Systems and methods for drug interaction analysis

Abstract

The present disclosure relates to systems and methods for analyzing drug-drug and gene-drug interactions of a user and providing suggestions therefrom. The systems and methods may utilize a database containing a list of genes associated with a plurality of drugs based on clinical relevance of the genes to the effects of the plurality of drugs. The systems and methods may include recommending alternative drugs to a user based on any identified problematic drug-drug or gene-drug interactions.

Description

[0001] priority

[0002] This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application No. 63 / 413,911, filed on October 6, 2022, entitled “SYSTEMS AND METHODS FOR DRUG INTERACTION ANALYSIS,” which application, and U.S. Provisional Patent Application No. 63 / 464,149, filed on May 4, 2023, entitled “SYSTEMS AND METHODS FOR DRUG INTERACTION ANALYSIS,” the entire contents of each of which are incorporated herein by reference. Background Art

[0003] The two main determinants of whether a drug will have the intended therapeutic effect or produce an adverse drug reaction are the patient's genetic makeup and the potential for two concomitantly administered drugs to interact (drug-gene interactions and drug-drug interactions, respectively). Currently, drugs are often prescribed to patients without detailed genetic analysis. Furthermore, if patients do not provide their healthcare providers with a complete and accurate list of all medications they are taking (either intentionally or unintentionally), healthcare providers may prescribe a drug that has a negative interaction with another drug. This can lead to poor or negative health outcomes for patients and inefficiencies associated with drug prescribing, often referred to as "trial-and-error" prescribing. Therefore, there is a need in the art for a system that can optimize the drug combination prescribed to a patient based on genetic makeup and drug-drug interactions. Without this information, one can never be sure that the drug they are taking is in fact exactly the right drug for a particular patient. Currently, some commercially available databases contain some information about drug-drug interactions and some information about the drugs themselves (e.g., major metabolites or enzymes that form the metabolites), but these commercial databases do not provide all the information needed to properly evaluate potential drug-drug and / or gene-drug interactions. Therefore, there is a need in the art for systems and methods that can comprehensively analyze all drug-drug and gene-drug interactions to provide better clinical outcomes and greater cost savings to the healthcare industry as a whole (whether by avoiding adverse or negative health outcomes for patients or by minimizing prescribing inefficiencies).

[0004] Pharmacogenomic tests identify genetic mutations or variants that may influence whether a specific drug will be effective for an individual or whether the individual will experience drug side effects. Current pharmacogenetic test reports list each gene, genotype, and phenotype separately, and include a list of drugs affected by each gene. Therefore, the responsibility for correctly digesting and interpreting the extensive information in pharmacogenetic test reports and providing optimal medication recommendations for patients falls on healthcare providers. Understandably, many healthcare providers are skeptical of pharmacogenetic test reports and struggle to incorporate test information into their regular medical practice due to (i) a general lack of expertise in interpreting pharmacogenetic data, (ii) time constraints imposed by the daily patient volume, (iii) the challenge of integrating data from multiple sections of a pharmacogenetic report related to different genes and understanding their significance for specific drugs, and (iv) the static nature of currently generated pharmacogenetic reports, limiting their utility following any changes to a patient's medication regimen. Therefore, there is a need for systems that can adapt and report the results and recommendations for analyzed drug-drug and gene-drug interactions in a more understandable and dynamic manner. Summary of the Invention

[0005] The present disclosure relates to a system and method for analyzing a user's drug-drug and / or drug-gene interaction. In some embodiments, the system and method include providing a user with advice on any identified drug interaction. A clinical decision support tool is described herein that allows doctors and patients to check the suitability of their drugs to each other (drug-drug interactions) and the suitability of drugs to the patient's genetics (gene-drug interactions) in a comprehensive manner. The system marks potential interactions and provides alternative drugs that are genetically suitable for the patient. This allows doctors to prescribe the best medication regimen for a particular patient.

[0006] In some embodiments, the present disclosure relates to a method for analyzing drug interactions for a user, the method comprising: receiving a patient's genetic test results and a drug taken by the user, wherein the drug belongs to a drug class; determining whether the drug exists in an internal database, wherein the internal database contains a list of genes associated with multiple drugs based on the clinical relevance of the genes to the effects of multiple drugs; when it is determined that the drug does not exist in the internal database: searching an external database for a major metabolite and an enzyme responsible for forming the major metabolite, wherein the major metabolite and the enzyme are associated with the drug, and determining whether there is a mutation in the gene associated with the enzyme that affects the pharmacokinetics of the drug based on the genetic test results; when it is determined that the drug exists in the internal database: determining a clinically relevant enzyme associated with the drug from the internal database, and determining whether there is a mutation in the clinically relevant gene based on the genetic test results; determining whether there is a problematic gene-drug interaction associated with the drug; when it is determined that a problematic gene-drug interaction exists, identifying an alternative drug from the drug class (as described above, from the internal database or the external database); and providing a report to the user, the report including the problematic gene-drug interaction and the alternative drug.

[0007] In some embodiments, the present disclosure relates to a method for analyzing drug interactions for a user, the method comprising: receiving a genetic test result and a drug or multiple drugs (one or more drugs) of the user; determining a major metabolite of the one or more drugs and a pathway of the one or more major metabolites; determining a class of the one or more drugs; determining one or more major enzymes pharmacokinetically associated with the one or more drugs based at least in part on the one or more major metabolites and the pathways; determining whether the user has a genetic mutation associated with the determined one or more major enzymes based on the genetic test result, the genetic mutation affecting the pharmacokinetics of the one or more drugs; when it is determined that the patient has the genetic mutation, determining whether the genetic mutation has a threshold pharmacokinetic effect on the one or more major enzymes; when it is determined that the genetic mutation has the threshold pharmacokinetic effect, determining one or more alternative drugs for the one or more drugs based on the determined class of the one or more drugs; and providing the user with an indication of the one or more alternative drugs and the pharmacokinetic effect of the genetic mutation.

[0008] In some embodiments, the present disclosure relates to a system for analyzing drug interactions for a user, the system comprising: a processor; and a memory coupled to the processor, the memory storing instructions that, when executed by the processor, cause the system to: receive genetic test results of a patient and a drug taken by the user, wherein the drug belongs to a drug class, determine whether the drug exists in an internal database, wherein the internal database contains a list of genes associated with multiple drugs based on their clinical relevance to the effects of the multiple drugs; when it is determined that the drug does not exist in the internal database: search an external database for major metabolites and enzymes responsible for forming the major metabolites, wherein the major metabolites and the enzymes are associated with the drug, and determine whether there is a mutation in the gene associated with the enzyme that affects the pharmacokinetics of the drug based on the genetic test results; when it is determined that the drug exists in the internal database: determine a clinically relevant enzyme associated with the drug from the internal database, and determine whether there is a mutation in the clinically relevant gene based on the genetic test results; determine whether there is a problematic gene-drug interaction associated with the drug; when it is determined that a problematic gene-drug interaction exists, identify an alternative drug from the drug class; and provide a report to the user, the report including the problematic gene-drug interaction and the alternative drug.

