Systems and methods for drug interaction analysis

Abstract

The present disclosure is directed to systems and methods for analyzing drug-drug and gene-drug interactions for a user and providing recommendations accordingly. The systems and methods can utilize a database containing a list of genes associated with multiple drugs based on the clinical relevance of the effects of the genes and the multiple drugs. The systems and methods can include recommending alternative drugs to the user based on any identified problematic drug-drug or gene-drug interactions.

Description

[Background technology]

[0001] (Priority) This application claims the benefit of priority under 35 U.S. Code Section 119(e) of U.S. Provisional Patent Application No. 63 / 413,911, filed October 6, 2022, entitled "SYSTEMS AND METHODS FOR DRUG INTERACTION ANALYSIS," and U.S. Provisional Patent Application No. 63 / 464,149, filed May 4, 2023, entitled "SYSTEMS AND METHODS FOR DRUG INTERACTION ANALYSIS," each of which is incorporated herein by reference in its entirety.

[0002] Two of the primary determinants of whether a drug will have its intended therapeutic effect or result in an adverse drug reaction are a patient's genetic makeup and the potential for two concomitantly administered drugs to interact (drug / gene and drug / drug interactions, respectively). Currently, drugs are generally prescribed to patients without performing a detailed genetic analysis. Furthermore, if a patient does not provide their healthcare provider with a complete and accurate list of all of the drugs they are taking (either intentionally or unintentionally), the healthcare provider may prescribe a drug that has a negative interaction with another drug. This can lead to negative health outcomes for the patient and inefficiencies associated with drug prescribing, commonly referred to as "trial-and-error" prescribing. Therefore, there is a need in the art for a system that optimizes the drug mix a patient is prescribed according to their genetic makeup and drug-drug interactions. Without such information, it can never be guaranteed that the drugs being taken are, in fact, entirely correct for a particular patient. While some currently commercially available databases have some information about drug / drug interactions and some information about the drugs themselves (e.g., major metabolites or the enzymes that form those metabolites), such commercial databases do not provide all of the information needed to make a proper assessment of potential drug / drug and / or gene / drug interactions. Thus, there is a need in the prior art for systems and methods that will fully analyze every drug / drug and gene / drug interaction in order to provide better clinical outcomes and more savings throughout the healthcare industry (whether by avoiding negative health outcomes for patients or by minimizing prescribing inefficiencies).

[0003] Pharmacogenomic testing identifies genetic mutations or variants that may affect whether a particular drug will be an effective treatment for an individual or whether the tested individual will suffer from adverse reactions to the drug. Current pharmacogenetic test reports list each gene, genotype, and phenotype separately and include a list of drugs affected by each gene. Thus, the onus is placed on healthcare providers to properly digest and interpret the myriad of information in pharmacogenetic test reports in order to provide the best medication recommendations for their patients. Not surprisingly, many healthcare providers find pharmacogenetic test reports confusing and have difficulty incorporating the test information into their usual medical care practice due to (i) a general lack of expertise in interpreting pharmacogenetic data, (ii) time constraints caused by their daily patient volume, (iii) the challenge of integrating and synthesizing data from multiple sections of the pharmacogenetic report related to different genes and understanding the significance of different sections with respect to a particular drug, and (iv) the fact that currently generated pharmacogenetic reports are static and therefore have limited usefulness following any changes to a patient's medication regimen. Thus, there is a need in the prior art for a system adapted to report results and recommendations from analyzed drug / drug and gene / drug interactions in a more understandable and dynamic manner. Summary of the Invention [Means for solving the problem]

[0004] The present disclosure is directed to systems and methods for analyzing drug-drug and / or drug-gene interactions for a user. In some embodiments, the systems and methods include providing the user with recommendations regarding any identified drug interactions. Described herein is a clinical decision support tool that allows physicians and patients to check their medications for compatibility with each other (drug-drug interactions) and with the patient's genetics (gene-drug interactions) in an integrated manner. The system flags potential interactions and offers alternative medications that are genetically appropriate for the patient. This allows physicians to prescribe a drug regimen that is optimal for a particular patient.

[0005] In some embodiments, the present disclosure provides a method for analyzing drug interactions for a user, comprising receiving genetic test results for a patient and drugs taken by the user, the drugs being in a drug category; determining whether the drug is present in an internal database, the internal database including a list of genes associated with a plurality of drugs based on clinical relevance of the effects of the genes and the plurality of drugs; and in response to determining that the drug is not present in the internal database, searching an external database for major metabolites and enzymes involved in forming the major metabolites, the major metabolites and enzymes associated with the drug; and determining whether the drug is present in an internal database for major metabolites and enzymes involved in forming the major metabolites, the major metabolites and enzymes associated with the drug based on the genetic test results. determining whether a mutation is present in the child; and in response to determining that the drug is present in the internal database, determining a clinically relevant enzyme associated with the drug from the internal database; determining whether a mutation is present in a clinically relevant gene based on genetic test results; determining whether a problematic gene / drug interaction is present associated with the drug; and in response to determining that the problematic gene / drug interaction is present, identifying an alternative drug from a drug category (either from the internal database as described above or an external database); and providing a report to a user, the report including the problematic gene / drug interaction and the alternative drug.

[0006] In some embodiments, the present disclosure is directed to a method for analyzing drug interactions for a user, the method including receiving genetic test results for the user and a drug or drugs; determining primary metabolites and pathways for the primary metabolites for the drug; determining a category for the drug; determining a primary enzyme pharmacokinetically associated with the drug based at least in part on the primary metabolites and pathway; determining, based on the genetic test results, whether the user has a genetic mutation associated with the determined primary enzyme that will pharmacokinetically affect the drug; in response to determining that the patient has the genetic mutation, determining whether the genetic mutation has a threshold pharmacokinetic effect on the primary enzyme; in response to determining that the genetic mutation has a threshold pharmacokinetic effect, determining an alternative drug for the drug based on the determined category for the drug; and providing to the user an indication of the pharmacokinetic effects of the genetic mutation and the alternative drug.

