Data-based mental disorder research and treatment systems and methods

Abstract

To provide a system for personalized depression treatment.SOLUTION: The system includes a server configured to communicate with existing healthcare resources and receive patient data corresponding to a patient, the server including an analysis module. The system further includes a first database configured to store empirical patient outcomes and further configured to communicate with the analysis module. Additionally, the system includes a user device having a graphical user interface (GUI) configured to communicate with the server and display at least one output generated by the analysis module. The analysis module is configured to determine at least one of personalized depression treatment and a personalized depression state prediction based on the empirical patient outcomes and the patient data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 62 / 882,466, filed August 2, 2019, the entire contents of which are incorporated herein by reference. Incorporation by reference of material submitted on compact disc or as a text file via the Office Electronic Filing System (efs-web).

[0002] This application contains a table that has been submitted in ASCII format via EFS-web and is hereby incorporated by reference in its entirety. Created on August 2, 2019, the ASCII copy is entitled TABLE-1-List-of-Genes.txt and is 147,138 bytes in size.

[0003] The present invention relates to systems and methods for capturing and employing data related to clinical, physical, and genomic patient characteristics, as well as diagnosis, treatment, and treatment effectiveness, to provide healthcare providers, researchers, and other stakeholders with a suite of tools that enable them to make clinical decisions, generate new psychiatric condition-treatment-outcome insights, and improve overall patient care. [Background technology]

[0004] Over the past 40 years, over 50 FDA-approved antipsychotic and antidepressant medications have become available, yet patients still undergo a trial-and-error approach to finding a successful treatment. Despite the abundance of available medications and clinical trials comparing the effectiveness of these commonly used antidepressants, over 70% of patients still do not respond to their initial treatment, and 10 to 30% of patients do not respond to medication at all. Knowledge about treatment outcomes is often based on empirical data over decades or even longer, during which physicians and / or researchers record treatment outcomes for many different patients and review those results to identify disease-specific treatments that are generally successful. Researchers and physicians administer medications or treat the illness in some other manner to patients, observe the results, and if the results are successful, use that treatment again for similar illnesses. If the treatment results are poor, they may not prescribe the related treatment for the next similar illness they encounter, but instead try some other treatment. Treatment results are sometimes published in medical journals and / or periodicals so that many physicians can benefit from the treating physician's insights and treatment results.

[0005] In the treatment of at least some mental illnesses, such as depression, treatment and outcome data are simply inconclusive. For example, in the treatment of depression, seemingly indistinguishable patients with similar conditions often respond differently to similar treatment regimens, with no causal relationship between the patients' conditions and the different treatment outcomes. For example, two women may be the same age, similarly healthy, and diagnosed with the same depressive condition (similar physical and psychological symptoms, BDI-II scores, PHQ-9 scores, etc.). Here, the first woman may respond well to a depression treatment regimen, experience few side effects, and experience rapid symptom improvement, while the second woman, despite receiving the same treatment regimen, may suffer from several severe side effects and never achieve relief from her initial depressive symptoms. Different treatment outcomes for seemingly similar depressive conditions negatively impact efforts to generate treatment and outcome datasets and prescribing activity.

[0006] Recognizing that in some cases virtually the same treatment can produce different results in different patients, researchers and physicians often develop additional guidelines for how to optimize illness treatment based on a patient's particular psychiatric illness, such as depression. For example, one treatment may be best for a young, relatively healthy woman with depression, while a second treatment with fewer side effects may be best for an older, more frail man with the same depression diagnosis.

[0007] In these cases, unfortunately, there are factors involved in psychiatric illness (e.g., depression factors) whose causal relationship with specific treatment outcomes is simply unknown at this time, and therefore, these factors cannot be used to optimize specific patient treatment at this time. Furthermore, over 70% of patients do not respond to the first course of drug treatment. With over 43,800,000 American patients receiving a mental health-related diagnosis, there is a need to learn from the ineffectiveness of over 70% of first courses of drug treatment to improve therapy selection at an early stage.

[0008] Genetic testing has been investigated to some extent as a possible factor (e.g., another patient's condition) that may influence the effectiveness of treatment for a psychiatric disorder (e.g., depression). It is believed that there may be many causal relationships between DNA and treatment outcome that have yet to be discovered. One problem with genetic testing is that it is expensive, and in many cases the cost can be prohibitive - often insurance companies refuse to cover the cost.

[0009] Another problem with genetic testing for treatment planning is that when genetic testing is performed, there is often no clear correlation between the resulting genetic factors and treatment effectiveness. In other words, in most cases, it is not fully known how the results of a genetic test can be used to prescribe a better treatment plan for the patient, so the additional costs associated with genetic testing in certain cases cannot be justified. Thus, while promising, genetic testing as part of psychiatric treatment planning has been marginal or sporadic at best.

[0010] For some psychiatric disorders (e.g., some depressive conditions), treatments and their associated outcomes are generally consistent and acceptable (e.g., minimal or at least understood side effects). However, in other cases, treatment outcomes associated with other depressive conditions are less than satisfactory and less accessible for several reasons.

[0011] First, there are many factors that affect treatment outcomes, including many different types of patient conditions, and some treatments are more effective for one patient than another, or for one patient as opposed to another, in different conditions. It is not easy to clearly identify specific patient conditions that are or may be causally related to treatment outcome, and some causal conditions may not be assessed or identified at all.