[0009] In some embodiments, the disclosed systems and methods provide dynamic reports that integrate pharmacogenetic test information for multiple genes associated with individual drugs. In some embodiments, the disclosed systems and methods clearly flag drug risks in a more understandable manner that does not require in-depth knowledge of pharmacogenetics for both patients and practitioners.

[0010] Figures in the specification

[0011] The accompanying drawings are incorporated in and form a part of the specification, illustrate embodiments of the present invention, and together with the written description, serve to explain the principles, characteristics, and features of the present invention. In the drawings:

[0012] Figure 1 A block diagram of a system for analyzing drug interactions for a user is depicted in accordance with an embodiment of the present disclosure.

[0013] Figure 2A A flow chart depicts a process for analyzing drug interactions for a user according to an embodiment of the present disclosure.

[0014] Figure 2B A flow chart depicting a process for analyzing drug-gene interactions and classifying drugs accordingly, according to an embodiment of the present disclosure.

[0015] Figure 3AAn illustrative graphical user interface (GUI) for inputting medications according to an embodiment of the present disclosure is depicted.

[0016] Figure 3B Depicted is an illustrative GUI for indicating drug interactions in accordance with an embodiment of the disclosure.

[0017] Figure 4 An illustrative internal or override table is depicted, according to an embodiment. DETAILED DESCRIPTION

[0018] The present disclosure is not limited to the specific systems, devices, and methods described, as these systems, devices, and methods may vary. The terms used in the specification are only used to describe specific versions or implementations and are not intended to limit the scope of the present disclosure.

[0019] For the purposes of this application, the following terms shall have the meanings set forth below. Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art. Nothing in this disclosure should be construed as an admission that the embodiments described herein are not entitled to any prior claim to advantage by virtue of prior invention.

[0020] definition

[0021] 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 drug" is a reference to one or more drugs and equivalents thereof known to those skilled in the art, and so forth.

[0022] As used herein, the term "about" refers to plus or minus 10% of the numerical value of the number used. Thus, about 50 mm means within the range of 45 mm to 55 mm.

[0023] As used herein, the terms "consisting of" or "composed of" mean that an apparatus or method includes only the elements, steps or components specifically recited in a particular claimed embodiment or claim.

[0024] In embodiments or claims where the term "comprising" is used as a transitional phrase, it is also contemplated that the term "comprising" may be replaced with the terms "consisting of" or "consisting essentially of.

[0025] As used herein, the terms "user," "subject," or "patient" are used interchangeably.

[0026] As used herein, the term "drug," "pharmaceutical" or "pharmaceutical composition" refers to a composition comprising an active pharmaceutical ingredient to be delivered to a subject for, for example, a therapeutic, preventive, diagnostic, protective or prognostic effect.

[0027] 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, preventive, diagnostic, protective, or prognostic effect). In various embodiments, the active pharmaceutical ingredient can be any of a variety of substances known in the art, such as small molecules, peptide mimetics, biologics, antisense RNA, small interfering RNA (siRNA), etc.

[0028] As used herein, "analyzing drug interactions" refers to evaluating the effect of a patient's genetics on drug metabolism, ie, analyzing how a drug is metabolized by a patient, where the determination is based on the patient's genetics.

[0029] As used herein, "genetic test results" refers to results from one or more molecular tests (e.g., targeted single variant testing, single gene testing, gene panels, and / or whole genome sequencing), chromosomal testing, gene expression testing, or biochemical testing used to identify the genotype and / or phenotype of a gene. In specific embodiments, the genetic test results are for genes associated with drug response as described herein.

[0030] As used herein, "drug class" refers to a classification of drugs based on therapeutic efficacy, mechanism of action, or other collective characteristics. Drug classes may include, for example, opioids, analgesics, beta blockers, and antipsychotics.

[0031] As used herein, "clinical relevance" refers to whether a gene directly or indirectly affects the metabolism of a drug. A skilled artisan can learn about genes associated with various drugs by, for example, consulting available scientific literature, FDA labels, and other recognized sources of drug information. In one illustrative embodiment, a gene mutation that enhances or decreases the metabolism of a particular drug by at least a threshold amount (e.g., 50% or more) can be considered to be clinically relevant to the drug.

[0032] As used herein, "major metabolite" refers to the product primarily produced by the action of an enzyme on a drug.

[0033] As used herein, "associated with a drug" or "pharmacokinetically associated with a drug" refers to an enzyme that interacts with a drug or a protein that otherwise directly or indirectly affects the drug response (e.g., by interacting with a receptor or membrane protein such as a transporter protein) to produce a primary metabolite (e.g., by utilizing the drug as a substrate) or otherwise affects the therapeutic response to the drug.

[0034] As used herein, "problematic," particularly in the context of problematic gene-drug interactions, refers to whether the drug's effect is affected by one or more of the user's genetic mutations. This effect could be to reduce or increase the drug's effect. Increasing or reducing the drug's effect could have adverse effects on the patient.

[0035] As used herein, a "threshold pharmacokinetic effect" refers to any factor that decreases or increases the metabolism of a drug by a threshold amount (eg, 50% or more).

[0036] As used herein, "determining whether there is a problematic gene-drug interaction associated with a drug" means determining whether a user's genetics (i.e., a mutation in a gene associated with a protein that directly or indirectly acts on the drug) affects the metabolism of the drug or the response to the drug. Problematic gene-drug interactions can be calculated by, for example, examining scientific data on drug metabolism or drug response and whether mutations in the protein encoded by the gene affect drug response or drug metabolism in a clinically relevant manner.

[0037] As used herein, an "internal database" refers to a database comprising one or more entries, wherein each database entry may include a variety of different information, such as clinically relevant genes or gene mutations for a particular drug, the extent to which the listed genes affect drug metabolism or drug response, and other gene-drug interactions.

[0038] As used herein, an "external database" refers to a database comprising one or more entries, wherein each database entry may include a variety of different information, such as the major metabolic pathways of a drug, the classification or category of the drug, and drug-drug interactions. Suitable external databases may include, for example, the DrugBank database, which can be accessed via https: / / go.drugbank.com.

