WO2014145829A1

WO2014145829A1 - Methods for determining and limiting the potential for drug-drug interactions

Info

Publication number: WO2014145829A1
Application number: PCT/US2014/030658
Authority: WO
Inventors: Amadeo J. Pesce
Original assignee: Millennium Laboratories
Priority date: 2013-03-15
Filing date: 2014-03-17
Publication date: 2014-09-18

Abstract

The disclosure provides for methods that integrate metabolic ratio testing results from a patient's biological sample with the subject's pharmacogenomic makeup so as to determine the subject's risk for drug-drug interactions for co-administered drugs.

Description

METHODS FOR DETERMINING AND LIMITING THE POTENTIAL FOR DRUG- DRUG INTERACTIONS

CROSS REFERENCE TO RELATED APPLICATIONS

[ 0001 ] This application claims priority under 35 U.S.C. §119 from Provisional Application Serial No. 61/800,225, filed March 15, 2013, the disclosure of which is incorporated herein by reference.

FIELD OF THE INVENTION

[ 0002 ] This disclosure relates to an improved in vitro method for determining the potential for drug-drug interaction involving cytochrome P450s (CYP) with new chemical entities. This disclosure further relates to the in vitro determination testing of, in vivo drug-drug interactions, particularly as they affect liver

metabolism, as an initial or primary screen for compounds as drug candidates .

BACKGROUND

[ 0003 ] It is important for clinicians to be informed of a patient's metabolic profile particularly as it relates to drug metabolism and drug profile. Drug-drug interaction (DDI) (i.e., when a medication is concurrently administered with another medication) can modify a drug's in vivo absorption and metabolic rate (Clinical Pharmacokinetics. Concepts and Applications, M.

Rowland and T. N. Tozer, Chapter 17. Drug Interactions, 1980, Lea & Febiger) . Unfavorable drug-drug interactions (DDI) are responsible for approximately 1-2% of clinically relevant DDI, which while a relatively small number, are nevertheless an important factor in determining proper dosing and administration. Understanding the metabolism of such drugs is important to be able to properly manage a patient's care.

SUMMARY

[ 0004 ] The methods of the disclosure provide information on how the metabolic pattern is related to the pharmacogenomic makeup of the patient for an administered drug. In a particular embodiment, the methods disclosed herein, use steps which utilize the metabolic ratio to integrate the pharmacogenomic information with the observed phenotypic observations of drug metabolism, so as to predict drug-drug interactions and their effect on the metabolic ratio of the drug. In a further embodiment, the disclosure provides methods that integrate biological sample (e.g., urine sample) testing results for determining the patient's metabolic ratio with identifying a patient's pharmacogenomic makeup so that patient safety is enhanced by identifying and/or predicting DDI with co-administered drugs.

[ 0005 ] In a particular embodiment, the disclosure provides a method for determining and limiting the potential for drug-drug interactions ("DDI"), comprising: quantifying the metabolic ratio for an administered drug from one or more biological samples taken from a patient; comparing the patient's metabolic ratio for the administered drug with a database comprising a population of patients which metabolic ratios have been quantified in the same manner so as to determine the metabolic phenotype of the patient; genotyping the patient based upon the determined metabolic phenotype; determining the patient's risk for DDI with a coadministered drug based upon the patient's genotype. In a further embodiment, the quantifying step is performed on biological samples taken from the patient on two or more different days.

[ 0006 ] In another embodiment, a method disclosed herein further comprises reporting to a care provider: the quantitative

concentrations of the administered drug and one or more

metabolites, the determined metabolic ratio from the patient, the pharmacogenomic makeup of the patient, and the potential risk for DDI with co-administered drugs. In a further embodiment, the administered drug and one or more metabolites are quantified by using LC-MS/MS.

[0007] The disclosure provides a method for determining and limiting the potential for drug-drug interactions (DDI),

comprising: quantifying the metabolic ratio for an administered drug from one or more biological samples taken from a subject;

comparing the subject's metabolic ratio for the administered drug with a database comprising a population of patients which metabolic ratios have been quantified in the same manner so as to determine the metabolic phenotype of the patient; genotyping the subject based upon the determined metabolic phenotype; determining the subject's risk for DDI with a co-administered drug based upon the patient's genotype. In one embodiment, the quantifying step is performed on biological samples taken from the subject on two or more different days. In another embodiment, one or more of the following are reported to a care provider: the quantitative concentrations of the administered drug and one or more

metabolites, the determined metabolic ratio, the pharmacogenomic makeup of the subject, and the potential risk for DDI with coadministered drugs. In yet another embodiment, the one or more biological samples are urine samples. In yet another embodiment, the administered drug and one or more metabolites are quantified by using liquid chromatography-tandem mass spectrometry (LC-MS/MS) , gas chromatography-1andem mass spectrometry (GC/MS) , test strips or by immunoassay.

[ 0008 ] The disclosure also provides a method for determining and limiting the potential for drug-drug interactions (DDI), comprising: quantitating mass fragments for an administered drug and one or more test metabolite (s) from one or more biological samples obtained from a subject using LC-MS/MS; determining the concentration of the drug and test metabolite (s) in the one or more biological samples by comparing the quantitated mass fragments for the drug and the one or more test metabolite (s) against a

calibration curve; quantifying the metabolic ratio for the administered drug based upon the concentrations determined for the administered drug and test metabolite (s) ; comparing the subject's metabolic ratio for the administered drug with a database

comprising a population of subjects which metabolic ratios have been quantified in the same manner so as to determine the metabolic phenotype of the subject; genotyping the subject based upon the determined metabolic phenotype; determining the subject's risk for DDI with a co-administered drug based upon the patient's genotype. In one embodiment, the quantitating step is performed on biological samples taken from the subject on two or more different days. In yet another embodiment, the one or more biological samples are urine samples. In a further embodiment, the urine samples are first treated with hydrolytic enzymes before been used in the quantitating step. In another embodiment, the mass fragments are quantitated using a triple-quad mass spectrometer and ESI as the ion source. In a further embodiment, the mass spectrometer is at least capable of femtogram-level (10^~15 grams) sensitivity. In another embodiment, the administered drug is a pain medication drug selected from morphine, oxymorphone, hydromorphone , codeine, oxycodone, 6-acetylmorphine, hydrocodone, meperidine, cocaine, meprobamate, phencyclidine, buprenorphine, proxyphene, methadone, amphetamine, methamphetamine , MDMA, fentanyl, tramadol, alprazolam, oxazepam, nordiazepam, carisoprodol, temazepam, 7-amino-clonazepam, tapentadol, desipramine, cyclobenzaprine, imipramine,