[0012] Second, for most depressive conditions, there are several different treatment options, and each general option can be customized for a particular psychiatric disorder (e.g., a particular depressive condition) and set of patient conditions. Often, with so many treatments and customization options, there are no clear, standardized guidelines for how to capture this type of information, making it difficult to normalize and accurately capture treatment and outcome data.

[0013] Third, in most cases, patient treatments and outcomes are not published for public consumption and therefore are not simply accessible to be combined with other treatment and outcome data to provide an over-the-top overall data set. In this regard, many physicians may see treatment results that fall within the expected range of efficacy and conclude that those results cannot be added to the overall depression treatment knowledge base, and those results are often not published. The problem here is that the expected range of efficacy may be large (e.g., 20% of patients experience a significant reduction in symptoms, 40% experience a moderate reduction in symptoms, 20% experience a mild reduction in symptoms, and 20% do not respond to the treatment plan), and therefore all treatment outcomes are within the expected range of efficacy, and the nuances in treatment outcomes are simply lost.

[0014] Fourth, there is currently no easy way to build or supplement the many existing disease-treatment-outcome databases. As such, as more data is generated, new data and associated results cannot be added to existing databases as evidence of treatment effectiveness or be probed. Thus, for example, when a researcher publishes a study in a medical journal, there is no easy way for other physicians or researchers to supplement the data captured in the study. Without supplemental data over time, the consequences of treatments and outcomes cannot be examined, confirmed, or scrutinized.

[0015] Fifth, the knowledge base surrounding depression treatment is expanding with clinical trials in various stages around the world, so even if a physician's knowledge is current today, it will become outdated within a few months. Thousands of articles related to mental illness broadly, or depression specifically, are published each year, many of which are lengthy and information-heavy, making them difficult to read and understand, especially for busy physicians with limited time to absorb new material and information. Narrowing the publications to those relevant to a particular physician's practice is a time-consuming and often non-rigorous task.

[0016] Sixth, in most cases, there is no clear incentive for physicians to memorize complete sets of treatment and outcome data, and indeed, the time required to memorize such data may act as an obstacle to collecting that data in a useful and complete form. Because of this, prescribing and treating physicians know what they know, and it may be perceived as a burden for them to laboriously capture complete sets of mental illness details (e.g., depression), treatment, and outcome data without receiving something in return (new insights, better prescribing tools, etc.).

[0017] In addition to the problems associated with collecting and storing treatment and outcome datasets, there are also problems with digesting or consuming the recorded data to generate useful conclusions. For example, recorded treatment and outcome data for mental illnesses (e.g., depression) are often incomplete. In most cases, physicians are not researchers and do not follow clearly defined research methods that track all aspects of depression, treatment, and outcomes. As a result, recorded data often lack important information, such as specific patient conditions that may be of current or future interest, why certain treatments were selected and others rejected, and specific outcomes. In many cases where a causal relationship exists between depression factors and treatment outcomes, if physicians are unable to identify and record the causal factors, those results cannot be linked to existing causal datasets and therefore simply cannot be consumed and meaningfully added to the overall depression knowledge dataset.

[0018] Another obstacle to digesting collected data is that physicians often capture mental illness (e.g., depression), treatment, and outcome data in a format that makes it difficult, if not impossible, to process the collected information so that the data can be normalized and used with other data from similar patient treatments to identify nuanced insights and draw stronger conclusions. For example, many physicians prefer to track patient treatment using paper and pen and / or use personal shorthand or abbreviations for different depression descriptors, patient conditions, treatments, outcomes, and even conclusions. Using software to glean accurate information from handwritten notes is difficult at best, but the task becomes even more difficult when the handwritten records contain personal abbreviations and shorthand representations of information that the software cannot simply identify with the physician's intended meaning.

[0019] To be useful, psychiatric disease, treatment, and outcome data, and the conclusions based on them, must be made accessible to physicians, researchers, and other interested parties. For example, in the case of depression treatment, where depressive states, treatments, outcomes, and conclusions are extremely complex and nuanced, physician and researcher interfaces must present vast amounts of information and show the consequences and relationships of much data. When vast amounts of information are presented through an interface, the interface often becomes extremely complex and daunting, which can lead to misunderstandings and prevent its full use.

[0020] Although treatments exist for many mental illnesses, such as depression, these are overwhelmingly directed at alleviating and treating symptoms, as opposed to "curing" the illness. In the absence of a treatment option that has been proven to be the best or even moderately effective, physicians often turn to clinical trials.

[0021] For example, research into depression is advancing daily at many hospitals and research institutions, and clinical trials are constantly being conducted to test new medications and treatment regimens. Depressed patients who have no other effective treatment options may choose to participate in a clinical trial if their depression meets the clinical trial's requirements and if the clinical trial has not yet fully enrolled (e.g., there are often limits on the number of patients who can participate in a clinical trial).

[0022] At any given time, thousands of clinical trials are ongoing worldwide, and identifying clinical trial options for a particular patient can be a daunting endeavor. Matching a patient's psychiatric disorder, such as depression, to a subset of ongoing clinical trials is complex and time-consuming. Reducing clinical trials to best matches given a given location, patient and physician requirements, and other factors further complicates the task of clinical trial participation consideration. In addition, considering whether to recommend a clinical trial for a particular patient is a tedious task that most physicians do not take lightly, especially considering the potential therapeutic effect of clinical trials where the treatment is experimental in nature in light of a particular patient condition.