[0039] Drug Interaction Analysis System

[0040] The present disclosure relates to systems and methods for analyzing patient medications (medication), including analyzing drug-drug interactions and gene-drug interactions. In addition, the systems and methods can recommend alternative medications to patients and / or third parties (e.g., healthcare providers) when the patient needs an alternative medication. Figure 1The system 100 is shown as including a computer system 102 that can be accessed by a user 112 and / or a healthcare provider 114 via a network 110 (e.g., the Internet). Furthermore, the computer system 102 can store or otherwise be communicatively coupled to a database 110 that can store various information associated with drug pharmacokinetics or drug-drug interactions, as described in greater detail below. In operation, the user 112 can access the computer system 102 to input data (e.g., medications being taken by the user), review pharmacogenetic analysis results and recommendations, and take other such actions.

[0041] In one embodiment, the computer system 102 can be operated or controlled via a testing entity that can receive, execute and / or instruct a genetic test 118 on a patient-provider sample to obtain the patient's genetic data. In another embodiment, the computer system 102 can be configured to receive a genetic test 118 from an external source (e.g., via a network 110). As described in more detail below, the user's genetic data can be used to analyze whether any alternative medicines can provide better results, or whether they may be more suitable for the user given the user's genetic makeup. In one embodiment, the user's genetic test results can be obtained by performing laboratory tests on a genetic sample collected from the user (e.g., by analyzing buccal mucosal epithelial cells collected by swabbing the inside of the cheek with a special swab). The genetic test results can be in the form of a genetic diplotype (i.e., an allele from each parent), which is then converted into a phenotype (i.e., the functional impact of the diplotype on protein function). In some embodiments, it may be beneficial to consider the user's diplotype because each allele may be associated with the functional aspects of the protein it encodes. Therefore, an individual's diplotype may affect how a male or female metabolizes a drug or how a drug affects an individual. For example, if one allele encodes a protein that exhibits normal functionality, while the second allele encodes a non-functional protein, the mixed phenotype can be used as a semi-functional protein. For example, for genes encoding metabolic enzymes, the individual's example phenotype can be classified as an "intermediate metabolizer." In one embodiment, genetic testing 118 can include a diplotype associated with one or more tested genes. In this embodiment, the computer system 102 can be programmed to match the diplotype received from the genetic test results with a previously determined functional table associated with each allele, and accordingly convert the diplotype into a predicted mixed phenotype of the gene. The phenotype can then be reported as a clinically relevant result for the gene. In some embodiments, corresponding labels can be specified for the phenotype from one or more labels, which represent the degree of influence of the user's diplotype on protein function. For example, a label can represent a severe impact, a moderate impact, or no impact. In one embodiment, the label of the specified phenotype can include a color (e.g., red, yellow, and green).

[0042] The computer system 102 may include a processor 104 and a memory 106 to execute various processes or algorithms for analyzing the medications being taken by the user and / or the user's data to provide recommendations to the user accordingly. In some embodiments, the computer system 102 may include a server or cloud-based computing system. In various embodiments, the computer system 102 may be configured to provide an interface (e.g., a GUI) that can be accessed and / or interacted with via a software application (e.g., running on a mobile device associated with the user 112) or a website. The interface may allow the user to input medications they are taking, such as Figure 3A In addition, the interface can display various suggestions or warnings to the user, such as Figure 3B shown.

[0043] In one embodiment, a user can manually enter the medications they are taking and / or prescribed. In another embodiment, the computer system 102 can be configured to obtain the medications the user is taking from the user's electronic health record (EHR) or other external source. For example, the computer system 102 can be communicatively coupled to a healthcare provider computer system and can access the user's EHR with the permission of the user or the user's healthcare provider 114 (e.g., a physician). In this embodiment, the computer system 102 can be configured to automatically retrieve a list of medications the user is taking and / or has been prescribed from the user's EHR after receiving appropriate permission.

[0044] The computer system 102 can be configured to communicate with an external database 116 that provides information about drugs, such as drug-drug interactions or information about the main metabolic pathways associated with the drug (e.g., the main circulating metabolites and the metabolic enzymes responsible for their formation). For example, the external database 116 can be accessed through a corresponding application programming interface (API). For example, the external database 116 can include a DrugBank database. One problem with current drug databases (such as DrugBank) is that, although they may have some information about drug-drug interactions and data about the main metabolic pathways of the drug (e.g., the main circulating metabolites and the metabolic enzymes responsible for their formation), the available information in such external databases 116 is not sufficient to determine whether any genetic mutations that an individual may have (as determined based on genetic testing) will affect the pharmacokinetics of a given drug. For example, although currently available drug information databases may include metabolic enzymes associated with the main circulating metabolites associated with the drug, 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 complete, clear information about whether a certain drug is suitable for them based on genetic testing.

[0045] Therefore, system 100 described herein includes a drug interaction table 109 associated with an internal database 108, and the internal database 108 is stored on or otherwise communicatively coupled to the computer system 102. Drug interaction table 109 (also referred to as "cover table" in some cases) has been developed to supplement and / or cover the drug information in the currently available drug information database. Drug interaction table 109 shows the drug-gene interaction for a drug for which the currently available external database 116 does not provide enough information to identify all possible drug-gene interactions. For the various drugs in drug interaction table 109, based on the clinical relevance of gene and drug action, gene is associated with the drug. Clinical relevance is determined based on experimental data (typically clinical data), which show that a gene mutation that affects the protein function encoded by a gene has an effect that affects the clinical attributes (e.g., efficacy, safety, or pharmacokinetics) of a drug. In some embodiments, drug interaction table 109 can be based on individual alleles and / or diplotypes associated with one or more genes shown. In one embodiment, the drug interaction table 109 is constructed in a table format, including each drug for which the external database 116 lacks sufficient information to identify associated gene-drug interactions and clinically relevant genes associated with each drug. The drug interaction table 109 may also include clinically relevant metabolic genes and clinically relevant response genes. For example, Figure 4 An illustrative portion of a drug interaction table 109 is depicted, indicating a pair of drug metabolite markers and reaction markers. In this example, the drugs are identified based on their DrugBank IDs (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.