nortriptyline, amitriptyline , doxepin, ritalinic acid,

methylphenidate , ketamine, O-DM-tramadol , and ZV-DM-tramadol . In a further embodiment, the mass fragments for the pain medication drug and/or one or more test metabolite (s) of the pain medication drug can be identified by using an acquisition method report that shows the sizes of ions and fragments and retention time which are expected for the pain medication drug and test metabolite (s) of the pain medication drug. In yet another embodiment, the calibration curve is constructed by using a plurality of synthetic biological samples that comprise known concentrations of the administered drug and of the one or more metabolites of the drug. In yet another embodiment, the administered drug and/or co-administered drug is of a drug class selected from: anticoagulant, thrombolytic, nonsteroidal anti-inflammatory agent, norepinephrine reuptake inhibitor (NRI), dopamine reuptake inhibitor (DARI), serotonin- norepinephrine reuptake inhibitor (SNRI), sedative, norepinephrine- dopamine reuptake inhibitor (NDRI) , serotonin-norepinephrine- dopamine-reuptake-inhibitor (SNDRI), monoamine oxidase inhibitor, hypothalamic phospholipid, endothelin converting enzyme (ECE) inhibitor, opioid, thromboxane receptor antagonist, potassium channel opener, thrombin inhibitor, growth factor inhibitor, platelet activating factor (PAF) antagonist, anti-platelet agent, anti-coagulant, low molecular weight heparin, Factor Via Inhibitor and Factor Xa Inhibitor, renin inhibitor, neutral endopeptidase (NEP) inhibitor, vasopepsidase inhibitor (dual NEP-ACE inhibitor) , HMG CoA reductase inhibitor, squalene synthetase inhibitor, fibrate, bile acid sequestrant, vitamin, anti-atherosclerotic agent, MTP Inhibitor, calcium channel blocker, potassium channel activator, alpha-adrenergic agent, diuretic, anti-diabetic agent, mineralocorticoid receptor antagonist, aP2 inhibitor,

phosphodiesterase inhibitor, protein tyrosine kinase inhibitor, anti-inflammatory, anti-proliferative, chemotherapeutic agent, immunosuppressant, anticancer agent and cytotoxic agent, antimetabolite, antibiotic, farnesyl-protein transferase inhibitor, hormonal agent, microtubule-disruptor agent, microtubule- stabilizing agent, topoisomerase inhibitor, prenyl-protein transferase inhibitor, cytotoxic drug, TNF-alpha inhibitor, anti- TNF antibody or soluble TNF receptor, cyclooxygenase-2 (COX-2) inhibitor, gold compound, and platinum coordination complex. In another embodiment, the method allows for the identification of a CYP or a MAO responsible for metabolism of the administered drug. In a further embodiment, the CYP is selected from CYP1A1, CYP1A2, CYP1B1, CYP2A6, CYP2A13, CYP2B6, CYP2C8, CYP2C9, CYP2C18, CYP2C19, CYP2D6, CYP2E1, CYP2G1, CYP2J2, CYP2R1, CYP2S1, CYP3A4, CYP3A5, CYP3A5P1, CYP3A5P2, CYP3A7, CYP4A11, CYP4B1, CYP4F2 CYP4F3, CYP4F8, CYP4F11, CYP4F12, CYP4X1, CYP4Z1, CYP5A1, CYP7A1, CYP7B1, CYP8A1, CYP8B1, CYP11A1, CYP11B1, CYP11B2, CYP17, CYP19, CYP21, CYP24, CYP26A1, CYP26B1, CYP27A1, CYP27B1, CYP39, CYP46, and CYP51, and wherein the MAO is either MAOA or MAOB. In another embodiment, one or more of the following are reported to a care provider: the quantitative concentrations of the administered drug and one or more metabolites, the determined metabolic ratio, the

pharmacogenomic makeup of the subject, and the potential risk for DDI with co-administered drugs.

DESCRIPTION OF DRAWINGS

[0009] Figure 1 presents a graph of the metabolic ratio of the drug metabolite divided by the administered drug from a patient, per patient visit. The upper and lower limits are determined from a database of a population of patients tested in a similar manner.

[0010 ] Figure 2 presents an example of a final report which can be provided to the care provider.

[0011 ] Figure 3 presents a typical chromatogram of analytes from a patient's urine sample.

[0012 ] Figure 4 provides a report showing the sizes of ions and fragments that are expected for the listed drug and drug metabolites using a triple-quad mass spectrometer and ESI as the ion source .

DETAILED DESCRIPTION

[ 0013] As used herein and in the appended claims, the singular forms "a," "and," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a sample" includes a plurality of such samples and reference to "the subject" includes reference to one or more subjects, and so forth .

[ 0014 ] Also, the use of "or" means "and/or" unless stated otherwise. Similarly, "comprise," "comprises," "comprising"

"include," "includes," and "including" are interchangeable and not intended to be limiting.

[ 0015] It is to be further understood that where descriptions of various embodiments use the term "comprising, " those skilled in the art would understand that in some specific instances, an embodiment can be alternatively described using language

"consisting essentially of" or "consisting of."

[ 0016] The term "patient" may include both a human patient, and a non-human animal patient that are undergoing a medical directed treatment. The term "subject" includes patients, but also includes persons or animals that are diagnosed or analyzed by the methods of the disclosure.

[ 0017 ] The term "parent drug", as used throughout this disclosure, includes pharmaceutical medicines, nutriceuticals , supplements, vitamins, minerals, nutraceuticals and the like, in any form. A "drug" may be used for treatment/therapy of acute or chronic disease, for prophylaxis and disease prevention, as well as for enhancing health, longevity and general "wellness."

[ 0018 ] The term "regression equation" refers to a technical statistical procedure by which two or more variables are shown to be consistently related that allows one or more variables to predict the corresponding value of the other variable and is one procedure that allows these predictions to occur.

[ 0019] In the present context the term "biological sample" refers to urine, blood, saliva, sweat, and spinal and brain fluids, or a combination thereof. [ 0020 ] Also as used herein, the term "test metabolite" is intended to indicate a substance the concentration of which in a biological sample is to be measured; the test metabolite is a substance that is a by-product of or corresponds to a specific prescribed drug.