[0023] Another problem with the current depression treatment planning process is the difficulty of integrating new appropriate treatment factors, treatment effect data, and insights into existing planning databases. In this regard, known treatment planning databases are developed with a predefined set of factors and insights, and modifying these databases often requires substantial effort on the part of software engineers to accommodate and integrate new factors or insights in a meaningful way that properly correlates them with other known factors and insights. In some cases, the substantial effort required simply means that the new factors or insights are never incorporated into the database or used to influence the plan, and in other cases, this effort means that the new factors or insights are simply added to the system with some delay time required to invest the effort.

[0024] Another problem with existing depression treatment effectiveness databases and systems is their inability to optimally support different types of system users. As a result, the data access, views, and interfaces required for optimal use often depend on what the system user is using the system for. For example, physicians often want to narrow down treatment options, outcomes, and effectiveness data to simple recommendations, while researchers often need even more detailed data access to develop new hypotheses related to the relationship between depression, treatment, and effectiveness. In known systems, data access, views, and interfaces are often developed with one consumer client in mind, such as a psychiatrist, general practitioner, radiologist, or treatment researcher, and are therefore optimized for that particular system user type, which means the system is not optimized for other user types.

[0025] Pharmacogenomics is the study of the role of the human genome in drug response. Aptly named by combining pharmacology and genomics, pharmacogenomics analyzes how an individual's genetic makeup influences drug response. It addresses the impact of genetic variation on a patient's drug response by correlating the pharmacokinetics (drug absorption, distribution, metabolism, and excretion) of gene expression with pharmacodynamics (the drug's action through its biological target). While both terms relate to drug response based on the influence of genes, pharmacogenetics focuses on single drug-gene interactions, while pharmacogenomics embraces a broader genome-wide association approach and incorporates genomics and epigenetics, addressing the influence of multiple genes on drug response. One goal of pharmacogenomics is to develop rational means to optimize drug therapy with respect to a patient's genotype, ensuring maximum efficacy while minimizing side effects. Pharmacogenomics and pharmacogenetics may be used interchangeably throughout this disclosure.

[0026] The human genome consists of 23 pairs of chromosomes, each containing 46 to 250 million base pairs (about 3 billion base pairs total), with each base pair having a complementary nucleotide (a pairing commonly described by the double helix). For each chromosome, the arrangement of base pairs can be referenced by its locus, or index number, for the base pair on that chromosome. Typically, each person receives one copy of a chromosome from their mother and the other copy from their father.

[0027] Traditional approaches to incorporating pharmacogenomics into precision medicine for the treatment, diagnosis, and analysis of psychiatric disorders, such as depression, include the use of single nucleotide polymorphism (SNP) genotyping and detection methods (such as through the use of SNP chips). SNPs are one of the most common types of genetic variation. SNPs are genetic variations that span only a single base pair at a particular locus. A SNP can be defined for a particular locus when individuals do not have the same nucleotide at that locus. SNPs are the most common type of genetic variation among people. Each SNP represents a difference in a single DNA building block. For example, a SNP may describe the substitution of the nucleotide cytosine (C) with the nucleotide thymine (T) at a locus.

[0028] Furthermore, different nucleotides may exist at the same locus within an individual. A person may have one nucleotide in the first copy of a particular chromosome and a different nucleotide in the second copy of that chromosome at the same locus. For example, a locus in a person's first copy of a chromosome may have the nucleotide sequence AAGCCTA, and the second copy may have the nucleotide sequence AAGCTTA at the same locus. In other words, either C or T may be present at the fifth nucleotide position within that sequence. A person's genotype at that locus can be described as a list of the nucleotides present in each copy of the chromosome at that locus. SNPs with two nucleotide options typically have three possible genotypes (a pair of matching nucleotides of the first type, one of each type, and a pair of matching nucleotides of the second type—AA, AB, and BB). In the above example, the three genotypes would be CC, CT, and TT. In a further example, the rs16260 variant has been defined for the gene CDH1 (chromosome 16) at loci 68, 737, and 131, where (C;C) is the normal genotype where C is expected at that locus, and (A;A) and (A;C) are variants of the normal genotype.

[0029] SNPs are common throughout human DNA, occurring on average almost once every 1,000 nucleotides, meaning that there are approximately 4 to 5 million SNPs in the human genome. Over 100 million SNPs have been detected in populations worldwide. These variations are typically found in the DNA between genes (regions of DNA known as "introns") and can act as biological markers that help scientists identify genes associated with diseases (such as psychiatric disorders).

[0030] SNPs are not the only possible genetic variations within the human genome. Any deviation in a person's genome sequence compared to a normal reference genome sequence can be referred to as a variant. In some cases, a person's physical health can be affected by a single variant, but in other cases, it can be affected only by a combination of specific variants located on the same chromosome. When variants within a gene are located on the same chromosome, the variants are located within the same allele of the gene. An allele is defined as a contiguous sequence of bases in a region of a DNA molecule observed within an individual, especially when the sequence of that region has been shown to vary between individuals. Some genetic tests, such as NGS, can detect multiple variants within a gene and determine whether the variants are located within the same allele. Some genetic tests do not have this capability.

[0031] Several groups of variants present together on the same chromosome may form specific alleles known to alter a person's health. Sometimes, a single allele may not affect a person's health unless they also possess a specific combination of alleles. Sometimes, alleles or allele combinations are reported or published in databases or other records along with their health significance (e.g., which alleles or allele combinations qualify a person as an ultrafast metabolizer, an intermediate metabolizer, or a poor metabolizer). Exemplary records include those from the American College of Medical Genetics and Genomics (ACMG), the Association for Molecular Pathology (AMP), or the Clinical Pharmacogenetics Implementation Consortium (CPIC). These published alleles may each have a designated identifier; one category of identifier is the * (star) allele system. For example, for each gene, each star allele may be numbered *1, *2, *3, etc., with *1 typically representing the reference or normal allele. As an example, the CYP2D6 gene has over 100 reported variant alleles.