[0046] In operation, computer system 102 receives a list of one or more medicines that a user is taking (for example, manually input by application GUI or website GUI) and the genetic test result of the user. For each medicine that a user takes, computer system 102 queries drug interaction table 109 to find the medicine in question, retrieves related genes from the metabolism and reaction gene data of the medicine, checks related genes for the patient's pharmacogenetic test results, and reports any abnormality (for example, mutation present in the gene of one of the clinically relevant enzymes associated with the medicine) of potential drug-gene interaction. If medicine is not present in drug interaction table 109, computer system 102 can retrieve corresponding data from external database 116 and therefrom determine any drug-gene interaction. The report provided to the user can include the description of abnormal properties and how it affects drug metabolism or drug reaction. Along with the appearance of new information (for example, new research and / or clinical data) about drug-gene interaction, drug interaction table 109 can be dynamically modified to include new information. Unlike the internal database 108, current commercially available databases (e.g., DrugBank) do not consider clinical relevance, but simply list drug properties (e.g., drug metabolizing enzymes or drug classes). These commercially available databases can be queried based on general principles of pharmacology (e.g., drug metabolism and dose-response relationships) in a manner that assumes that certain properties have clinical relevance (i.e., clinical relevance may not be supported by experimental data), but these assumptions do not apply to all drugs. Therefore, any system that attempts to identify possible drug-gene interactions based solely on this commercially available information is incomplete and, therefore, cannot provide correct results in all cases. If a drug in an external database has a drug property that is relevant to the pharmacogenetic gene being tested, the relevant gene information can be reported in the event of an anomaly.

[0047] In one embodiment, the system 100 can be embodied as a web-based application 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 an expanded description of the problematic interaction and information on how to manage it clinically. The system 100 can further identify gene-drug interactions by comparing the patient's medications with an internal database of specific drugs, as well as genes encoding proteins that affect the biotransformation of the specific drug (i.e., metabolic genes), genes encoding proteins that affect the transport of the specific drug in 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 for various drugs.

[0048] Any problematic drug-drug and / or drug-gene interactions identified by the system 100 can be labeled based on the phenotype associated with the abnormal function of the encoded protein. In addition, the results can be presented to the user in a variety of different formats. For example, the results can be color-coded using green, yellow, or red based on the severity of the clinical impact of the gene mutation and / or drug-drug interaction, where green can indicate normal function, yellow can indicate an increased risk of adverse reactions or reduced clinical efficacy, and red can indicate an extremely high risk of adverse reactions or a lack of clinical efficacy.

[0049] Drug interaction analysis process

[0050] Figure 2A One embodiment of a process 200 for analyzing a user's drug interactions is shown. In one embodiment, process 200 can be embodied as instructions stored in a 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 between various 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 associated with user 112 (e.g., a computer, laptop, or smartphone). In various embodiments, system 100 performing process 200 can utilize distributed processing, parallel processing, cloud processing, and / or edge computing technologies. Process 200 described below is performed by system 100; therefore, it should be understood that the functions described can be performed by one or more devices or systems individually or collectively.

[0051] The system 100 performing the process 200 may receive 202 one or more medications associated with a user and receive 204 genetic information (e.g., genetic test results) associated with the user. In one embodiment, the user may manually input one or more medications that she or he is taking by interacting with the computer system 102, such as by entering the medications through a website interface. For example, Figure 3A A GUI 300 is shown including a medication input field 302 where a user can manually enter medications that they are taking and / or have been prescribed. Figure 3AIn the specific example shown, the user has entered two medications 304 that the user is currently taking (propranolol and clopidogrel). In another embodiment, the computer system 102 can retrieve the user's EHR (e.g., from a database associated with the patient's healthcare provider) and determine therefrom the medications that the user is currently taking and / or has been prescribed. In one embodiment, the system 100 can receive 204 genetic information from a genetic test performed by the entity operating the computer system 102. In another embodiment, the system 100 can receive 204 the user's genetic information from a third party or another external source.

[0052] For each of the patient's medications, the system 100 may identify 206 any drug-gene interactions associated with the received 202 list of medications and the received 204 genetic information. In parallel with or otherwise independently of identifying 206 the drug-gene interactions, the system 100 may further identify 208 any drug-drug interactions. In one embodiment, the system 100 may identify 206 the drug-drug interactions using a commercially available database (e.g., DrugBank).

[0053] The system 100 can identify 206 drug-gene interactions between the received 202 list of drugs and the received 204 genetic information of the user in various ways. Figure 2AIn one embodiment shown, the system 100 can determine 210 whether a drug exists in the internal database 108 (i.e., the drug interaction table 109). If the system 100 determines 210 that the drug exists in the internal database 108, the system 100 can determine 212 whether any drug-gene interactions exist based on the pre-characterized drug-gene data in the drug interaction table 109. If the system 100 determines 210 that the drug does not exist in the internal database 108, the system 100 can search 214 for the major metabolites associated with the given drug and the enzymes responsible for forming the major metabolites in the external database 116 (e.g., DrugBank). If a drug does not exist in the internal database 108, it can be assumed that the information present in the external database 116 is sufficient to assess the clinical effect of the drug's pharmacogenetic data. Based on the data from the external database 116, the system 100 can identify the drug's major enzyme, which can then be used to determine 216 whether any drug-gene interactions exist based on the user's genetic test information and the major enzyme determined to be associated with the drug. In other words, the system 100 can determine 216 whether the user has a genetic mutation associated with the identified primary enzyme that would have a clinical impact on the drug. In this context, a genetic mutation may clinically impact an enzyme if it inactivates the enzyme, causes it to overfunction, or causes it to underfunction. For example, CYP2C19 encodes an enzyme that is 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 function poorly, clopidogrel will not produce a clinically relevant drug effect. Conversely, if a CYP2C19 mutation causes the enzyme to function too well, the drug effect will be too strong, and the patient's blood will become too thin, resulting in an increased risk of bleeding. The system 100 can determine 212, 216 whether the user has a mutation in the CYP2C19 gene or any other gene that has a clinical impact on the drug using a pre-characterized drug interaction table 109 or information obtained from an external database 116.

[0054] Thus, the system 100 can determine 218 whether there are any problematic drug-gene interactions. Different genetic mutations can have different effects on the pharmacokinetics of a drug; therefore, in some embodiments, it may be desirable to quantify or otherwise determine the extent to which a particular genetic mutation affects the pharmacokinetics of a drug. In other words, it may be desirable to recommend an alternative drug only if the patient's genetic mutation has a significant or non-trivial pharmacokinetic effect on the drug. Thus, in one embodiment, the system 100 can determine 218 whether there are any problematic drug-gene interactions based on whether the pharmacokinetics associated with the enzyme encoded by the particular gene in question would adversely affect the drug to at least a threshold level or degree.