[ 0021 ] The term "normative database" refers to the concept of a collected set of data that is related to the specific population it is intended to predict. Statistical analysis of such a set of data enables a person of ordinary skill in the art to perform predictive analysis based on the norms of the data set under the basic assumptions set forth by statistical analysis. From which, a set of nominal values for each drug' s metabolic ratio can be established, including upper and lower limits.

[ 0022 ] The term "self-correction" refers to the concept where each new observation (or piece of data) is subsequently added into the normative database discussed herein above as it occurs, which leads to improvement of the norms from that database. This leads to a normative database and statistical analyses that more

consistently describe the true population it is intended to describe and ultimately predict.

[ 0023] Although methods and materials similar or equivalent to those described herein can be used in the practice of the disclosed methods and compositions, the exemplary methods, devices and materials are described herein.

[ 0024 ] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this disclosure belongs.

[ 0025] All publications mentioned herein are incorporated by reference in full for the purpose of describing and disclosing the methodologies, which are described in the publications, which might be used in connection with the description herein. The

publications discussed above and throughout the text are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the inventors are not entitled to antedate such disclosure by virtue of prior disclosure. Moreover, with respect to any term that is presented in one or more publications that is similar to, or identical with, a term that has been expressly defined in this disclosure, the definition of the term as expressly provided in this disclosure will control in all respects.

[ 0026 ] Multiple medication prescriptions have been shown to dramatically lower patient compliance. In addition, combinations of medications (both licit and illicit) can have drug-drug

interactions that lower efficacy and cause side effects. Many older patients are faced with up to a dozen or sometimes more separate prescribed medications ranging from pills to eye drops, requiring complex regimens, sorting and scheduling. Patient and family/caregiver education about the problems being treated or prevented, and understanding the prescribing clinicians

instructions on the dosing is important. These issues, and others, can lead to poor adherence/compliance, mix-ups, under dosing and overdoses. Accordingly, clinical outcomes suffer, leading to further disease progression, pathology, clinical needs,

hospitalizations, increased healthcare costs, as well as increased morbidity and mortality. It has been estimated that 10% of hospital admissions are related to medication errors and problems with compliance and DDI .

[ 0027 ] Pharmacogenomics refers to the entire spectrum of genes that determine drug behavior and sensitivity, whereas

pharmacogenetics is often used to define the narrower spectrum of inherited differences in drug metabolism and disposition. The benefits of pharmacogenomics are numerous. For example, prescribing clinicians, as well as pharmaceutical companies could exclude those people who are known to have a negative response to the drug, which can be determined by clinical trials and correlating side effects or other issues to one or more genes or gene variants (as

determined by Single Nucleotide Polymorphism (SNP) analysis (which is available and common today) to sequencing (becoming lower cost and more common) ) . This, of course, increases the probability of the effectiveness of the drug with a particular patient population. Pharmacogenetic and ever cheaper and more available genotyping will identify many new disease-related genes and provide new targets to pursue; and pharmacogenomics profiling (with or without additional patient specific information) will lead to patient stratification, and these new targets, as well as existing targets, will be divided into subsets. It has been estimated that genotyping will identify new disease related genes that will lead to between 5,000 and 10,000 new potential targets. Accordingly, pharmacogenomics and pharmacogenetics can be used to personalize a subject's drug prescriptions, however, drug-drug interactions and the use of illicit drugs, over the counter drugs and the like can alter the metabolism of prescription drugs. Thus, it is important to identify such interactions and changes in metabolism even if subject has had their pharmacogenomic/genetic profiles examined.

[ 0028 ] Some drugs are metabolized by several pharmacologic polymorphic genes including, for example, CYP (cytochrome P450) a family of liver enzymes responsible for breaking down over 30 different classes of drugs; and MAO (monoamine oxidase) which is also responsible for breaking down drugs. Other drugs and/or dietary intake of various vitamins or other compounds can inhibit or induce these same enzymatic as well as other genes/proteins. For example, Vitamin K intake (which may be provided from a diet including leafy green vegetables) can interact with warfarin

(Coumadin) , and components in grapefruit can interfere with several kinds of prescription medications. These combinations and their various effects should be considered when prescribing medications, but often are not known (genetics of patient aren't known) and/or not presented to the prescribing clinician, and the impact of various patient attributes (ranging from weight, to renal function) on various multi-drug effects not determined or calculated. This can lead to drug toxicity, and drug overdoses, and contribute to many of the drug related side effects, complications, morbidity and deaths which occur in the US and rest of world each year.

[ 0029 ] In this regard, drug interactions with cytochrome P450s

(CYPs) and MAOs are particularly important. Examples of MAOs, include, but are not limited to, MAOA, and MAOB. Examples of CYPs include, but are not limited to, CYP1A1, CYP1A2, CYP1B1, CYP2A6, CYP2A13, CYP2B6, CYP2C8, CYP2C9, CYP2C18, CYP2C19, CYP2D6, CYP2E1, CYP2G1, CYP2J2, CYP2R1, CYP2S1, CYP3A4, CYP3A5, CYP3A5P1, CYP3A5P2, CYP3A7, CYP4A11, CYP4B1, CYP4F2 CYP4F3, CYP4F8, CYP4F11, CYP4F12, CYP4X1, CYP4Z1, CYP5A1, CYP7A1, CYP7B1, CYP8A1, CYP8B1, CYP11A1, CYP11B1, CYP11B2, CYP17, CYP19, CYP21, CYP24, CYP26A1, CYP26B1, CYP27A1, CYP27B1, CYP39, CYP46, and CYP51. Of the CYPs, CYP1A2, CYP2C, CYP2D6 and CYP3A4 represent greater than 90% of total hepatic P450 and nearly 80% of therapeutic drugs are metabolized by these same enzymes. Further, some of the CYPs have been found to be polymorphically-expressed, including CYP2C8, CYP2C9, CYP2C19, and CYP2D6. Interaction with one or more of these CYP and/or MAO enzymes in vivo would pose a potentially relevant event in the clinic. Recently, it has been established that in vitro systems have proven capable of predicting the likelihood of DDI as they allow identification of the CYPs responsible for metabolism as well as determination of the relative contribution to overall

elimination of the inhibited pathways.