[0032] Developed before next-generation sequencing (NGS), microarray assays were a common genetic test for detecting variants. Microarray assays use biochips with DNA probes attached to the surface (usually in a grid pattern). Mass arrays can also be used for genetic testing. Some of these biochips are called SNP chips. A solution containing DNA molecules from one or more biological samples is introduced to the biochip surface. Each DNA molecule from the sample is attached with a fluorescent dye or another type of dye. The color of the dye is often unique to the sample, allowing the assay to distinguish between two samples when multiple samples are introduced to the biochip surface simultaneously.

[0033] If the solution contains a DNA sequence complementary to one of the probes attached to the biochip, the DNA sequence will bind to the probe. After all unbound DNA molecules are washed away, any sample DNA bound to the probe will fluoresce or produce another visually detectable signal. The location and sequence of each probe are known, so the location of the visually detectable signal indicates which bound complementary DNA sequences were present in the sample, and the color of the dye indicates which sample the DNA sequence originated from. Each probe sequence on the biochip contains only one sequence, and each probe specifically binds to one complementary sequence in the DNA, meaning that most probes can only detect one type of mutation or genetic variant. This also means that microarrays will not detect sequences that are not targeted by the probes on the biochip, meaning they cannot be used to identify new variants. This is one reason why next-generation sequencing is more useful than microarrays.

[0034] The fact that a probe detects only one specific DNA sequence means that a microarray cannot determine whether two detected variants are the same allele unless the variants' loci are close enough that a single probe can span both loci. In other words, the number of nucleotides between the two variants plus the number of nucleotides in each variant must be less than the number of nucleotides in the probe; otherwise, the microarray cannot detect whether the two variants are in the same DNA strand, and therefore the same allele.

[0035] Each probe binds to its complementary sequence within the specific temperature and concentration range of the components in the DNA solution introduced into each biochip. Because it is difficult to simultaneously achieve optimal binding conditions for all probes on a microarray (such as those used in SNP chips), any DNA from the sample may hybridize to a probe that is not perfectly complementary to the sample DNA sequence, potentially resulting in inaccurate test results.

[0036] Furthermore, the drawbacks of microarrays include the limited number of probes targeting biomarkers due to the surface area of ​​the biochip, the misclassification of variants that do not bind to the probes as normal genotypes, and the overall misclassification of a patient's genotype. Because the throughput of SNP chips is limited, traditional microarray approaches are inefficient in detecting biomarkers and the many mutations they contain.

[0037] TaqMan assays have the same limitations as microarrays. If a TaqMan assay probe is a perfect match for a complementary sequence within a DNA molecule from a sample, the DNA molecule is extended, similar to NGS. However, instead of reporting what sequences of each nucleotide type are present in the DNA extension, the assay only reports whether an extension has occurred. This presents the same limitations as SNP chips. Other genetic tests, such as dot blots and Southern blots, have similar limitations.

[0038] Therefore, there is a need in the art to address the above-mentioned shortcomings. With respect to psychiatric disorders such as depression, in many cases, patient conditions related to the disorder can be gleaned piecemeal from clinical medical records via medical examinations, genetic analysis, and / or patient interviews and used to develop personalized treatment plans for the particular illness. Therefore, there is a need in the art for a system that collects data on as many factors as possible that have some causal relationship with treatment outcome and uses those factors to design optimal personalized treatment plans.

[0039] Additionally, what is needed is a well-designed interface that makes complex data sets easy to understand and digest. For example, in the case of psychiatric disorders such as depression, treatments, and outcomes, it would be beneficial to provide an interface that allows physicians to view de-identified patient data for many patients, with the data specifically arranged to trigger important treatment and outcome insights. It would also be beneficial for the interface to have interactive capabilities so that physicians can use filters to access different treatment and outcome data sets, again to trigger different insights, examine anomalies within the data sets, and better think through treatment plans for their particular patients.

[0040] It would also be advantageous to have a tool that could help physicians identify clinical trial options for specific patients with specific psychiatric disorders, such as specific depressive conditions, and access information related to the trial options.

[0041] What is needed, therefore, is a system that can efficiently capture all treatment-related data, including factors associated with mental illness, such as depression factors, treatment decisions, treatment outcomes, and exploratory factors (such as factors believed to be causally related to treatment outcomes), and structure the data to optimally drive different system activities, including data and treatment decision storage, database analysis, and user applications and interfaces. In addition, the system should be highly adaptable so that it can be modified to absorb new data types and new treatment and research insights, as well as to enable the development of new user applications and interfaces optimized for specific user activities. Summary of the Invention [Means for solving the problem]

[0042] One implementation of the present disclosure is a system for personalized mental illness treatment. In one embodiment, the mental illness is a depression disorder. The system includes a server configured to communicate with existing healthcare resources and receive patient data corresponding to the patient, the server comprising an analysis module. The system further includes a first database configured to store empirical patient outcomes and further configured to communicate with the analysis module. In addition, the system includes a user device communicating with the server and having a graphical user interface (GUI) configured to display at least one output generated by the analysis module. The analysis module is configured to determine at least one of a personalized depression treatment and a personalized depression state prediction based on the empirical patient outcomes and the patient data.