[0007] In some embodiments, the present disclosure provides a system for analyzing drug interactions for a user, comprising: a processor; and a memory coupled to the processor, the system, when executed by the processor, comprising: receiving a genetic test result for the patient and a drug taken by the user, the drug being in a drug category; determining whether the drug is present in an internal database, the internal database including a list of genes associated with a plurality of drugs based on clinical relevance of the effects of the genes and the plurality of drugs; and, in response to determining that the drug is not present in the internal database, searching an external database for major metabolites and enzymes involved in forming the major metabolites, the major metabolites and enzymes associated with the drug; and determining whether a mutation is present in a gene associated with an enzyme that pharmacokinetically affects the drug based on the genetic test results; determining a clinically relevant enzyme associated with the drug from the internal database in response to determining that the drug is present in the internal database; determining whether a mutation is present in the clinically relevant gene based on the genetic test results; determining whether a problematic gene / drug interaction is present associated with the drug; and identifying an alternative drug from the drug category in response to determining that the problematic gene / drug interaction is present; and providing a report to a user, the report including the problematic gene / drug interaction and the alternative drug.

[0008] In some embodiments, the disclosed systems and methods provide dynamic reports that integrate pharmacogenetic testing information across multiple genes associated with individual drugs. In some embodiments, the disclosed systems and methods label drug risk in a clear manner that is more easily understood in a manner that does not require in-depth knowledge of pharmacogenetics, applicable to both patients and medical practitioners. [Brief explanation of the drawings]

[0009] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the present invention and, together with the written description, serve to explain the principles, characteristics and features of the invention.

[0010] [Figure 1] FIG. 1 depicts a block diagram of a system for analyzing drug interactions for a user, according to an embodiment of the present disclosure.

[0011] [Figure 2A] FIG. 2A depicts a flow diagram of a process for analyzing drug interactions for a user according to an embodiment of the present disclosure.

[0012] [Figure 2B] FIG. 2B depicts a flow diagram of a process for analyzing drug / gene interactions and categorizing drugs accordingly, according to an embodiment of the present disclosure.

[0013] [Figure 3A] FIG. 3A depicts an illustrative graphical user interface (GUI) for entering medications, according to an embodiment of the present disclosure.

[0014] [Figure 3B] FIG. 3B depicts an illustrative GUI for showing drug interactions according to an embodiment of the present disclosure.

[0015] [Figure 4] FIG. 4 depicts an illustrative internal table or override table, according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0016] (Detailed explanation) This disclosure is not limited to the particular systems, devices, and methods described, as these may vary, and the terminology used in this description is for the purpose of describing particular versions or embodiments only and is not intended to limit the scope of the disclosure.

[0017] For purposes of this application, the following terms shall have the respective meanings set forth below. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Nothing in this disclosure shall be construed as an admission that the embodiments described in this disclosure are not entitled to antedate such disclosure by virtue of prior invention.

[0018] (definition) As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to a "pharmaceutical" is a reference to one or more pharmaceutical agents and equivalents thereof known to those skilled in the art, and so forth.

[0019] As used herein, the term "about" means ±10% of the numerical value of the number with which it is used, so about 50 mm means within the range of 45 mm to 55 mm.

[0020] As used herein, the terms "consists of" or "consisting of" mean that the device or method includes only those elements, steps, or ingredients that are specifically recited in a particular claimed embodiment or claim.

[0021] In embodiments or claims in which the term "comprising" is used as a transitional phrase, such embodiments can also be envisioned with the term "comprising" replaced with the term "consisting of" or "consisting essentially of."

[0022] As used herein, the terms "user," "subject," or "patient" may be used interchangeably.

[0023] As used herein, the terms "drug," "medication," or "pharmaceutical composition" refer to a composition comprising an active pharmaceutical ingredient to be delivered to a subject, e.g., for a therapeutic, preventative, diagnostic, preventative, or prognostic effect.

[0024] As used herein, the term "active pharmaceutical ingredient" or "API" refers to a substance in a pharmaceutical composition that provides a desired effect, e.g., a therapeutic, preventative, diagnostic, preventative, or prognostic effect. In various embodiments, the active pharmaceutical ingredient can be any of a variety of substances known in the art, e.g., a small molecule, a polypeptide mimetic, a biologic, an antisense RNA, a small interfering RNA (siRNA), etc.

[0025] As used herein, "analyzing drug interactions" refers to assessing the influence of a patient's genetics on the metabolism of a drug, i.e., analyzing how a drug is metabolized by a patient, where the determination is based on the patient's genetics.

[0026] As used herein, "genetic test results" refers to results from one or more of molecular testing (e.g., targeted single-variant testing, single-gene testing, gene panels, and / or whole-genome sequencing), chromosomal testing, gene expression testing, or biochemical testing to identify a gene's genotype and / or phenotype. In certain embodiments, the genetic test results are for genes that are relevant for drug response, as described herein.

[0027] As used herein, "drug category" refers to a classification of drugs based on therapeutic effect, mechanism of action, or other collective characteristics. Drug categories may include, for example, opioids, analgesics, beta-blockers, and antipsychotics.

[0028] As used herein, "clinical relevance" refers to whether a gene directly or indirectly affects the metabolism of a drug. Relevant genes for various types of drugs will be known by those skilled in the art, for example, by referring to available scientific literature, FDA labeling, and other recognized sources of drug information. In one illustrative embodiment, mutations in genes that enhance or reduce the metabolism of a particular drug by at least a threshold amount (e.g., 50% or more) may be considered clinically relevant for the drug.

[0029] As used herein, "major metabolite" refers to the product primarily produced by the action of an enzyme on a drug.

[0030] As used herein, "associated with the drug" or "pharmacokinetically associated with the drug" refers to an enzyme that interacts with a drug or protein that either directly or indirectly affects the response to the drug (e.g., by interacting with a membrane protein such as a receptor or transporter), to produce a major metabolite (e.g., by using the drug as a substrate), or otherwise affects the therapeutic response to the drug.

[0031] As used herein, "problematic," particularly within the context of "problematic gene-drug interaction," refers to whether the action of a drug is affected by one or more genetic mutations in the user. The effect can be to reduce the drug's effect or to increase the drug's effect. Increasing or decreasing the drug's effect can lead to adverse effects for the patient.

[0032] As used herein, "threshold pharmacokinetic impact" refers to any factor that reduces or enhances drug metabolism by a threshold amount (e.g., by 50% or more).

[0033] As used herein, "determining whether there is a problematic gene-drug interaction associated with the drug" refers to determining whether a user's gene (i.e., a mutation in a gene associated with a protein that acts either directly or indirectly on the drug) affects the metabolism of or response to the drug. A problematic gene / drug interaction can be calculated, for example, by reviewing scientific data regarding the metabolism or response to a drug and whether mutations in the protein encoded by the gene may affect the response or metabolism of the drug in a clinically relevant manner.

[0034] As used herein, "internal database" refers to a database containing one or more entries, each of which may contain a variety of different information, such as clinically relevant genes or genetic mutations for a particular drug, the extent to which the listed genes affect drug metabolism or drug response, and other gene / drug interactions.