[0055] In one embodiment, the system 100 may code the analyzed drugs into one or more categories based on whether the system 100 identifies any problematic drug-gene interactions. For example, a first category may correspond to drugs for which no clinically important genetic indicators have been identified based on the subject's genetic data, a second category may correspond to drugs for which genetic indicators have been identified that warrant caution, and a third category may correspond to drugs for which genetic indicators have been identified that warrant extreme caution or avoidance. To help a user quickly identify and assess which category an analyzed drug falls into, the categories may be associated with various indicia (e.g., colors), which may be presented via a graphical user interface, such as Figure 3A and 3B shown.

[0056] In embodiments, given the subject's genetic data, the system 100 can determine 218 whether there are any problematic drug-gene interactions by calculating a score for the drug and comparing the calculated score to various thresholds to assess whether a particular drug or group of drugs is problematic. In embodiments, the drug score can include one or more separate calculations and / or components.

[0057] In one embodiment, determining and / or calculating a drug score may include determining and / or calculating a metabolic component value (MCV) and a reaction component value (RCV). The MCV may be a calculation based on the phenotypic results of the relevant metabolic genes. The RCV may be a calculation based on the expected response or adverse reaction based on the phenotype determined from the genetic information of the subject. In one embodiment, the MCV may be calculated for n genes according to the following equation:

[0058]

[0059] Wherein PCD is the phenotypic color designation of a particular gene, and I is the relative importance of a particular gene (e.g., expressed as a weight or percentage). In other words, MCV is calculated by weighted summing the PCDs of n genes associated with the particular drug being analyzed. It is noteworthy that the relative importance (I) of genes associated with a drug sums to 1. In one embodiment, PCD can be a value corresponding to the designated code of the gene (e.g., a genetic indicator of no clinical importance, a genetic indicator that requires caution, or a genetic indicator that requires extreme caution or avoidance). For example, the PCD value of a "green" or low-risk gene for a particular drug can be 1, the PCD value of a "yellow" or medium-risk gene can be 5, and the PCD value of a "red" or high-risk gene can be 10. In other embodiments, PCD values ​​can be different static values, functions based on one or more variables, and the like. In one embodiment, correlation values ​​can be values ​​assigned to individual values ​​based on their pharmacological and toxicological properties. For example, the relevance value can be based on the overall contribution of each tested gene to the total metabolism of the drug and the drug metabolites produced thereby, the clinical relevance of the metabolites of each tested gene (e.g., whether an active metabolite is produced, whether a toxic metabolite is produced, and whether the metabolite is major for the drug response), known pharmacogenetic-related metabolic effects (e.g., obtained from drug labels), and relevant information from the scientific literature (e.g., data from in vitro studies and clinical studies using human hepatocytes).

[0060] In one embodiment, determining and / or calculating a drug score can include determining and / or calculating a response component value (RCV). For example, the RCV calculation can be based on the PCD obtained for a response-associated gene or the PCD obtained for a group of response-associated genes (if more than one response gene is associated with the drug). For response genes, the relative importance of each response gene is not weighted because any deleterious mutation in any of the associated response genes will render the drug ineffective. Therefore, the highest PCD value among the associated response genes is designated as the RCV. Thus, for example, if a drug has two associated response genes with PCD values ​​of 1 and 5, the RCV will be 5.

[0061] In one embodiment, the system 100 may use a combination of the MCV and the RCV to determine 218 whether there are any problematic drug-gene interactions. Figure 2BAn example of a process for determining 218 whether any problematic drug-gene interactions exist, given a subject's genetic information and one or more medications associated with the subject (i.e., medications currently being taken by the subject or medications to be prescribed to the subject), is shown. In the illustrated embodiment, given the subject's genetic information and the one or more medications received 202, 204, the system 100 calculates 215 the MCV, as described above. Furthermore, the system 100 determines 252 whether any response and / or adverse event markers exist. The system 100 examines the genetic information obtained by testing for pharmacogenetic mutations (i.e., PGx test results) and matches it with genes associated with medications that determine PCD. If no response and / or adverse event markers exist, the system 100 assigns 254 the MCV as a score for the given genetic and / or medication information. If no response and / or adverse event markers exist, the system 100 determines 256 the RCV, as described above, given the subject's genetic information and the one or more medications received 202, 204. The system 100 compares the RCV and the MCV and assigns the larger of the two values. Therefore, the system 100 determines 258 whether the RCV is greater than the MCV. If the MCV is greater, the system 100 assigns 254 the MCV as the score for the given genetic and / or drug information. If the RCV is greater, the system 100 assigns 260 the RCV as the score for the given genetic and / or drug information. Therefore, the system 100 classifies 262 the drug based on the assigned scores 254, 260.

[0062] If the system 100 determines 218 that the user has a problematic drug-gene interaction, the system 100 may identify 220 one or more alternative medications for the analyzed medication. In one embodiment, the alternative medications may be determined based on the product category of the medication in question. For example, if the analyzed medication is a beta-blocker, the system 100 may identify 220 alternative beta-blockers for the user. As another example, if the analyzed medication is an opioid, the system 100 may identify 220 alternative opioids for the user. In one embodiment, the identified 220 alternative medications may be further analyzed to identify 206 any drug-gene interactions associated with the identified 220 alternative medications, as shown in FIG. 2 . This embodiment may be beneficial because it may be desirable to examine a suggested medication for any problematic gene-drug interactions so that the system 100 only suggests alternative medications that have also been analyzed for their gene-drug interactions. Furthermore, the system 100 may repeatedly identify 206 the process of identifying 206 gene-drug interactions until a suitable alternative medication is identified that belongs to the same product category as the initially analyzed medication (i.e., a medication that does not have any problematic gene-drug interactions).

[0063] Once the system 100 performing process 200 has identified a suitable alternative medication, the system 100 may provide 222 the result. In one embodiment, the system 100 may provide 222 the result to the user 112. In another embodiment, the system 100 may provide 222 the result to the user's healthcare provider 114. In yet another embodiment, the system 100 may provide 222 the result to both the user 112 and the healthcare provider 114. The result may include whether any gene-drug or drug-drug interactions were identified and / or suggested alternative medications. In one embodiment, the system 100 may provide data, reports, or summaries to the patient and / or a third party indicating the pharmacokinetic effects of the patient's specific one or more genetic mutations on one or more medications currently being taken by the patient. Such embodiments may be useful in further educating patients to manage their own healthcare and take an active role. For example, Figure 3B A report GUI 310 provided by the system 100 is shown. In this example, the report GUI 310 includes an alert 311 indicating that a problematic drug-drug interaction and / or gene-drug interaction has been identified for the user. In this example, a widget 312 of the report GUI 310 indicates that a problematic drug-drug interaction exists between two medications the user is taking. In addition, a second widget 314 of the report GUI 310 further explains the identified problematic gene-drug interaction. Finally, the report GUI 310 lists alternative medications 316 for each medication the user is taking that has been identified as having a problematic gene-drug interaction and / or drug-drug interaction.