[0043] Another implementation of the present disclosure is a method for analyzing clinical data. The method includes combining molecular data with clinical data from a patient diagnosed with a psychosis to generate combined patient data. The method further includes comparing the combined patient data with a knowledge base to generate information. Additionally, the method includes generating a clinical report including information related to the comparison and providing the clinical report to a physician.

[0044] Another implementation of the present disclosure is a method that includes receiving a list of clinical trial criteria from one or more clinical trials targeting a psychiatric disorder, such as a depression clinical trial, and receiving patient data from the patient's medical record from an electronic health record system. The method further includes deriving one or more patient metrics, each patient metric corresponding to a clinical trial criterion from the list of clinical trial criteria. Additionally, the method includes comparing each patient metric to the list of clinical trial criteria and indicating that the patient is eligible for the one or more clinical trials if each criterion in the corresponding list of clinical trial criteria is met.

[0045] In one aspect, the present disclosure provides a method for generating treatment information for a patient diagnosed with at least one psychiatric disease, the method comprising, in a computer system having one or more processors and a memory storing one or more programs for execution by the one or more processors, obtaining molecular data from a multigene panel sequencing reaction on a sample from the patient, the molecular data including a plurality of nucleic acid sequences obtained from whole exome sequence data, mass array data, sequence data from one or more introns associated with metabolism-related genes, and sequence data from one or more promoter regions associated with metabolism-related genes; aligning the molecular data to a human reference sequence; and providing a first set of clinical data associated with the patient, the first set of clinical data including a listing of prior therapeutic medications and a listing of one or more diagnoses. generating a first report from a therapeutic engine based on the patient's molecular data and a first set of clinical data, the report presenting, for each one of at least a portion of a plurality of nucleic acid sequences in the patient's molecular data, a phenotype associated with the nucleic acid sequence in a laboratory results section of the report, and a listing of one or more medications associated with the nucleic acid sequence and a classification for each medication in the listing in a supplemental section of the report, the listing of the one or more medications being determined at least in part by a listing of a prior therapeutic medication, the method including presenting the report to a user; obtaining a second set of clinical data associated with the patient, the second set of clinical data describing the patient's clinical activity after presentation of the report; and updating the therapeutic engine with at least a portion of the second set of clinical data.

[0046] In some embodiments, the classification may relate to one or more of drug administration, drug risks, and contraindications.

[0047] In some embodiments, the clinical activity described in the second set of clinical data can include one or more of prescribed medications, medication dosages, patient compliance, and patient outcomes after taking the prescribed medications.

[0048] In some embodiments, the patient may be diagnosed with multiple psychiatric illnesses.

[0049] In some embodiments, the therapeutic engine can include a knowledge database, the knowledge database including data related to interactions between one or more specific drugs and one or more nucleic acid sequences associated with drug metabolism; primary drug metabolic pathway data; and a first cohort dataset derived from a cohort of psychiatric subjects at time 1, the first cohort dataset including one or more drugs used in treatment, pretreatment diagnoses, treatment outcomes, and drug information data collected from one or more sources: scientific publications, the U.S. Food and Drug Administration (FDA), the Clinical Pharmacogenomics Implementation Consortium (CPIC), the Dutch Pharmacogenomics Working Group (DPWG), the Pharmacogenomics Knowledge Base Review, and the Psychoactive drug Screening Program Ki Database.

[0050] In some embodiments, the cohort dataset may not be derived from clinical trial data, hi some embodiments, at least some of the psychiatric subjects in the cohort may be diagnosed as suffering from multiple psychiatric illnesses.

[0051] In some embodiments, the knowledge database can further include a second cohort dataset derived at time 2, where the second cohort dataset can include information from at least one of the first cohort subjects. In some embodiments, the knowledge database can further include an Nth cohort dataset derived at time N, where the Nth cohort dataset includes information from at least one of the previous cohort subjects. In some embodiments, the method can further include providing an Nth set of clinical data associated with the patient, where the Nth set of clinical data can be acquired at a time after the (N-1)th clinical dataset is acquired. In some embodiments, each of the clinical datasets can describe the patient's clinical activity since the submission of the immediately preceding report. In some embodiments, the method can further include updating the therapy engine with at least a portion of the Nth clinical dataset.

[0052] In some embodiments, the report may further provide supporting information for the classification. In some embodiments, the report may further provide hyperlinks to source documents or websites with information about the drug classification.

[0053] In some embodiments, the report can further provide a listing of drugs that are associated with the patient's diagnosis but have no known nucleic acid association.

[0054] In some embodiments, the clinical activity described in any of the Nth clinical datasets may include one or more of prescribed medications, medication dosages, patient compliance, and patient outcomes after taking the prescribed medications.

[0055] In some embodiments, the method can further include generating a second report based on input from the second clinical dataset.

[0056] In some embodiments, the listing of the prior therapeutic agent can include at least one drug dosage.

[0057] In some embodiments, the listing of prior therapeutic agents can include at least one patient response to the agent.

[0058] In some embodiments, the treatment engine can identify possible side effects of the listed drugs and provide that side effect information in the report.

[0059] In some embodiments, the therapeutic engine can identify a recommended dosage for each drug included in the one or more drug listings and can provide that dosage information in the report.

[0060] In some embodiments, the treatment engine can identify the next potential drug recommendation by excluding from the report at least one drug included in the prior treatment drug listing.

[0061] In some embodiments, the treatment engine may include a classifier for identifying a subtype of depression, and the psychiatric illness for which the patient has been diagnosed may be depression, and the subtype of depression may be listed in the report.

[0062] In some embodiments, the treatment engine can include a classifier to identify drug resistance, which can be listed in a report.