[0035] As used herein, "external database" refers to a database that includes one or more entries, where each database entry may include a variety of different information, such as a drug's primary metabolic pathway, a drug's classification or category, and drug / drug interactions. Suitable external databases may include, for example, the DrugBank database, accessible at https: / / go.drugbank.com.

[0036] (Drug interaction analysis system) The present disclosure is directed to systems and methods for analyzing medications for a patient, including analyzing drug-drug and gene-drug interactions. Additionally, the described systems and methods can recommend alternative medications to a patient and / or a third party (e.g., a healthcare provider) if alternative medications may be needed for the patient. FIG. 1 illustrates a system 100 that includes a computer system 102 that can be accessed via a network 110 (e.g., the Internet) by a user 112 and / or a healthcare provider 114. Additionally, the computer system 102 can store or otherwise be communicatively coupled to a database 110 that can store a variety of different information associated with drug pharmacokinetics or drug-drug interactions, as described in more detail below. In operation, a user 112 can access the computer system 102, input data (e.g., medications the user is taking), review pharmacogenetic analysis results and recommendations, and take other such actions.

[0037] In one embodiment, the computer system 102 may be operated or controlled by a testing entity that is capable of receiving, performing, and / or prescribing genetic tests 118 on patient / healthcare provider samples and obtaining genetic data about the patient. In another embodiment, the computer system 102 may be differently configured to receive genetic tests 118 from an external source (e.g., via the network 110). As described in more detail below, the user's genetic data may be utilized to analyze whether any alternative medications may provide better results or may otherwise be more suitable for the user, given the user's genetic makeup. In one embodiment, the user's genetic test results may be obtained by laboratory testing of a genetic sample collected from the user (e.g., through analysis of buccal mucosal epithelial cells collected via swabbing the inside of the cheek with a specially manufactured cotton swab). The genetic test results may be in the form of genetic diplotypes (i.e., one allele from each parent), which are then translated into a phenotype (i.e., the functional impact of the diplotype on protein function). In some embodiments, it may be beneficial to consider a user's diplotype, as each allele may be associated with a functional aspect of the protein it encodes. Thus, an individual's diplotype may influence how a drug is metabolized or how a drug otherwise affects the individual. For example, if one allele encodes a protein that exhibits normal functionality and a second allele encodes a non-functional protein, the combined phenotype may behave as a semi-functional protein. For genes encoding metabolic enzymes, an exemplary phenotype for an individual may be categorized as, for example, an "intermediate metabolizer." In one embodiment, a genetic test 118 may include diplotypes associated with one or more tested genes.In this embodiment, the computer system 102 can be programmed to match the diplotypes received from the genetic test results with a table of previously determined functionalities associated with individual alleles and convert the diplotypes accordingly into a predicted composite phenotype for the gene. The phenotype can then be reported as a clinically relevant result for that gene. In some embodiments, the phenotype can be assigned a corresponding index from one or more indexes representing the degree of impact on protein function for the user's diplotype. For example, the index can represent "severe," "intermediate," and "no impact." In one embodiment, the index for the assigned phenotype can include a color (e.g., red, yellow, and green).

[0038] Computer system 102 may include a processor 104 and memory 106 for executing various processes or algorithms to analyze the medications the user is taking and / or the user's data and, accordingly, provide recommendations to the user. In some embodiments, computer system 102 may include a server or cloud-based computing system. In various embodiments, computer system 102 may be configured to provide an interface (e.g., a GUI) accessed by and / or interacted with through a software application or website (e.g., launched on a mobile device associated with user 112). The interface may allow the user to input the medications they are taking, such as those shown in FIG. 3A. Additionally, the interface may display various recommendations or alerts to the user, such as those shown in FIG. 3B.

[0039] In one embodiment, the user may manually enter the medications they are taking and / or have been prescribed. In another embodiment, the computer system 102 may be configured to retrieve the medications the user is taking from the user's electronic health record (EHR) or other external source. For example, the computer system 102 may be communicatively coupled to a healthcare provider computer system and have access to the user's EHR through authorization from either the user or the user's healthcare provider 114 (e.g., a doctor). In this embodiment, the computer system 102 may be configured to automatically retrieve from the user's EHR a list of the medications the user is taking and / or has been prescribed, upon receipt of appropriate authorization.

[0040] The computer system 102 can be configured to communicate with an external database 116 that provides information about drugs, such as information about drug / drug interactions or major metabolic pathways associated with drugs (e.g., major circulating metabolites and metabolic enzymes involved in their formation). The external database 116 can be accessed, for example, via a corresponding application programming interface (API). The external database 116 can include, for example, the DrugBank database. One challenge with current drug databases, such as DrugBank, is that while they may have some information about drug / drug interactions and major metabolic pathway data about drugs (e.g., major circulating metabolites and metabolic enzymes involved in their formation), the information available in such external databases 116 is not sufficient to determine whether any genetic mutations an individual may have (as determined from genetic testing) can affect the pharmacokinetics of a given drug. For example, currently available drug information databases may include metabolic enzymes associated with major circulating metabolites associated with a drug, but the pharmacokinetics of a given drug may be affected by other enzymes and / or other genetic mutations. Therefore, currently available databases are not sufficient to provide individuals with a complete and clear picture of whether a drug is appropriate for them to use based on genetic testing.

[0041] Thus, the system 100 described herein includes a drug interaction table 109 associated with an internal database 108 stored on or otherwise communicatively coupled to the computer system 102. In some instances, the drug interaction table 109, also referred to as an "override table," is deployed to supplement and / or override drug information in currently commercially available drug information databases. The drug interaction table 109 describes drug / gene interactions for drugs for which currently available external databases 116 do not provide sufficient information to identify all possible drug / gene interactions. For each drug in the drug interaction table 109, a gene is associated with the drug based on the gene's clinical relevance to the drug's action. Clinical relevance is determined based on experimental data (typically clinical data) indicating that genetic mutations affecting the function of the protein encoded by the gene have an effect that affects the drug's clinical attributes, such as efficacy, safety, or pharmacokinetics. In some embodiments, the drug interaction table 109 may be based on individual alleles and / or diplotypes associated with a stated gene or genes. In one embodiment, the drug interaction table 109 is constructed in a tabular format, including each drug for which the external database 116 lacks sufficient information to identify a relevant gene / drug interaction and the clinically relevant genes associated with each of the drugs. The drug interaction table 109 may further include clinically relevant metabolic genes and clinically relevant response genes. FIG. 4 depicts an illustrative portion of the drug interaction table 109, for example, showing metabolic and response markers for drug pairs. In this example, the drugs are listed based on their DrugBank ID (e.g., DB00802 corresponds to alfentanil). Thus, the system 100 can query DrugBank or other external databases based on the specific IDs associated with those drugs from the drug interaction table 109.