[0064] 2 can be repeated for each medication that the user is taking. The analysis of each medication by process 200 performed by system 100 can be incorporated into a report 222 provided to the user.

[0065] It should also be noted that although the functions and / or steps of process 200 are described in a specific order or arrangement, the order and / or arrangement of the steps and / or functions depicted is for illustrative purposes only. Unless expressly described to the contrary herein, the various steps and / or functions of process 200 may be performed in a different order, in parallel with each other, in an interleaved manner, etc.

[0066] Drug Interaction Analysis System—Application Programmable Interface (API)

[0067] As described above, a variety of external systems (e.g., third-party systems) can be communicatively coupled to the computer system 102 or otherwise configured to interact with the computer system 102. In some embodiments, the external systems can be communicatively coupled to the computer system 102 via an application programmable interface (API). An API is a set of protocols, routines, and tools used to build software applications. Thus, the API defines a set of standard rules that allow external systems to communicate with the computer system 102. The API can enable the external systems to exchange data with the computer system 102 in order to provide access to drug-drug and gene-drug analyses (as described above) provided by the computer system 102 to determine drug effectiveness. The API can allow a variety of external systems to submit queries to the computer system 102, or otherwise utilize the computer system 102, in order to immediately allow the external systems to receive responses from the computer system 102, without having to generate manual queries as is common in the art.

[0068] The API can support a variety of different endpoints, such as an API call to retrieve patient results based on a drug lookup. The API call can be structured to, for example, receive a specific drug (or group of drugs) for a subject and identification information for the subject. In response to the call, the computer system 102 can return data indicating that the patient data is located within the internal database 108, the specified category of the drug (i.e., whether any drug-gene and / or drug-drug interactions are identified and the severity of these interactions), and the drug-gene and / or drug-drug interactions that are identified. The data can then be presented to the user (e.g., via a graphical user interface, such as Figure 3A and 3B ), or otherwise utilized by an external system that initiated the query (e.g., healthcare provider system 114). As generally discussed above, genetic testing data 118 for a particular subject may have been previously received by computer system 102 from an appropriate genetic testing facility and / or otherwise uploaded to internal database 108.

[0069] Aspects of the invention will now be described by way of example only and with reference to the following experiments.

[0070] Example 1—Efavirenz

[0071] Efavirenz, in Efavirenz is marketed under the name , and is a non-nucleoside reverse transcriptase inhibitor (NNRTI) against human immunodeficiency virus type 1 (HIV-1). Efavirenz is metabolized by CYP3A4 / 5, CYP2B6, CYP2C9, and CYP2C19; therefore, these genes are analyzed for any problematic interactions based on genetic test results associated with these genes. In this example, an illustrative patient has the following genetic test results: 3A4 PM, 3A5 IM, 2B6EM, 2C9 IM, 2C19 PM, where PM, IM, and EM are metabolic phenotypes. The metabolic phenotype is determined by (i) converting the mutation into the associated phenotype and (ii) classifying the two phenotypes (one from each parent) into a composite phenotype and an associated PCD, also known as a diplotype, by consulting a diplotype-phenotype conversion table. Based on the above genetic result data, the PCD of CYP3A5 is 5 (i.e., "yellow"), the PCD of CYP2B6 is 1 (i.e., "green"), the PCD of CYP2C9 is 5 (i.e., "yellow"), and the PCD of CYP2C19 is 10 (i.e., "red"). As described above, computer system 102 assigns relative importance to each of the different components of the MCV calculation. These weights are shown below. Therefore, considering the above genetic information, the MCV of efavirenz is calculated as follows:

[0072] MCV 依法韦仑 =(PCD CYYP3A5 ×I CYP3A5 )+(PCD CYP2B6 ×I CYP2B6 )+(PCD CYP2C9 ×I CYP2C9 )+(PCD CYP2C19 ×I CYP2C19 )

[0073] MCV 依法韦仑 =(5×0.60)+(1×0.30)+(5×0.05)+(10×0.05)=4.05

[0074] Therefore, for this particular patient, the MCV of efavirenz is 4.05. Therefore, the computer system 100 will assign a yellow phenotypic color designation, which indicates that this drug has a medium risk for the patient. As described above, this classification can be displayed to the user via a graphical user interface.

[0075] It is noteworthy that although efavirenz is metabolized by both CYP3A4 and CYP3A5, computer system 102 ignores CYP3A4 because CYP3A4 and CYP3A5 are considered combined functions because they have the same effect on the drugs they affect. Therefore, computer system 102 and the processes described herein can calculate the score for a drug using the best result between CYP3A4 and CYP3A5.

[0076] Example 2—Simvastatin

[0077] Simvastatin is used to lower low-density lipoprotein (LDL-C) cholesterol in the blood. In patients of African descent, simvastatin is metabolized by CYP3A4 / 5 and is associated with an adverse effect gene (SLCO1B1); therefore, these genes are used to analyze whether there are any problematic interactions based on the subject's genetic test results associated with these genes. In this example, the illustrative patient has the following genetic test results: 3A4 IM, 3A5 EM, SLCO1B1. Based on the above genetic result data, the PCD of CYP3A5 is 1 (i.e., "green"). As described above, the computer system 102 assigns relative importance to each of the different components of the MCV calculation. These weights are as follows. Therefore, taking into account the above genetic information, the MCV of simvastatin is calculated as follows:

[0078] MCV 辛伐他汀 =((PCD CYP3A5 ×I CYP3A5 )

[0079] MCV 辛伐他汀 =(1×1.0)=1.0

[0080] Similarly, it should be noted that although simvastatin is metabolized by both CYP3A4 and CYP3A5, only one of these two genes was analyzed for the reasons described above. Therefore, for this particular patient, the MCV for simvastatin is 1.0. However, because simvastatin also has adverse event markers associated with it, the computer system 102 will separately determine the RCV for simvastatin. In this case, an RCV of 5 (i.e., "yellow") has been assigned to the drug's SLCO1B1. Because the RCV is greater than the calculated MCV, the computer system 102 will assign the RCV as the drug score. Therefore, the computer system 100 will assign a yellow phenotypic color designation, indicating that this drug has a medium risk for the patient.