[0063] In some embodiments, the listing of one or more drugs may be determined at least in part based on at least one diagnosis.

[0064] In another aspect, the present disclosure provides a system for generating information regarding treatment for a patient diagnosed with a psychosis. The system includes at least one memory and at least one processor coupled to the at least one memory. The system causes the at least one processor to execute instructions stored in the at least one memory to: obtain molecular data from a multi-gene panel sequencing reaction on a sample from the patient, the molecular data including a plurality of nucleic acid sequences obtained from whole exome sequence data, mass array data, sequence data from one or more introns associated with metabolism-related genes, and sequence data from one or more promoter regions associated with metabolism-related genes; align the molecular data to a human reference sequence; and provide a first set of clinical data associated with the patient, the first set of clinical data including a listing of prior therapeutic medications and a listing of one or more diagnoses. and generating a first report from the therapeutic engine based on the first set of clinical data, the report presenting, for each one of at least a portion of the plurality of nucleic acid sequences in the patient molecular data, a phenotype associated with the nucleic acid sequence, a listing of one or more drugs associated with the nucleic acid sequence, and a classification for each drug in the listing, the listing of the one or more drugs being determined at least in part by the listing of the prior therapeutic medication; causing the report to be presented to a user; obtaining a second set of clinical data associated with the patient, the second set of clinical data describing the patient's clinical activity after presentation of the report; and updating the therapeutic engine with at least a portion of the second set of clinical data.

[0065] In some embodiments, the clinical activity described in the second set of clinical data can include one or more of prescribed medications, medication dosages, patient compliance, and patient outcomes after taking the prescribed medications.

[0066] In some embodiments, the cohort dataset may not be derived from clinical trial data.

[0067] In some embodiments, the subject may be diagnosed with multiple psychiatric illnesses.

[0068] In some embodiments, at least a portion of the psychiatric subjects in the cohort may be diagnosed with multiple psychiatric illnesses.

[0069] To the accomplishment of the foregoing and related ends, the invention, then, comprises the features hereinafter fully described. The following description and the annexed drawings set forth in detail certain illustrative aspects of the invention. These aspects are indicative, however, of but a few of the various ways in which the principles of the invention may be employed. Other aspects, advantages and novel features of the invention will become apparent from the following detailed description of the invention when considered in conjunction with the drawings. [Brief explanation of the drawings]

[0070] [Figure 1] FIG. 1 is a block diagram of a data-based therapy system according to an aspect of the present disclosure. [Figure 2] 1 is an image of an exemplary graphical user interface (GUI) according to an aspect of the present disclosure. [Figure 3A] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 3B] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 3C] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 3D] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 4A] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 4B]3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 4C] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 4D] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 4E] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 5A] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 5B] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 6A] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 6B] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 6C] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 6D] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 6E] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 7A] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 7B] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 7C] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 7D] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 8A] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 8B] 3 is another image of the example GUI of FIG. 2 according to an embodiment of the present disclosure. [Figure 9A] FIG. 10 illustrates an alternative embodiment of a patient report. [Figure 9B] FIG. 10 illustrates an alternative embodiment of a patient report. [Figure 9C] FIG. 10 illustrates an alternative embodiment of a patient report. [Figure 9D] FIG. 10 illustrates an alternative embodiment of a patient report. [Figure 9E] FIG. 10 illustrates an alternative embodiment of a patient report. [Figure 9F] FIG. 10 illustrates an alternative embodiment of a patient report. [Figure 9G] FIG. 10 illustrates an alternative embodiment of a patient report. [Figure 9H] FIG. 10 illustrates an alternative embodiment of a patient report. [Figure 10] FIG. 10 is a diagram of a portion of another alternative embodiment of a patient report. [Figure 11] FIG. 11 is another portion of the patient report of FIG. 10. [Figure 12] FIG. 11 is a diagram of yet another portion of the patient report of FIG. 10. [Figure 13] FIG. 11 is a diagram of an additional portion of the patient report of FIG. 10. [Figure 14] FIG. 11 is a further portion of the patient report of FIG. 10. [Figure 15] FIG. 11 is a view of yet a further portion of the patient report of FIG. 10. [Figure 16] FIG. 11 is another additional portion of the patient report of FIG. 10. [Figure 17] FIG. 11 is a diagram of yet another additional portion of the patient report of FIG. 10. [Figure 18] FIG. 11 is yet another additional portion of the patient report of FIG. 10. [Figure 19] FIG. 1 illustrates an exemplary process for generating treatment information for a patient diagnosed with a psychosis. DETAILED DESCRIPTION OF THE INVENTION

[0071] While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and are herein described in detail. However, the description herein of specific embodiments is not intended to limit the invention to the particular forms disclosed; on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.

[0072] The present invention will now be described with reference to various accompanying drawings, wherein like reference numerals correspond to like elements throughout the several drawings. It should be understood, however, that the drawings and the following related detailed description are not intended to limit the claimed subject matter to the particular forms disclosed. Rather, the invention covers all modifications, equivalents, and alternatives falling within the spirit and scope of the claimed subject matter.

[0073] The present disclosure is described in the context of a system related to the research, diagnosis, treatment, and outcome analysis of mental illnesses (such as depression, bipolar disorder, obsessive-compulsive disorder, borderline personality disorder, and anxiety). More specifically, exemplary embodiments related to depressive disorders ("depression") are described herein. Nevertheless, it should be understood that the present disclosure is intended to teach concepts, features, and aspects that will be useful in many different health-related contexts, and thus, the present specification should not be considered limited to depression-related systems unless specific reference is made to certain system aspects. Furthermore, the present disclosure is described in the context of a system related to the research, diagnosis, treatment, and analysis of results from next-generation sequencing (NGS).