[0042] In operation, computer system 102 receives (e.g., by manually entering via an app GUI or website GUI) a list of one or more medications the user is taking and the user's genetic test results. For each medication taken by the user, computer system 102 queries drug interaction table 109 for that medication, retrieves relevant genes from the metabolism and response gene data for the medication, checks the relevant genes against the patient's pharmacogenetic test results, and reports any abnormalities indicating potential drug / gene interactions (e.g., mutations present in genes coding for one of the clinically relevant enzymes associated with the medication). If a medication is not present in drug interaction table 109, computer system 102 may retrieve corresponding data from external database 116 and determine any drug / gene interactions therefrom. The report to the user may include a description of the nature of the abnormality and how it affects either drug metabolism or drug response. As new information about drug / gene interactions (e.g., new research and / or clinical data) becomes available, drug interaction table 109 can be dynamically modified to incorporate the new information. Unlike the internal database 108, currently commercially available databases (e.g., DrugBank) merely list drug attributes (e.g., drug-metabolizing enzymes or drug classes) rather than considering clinical relevance. These commercially available databases can be queried in a manner that assumes that certain attributes have clinical relevance based on general principles of pharmacology (e.g., drug metabolism and dose / response relationships) (i.e., clinical relevance may not be supported by experimental data), but these assumptions will not be correct for all drugs. Thus, any system that attempts to identify possible drug / gene interactions based solely on this commercially available information will be imperfect and therefore will not provide correct results in all cases.If a drug in the external database has a drug attribute to which the pharmacogenetic gene being tested is associated, the associated genetic information can be reported if abnormal.

[0043] In one particular embodiment, system 100 can be embodied as a web-based app that can search a commercial database of drug / drug interactions (e.g., DrugBank), identify problematic drug combinations, and display any problematic drug combinations to a physician and / or patient-user, along with a detailed description of the problematic interactions and information on how to manage them clinically. System 100 can further identify gene / drug interactions by comparing the patient's medications to an internal database of genes that code for specific drugs and proteins that affect either the biotransformation of the specific drug (i.e., metabolic genes), the transport of the specific drug within the body, and / or the therapeutic response to the specific drug (i.e., response genes). The internal database can be established using clinical and / or research data on various drugs.

[0044] Any problematic drug / drug and / or drug / gene interactions identified by system 100 can be indicated based on the phenotype associated with the abnormal function of the encoded protein. Additionally, results can be presented to the user in a variety of different formats. For example, results can be color-coded green, yellow, or red based on the clinical impact of the genetic mutation and / or the severity of the drug / drug interaction, where green can indicate normal function, yellow can indicate an increased risk of adverse effects or decreased clinical efficacy, and red can indicate an extreme risk of adverse effects or a lack of clinical efficacy.

[0045] (Drug interaction analysis process) One embodiment of a process 200 for analyzing a user's drug interactions is shown in FIG. 2A. In one embodiment, process 200 can be embodied as instructions stored in memory (e.g., memory 106) that, when executed by a processor (e.g., processor 104), cause computer system 102 to perform process 200. In various embodiments, process 200 can be embodied as software, hardware, firmware, and various combinations thereof. In various embodiments, process 200 can be performed by and / or among a variety of different devices or systems. For example, various combinations of the steps of process 200 can be performed by computer system 102, network 110, and / or a device (e.g., a computer, laptop, or smartphone) associated with user 112. In various embodiments, system 100 performing process 200 can utilize distributed processing, parallel processing, cloud processing, and / or edge computing techniques. Process 200 is described below as being performed by system 100, and thus it should be understood that the functions may be performed individually or collectively by one or more devices or systems.

[0046] System 100 executing process 200 may receive 202 one or more medications associated with a user and may receive 204 genetic information (e.g., genetic test results) associated with the user. In one embodiment, a user may manually enter medications they are taking by interacting with computer system 102, such as by entering the medications into a website interface. For example, FIG. 3A illustrates a GUI 300 including a medication entry field 302 in which a user may manually obtain medications they are taking and / or prescribed. In the particular example shown in FIG. 3A, the user has entered two medications 304 (propranolol and clopidogrel) they are taking. In another embodiment, computer system 102 may retrieve the user's EHR (e.g., from a database associated with the patient's healthcare provider) and determine therefrom the medications they are taking and / or prescribed for the user. In one embodiment, system 100 may receive 204 genetic information from a genetic test performed by an entity operating computer system 102. In another embodiment, the system 100 may receive 204 the user's genetic information from a third party or another external source.

[0047] For each patient's medication, system 100 can identify (206) any drug / gene interactions associated with the received (202) medication list and the received (204) genetic information. In parallel with identifying (206) the drug / gene interactions, or otherwise separately, system 100 can further identify (208) any drug / drug interactions. In one embodiment, system 100 can utilize a commercially available database (e.g., DrugBank) to identify (206) the drug / drug interactions.

[0048] System 100 can identify (206) drug / gene interactions between a received (202) drug list and received (204) genetic information about a user in a variety of different ways. In one embodiment shown in FIG. 2A , system 100 can determine (210) whether a drug is present in internal database 108 (i.e., drug interaction table 109). If system 100 determines (210) that a drug is present in internal database 108, system 100 can determine (212) whether any drug / gene interactions exist based on the pre-characterized drug / gene data in drug interaction table 109. If system 100 determines (210) that a drug is not present in internal database 108, system 100 can search (214) an external database 116 (e.g., DrugBank) for major metabolites and enzymes involved in forming the major metabolites associated with a given drug. If a drug is not present in the internal database 108, it may be assumed that the information present in the external database 116 is sufficient to assess the clinical effect of the pharmacogenetic data for the drug. Based on data from the external database 116, the system 100 can identify the primary enzyme for the drug, which can then be utilized to determine (216) whether any drug / gene interactions exist based on the user's genetic test information and the determined primary enzyme associated with the drug. In other words, the system 100 can determine (216) whether the user has a mutation in the gene associated with the identified primary enzyme that clinically affects the drug. In the present context, a genetic mutation can clinically affect an enzyme if it causes the enzyme to be inactive, makes the enzyme hyperfunctional, or makes the enzyme underfunctional. For example, CYP2C19 codes for the enzyme clinically relevant to clopidogrel. Clopidogrel is a prodrug that is converted to its active form by CYP2C19.If CYP2C19 has a mutation that causes the enzyme to be under-functional, there will be no clinically relevant drug effect for clopidogrel. Conversely, if CYP2C19 has a mutation that causes the enzyme to be over-functional, there will be an excessive drug effect, and the patient's blood will be overly thinned, leading to an increased risk of bleeding. System 100 can determine (212, 216) whether the user has a mutation in the CYP2C19 gene or any other gene that has a clinical effect on the drug, either through information obtained from a pre-characterized drug interaction table 109 or an external database 116.