[0030 ] In a particular embodiment, the methods disclosed herein prevent unwanted DDIs for a patient taking a pain medication drug. Examples of pain medication drugs, include, but are not limited to: morphine, oxymorphone, hydromorphone , codeine, oxycodone, 6- acetylmorphine , hydrocodone, meperidine, cocaine, meprobamate, phencyclidine , buprenorphine , proxyphene, methadone, amphetamine, methamphetamine , MDMA, fentanyl, tramadol, alprazolam, oxazepam, nordiazepam, carisoprodol, temazepam, 7-amino-clonazepam,

tapentadol, desipramine, cyclobenzaprine, imipramine,

nortriptyline, amitriptyline , doxepin, ritalinic acid,

methylphenidate , ketamine, O-DM-tramadol , and ZV-DM-tramadol .

[0031 ] In a further embodiment, the disclosure provides for the methods disclosed herein comprise an administered drug or coadministered drug of the drug class, including, but not limited to: anticoagulants, such as bivalirudin; thrombolytics, such as streptokinase; non-steroidal anti-inflammatory agents, such as aspirin; antiplatelet agents, such as clopidogrel; norepinephrine reuptake inhibitors (NRIs) such as atomoxetine; dopamine reuptake inhibitors (DARIs) , such as methylphenidate; serotonin- norepinephrine reuptake inhibitors (SNRIs) , such as milnacipran; sedatives, such as diazepham; norepinephrine-dopamine reuptake inhibitor (NDRIs) , such as bupropion; serotonin-norepinephrine- dopamine-reuptake-inhibitors (SNDRIs) , such as venlafaxine;

monoamine oxidase inhibitors, such as selegiline; hypothalamic phospholipids; endothelin converting enzyme (ECE) inhibitors, such as phosphoramidon; opioids, such as tramadol; thromboxane receptor antagonists, such as ifetroban; potassium channel openers; thrombin inhibitors, such as hirudin; growth factor inhibitors, such as modulators of PDGF activity; platelet activating factor (PAF) antagonists; anti-platelet agents, such as GPIIb/IIIa blockers (e.g., abdximab, eptifibatide, and tirofiban) , P2Y (AC) antagonists (e.g., clopidogrel, ticlopidine and CS-747) , and aspirin; anticoagulants, such as warfarin; low molecular weight heparins, such as enoxaparin; Factor Via Inhibitors and Factor Xa Inhibitors;

renin inhibitors; neutral endopeptidase (NEP) inhibitors;

vasopepsidase inhibitors (dual NEP-ACE inhibitors) , such as omapatrilat and gemopatrilat; HMG CoA reductase inhibitors, such as pravastatin, lovastatin, atorvastatin, simvastatin, NK-104 (a.k.a. itavastatin, nisvastatin, or nisbastatin) , and ZD-4522 (also known as rosuvastatin, or atavastatin or visastatin) ; squalene synthetase inhibitors; fibrates; bile acid sequestrants , such as questran; niacin; anti-atherosclerotic agents, such as ACAT inhibitors; MTP Inhibitors; calcium channel blockers, such as amlodipine besylate; potassium channel activators; alpha-adrenergic agents; diuretics, such as chlorothiazide, hydrochlorothiazide, flumethiazide, hydroflumethiazide, bendroflumethiazide, methylchlorothiazide, trichioromethiazide, polythiazide, benzothlazide, ethacrynic acid, tricrynafen, chlorthalidone, furosenilde, musolimine, bumetanide, triamterene, amiloride, and spironolactone; thrombolytic agents, such as tissue plasminogen activator (tPA) , recombinant tPA, streptokinase, urokinase, prourokinase, and anisoylated plasminogen streptokinase activator complex (APSAC) ; anti-diabetic agents, such as biguanides (e.g. metformin), glucosidase inhibitors (e.g., acarbose) , insulins, meglitinides (e.g., repaglinide) ,

sulfonylureas (e.g., glimepiride, glyburide, and glipizide), thiozolidinediones (e.g. troglitazone, rosiglitazone and

pioglitazone) , and PPAR-gamma agonists; mineralocorticoid receptor antagonists, such as spironolactone and eplerenone; growth hormone secretagogues ; aP2 inhibitors; phosphodiesterase inhibitors, such as PDE III inhibitors (e.g., cilostazol) and PDE V inhibitors (e.g., sildenafil, tadalafil, vardenafil) ; protein tyrosine kinase inhibitors; anti-inflammatories ; anti-proliferatives, such as methotrexate, FK506 (tacrolimus, Prograf) , mycophenolate mofetil; chemotherapeutic agents; immunosuppressants; anticancer agents and cytotoxic agents (e.g., alkylating agents, such as nitrogen mustards, alkyl sulfonates, nitrosoureas, ethylenimines , and triazenes) ; anti-metabolites, such as folate antagonists, purine analogues, and pyrridine analogues; antibiotics, such as

anthracyclines , bleomycins, mitomycin, dactinomycin, and

plicamycin; enzymes, such as L-asparaginase ; farnesyl-protein transferase inhibitors; hormonal agents, such as glucocorticoids (e.g., cortisone), estrogens/antiestrogens,

androgens/antiandrogens , progestins, and luteinizing hormone- releasing hormone anatagonists , and octreotide acetate;

microtubule-disruptor agents, such as ecteinascidins ; microtubule- stablizing agents, such as pacitaxel, docetaxel, and epothilones A- F; plant-derived products, such as vinca alkaloids,

epipodophyllotoxins , and taxanes; and topoisomerase inhibitors; prenyl-protein transferase inhibitors; and cyclosporins; cytotoxic drugs, such as azathiprine and cyclophosphamide; TNF-alpha inhibitors, such as tenidap; anti-TNF antibodies or soluble TNF receptor, such as etanercept, rapamycin, and leflunimide; and cyclooxygenase-2 (COX-2) inhibitors, such as celecoxib and rofecoxib; and miscellaneous agents such as, hydroxyurea,

procarbazine, mitotane, hexamethylmelamine , gold compounds, platinum coordination complexes, such as cisplatin, satraplatin, and carboplatin.

[ 0032 ] Current urine drug testing measures the administered parent drug and one or more of its metabolites in urine. One of the observed sets of values is the metabolic ratio, which is the concentration of the drug metabolite divided by the parent drug concentration. This is often used to establish medication

compliance. The presence of the metabolite is used to establish that the patient took the prescribed drug. For individual patients the expected range of values for this metabolic ratio falls within a measurable range. This measurable range is due to the

pharmacogenomic makeup of the patient. That is patients who are poor metabolizers will have a lower metabolic ratio than patients who are extensive metabolizers or ultra-rapid metabolizers . There should be a direct correlation between the observed metabolic ratio and the pharmacogenomic makeup of the individual patient. This metabolic ratio can be influenced by co-administered drugs which compete or inhibit the specific CYP450 or MAO enzyme and thus reduce the amount of metabolite formed.