[0081] Example 3—Desvenlafaxine

[0082] Desvenlafaxine is a selective serotonin and norepinephrine reuptake inhibitor (SNRI) used to treat depression. In non-African patients, simvastatin is metabolized by CYP3A4 / 5 and CYP2D6; therefore, these genes were analyzed to determine if there were any problematic interactions based on the subject's genetic test results associated with these genes. In this example, the illustrative patient had the following genetic test results: 3A4 EM, 3A5 PM, and 2D6 EM. Based on the genetic result data, the PCD for CYP3A5 was 1 (i.e., "green"). As described above, computer system 102 assigns relative importance to each of the different components of the MCV calculation. These weights are shown below. For certain drugs, computer system 102 may further apply a general metabolic relevance factor (MRF) to the MCV calculation, where only a small subset of the associated genes metabolize the drug in the body. For desvenlafaxine, only 5-10% of the CYP genes metabolize the drug. Therefore, computer system 102 may apply a general metabolic relevance factor or weight to the overall MCV calculation. Therefore, taking into account the above genetic information, the MCV of simvastatin is calculated as follows:

[0083] MCV 去甲文拉法辛 =MRF×[(PCD CYP3A5 ×I CYP3A5 )+(PCD CYP2D6 ×I CYP2D6 )]

[0084] MCV 去甲文拉法辛 =0.1×[(1×0.9)+(1×0.1)]=0.1

[0085] Also, it should be noted that although simvastatin is metabolized by both CYP3A4 and CYP3A5, only one of these two genes was analyzed for the reasons mentioned above. Therefore, for this particular patient, the MCV for desvenlafaxine is 0.1. Therefore, computer system 100 will assign a green phenotypic color designation, indicating that the drug poses little risk to the patient.

[0086] Although various illustrative embodiments incorporating the principles of the present teachings have been disclosed, the present teachings are not limited to the disclosed embodiments. However, this application is intended to cover any variations, uses, or adaptations of the present teachings, and to employ their general principles. Furthermore, this application is intended to cover modifications within the scope of the present disclosure as are known or commonly practiced in the art.

[0087] In the foregoing detailed description, reference is made to the accompanying drawings, which form a part hereof. In the accompanying drawings, similar symbols generally indicate similar parts, unless the context dictates otherwise. The illustrative embodiments described in this disclosure are not intended to be limiting. Other embodiments may be used, 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 (as generally described herein and shown in the accompanying drawings) may be arranged, substituted, combined, separated, and designed in a variety of different configurations, all of which are expressly contemplated herein.

[0088] The present disclosure is not limited to the specific embodiments described in this application, and these embodiments are intended to illustrate various features. Many modifications and variations can be carried out without departing from its spirit and scope, which will be apparent to those skilled in the art. In addition to those methods and devices listed herein, those skilled in the art will understand from the foregoing description that functional equivalent methods and devices within the scope of this disclosure. It should be understood that the present disclosure is not limited to ad hoc methods, reagents, compounds, compositions or biosystems, and of course, these methods, reagents, compounds, compositions or biosystems can change. It should also be understood that the terms used herein are only for describing specific embodiments, rather than for limiting purposes.

[0089] For substantially any plural and / or singular terms used herein, those skilled in the art can convert from the plural to the singular and / or from the singular to the plural, as appropriate, as is appropriate to the context and / or application. For clarity, various singular / plural permutations may be explicitly described herein.

[0090] Those skilled in the art will understand that, in general, the terms used herein are generally intended to be "open" terms (e.g., the term "comprising" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," "including" should be interpreted as "including but not limited to," etc.). Although various compositions, methods, and apparatus are described as "comprising" various components or steps (interpreted as "including but not limited to"), the compositions, methods, and apparatus may also "consist essentially of" or "consisting of" various components and steps, and such terms should be interpreted as defining substantially closed groups of members.

[0091] Furthermore, even if specific numbers are explicitly referenced, those skilled in the art will recognize that such references should be interpreted as referring to at least the referenced numbers (e.g., a shorthand reference of "two references," without other modifiers, means at least two references, or two or more references). Furthermore, in those instances where a convention similar to "at least one of A, B, and C, etc." is used, generally, such construction is intended to be in the sense that one skilled in the art would understand the convention (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 instances where a convention similar to "at least one of A, B, or C, etc." is used, generally, such construction is intended to be in the sense that one skilled in the art would understand the convention (e.g., "a system having at least one of A, B, or 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.). Those skilled in the art will further understand that virtually any disjunctive word and / or phrase presenting two or more alternative terms, whether in the specification, sample embodiments, or drawings, should be understood to include the possibility of one, any one, or both of these terms. For example, the phrase "A or B" will be understood to include the possibility of "A" or "B" or "A and B."

[0092] In addition, 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 capable of describing the invention in terms of any individual member or subgroup of members of that Markush group.

[0093] It will be understood by those skilled in the art that, for any and all purposes, such as with respect to providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations thereof. Any listed range can easily be considered to fully describe and allow the same range to be decomposed into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein can easily be decomposed into lower thirds, middle thirds, and upper thirds, etc. It will also be understood by those skilled in the art that all language such as "up to," "at least," and similar language include the stated numbers and refer to ranges that can subsequently be decomposed into subranges as described above. Finally, as will be understood by those skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 cells refers to a group having 1, 2, or 3 cells. Similarly, a group having 1-5 cells refers to a group having 1, 2, 3, 4, or 5 cells, and so on.

[0094] As used herein, the term "about" refers to variations in numerical amounts, for example, by real-world measurement or processing procedures; by unintentional errors in these procedures; by differences in the manufacture, source, or purity of compositions or reagents; and the like. Typically, the term "about" as used herein means greater than or less than 1 / 10 of the value being described, for example, ±10%. The term "about" also refers to variations that one skilled in the art would consider to be equivalent, as long as such variations do not include known values ​​practiced in the prior art. The individual values ​​or ranges of values ​​preceding the term "about" are also intended to encompass embodiments of the absolute values ​​or ranges of values. Whether or not modified by the term "about," the values ​​described in this disclosure include equivalents of the values, for example, variations in the numerical amounts of such values ​​that may occur, but that would be considered equivalent by one skilled in the art.