[0049] Thus, system 100 can determine (218) whether any problematic drug / gene interactions exist. Different genetic mutations can have varying effects on the pharmacokinetics of a drug, and therefore, in some implementations, it may be desirable to quantify or otherwise determine the degree of pharmacokinetic impact that a particular genetic mutation will have on a drug. In other words, it may be desirable to recommend an alternative drug only in situations where a patient's genetic mutation will have a significant or non-trivial pharmacokinetic impact on the drug. Thus, in one embodiment, system 100 can determine (218) whether any problematic drug / gene interactions exist based on whether the pharmacokinetics associated with the enzyme encoded by the particular gene at issue will be adversely affected for the drug by at least a threshold level or degree.

[0050] In one embodiment, system 100 can code analyzed drugs into one or more categories depending on whether system 100 identifies any problematic drug / gene interactions. For example, a first category may correspond to drugs for which no genetic indicators of clinical significance have been identified based on the subject's genetic data, a second category may correspond to drugs for which genetic indicators requiring caution have been identified, and a third category may correspond to drugs for which genetic indicators requiring extreme caution or avoidance have been identified. To assist users in quickly identifying and assessing the category into which an analyzed drug falls, the categories can be associated with various indicators (e.g., colors), which may be presented via a graphical user interface such as that shown in FIGS. 3A and 3B.

[0051] In embodiments, system 100 can determine (218) whether any problematic drug / gene interactions exist by calculating a score for the drug, comparing the calculated score to various thresholds, and assessing whether a particular drug or set of drugs is problematic given the subject's genetic data. In embodiments, the drug score can include one or more separate calculations and / or components.

[0052] In one embodiment, determining and / or calculating a drug score may include determining and / or calculating a metabolic component value (MCV) and a response component value (RCV). The MCV can be a calculation based on the phenotypic results for the relevant metabolic genes. The RCV can be a calculation based on the expected response or adverse effect according to the phenotype determined from the genetic information of the subject. In one embodiment, the MCV can be calculated for n genes according to the following equation: [ka] where PCD is the phenotypic color designation for a particular gene, and I is the relative importance (e.g., expressed as a weight or percentage) for the particular gene. In other words, MCV is calculated via a weighted summation of the PCDs for n genes associated with the particular drug being analyzed. Notably, the relative importance (I) for genes associated with a drug sum to 1. In one embodiment, the PCD may be a value corresponding to a coding assigned for a gene (e.g., no genetic indicator of clinical importance, a genetic indicator requiring caution, or a genetic indicator requiring undue caution or avoidance). For example, a PCD value for "green" or low-risk genes for a particular drug may be 1, a PCD value for "yellow" or moderate-risk genes may be 5, and a PCD value for "red" or high-risk genes may be 10. In other embodiments, the PCD value may be a different static value, i.e., a function based on one or more variables, etc. In one embodiment, the relevance value may be a value assigned to each gene based on its pharmacological and toxicological attributes. For example, the relevance value may be based on the overall contribution of each tested gene to the overall metabolism of the drug and resulting drug metabolites, the clinical relevance of metabolic products from each tested gene (e.g., whether an active metabolite is produced, whether a toxic metabolite is produced, and whether the metabolite is primary to the drug response), known pharmacogenetic-associated metabolic effects (e.g., obtained from drug labels), and relevant information from the scientific literature (e.g., data from in vitro and clinical studies using human hepatocytes).

[0053] In one embodiment, determining and / or calculating a drug score may include determining and / or calculating a response component value (RCV). For example, RCV calculation can be based on the PCD obtained for a response-related gene or multiple PCDs for a group of response-related genes (if more than one response gene is relevant for the drug). With regard to response genes, there is no weighting of the relative importance of individual response genes, because any deleterious mutation in any relevant response gene will make the drug ineffective. Therefore, the highest PCD value among the relevant response genes is assigned as the RCV. So, for example, if a drug has two relevant response genes with PCD values ​​of 1 and 5, the RCV will be 5.

[0054] In one embodiment, system 100 can use a combination of MCV and RCV to determine (218) whether any problematic drug / gene interactions exist. An example of a process for determining (218) whether any problematic drug / gene interactions exist, given a subject's genetic information and the drugs associated with the subject (i.e., drugs currently being taken by the subject or to be prescribed to the subject), is shown in FIG. 2B. In this embodiment, system 100 calculates (215) the MCV given the genetic information and drugs received (202, 204) for the subject, as described above. Furthermore, system 100 determines (252) whether any response and / or adverse event markers exist. System 100 examines genetic information obtained by testing for pharmacogenetic mutations (i.e., PGx test results) and matches this to genes relevant for the drug whose PCD is being determined. If no response and / or adverse event markers are present, system 100 assigns the MCV as the score for the given genetic and / or drug information (254). If response and / or adverse event markers are present, system 100 determines the RCV (256) given the genetic information and drugs received for the subject (202, 204), as described above. System 100 compares the RCV and MCV and assigns the greater of the two values. Thus, system 100 determines (258) whether the RCV exceeds the MCV. If the MCV is greater, system 100 assigns the MCV as the score for the given genetic and / or drug information (254). If the RCV is greater, system 100 assigns the RCV as the score for the given genetic and / or drug information (260). Thus, the system 100 categorizes (262) the drugs according to the scores assigned (254, 260).

[0055] If system 100 determines (218) that a problematic drug / gene interaction exists for the user, system 100 may identify (220) one or more alternative drugs for the analyzed drug. In one embodiment, the alternative drugs may be determined from the product category of the drug in question. For example, if the drug being analyzed is a beta-blocker, system 100 may identify (220) an alternative beta-blocker for the user. As another example, if the drug being analyzed is an opioid, system 100 may identify (220) an alternative opioid for the user. In one embodiment, the identified (220) alternative drugs may be further analyzed to identify (206) any drug / gene interactions associated with the identified (220) alternative drug, as shown in FIG. 2 . This embodiment may be beneficial because it may be desirable to check the proposed drugs for any problematic gene / drug interactions, so that system 100 only suggests alternative drugs that have been similarly analyzed with respect to those gene / drug interactions. Furthermore, system 100 can continually repeat the process of identifying gene / drug interactions (206) until a suitable replacement drug in the same product category as the originally analyzed drug (i.e., a drug that does not have any problematic gene / drug interactions) is identified.