[ 0033 ] The metabolic ratio is an overall indication of the ability of the patient to properly metabolize and thus eliminate the administered drug. Changes in the metabolic ratio overtime can indicate either physiologic alterations in the patient metabolism or drug-drug interactions which have changed the drug's metabolism. Patients who cannot metabolize the drug may be at risk for adverse events from unexpectedly high parent drug concentrations.

Therefore, knowledge that the drug is being properly metabolized as determined by the metabolic ratio contributes to the physician consideration that the patient is not at risk for an adverse drug event. The methods disclosed herein, use steps which utilize the metabolic ratio to integrate the pharmacogenomic information with the observed phenotypic observations of drug metabolism, and integrate known drug-drug interactions and their effect on the metabolic ratio. Accordingly, the methods of the disclosure provide the information on how the metabolic pattern is related to the pharmacogenomic makeup of the patient.

[ 0034 ] In a particular embodiment, the disclosure provides methods that integrate a biological sample (e.g., urine sample) testing results for determining the patient's metabolic ratio with identifying a patient's pharmacogenomic makeup so that patient safety is enhanced by identifying and/or predicting DDI with coadministered drugs. The biological sample can be used directly in the methods disclosed herein, or alternatively, the biological sample can be diluted first. In another embodiment, the biological samples are prepared by quantitatively aliquoting the samples into 6-well, 24-well, 96-well, or 384-well microtiter plates. In a further embodiment, the biological sample is a urine sample that contains between 1 mL to 2 mL of urine. In yet a further

embodiment, the urine sample contains no added preservatives. [0035] Beta-glucuronidation of hydroxyl-, carboxyl-, amino- and thiol-groups as well as sulfation of hydroxyl- and amino groups are major phase II metabolism pathways. The resulting glucuronated and sulfated conjugates are relatively stable, but for more accurate detection of the parent compound and phase I metabolites, the biological samples should be first enzymatic hydrolyzed in order to remove these groups. Different types of enzymes are commercially available, but the most frequently used are Beta-glucuronidase from E. coli or Helix pomatia, sometimes combined with arylsulfatase . In order to achieve reliable results, the pH and temperature optima for different preparations of purified glucuronidase and sulfatase should be taken into consideration. Beta-glucuronidase from E. coli provides the largest pH optimum of all glucuronidases, which is situated between 5.5 and 7.5, whereas Beta-glucuronidase from Helix pomatia works best between pH 4.5 and 5.5. The temperature optimum for Beta-glucuronidase from E. coli and Helix pomatia lies at 50°C and 60°C, respectively. Beta-glucuronidase-arylsulfatase from Helix pomatia provides the advantage of the cleavage of glucuronide and sulfate conjugates at the same time, but the glucuronidase activity is not as high as in the E. coli preparation. Moreover, E. coli solution leads to cleaner extracts in comparison to Helix pomatia Beta-glucuronidase/ arylsulfatase . In a particular embodiment, the biological sample is first enzymatically hydrolyzed prior to being used in the methods disclosed herein. A typical procedure for the enzymatic hydrolysis of glucuronides is to mix 1 mL to 2 mL of urine with internal standards (e.g., glucuronides) and 1 to 2 mL of buffer and then to add Beta-glucuronidase (approx. between 1.000 and 20.000 units per mL urine) and possibly sulfatase and incubate at 37° overnight (approx. 16 h) , or at least 90 min at 50 °C. After incubation, the pH of the solution is adjusted appropriately for liquid-liquid or solid-phase extraction.

The cleavage of ether groups such as morphine-6-glucuronide generally takes more time than hydrolysis of phenolic glucuronides like morphine-3-glucuronide .

[0036] Generally, in practicing the methods disclosed herein, there are several different embodiments that can be used to determine the presence and concentration of at least one or more metabolites and/or drug in a biological sample from a patient. For example, one or more of the following methods can be used to detect and quantify drug and/or one or more metabolites from a patient: immunoassay, chromatography, chromatography/mass spectrometry, and test strips.

[ 0037 ] Immunoassay methods are based on an antibody-antigen reaction, small amounts of the drug or metabolite ( s ) can be detected. Antibodies specific to a particular drug are produced by injecting laboratory animals with the drug or human metabolite. These antibodies are then tagged with markers such as an enzyme

(enzyme immunoassay, EIA) , a radio isotope (radioimmunoassay, RIA) or a fluorescence (fluorescence polarization immunoassay, FPIA) label. Reagents containing these labeled antibodies can then be introduced into urine samples, and if the specific drug or metabolite against which the antibody was made is present, a reaction will occur.

[ 0038 ] Chromatography, such as gas chromatography (GC) and high pressure liquid chromatography (HPLC) , can also be used to detect and quantify drug and/or one or more metabolites from a patient. In GC, the sample to be analyzed is introduced via a syringe into a narrow bore (capillary) column which sits in an oven. The column, which typically contains a liquid adsorbed onto an inert surface, is flushed with a carrier gas such as helium or nitrogen. In a properly set up GC system, a mixture of substances introduced into the carrier gas is volatilized, and the individual components of the mixture migrate through the column at different speeds.

Detection takes place at the end of the heated column and is generally a destructive process. Very often the substance to be analyzed is "derivatized" to make it volatile or change its chromatographic characteristics. In contrast, for HPLC a liquid under high pressure is used to flush the column rather than a gas. Typically, the column operates at room or slightly above room temperature. This method is generally used for substances that are difficult to volatilize (e.g., steroids) or are heat labile (e.g., benzodiazepines) .

[ 0039 ] Gas chromatography/mass spectrometry (GC/MS) is a combination of two sophisticated technologies. GC physically separates (chromatographs or purifies) the compound, and MS fragments it so that a fingerprint of the chemical (drug) can be obtained. Although sample preparation is extensive, when the methods are used together the combination is regarded as the most accurate. This combination is sensitive (i.e., can detect low levels) and specific (i.e., identify all types of drugs in any body fluid) . Furthermore, assay sensitivity can be enhanced by treating the test substance with reagents.

