that the patient drinks grapefruit juice. Fields 116 and 120-120 are not selected since the patient does not smoke or drink alcohol or green tea.

[0107] Step 9: As can be seen in Fig. 12, a next step - enter genetic factors -anther screen will be provided in which the physician selects in field 126 that the patient CYP2C9 poor metabolism. As will be appreciated by those of ordinary skill, such genetic data may also be entered automatically with the assistance of hie system that analyzes the patient’s genetic data.

[0108] Step 10: As can be seen in Fig. 13, a next step - enter adherence variables -- another screen will be provided in which the physician selects whether the listed variables are present or not, or are unknown. In this example, all listed variables are selected as not being present in fields 132, 136-144 and 148-150, except for fields 134 and 146, which are selected as unknown.

[0109] Step 11: As can be seen in Fig. 14, a next step provides the output of the disease matrix algorithm to the physician based upon the previous inputs. As can be seen in this exemplary output, column 152 lists the recommended chugs for treating the patient, column 154 provides the score for each drug listed, column 156 provides a filed in which the physician can select to prescribe the drag, column 158 provides the recommended dosage for the patient, column 160 provides a bar-graph display for each drug listed that provides the five most relevant features in generating foe score (the features are defined/explained in the box 161 to the right), and field 162 indicates the adherence percentage estimate for the patient. In this example, topiramate is recommended by the algorithm for the patient, having a score of2850 and a recommended dosage of claim 100% of the listed dosage. The patient is calculated to have a 90% chance of adhering to the drag treatment.

Conclusion [0110] Having described the invention with reference to the exemplary embodiments, it is to be understood that it is not intended that any limitations or elements describing the exemplary embodiment set forth herein are to be incorporated into the meanings of the patent claims unless such limitations or elements are explicitly listed in the claims. Likewise, it is to be understood that it is not necessary to meet any or all of the identified advantages or objects of the invention disclose herein in order to fall within the scope of any claims, since the invention is defined by the claims and since inherent and/or unforeseen advantages of the present invention may exist even though they may not be explicitly discussed herein.

[0111] Finally, it is to be understood that it is also within the scope of the invention to provide any computer, computer-system and/or computerized tool as is known by one of ordinary skill in the art that is designed, programmed or otherwise configured to perform any of the above-discussed methods, algorithms or processes.

[0112] All publications, patents, and patent applications mentioned in foe specification are indicative of foe level of those skilled in foe art to which this invention pertains. All publications, patents, and patent applications are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually incorporated by reference.

[0113] The relevant skilled addressee will folly appreciate that by referring to Figure 4 to Figure 14 (inclusive) a computer-assisted method or a computerised method for selecting one or more drugs for a patient for treatment of a disease as described herein also means a computer-integrated method for selecting one or more drugs for a patient for treatment of a disease as described herein.

[0114] The present Application is a Divisional Application based on Australian Patent Application No. 2012203861, the disclosures of the Specification of that published Application (as understood by the relevant skilled addressee) clearly, folly and unambiguously form part of this Specification.

[0115] Throughout this specification and the claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" and "comprising", will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.

[0116] The reference to any prior art in this specification is not and should not be taken as an acknowledgement or any form of suggestion that the prior art forms part of the common general knowledge.

Claims (15)

1. The claims defining the invention are as follows:

   1. A method for treating a patient with a therapeutic drug, the method comprising receiving a drug profile comprising the drug’s therapeutic range; receiving patient data comprising demographic data and clinical data; detennining the patient’s genotype at one or more genetic loci encoding a cytochrome P450 gene selected from the group consisting of CYP2D6, CYP1A2, CYP2C19, CYP2C9, and CYP2E1, each genetic loci containing one or more genetic variants; generating a first pharmacokinetic (PK) model of the area under the time concentration curve (AUC) based on the patient data, genotype, and drug profile, wherein the model is a Bayesian model and incorporates the one or more genetic variations, a population frequency for each genetic variation, and AUC data for each genetic variation; administering a first dosing regimen of the drug to the patient based upon the first PK model; detennining a blood level of the drug in the patient; generating a second PK model incorporating the blood level of the drug; administering a second dosing regimen of the drug to the patient based upon the second PK model.
2. 2. The method of claim 1, wherein the step of detennining the blood level of the drug comprises detennining a pre-dose blood level of the drug in the patient before administering the first dosing regimen.
3. 3. The method of claim 2, wherein the method further comprises incorporating the pre-dose blood level into the PK model.
4. 4. The method of any preceding claim, wherein the therapeutic drug is a neuropsychiatric medication.
5. 5. The method of claim 4, wherein the neuropsychiatric medication is a selective serotonin reuptake inhibitor or a tricyclic antidepressant.
6. 6. The method of claim 4, wherein the neuropsychiatric medication is a substrate of CYP2D6.
7. 7. The method of claim 6, wherein the medication is selected from the group consisting of amitriptyline, nortriptyline, haloperidol, and desipramine.
8. 8. The method of claim 4, wherein the neuropsychiatric medication is a substrate of CYP1A2.
9. 9. The method of claim 8, wherein the medication is selected from clozapine and imipramine.
10. 10. The method of claim 4, wherein the neuropsychiatric medication is a substrate of CYP2C19.
11. 11. The method of claim 10, wherein the medication is selected from imipramine, citalopram, and diazepam.
12. 12. The method of claim 4, wherein the neuropsychiatric medication is a substrate of CYP2C9.
13. 13. The method of claim 12, wherein the medication is selected from acetaminophen and antabuse (disulfiram).
14. 14. The method of claim 4, wherein the neuropsychiatric medication is a substrate of CYP2E1.
15. 15. The method of claim 14, wherein the medication is selected from phenytoin and carbamazepine.