[0056] Once system 100 executing process 200 identifies an appropriate alternative medication, system 100 can provide 222 the results. In one embodiment, system 100 can provide 222 the results to user 112. In another embodiment, system 100 can provide 222 the results to the user's healthcare provider 114. In yet another embodiment, system 100 can provide 222 the results to both user 112 and healthcare provider 114. The results can include whether any gene / drug or drug / drug interactions were identified and / or suggested alternative medications. In one embodiment, system 100 can provide data, reports, or summaries to the patient and / or a third party showing the pharmacokinetic impact of the patient's specific genetic mutation on medications currently being taken by the patient. Such embodiments can be beneficial for further educating patients in managing their own healthcare and taking an active role in doing so. For example, FIG. 3B illustrates a reporting GUI 310 provided by system 100. In this example, the reporting GUI 310 includes an alert 311 indicating that a problematic drug / drug and / or gene / drug interaction has been identified for the user. In this example, a widget 312 of the reporting GUI 310 indicates that a problematic drug / drug interaction exists between two of the medications being taken by the user. Additionally, a second widget 314 of the reporting GUI 310 further explains the identified problematic gene / drug interaction. Finally, the reporting GUI 310 lists alternative medications 316 for each of the medications being taken by the user for which a problematic gene / drug interaction and / or drug / drug interaction has been identified.

[0057] 2 can be repeated for each medication the user is taking. The analysis for each medication by process 200 performed by system 100 can be incorporated into a report provided 222 to the user.

[0058] It should be further noted that, although the functions and / or steps of process 200 are depicted in a particular order or arrangement, the depicted order and / or arrangement of steps and / or functions is provided for illustrative purposes only. Unless expressly described to the contrary herein, various steps and / or functions of process 200 may be performed in different orders, in parallel with one another, in an alternating manner, etc.

[0059] (Drug Interaction Analysis System - Application Programmable Interface) Generally, as described above, a variety of different external systems (e.g., third-party systems) can be communicatively coupled to or otherwise configured to interface with computer system 102. In some embodiments, the external systems can be communicatively coupled to computer system 102 via an application programmable interface (API). An API is a set of protocols, routines, and tools for building software applications. Thus, an API defines a standard set of rules that allow external systems to communicate with computer system 102. The API can enable external systems to exchange data with computer system 102 to provide access to the drug / drug and gene / drug analyses provided by computer system 102 (as described above) to determine drug efficacy. The API can enable a variety of different external systems to submit queries to or otherwise use computer system 102, or to instantly allow the external systems to receive responses from computer system 102, without the need for manual queries to be generated as is typical for this field.

[0060] The API can support a variety of different endpoints, such as an API call for retrieving patient results based on a drug lookup. The API call can be structured to receive, for example, a specific drug (or set of drugs) for a subject and identifying information about the subject. In response to the call, the computer system 102 can return data indicating where the patient data was located in the internal database 108, the category assigned for the drug (i.e., whether any drug / gene and / or drug / drug interactions were identified and the severity of those interactions), and the identified drug / gene and / or drug / drug interactions. This data can then either be presented to the user (e.g., via a graphical user interface such as shown in FIGS. 3A and 3B) or otherwise utilized by an external system (e.g., the healthcare provider system 114) that initiates a query. Generally, as discussed above, genetic testing data 118 for a particular subject may have been previously received by the computer system 102 from an appropriate genetic testing facility and / or otherwise uploaded to the internal database 108.

[0061] Aspects of the present invention will now be illustrated, by way of example only, with reference to the following experiments. [Example]

[0062] Example 1 Efavirenz Efavirenz, marketed as SUSTIVA®, is a human immunodeficiency virus type 1 (HIV-1)-specific non-nucleoside reverse transcriptase inhibitor (NNRTI). Efavirenz is metabolized by CYP3A4 / 5, CYP2B6, CYP2C9, and CYP2C19, and accordingly, these genes are utilized to analyze whether any problematic interactions exist based on the subject's genetic test results associated with these genes. In this example, the exemplary patient has the following genetic test results: 3A4 PM, 3A5 IM, 2B6 EM, 2C9 IM, and 2C19 PM (PM, IM, and EM are metabolic phenotypes). Metabolic phenotypes are determined by (i) conversion of mutations into associated phenotypes and (ii) categorization of two phenotypes (one from each parent) into a composite phenotype and associated PCD, also known as a diplotype, via reference to a diplotype-to-phenotype conversion table. Based on the foregoing genetic outcome data, the PCD for CYP3A5 is 5 (i.e., "yellow"), the PCD for CYP2B6 is 1 (i.e., "green"), the PCD for CYP2C9 is 5 (i.e., "yellow"), and the PCD for CYP2C19 is 10 (i.e., "red"). As described above, a relative importance is assigned by computer system 102 to each of the various components of the MCV calculation. These weights are set forth below. Accordingly, the MCV is calculated for efavirenz given the above genetic information as follows: [ka] Thus, for this particular patient, the MCV of efavirenz is 4.05. In response, computer system 100 will assign a yellow phenotype color designation, indicating a moderate risk associated with this drug for this patient. As described above, this categorization may be displayed to the user via a graphical user interface.

[0063] Notably, efavirenz is metabolized by CYP3A4 and CYP3A5, but CYP3A4 is ignored by computer system 102 because CYP3A4 and CYP3A5 are treated as combined functions because they act identically with respect to the drugs they affect. Thus, computer system 102 and processes described herein can utilize the best results between CYP3A4 and CYP3A5 to calculate a score for a drug.