[0040 ] When coupled with MS, LC/MS is the method of choice for substances that are difficult to volatilize (e.g., steroids) . For the methods disclosed herein, liquid chromatography tandem mass spectrometry (LC-MS/MS) is the preferred method for detecting and quantitating drug and/or one or more metabolites from a patient. Due to the higher sensitivity of LC-MS/MS, better detection can be achieved for specific drugs and metabolites using a minimal volume

(e.g., 1 - 2 mL useable urine) of a biological sample. Moreover, the need for specimen re-tests due to insufficient quantity is virtually eliminated. In another embodiment, the methods disclosed herein use an HPLC in combination with a triple-quad mass

spectrometer (MS/MS) . In a further embodiment, the HPLC is standardized on 600 bar system pressure, 80 Hz data-acquisition speed and 2x or lOx higher UV sensitivity. In another embodiment, the triple-quad mass spectrometer provides at least femtogram-level

(10^~15 grams) sensitivity. In yet a further embodiment, the triple- quad mass spectrometer is capable of polarity switching scans every 500 msec.

[0041 ] Alternatively, a drug and/or metabolite concentration can be derived by use of a test strip which is capable of being flexible so as to be submerged readily into a biological sample. The test strips may be either one-sided or multi-sided and vertical in nature, or horizontal in nature. The test strip may also be housed in a hard plastic case with an opening for introduction of a biological sample such as a "drop" of urine and a rectangular opening for display of the results (e.g., see SunLine^R™ in vitro urine drug screening test strips, described in U.S. Pat. Nos .

5,238,652; 6,046,058; 5,962,336; and 6,372,516) or have a multi- threshold and multi-level arrangement, as in U.S. Pat Appl . No. 2012/0041778.

[0042 ] The disclosure further provides for methods disclosed herein, a step of determining the metabolic ratio from a patient and then determining the patients metabolic phenotype based on the analysis of results stored in a large database of a similarly tested patient population. This large database can be generated by determining the metabolic profiles of parent drug from a population of patients using the same testing procedures. Moreover, this database may be a "normative database" as defined herein. The database can then be used to calculate the expected metabolic ratios of individual drugs, and establish upper and lower ranges. From which, by matching these metabolic ratios to a patient's pharmacogenomic makeup, one can determine the CYP450 enzyme or MAO enzyme responsible for the parent drug's metabolism, as well as determined whether the patient is a poor, normal or extensive metabolizer. Based upon this information, the effect of

concomitant medications that would inhibit or activate the CYP or MAO responsible for the parent drug's metabolism could be detected, thereby lowering the risk for DDI. Further tests, like those presented herein in the Examples section, could be used to verify the effects of concomitant medication on the identified CYP or MAO.

[ 0043] The disclosure also provides for methods disclosed herein, a step of providing enhanced information to the care provider that integrates the observed quantitative concentrations of parent drug and metabolite, the metabolic ratio calculation, the pharmacogenomic makeup of the specific patient, and the potential influence of co-administered inhibitory drugs.

[ 0044 ] The following examples are intended to illustrate but not limit the disclosure. While they are typical of those that might be used, other procedures known to those skilled in the art may alternatively be used.

EXAMPLES

[ 0045] Examples of enhanced information that can be provided to the care provider by way of a final report: An example of the enhanced information to be given to a care provider by using the methods disclosed herein, includes the calculation of the metabolic ratio from the patient's biological sample, identifying the patient's genotype retrieved from the laboratory information system, and potential drug interactions derived from the medication list provided with the patient specimen. All of this enhanced information can be provided to the care provider by way of a final report .

[0046] A first part of the final report would include graphs of the metabolic ratio of the drug metabolite divided by the parent drug (e.g., see FIG. 2, top left and right) . This can be done for a number of the administered parent drugs. The graph would plot the metabolic ratio versus each biological sample test that is performed. It would also serve as a historical record of the drug metabolism for a particular patient (e.g., see FIG. 1) . The upper and lower limits would be determined from a database, which is constructed from a population of patients that were tested in the same manner for the parent drug and/or one or more metabolites. Large changes in the metabolic ratio would indicate changes in the patient's ability to appropriately metabolize the drug (e.g., see FIG. 2, top left) . Limits can be placed on the calculation, such as the patient must excrete at least a minimum amount of drug (e.g., 500 ng) . If the metabolic ratio exceeds the expected values, the computer should issue a warning flag to the physician that the patient's status has changed.

[0047] A second part of the final report would match the CYP genotypes with the metabolic phenotype revealed by the metabolic ratio data (e.g., See FIG. 2, lower left) . The drug screen quantitative measurement of the metabolic ratio is within this expected range .

[0048] A third part of the final report should include flagging medications that interfere with the parent drug's metabolism (e.g., see FIG. 2, lower right) . An enhanced LIMS system should be able to search and flag these if they are entered in the patient's medication list.

[0049] Example of determining a metabolic ratio for a patient taking a drug using LC-MS/MS : In order to determine the metabolic ratio for a patient taking a drug: mass fragment peaks attributed to the drug and its metabolites are first identified from a chromatogram of the biological sample (e.g., see FIG. 3 and FIG. 4) and then quantitated. The concentration of the parent drug and test metabolite (s) are then determined by using a calibration curve. The calibration curve is constructed by using LC-MS/MS with synthetic biological samples that contain known concentrations (e.g., a high calibrator and a low calibrator) of parent drug and test metabolite (s) . For example, a calibration curve for a pain medication drug would comprise a high and a low calibrator at preselected concentrations per analyte shown in TABLE 1.

Upper

Low High Limit of Limit of Carryover . ..

Analyte Limit of

Calibrator Calibrator Detection Quantitation Limit

Linearity

Morphine 50 6,400 50 50 100, 000 100, 000 Oxymorphone 50 6,400 50 50 100, 000 100, 000 Hydromorphone 50 6,400 50 50 100, 000 100, 000 Codeine 50 6,400 50 50 100, 000 100, 000 Oxycodone 50 6,400 50 50 100, 000 100, 000 6-acetylmorphine 10 1,280 10 10 5, 000 5,000 Hydrocodone 50 6,400 50 50 100, 000 100, 000 Meperidine 50 6,400 50 50 100, 000 100, 000 Normeperidine 50 6,400 25 50 50, 000 50, 000 Benzoylecgonine 50 6,400 25 50 50, 000 100, 000 Meprobamate 50 6,400 50 50 50, 000 100, 000 Phencyclidine 10 640 10 50 100, 000 100, 000 Buprenorphine 10 1,280 10 10 5, 000 5,000 Norbuprenorphine 20 2,560 20 20 2,000 5,000 Propoxyphene 100 12,800 50 50 100, 000 100, 000