[0095] The various features and functions disclosed above and their alternatives can be combined into many other different systems or applications. Those skilled in the art may subsequently make various currently unforeseen or unanticipated alternatives, modifications, variations or improvements thereto, which are also intended to be included in the disclosed embodiments.

[0096] The functions and process steps described herein may be automatically executed or performed in whole or in part in response to user commands. Automatically executed activities (including steps) are performed in response to one or more executable instructions or device operations without the user directly initiating the activity.

Claims

1. A method for analyzing drug interactions for a user, the method comprising: receiving genetic test results of a patient and a medication taken by the user, wherein the medication belongs to a drug class; determining whether the drug is present in an internal database, wherein the internal database comprises a list of genes associated with a plurality of drugs based on clinical relevance of the genes to the effects of the plurality of drugs; When it is determined that the drug does not exist in the internal database: searching an external database for a major metabolite and an enzyme responsible for forming the major metabolite, wherein the major metabolite and the enzyme are associated with the drug, and determining, based on genetic testing results, whether a mutation in a gene associated with the enzyme that affects the pharmacokinetics of the drug is present; When the drug is confirmed to exist in the internal database: Identify clinically relevant enzymes associated with drugs from an in-house database, and Determine the presence of mutations in clinically relevant genes based on genetic testing results; Determine if there are problematic gene-drug interactions associated with the drug; Identifying alternative medications from the drug class when a problematic gene-drug interaction is determined to exist; and Reports are provided to the user that include problematic gene-drug interactions and alternative medications.

2. The method according to claim 1, further comprising: The report is provided to a healthcare provider associated with the user.

3. The method according to claim 1 or claim 2, further comprising: Determine if the user has gene-drug interactions that are problematic for alternative medications.

4. The method of any one of claims 1 to 3, wherein determining whether there is a problematic gene-drug interaction associated with a drug comprises determining whether a mutation causes at least a threshold pharmacokinetic effect on the drug.

5. The method of claim 4, wherein the threshold pharmacokinetic effect comprises excessive metabolic function of the enzyme with respect to the drug.

6. The method of claim 4, wherein the threshold pharmacokinetic effect comprises insufficient metabolism of the drug by the enzyme.

7. The method of any one of claims 1 to 6, wherein determining whether there is a problematic gene-drug interaction associated with the drug comprises: determining a metabolite value of the drug based on the received genetic test results; determining a response component value for the drug based on the received genetic test results; A drug category is assigned from a plurality of drug categories based on the greater of the metabolite value and the reaction component value, wherein the drug category indicates a severity of any drug-gene interaction between the user and the drug.

8. A method for analyzing drug interactions for a user, the method comprising: receiving genetic test results and medications from the user; Identify the major metabolites of the drug and the pathways of the major metabolites; Determine the class of the drug; identifying a primary enzyme pharmacokinetically associated with the drug based at least in part on the primary metabolite and the pathway; determining whether the user has a genetic mutation associated with the identified primary enzyme that affects the pharmacokinetics of the drug based on the genetic test results; When it is determined that the patient has the gene mutation, determining whether the gene mutation has a threshold pharmacokinetic effect on the major enzyme; When it is determined that the gene mutation has the threshold-effect pharmacokinetic effect, determining an alternative drug to the drug based on the determined drug class; An indication of the alternative drug and the pharmacokinetic impact of the genetic mutation is provided to the user.

9. The method according to claim 7-8, further comprising, An indication of the alternative medication and the pharmacokinetic impact of the genetic mutation is provided to a healthcare provider associated with the user.

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

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

12. The method according to any one of claims 8-11, wherein the adverse effect comprises excessive metabolic function of the enzyme with respect to the drug.

13. The method of any one of claims 8-11, wherein the adverse effect comprises insufficient metabolism of the drug by the enzyme.

14. The method of any one of claims 8-13, wherein determining whether the genetic mutation has a threshold pharmacokinetic effect on the primary enzyme comprises: determining a metabolite value of the drug based on the received genetic test results; determining a response component value for the drug based on the received genetic test results; A drug category is assigned from a plurality of drug categories based on the greater of the metabolite value and the reaction component value, wherein the drug category indicates a severity of any drug-gene interaction between the user and the drug.

15. A system for analyzing drug interactions for a user, the system comprising: processor; and a memory coupled to the processor, the memory storing instructions that, when executed by the processor, cause the system to: receiving genetic test results of a patient and a medication taken by the user, wherein the medication belongs to a drug class; determining whether the drug is present in an internal database, wherein the internal database comprises a list of genes associated with a plurality of drugs based on clinical relevance of the genes to the effects of the plurality of drugs; When it is determined that the drug does not exist in the internal database: searching an external database for a major metabolite and an enzyme responsible for forming the major metabolite, wherein the major metabolite and the enzyme are associated with the drug, and Determining whether there is a mutation in the gene associated with the enzyme that affects the pharmacokinetics of the drug based on genetic testing results, When the drug is confirmed to exist in the internal database: Identify clinically relevant enzymes associated with drugs from an in-house database, and Determine the presence of mutations in clinically relevant genes based on genetic testing results; Determine if there are problematic gene-drug interactions associated with the drug; Identifying alternative medications from the drug class when a problematic gene-drug interaction is determined to exist; and Reports are provided to the user that include problematic gene-drug interactions and alternative medications.

16. The system of claim 15, wherein the memory stores instructions that, when executed by the processor, cause the computer system to: The report is provided to a healthcare provider associated with the user.

17. The system of claim 15 or claim 16, wherein the memory stores instructions that, when executed by the processor, cause the computer system to determine whether a user has a gene-drug interaction that is problematic for an alternative medication.

18. The system of any one of claims 15 to 17, wherein the memory stores instructions that, when executed by the processor, cause the computer system to determine whether there is a problematic gene-drug interaction associated with the drug by: A determination is made as to whether the mutation causes at least a threshold pharmacokinetic effect on the drug.

19. The system of claim 18, wherein the threshold pharmacokinetic effect comprises excessive metabolic function of the enzyme with respect to the drug.

20. The system of claim 18, wherein the threshold pharmacokinetic effect comprises insufficient metabolism of the drug by the enzyme.

21. The system of any one of claims 15-20, wherein the memory stores instructions that, when executed by the processor, cause the computer system to determine whether there is a problematic gene-drug interaction associated with the drug by: determining a metabolite value of the drug based on the received genetic test results; determining a response component value for the drug based on the received genetic test results; A drug category is assigned from a plurality of drug categories based on the greater of the metabolite value and the reaction component value, wherein the drug category indicates a severity of any drug-gene interaction between the user and the drug.

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