[0064] Example 2: Simvastatin Simvastatin is used to lower low-density lipoprotein cholesterol (LDL-C) in the blood. In patients of African descent, simvastatin is metabolized by CYP3A4 / 5 and is associated with an adverse effect gene (SLCO1B1). Accordingly, these genes are utilized to analyze whether any problematic interactions exist based on the subject's genetic test results associated with these genes. In this example, the exemplary patient has the following genetic test results: 3A4 IM, 3A5 EM, and SLCO1B1. Based on the aforementioned genetic result data, the PCD for CYP3A5 is 1 (i.e., "green"). As described above, relative importance is assigned by the computer system 102 to each of the various components of the MCV calculation. These weights are described below. Accordingly, the MCV is calculated for simvastatin given the above genetic information as follows: [ka]

[0065] Again, note that simvastatin is metabolized by both CYP3A4 and CYP3A5, but only one of these two genes is analyzed for the reasons discussed above. Thus, for this particular patient, the MCV of simvastatin is 1.0. However, because simvastatin additionally has an adverse event marker associated with it, computer system 102 would additionally determine the RCV for simvastatin. In this case, an RCV of 5 (i.e., "yellow") has been assigned to SLCO1B1 for this drug. Because the RCV is greater than the calculated MCV, computer system 102 would assign the RCV as the drug score. In response, computer system 100 would assign a yellow phenotype color designation, indicating a moderate risk associated with this drug for this patient.

[0066] Example 3: Desvenlafaxine In non-African patients, simvastatin is metabolized by CYP3A4 / 5 and CYP2D6, and accordingly, these genes are utilized to analyze whether any problematic interactions exist based on the subject's genetic test results associated with these genes. In this example, the exemplary patient has the following genetic test results: 3A4 EM, 3A5 PM, and 2D6 EM. Based on the aforementioned genetic result data, the PCD for CYP3A5 is 1 (i.e., "green"). As described above, relative importance is assigned by computer system 102 to each of the various components of the MCV calculation. These weights are described below. For some drugs, computer system 102 can further apply a general metabolic relevance factor (MRF) to the MCV calculation, where only a small proportion of the relevant genes metabolize the drug in vivo. For desvenlafaxine, only 5-10% of the CYP genes metabolize the drug. Thus, the computer system 102 can apply a general relevance factor or weighting to the overall MCV calculation. Accordingly, the MCV is calculated for simvastatin given the genetic information above as follows: [ka]

[0067] Again, note that simvastatin is metabolized by both CYP3A4 and CYP3A5, but only one of these two genes is analyzed for the reasons discussed above. Thus, for this particular patient, the MCV of desvenlafaxine is 0.1. Accordingly, computer system 100 would assign a green phenotype color designation, indicating that there is little or no risk associated with this drug for this patient.

[0068] While various illustrative embodiments incorporating principles of the present teachings have been disclosed, the present teachings are not limited to the disclosed embodiments. Instead, this application is intended to cover any variations, uses, or adaptations of the present teachings using their general principles. Further, this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which these teachings pertain.

[0069] In the above detailed description, reference is made to the accompanying drawings, which form a part of this specification. In the drawings, like symbols typically identify like components unless context dictates otherwise. The illustrative embodiments described in this disclosure are not intended to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that the various features of the present disclosure, generally as described herein and illustrated in the figures, can be arranged, substituted, combined, separated, and designed into a wide variety of different configurations, all of which are expressly contemplated herein.

[0070] The present disclosure should not be limited in terms of the specific embodiments described herein, which are intended as illustrations of various features. Many modifications and variations can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatuses within the scope of the present disclosure, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing description. It is to be understood that the present disclosure is not limited to particular methods, reagents, compounds, compositions, or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0071] With respect to the use of virtually any plural and / or singular term herein, those skilled in the art can convert from plural to singular and / or from singular to plural as appropriate to the context and / or application. Various singular / plural permutations may be expressly set forth herein for clarity.

[0072] In general, it will be understood by those skilled in the art that the terms used herein are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.). Although various compositions, methods, and devices are described in terms of "comprising" (interpreted to mean "including, but not limited to") various components or steps, compositions, methods, and devices can also "consist essentially of" or "consist of" various components or steps, and such terminology should be interpreted as defining an essentially closed group of members.

[0073] Additionally, even when specific numbers are explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the literal recitation of "two recitations" without other modifiers means at least two recitations or two or more than two recitations). Furthermore, in those cases where notation similar to "at least one of A, B, and C, et cetera" is used, such structure is generally intended in the sense that one skilled in the art would understand the notation (e.g., "a system having at least one of A, B, and C" would include, but is not limited to, systems having A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). In those cases where notation similar to "at least one of A, B, or C, et cetera" is used, generally, such structure is intended in the sense that one of ordinary skill in the art would understand the notation (e.g., "a system having at least one of A, B, and C" would include, but is not limited to, systems having A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). It will further be understood by those of ordinary skill in the art that virtually any disjunctive word and / or phrase presenting two or more alternative terms, whether in the description, sample embodiments, or drawings, should be understood to contemplate the possibility of including one of the terms, either of the terms, or both terms. For example, the phrase "A or B" will be understood to include the possibilities of "A," or "B," or "A and B."

[0074] Additionally, where features of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual members or subgroups of members of the Markush group.

[0075] As will be understood by those skilled in the art, for all purposes, including in terms of providing a written description, all ranges disclosed herein also encompass all possible subranges and combinations of those subranges. Any listed range can be readily recognized as fully descriptive and allows for the same range to be broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third, upper third, etc. As will also be understood by those skilled in the art, phrases such as "up to," "at least," and the like all refer to ranges that include the recited numbers and can subsequently be broken down into subranges as discussed above. Finally, as will be understood by those skilled in the art, a range includes each individual member. Thus, for example, a group having 1 to 3 cells refers to a group having 1, 2, or 3 cells. Similarly, a group having 1 to 5 cells refers to groups having 1, 2, 3, 4, or 5 cells, etc.

[0076] As used herein, the term "about" refers to variations in a quantity that may occur, for example, through real-world measurement or handling procedures, through inadvertent errors within these procedures, through differences in the manufacture, source, or purity of a composition or reagent, and the like. Typically, the term "about" as used herein means 1 / 10th the stated value or range of values, e.g., ±10%, of the stated value. The term "about" also refers to variations that would be recognized by those of ordinary skill in the art as equivalents, unless such variations encompass known values ​​practiced by the prior art. Each value or range of values ​​preceded by the term "about" is also intended to encompass embodiments of the stated absolute value or range of values. Whether modified by the term "about," quantitative values ​​recited in this disclosure include equivalents of the recited values, e.g., variations in the quantities of such values ​​that may occur but would be recognized as equivalents by those of ordinary skill in the art.

[0077] Various of the above-disclosed and other features and functions, or alternatives thereof, may be combined into many other different systems or applications. Various presently unforeseen or unanticipated alternatives, modifications, variations, or improvements thereon may subsequently occur to those skilled in the art, each of which is also intended to be encompassed by the disclosed embodiments.

[0078] The functions and process steps herein may be performed automatically or fully or partially in response to user commands. An activity (including a step) that is performed automatically is performed in response to one or more executable instructions or device operations without direct user initiation of the activity.