Methadone 100 12,800 50 50 100, 000 100, 000 Amphetamine 100 12,800 50 50 100, 000 100, 000 Methamphetamine 100 12,800 50 50 100, 000 100, 000 MDMA 100 12,800 50 50 100, 000 100, 000 Norfentanyl 8 1,024 8 8 5000 5000 Fentanyl 2 256 2 2 2000 2000 Tramadol 100 12,800 50 100 100, 000 100, 000 EDDP 100 12,800 100 100 100, 000 100, 000 Alpha- hydroxyalprazola 20 2,560 20 20 100, 000 100, 000 m

Oxazepam 40 5,120 50 50 100, 000 100, 000 Norpropoxyphene 100 12,800 50 50 100, 000 100, 000 Lorazepam 40 5,120 40 40 100, 000 100, 000 Nordiazepam 40 5,120 40 40 100, 000 100, 000 Carisoprodol 100 12,800 50 50 100, 000 100, 000

Temazepam 50 6,400 50 50 100, 000 100, 000

7-amino-

20 2, 560 20 20 100, 000 100, 000 clonazepam

Tapentadol 50 6, 400 50 50 100, 000 100, 000

Desipramine 50 6,400 50 50 100, 000 100, 000

Cyclobenzaprine 50 6,400 50 50 50, 000 100, 000

Imipramine 50 6,400 50 50 100, 000 100, 000

Nortriptyline 50 6,400 50 50 100, 000 100, 000

Amitriptyline 50 6,400 50 50 100, 000 100, 000

Doxepin 50 6, 400 50 50 100, 000 100, 000

Ritalinic Acid 50 6,400 50 50 100, 000 50, 000

Methylphenidate 50 6, 400 50 50 100, 000 50, 000

Ketamine 50 6,400 50 50 100, 000 TBD

Norketamine 50 6,400 50 50 100, 000 50, 000

O-DM-Tramadol 100 12, 800 50 100 130, 000 130, 000

N-DM-Tramadol 100 12, 800 50 100 60, 000 60, 000

TABLE 1

The quantitated peaks of the mass fragments attributed to the parent drug and the test metabolite (s) from the biological sample are compared to identical peaks form the calibration curve in order to determine the concentration of the parent drug and test

metabolite (s) in the biological sample. By dividing the

concentration of the test metabolite by the concentration of the parent drug, one is able to determine the subject's metabolic ratio for that particular drug.

[ 0050 ] In vitro Liver Microsomal Stability Assay: Liver microsomal stability assays are conducted at 1 to 5 mg per mL liver microsome protein with an NADPH-generating system in 2% NaHC0₃ (2.2 mM NADPH, 25.6 mM glucose 6-phosphate, 6 units per mL glucose 6- phosphate dehydrogenase and 3.3 mM MgCl2) . Test compounds are prepared as solutions in 20% acetonitrile-water and added to the assay mixture (final assay concentration 1 μΜ) and incubated at 37 °C. Final concentration of acetonitrile in the assay should be < 1%. Aliquots (50 L) are taken out at times 0, 15, 30 min, 1 hour, 2 hour, 3 hour, and diluted with ice cold acetonitrile (200 L) to stop the reactions. Samples are centrifuged at 12,000 RPM for 10 min to precipitate proteins. Supernatants are transferred to micro centrifuge tubes and stored for LC/MS/MS analysis of the degradation half-life of the test compounds.

[ 0051 ] In vitro Metabolism using Human Cytochrome P₄₅₀ Enzymes :

The cytochrome P450 enzymes are expressed from the corresponding human cDNA using a baculovirus expression system (BD Biosciences, San Jose, Calif.) . A 0.25 milliliter reaction mixture containing 0.8 milligrams per milliliter protein, 1.3 millimolar NADP⁺, 3.3 millimolar glucose- 6-phosphate , 0.4 U/mL glucose- 6-phosphate dehydrogenase, 3.3 millimolar magnesium chloride and 0.2 millimolar of a compound or a standard or control in 100 millimolar potassium phosphate (pH 7.4) is incubated at 37 °C for 20 min. After

incubation, the reaction is stopped by the addition of an

appropriate solvent (e.g., acetonitrile, 20% trichloroacetic acid, 94% acetonitrile/ 6% glacial acetic acid, 70% perchloric acid, 94% acetonitrile/ 6% glacial acetic acid) and centrifuged (10,000 g) for 3 min. The supernatant is analyzed by LC-MS/MS. Standards to test TABLE 2.

Cytochrome P450 Standard

CYP1A2 Phenacetin

CYP2A6 Coumarin

CYP2B6 [¹³C] - (S) -mephenytoin

CYP2C8 Paclitaxel

CYP2C9 Diclofenac

CYP2C19 [¹³C] - (S) -mephenytoin

CYP2D6 (+/-) -Bufuralol

CYP2E1 Chlorzoxazone

CYP3A4 Testosterone

CYP4A [¹³C] -Laurie acid

TABLE 2

[0052] Monoamine Oxidase A Inhibition and Oxidative Turnover:

The procedure is carried out using the methods described by Weyler et al. (Journal of Biological Chemistry 260:13199-13207 (1985)) and references cited therein, which is hereby incorporated by reference in its entirety. Monoamine oxidase A activity is measured

spectrophotometrically by monitoring the increase in absorbance at 314 nm on oxidation of kynuramine with formation of 4- hydroxyquinoline . The measurements are carried out, at 30 °C., in 50 mM NaPi buffer, pH 7.2, containing 0.2% Triton X-100 (monoamine oxidase assay buffer) , plus 1 mM kynuramine, and the desired amount of enzyme in 1 mL total volume.

[0053] The procedure can alternatively be carried out by using the methods described by Uebelhack et al . , (Pharmacopsychiatry 31:187-192 (1998)) and references cited therein, which is hereby incorporated by reference in its entirety.

[0054] A number of embodiments have been described herein.

Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of this disclosure. Accordingly, other embodiments are within the scope of the following claims .

Claims

WHAT IS CLAIMED IS:

1. A method for determining and limiting the potential for drug- drug interactions (DDI), comprising:

quantifying the metabolic ratio for an administered drug from one or more biological samples taken from a subject;

comparing the subject's metabolic ratio for the administered drug with a database comprising a population of patients which metabolic ratios have been quantified in the same manner so as to determine the metabolic phenotype of the patient;

genotyping the subject based upon the determined metabolic phenotype ;

determining the subject's risk for DDI with a co-administered drug based upon the patient's genotype.