[0074] Hereinafter, unless otherwise indicated, the following terms and phrases are used in this disclosure as explained: The term "provider" is used to refer to the entity that operates the entire system disclosed herein and will most likely include a company or other entity that runs the servers, maintains the databases, and employs people with the many different skill sets necessary to build, maintain, and adapt the disclosed system to accommodate new data types, new medical and treatment insights, and other needs. Exemplary provider employees may include researchers, data abstractors, neurologists, psychiatrists, data scientists, and many others with specialized skill sets.

[0075] The term "physician" is used generally to refer to any health care provider, including, but not limited to, primary care physicians, specialists, neurologists, psychiatrists, psychologists, nurses, and medical assistants, among others.

[0076] The term "researcher" is used generally to refer to any person who conducts research, including, but not limited to, a radiologist, neurologist, data scientist, or any other healthcare provider. Some people may be both physicians and researchers, while others may only function in one of these capacities.

[0077] The phrase "systems specialist" is generally used to refer to a provider employee who works within the disclosed system to collect, develop, analyze, or otherwise process system data, tissue samples, or other information types (e.g., medical images) to generate intermediate system work products or final work products, including any data sets, conclusions, tissue or other samples, or other information for consumption by one or more other systems specialists, and final work products including data, conclusions, or other information that are documented in a final or definitive report for a system client or that perform research and operate within the system to adapt the system to changing needs, data types, or client requirements. For example, an "abstraction specialist" is used to refer to a person who consumes data available in clinical notes provided by a physician (e.g., a primary care physician or psychiatrist) and generates normalized and structured data for use by other systems specialists. The phrase "programming specialist" is used to refer to a person who generates or modifies application program code to accommodate new data types and / or clinical insights, etc.

[0078] The phrase "system user" is used generally to refer to anyone who uses the disclosed system to access or manipulate system data for any purpose, and thus generally includes physicians and researchers working for or partnering with a provider to perform work on behalf of patients or other partner institutions, as well as system professionals working for the provider.

[0079] The term "depressed state" is used to refer to the overall state of a depressed patient, including diagnosed depression, mental and physical depressive symptoms, other patient conditions (such as age, sex, weight, race, habits (smoking, alcohol, diet, etc.)), other related medical conditions (such as anxiety, high blood pressure, dry skin, other illnesses), medications, allergies, other related medical history, current side effects of any depression treatments and other medications, etc.

[0080] The phrase "consume" is used to refer to any type of consideration, use, modification, or other activity involving any type of system data, saliva sample, etc., whether the consumption is exhaustive (e.g., used only once, as in the case of a non-reproducible saliva sample) or non-exhaustive (e.g., used multiple times, as in the case of a simple data value) such that the data, sample, etc. persists for consumption by multiple entities. The term "consumer" is used to refer to any system entity that consumes any system data, sample, or other information in any manner, including experts, physicians, researchers, clients that consume any system work products, and each of software application programs or operational code that automatically consumes data, samples, information, or other system work products independent of any initiating human activity.

[0081] The phrase "treatment planning process" is used to refer to the entire process, including one or more subprocesses, that process clinical data and other patient data and samples (e.g., saliva samples) to produce intermediate data deliverables and, ultimately, final work products in the form of one or more final reports provided to a system client. These processes typically involve various levels of exploration of treatment options for a patient's particular depressive condition, but typically relate to the treatment of a specific patient, as opposed to more general exploration aimed at more general research activities. Thus, treatment planning may include data generation, the processes used to generate that data, consideration of different treatment options and their effects on the patient's condition, etc., resulting in a final prescription for treating a specific patient's illness.

[0082] A medical treatment prescription or plan is typically based on an understanding of the extent to which a treatment will affect the disease (e.g., treatment outcome), including the extent to which a particular treatment will eradicate the disease, the duration of the particular treatment, the duration of the healing process associated with the particular treatment, and typical treatment-specific side effects. Ideally, the treatment will result in a complete cure of the disease within a short period of time with minimal or no side effects. In some cases, cost is also a consideration when selecting a particular medical treatment for a particular disease.

[0083] As used herein, the terms "component," "system," and similar terms are intended to refer to a computer-related entity, i.e., either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and / or a computer. For example, both an application running on a computer and the computer may be a component. One or more components may reside within a process and / or thread of execution, and a component may be local to one computer and / or distributed between two or more computers or processors.

[0084] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any aspect or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs.

[0085] Furthermore, the disclosed subject matter may be implemented as a system, method, apparatus, or article of manufacture that uses programming and / or engineering techniques to produce software, firmware, hardware, or any combination thereof, to control a computer or processor-based device to implement aspects detailed herein. The term "article of manufacture" (or alternatively, "computer program product") as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or medium. For example, computer-readable media may include, but are not limited to, magnetic storage devices (hard disks, floppy disks, magnetic strips, etc.), optical disks (compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (cards, sticks, etc.). In addition, it should be understood that carrier waves may be employed to carry computer-readable electronic data, such as those used to send and receive email or access networks such as the Internet or a local area network (LAN). Transient computer-readable media (carrier wave and signal-based) should be considered separately from non-transient computer-readable media such as those described above. Of course, those skilled in the art will recognize that many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter.