Claims

1. 1. A method for analyzing drug interactions for a user, the method comprising: receiving a genetic test result for the patient and a medication taken by the user, the medication being in a medication category; determining whether the drug is present in an internal database, the internal database including a list of genes associated with the plurality of drugs based on clinical relevance of the effects of genes and the plurality of drugs; in response to determining that the drug is not present in the internal database, searching an external database for major metabolites and enzymes involved in forming said major metabolites, said major metabolites and said enzymes being associated with said drug; determining whether a mutation is present in the gene associated with the enzyme that affects the pharmacokinetics of the drug based on the genetic test results; and in response to determining that the drug is present in the internal database; determining a clinically relevant enzyme associated with said drug from said internal database; determining whether a mutation is present in the clinically relevant gene based on the genetic test results; and determining whether there is a problematic gene / drug interaction associated with said drug; identifying an alternative drug from the drug category in response to determining that the problematic gene / drug interaction exists; providing a report to the user, the report including the gene / drug interaction in question and the alternative drug; A method comprising:

2. The method of claim 1 , further comprising providing the report to a healthcare provider associated with the user.

3. 3. The method of claim 1 or claim 2, further comprising determining whether the user has a problematic gene / drug interaction with the alternative drug.

4. 4. The method of claim 1, wherein determining whether the problematic gene / drug interaction associated with the drug exists comprises determining whether there is at least a threshold pharmacokinetic effect on the drug caused by the mutation.

5. 5. The method of claim 4, wherein the threshold pharmacokinetic effect comprises excessive metabolic functionality of the enzyme for the drug.

6. 5. The method of claim 4, wherein the threshold pharmacokinetic effect comprises a metabolic under-functionality of the enzyme for the drug.

7. Determining whether there is a problematic gene / drug interaction associated with the drug comprises: determining a metabolic component value for the drug based on the received genetic test result; determining a response component value for the drug based on the received genetic test result; assigning a drug category from a plurality of categories for the drug based on the greater of the metabolic component value and the response component value, the drug category indicating the severity of any drug / gene interaction for the user and the drug; The method of any one of claims 1 to 6, comprising:

8. 1. A method for analyzing drug interactions for a user, the method comprising: receiving genetic test results and medications for the user; determining primary metabolites for the drug and pathways for the primary metabolites; determining a category for the drug; determining a primary enzyme pharmacokinetically associated with the drug based, at least in part, on the primary metabolite and the pathway; determining, based on the genetic test results, whether the user has a genetic mutation associated with the determined primary enzyme that will pharmacokinetically affect the drug; responsive to determining that the patient has the genetic mutation, determining whether the genetic mutation has a threshold pharmacokinetic effect on the primary enzyme; determining an alternative drug for the drug based on the determined category for the drug in response to determining that the genetic mutation has a pharmacokinetic impact of the threshold effect; providing the user with an indication of the pharmacokinetic impact of the genetic mutation and the alternative drug; A method comprising:

9. The method of claims 7-8, further comprising providing an indication of the genetic mutation and the pharmacokinetic effects of the alternative drug to a healthcare provider associated with the user.

10. 10. The method of claim 8 or claim 9, wherein the category is selected from the group consisting of opioids or beta-blockers.

11. The method of any one of claims 8-10, wherein the pharmacokinetic effect of the threshold effect corresponds to a deleterious effect of the genetic mutation on the pharmacokinetics associated with the enzyme.

12. The method of any one of claims 8-11, wherein said adverse effect comprises excessive metabolic functionality of said enzyme towards said drug.

13. The method of any one of claims 8-11, wherein said adverse effect comprises metabolic under-functionality of said enzyme towards said drug.

14. Determining whether the genetic mutation has a threshold pharmacokinetic effect on the primary enzyme comprises: determining a metabolic component value for the drug based on the received genetic test result; determining a response component value for the drug based on the received genetic test result; assigning a drug category from a plurality of categories for the drug based on the greater of the metabolic component value and the response component value, the drug category indicating the severity of any drug / gene interaction for the user and the drug; The method of any one of claims 8 to 13, comprising:

15. 1. A system for analyzing drug interactions for a user, the system comprising: a processor; a memory coupled to the processor; Equipped with The memory, when executed by the processor, provides the system with: receiving a genetic test result for the patient and a medication taken by the user, the medication being in a medication category; determining whether the drug is present in an internal database, the internal database including a list of genes associated with the plurality of drugs based on clinical relevance of the effects of genes and the plurality of drugs; in response to determining that the drug is not present in the internal database, searching an external database for major metabolites and enzymes involved in forming said major metabolites, said major metabolites and said enzymes being associated with said drug; determining whether a mutation is present in the gene associated with the enzyme that affects the pharmacokinetics of the drug based on the genetic test results; and in response to determining that the drug is present in the internal database; determining a clinically relevant enzyme associated with said drug from said internal database; determining whether a mutation is present in the clinically relevant gene based on the genetic test results; and determining whether there is a problematic gene / drug interaction associated with said drug; identifying an alternative drug from the drug category in response to determining that the problematic gene / drug interaction exists; providing a report to the user, the report including the gene / drug interaction in question and the alternative drug; A system storing instructions for performing the above.

16. The memory, when executed by the processor, causes the computer system to: The system of claim 15 , further comprising: storing instructions for causing a healthcare provider associated with the user to provide the report.

17. The memory, when executed by the processor, causes the computer system to:

17. The system of claim 15 or claim 16, storing instructions for determining whether the user has a problematic gene / drug interaction with the alternative drug.

18. The memory, when executed by the processor, causes the computer system to: The system of any one of claims 15-17, storing instructions for determining whether the problematic gene / drug interaction associated with the drug exists by determining whether there is a pharmacokinetic effect on the drug of at least a threshold value caused by the mutation.

19. 20. The system of claim 18, wherein the pharmacokinetic effect of the threshold comprises excessive metabolic functionality of the enzyme for the drug.

20. 20. The system of claim 18, wherein the pharmacokinetic effect of the threshold comprises a metabolic under-functionality of the enzyme for the drug.

21. The memory, when executed by the processor, causes the computer system to: determining a metabolic component value for the drug based on the received genetic test result; determining a response component value for the drug based on the received genetic test result; assigning a drug category from a plurality of categories for the drug based on the greater of the metabolic component value and the response component value, the drug category indicating the severity of any drug / gene interaction for the user and the drug; 21. The system of claim 15, further comprising instructions for determining whether there is a gene / drug interaction of interest associated with the drug by performing the steps:

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