2. The method of claim 1, wherein the quantifying step is performed on biological samples taken from the subject on two or more different days.

3. The method of claim 1, wherein one or more of the following are reported to a care provider: the quantitative concentrations of the administered drug and one or more metabolites, the determined metabolic ratio, the pharmacogenomic makeup of the subject, and the potential risk for DDI with co-administered drugs.

4. The method of claim 1, wherein the one or more biological samples are urine samples.

5. The method of claim 1, wherein the administered drug and one or more metabolites are quantified by using liquid chromatograph y- tandem mass spectrometry (LC-MS/MS) , gas chromatography-tandem mass spectrometry (GC/MS) , test strips or by immunoassay.

6. A method for determining and limiting the potential for drug- drug interactions (DDI), comprising:

quantitating mass fragments for an administered drug and one or more test metabolite ( s ) from one or more biological samples obtained from a subject using LC-MS/MS; determining the concentration of the drug and test metabolite (s) in the one or more biological samples by comparing the quantitated mass fragments for the drug and the one or more test metabolite (s) against a calibration curve;

quantifying the metabolic ratio for the administered drug based upon the concentrations determined for the administered drug and test metabolite (s) ;

comparing the subject's metabolic ratio for the administered drug with a database comprising a population of subjects which metabolic ratios have been quantified in the same manner so as to determine the metabolic phenotype of the subject;

genotyping the subject based upon the determined metabolic phenotype ;

7. The method of claim 6, wherein the quantitating step is performed on biological samples taken from the subject on two or more different days.

8. The method of claim 6, wherein the one or more biological samples are urine samples.

9. The method of claim 8, wherein the urine samples are first treated with hydrolytic enzymes before been used in the quantitating step .

10. The method of claim 6, wherein the mass fragments are

quantitated using a triple-quad mass spectrometer and ESI as the ion source .

11. The method of claim 10, wherein the mass spect omete is at least capable of femtogram-level (10^~15 grams) sensitivity.

12. The method of claim 6, wherein the administered drug is a pain medication drug selected from morphine, oxymorphone, hydromorphone , codeine, oxycodone, 6-acetylmorphine, hydrocodone, meperidine, cocaine, meprobamate, phencyclidine, buprenorphine, proxyphene, methadone, amphetamine, methamphetamine, MDMA, fentanyl, tramadol, alprazolam, oxazepam, nordiazepam, carisoprodol, temazepam, clonazepam, tapentadol, desipramine, cyclobenzaprine, imipramine, nortriptyline, amitriptyline , doxepin, ritalinic acid,

methylphenidate, ketamine, O-DM-tramadol , and ZV-DM-tramadol .

13. The method of claim 12, wherein the mass fragments for the pain medication drug and/or one or more test metabolite (s) of the pain medication drug can be identified by using an acquisition method report that shows the sizes of ions and fragments and retention time which are expected for the pain medication drug and test metabolite ( s ) of the pain medication drug.

14. The method of claim 6, wherein the calibration curve is constructed by using a plurality of synthetic biological samples that comprise known conce rations of the administered drug and of the one or more metabolites of the drug.

15. The method of claim 6, wherein the administered drug and/or co-administered drug is of a drug class selected from:

anticoagulant, thrombolytic, non-steroidal anti-inflammatory agent, norepinephrine reuptake inhibitor (NRI), dopamine reuptake inhibitor (DARI), serotonin-norepinephrine reuptake inhibitor (SNRI), sedative, norepinephrine-dopamine reuptake inhibitor (NDRI), serotonin-norepinephrine-dopamine-reuptake-inhibitor (SNDRI) , monoamine oxidase inhibitor, hypothalamic phospholipid, endothelin converting enzyme (ECE) inhibitor, opioid, thromboxane receptor antagonist, potassium channel opener, thrombin inhibitor, growth factor inhibitor, platelet activating factor (PAF) antagonist, antiplatelet agent, anti-coagulant, low molecular weight heparin, Factor Via Inhibitor and Factor Xa Inhibitor, renin inhibitor, neutral endopeptidase (NEP) inhibitor, vasopepsidase inhibitor (dual NEP-ACE inhibitor) , HMG CoA reductase inhibitor, squalene synthetase inhibitor, fibrate, bile acid sequestrant, vitamin, anti- atherosclerotic agent, MTP Inhibitor, calcium channel blocker, potassium channel activator, alpha-adrenergic agent, diuretic, anti- diabetic agent, mineralocorticoid receptor antagonist, aP2

inhibitor, phosphodiesterase inhibitor, protein tyrosine kinase inhibitor, anti-inflammatory, anti-proliferative, chemotherapeutic agent, immunosuppressant, anticancer agent and cytotoxic agent, anti-metabolite, antibiotic, farnesyl-protein transferase inhibitor, hormonal agent, microtubule-disruptor agent, microtubule-stabilizing agent, topoisomerase inhibitor, prenyl-protein transferase

inhibitor, cytotoxic drug, TNF-alpha inhibitor, anti-TNF antibody or soluble TNF receptor, cyclooxygenase-2 (COX-2) inhibitor, gold compound, and platinum coordination complex.

16. The method of claim 6, wherein the method allows for the identification of a CYP or a MAO responsible for metabolism of the administered drug.

17. The method of claim 16, wherein the CYP is selected from

CYP1A1, CYP1A2, CYP1B1, CYP2A6, CYP2A13, CYP2B6, CYP2C8, CYP2C9, CYP2C18, CYP2C19, CYP2D6, CYP2E1, CYP2G1, CYP2J2, CYP2R1, CYP2S1, CYP3A4, CYP3A5, CYP3A5P1, CYP3A5P2, CYP3A7, CYP4A11, CYP4B1, CYP4F2 CYP4F3, CYP4F8, CYP4F11, CYP4F12, CYP4X1, CYP4Z1, CYP5A1, CYP7A1, CYP7B1, CYP8A1, CYP8B1, CYP11A1, CYP11B1, CYP11B2, CYP17, CYP19, CYP21, CYP24, CYP26A1, CYP26B1, CYP27A1, CYP27B1, CYP39, CYP46, and CYP51, and wherein the MAO is either MAO_A or MAO_B.

18. The method of claim 6, wherein one or more of the following are reported to a care provider: the quantitative concentrations of the administered drug and one or more metabolites, the determined metabolic ratio, the pharmacogenomic makeup of the subject, and the potential risk for DDI with co-administered drugs.