[0086] Unless otherwise indicated, the disclosed system may be used for many different purposes (data collection, data analysis, data display, treatment, research, etc.), but for simplicity and consistency, the entire disclosed system will be referred to hereinafter as the "disclosed system."

[0087] As used herein, the term "clinical data" refers to information relating to a patient or cohort of subjects that is typically obtained by questioning, observing, or examining the subject. Exemplary clinical data include, but are not limited to, physical characteristics (e.g., sex, height, weight, age, general health, etc.), medical history, current and past diagnoses, current and past treatment regimens administered, patient compliance, treatment outcomes, imaging analyses such as X-rays, CT scans, facial images, and recordings of body movements, physician observations and notes regarding behavior, thought patterns, sleep cycles, physical conditions, changes, etc.

[0088] In one example, the presently disclosed invention may be a system, other class of device, and / or method that helps healthcare providers make clinical decisions based on a combination of molecular and clinical data, which may include comparing a patient's molecular and clinical data to an aggregated dataset of molecular and / or clinical data from multiple patients (e.g., a cohort of subjects) and / or a knowledge database (KDB) of clinical genomics data. In addition, the presently disclosed invention may be used to capture, ingest, cleanse, structure, and combine robust clinical and detailed molecular data to determine the significance of correlations, patterns, and trends to generate physician-ready reports, analyze or confirm the accuracy of diagnoses, predict the likelihood that a patient will respond to a particular treatment, recommend or withhold a particular treatment for a patient, support biomarker discovery, enhance clinical research efforts, monitor treatment and dosing decisions, expand indications of use for currently marketed treatments and clinical trials, and expedite federal or regulatory approval of therapeutic compounds. In one example, the invention disclosed herein may help academic medical centers, pharmaceutical companies, and community providers improve care options and treatment outcomes for patients, particularly patients experiencing any psychiatric illness or disease, including, but not limited to, treatment-resistant depression, major depressive disorder, bipolar disorder, schizophrenia, etc. In one example, an implementation of this system may be in the form of software.

[0089] As used herein, "drug metabolism" refers to the metabolic breakdown of drugs by the body, usually through specialized enzyme systems. Genes that encode such enzymes or that encode regulators of enzyme-expressing genes are considered drug-metabolizing genes.

[0090] The terms "subject" and "patient" are used interchangeably herein. The subject is preferably a human subject, although it should be understood that the methods described herein are effective with respect to all vertebrate species that are intended to be encompassed by the term "subject." Thus, a "subject" can include a human subject for medical purposes, such as treatment of an existing condition or disease, or prophylactic treatment to prevent the onset of a condition or disease, or an animal subject for medical, veterinary, or development purposes. Suitable animal subjects include mammals, including, but not limited to, primates, e.g., monkeys, apes, and the like, bovines, e.g., cattle, oxen, and the like, ovines, e.g., sheep, and the like, caprines, e.g., goats, and the like, porcines, e.g., piglets, piglets, and the like, equines, e.g., horses, donkeys, zebras, and the like, felines, including wild cats, domestic cats, canines, including dogs, lagomorphs, including rabbits, hares, and the like, rodents, including mice, rats, and the like. Additionally, "subject" can include a patient diagnosed with or suspected of having a condition or disease, such as a psychiatric illness.

[0091] As used herein, the phrases "treatment" or "treating" refer to both prophylactic or preventative treatment, as well as curative or disease-modifying treatment, including treatment of patients at risk of or suspected of being infected with a disease, as well as patients who are sick, suffering from a disease, or diagnosed with a condition, and also include suppression of clinical recurrence. A treatment may be administered to a subject who has a medical disease or who may ultimately acquire a disease, thereby preventing, curing, delaying the onset of, reducing the severity of, or ameliorating one or more symptoms of a disease or recurring disease, or prolonging the subject's survival beyond that expected in the absence of such treatment. A "treatment regimen" refers to a pattern of treatment for a disease, such as a mental illness, e.g., a pattern of medications or other treatments (e.g., counseling, group therapy, etc.) used during treatment.

[0092] As used herein, "control," "control sample," "reference," "reference sample," "normal," and "normal sample" describe a sample from non-diseased tissue. In some embodiments, such a sample is from a subject without a particular condition (e.g., a diagnosed psychiatric disorder). In other embodiments, such a sample is an internal control, e.g., from a subject who may or may not have a particular disease or disorder, and is from a pre-treatment sample from the subject. For example, if a blood or saliva sample is obtained from a subject diagnosed with one or more psychiatric disorders, the internal control sample may be obtained from the subject before any treatment. The pre-treatment sample may, for example, show elevated expression levels from one or more genes. After treatment, another sample may be analyzed to determine whether the treatment accordingly alters expression levels. Thus, a reference sample may be obtained from the subject or from a database, e.g., a second subject.

[0093] As used herein, "molecular data" includes information such as the sequence and / or quantity (e.g., expression levels or duplication / deletion information) of one or more protein, DNA, or RNA samples of a subject, control subject, or cohort. By way of example, and not limitation, in some embodiments, molecular data includes DNA sequence information, including, but not limited to, whole-exome genetic data, single nucleotide variants (SNVs), insertions / deletions (indels), copy number variations (CNVs), fusion variants, RNA expression data (including miRNA expression), microbiome information, haplotype or allele information including star alleles, haplotype groups or diplotypes including star allele combinations, mass array data, and microarray data. Whole-exome genetic data associated with any of the exons in the human genome may further include, for example, intronic regions targeted by intron-specific probes spiked into a whole-exome panel. Molecular data, as used herein, also includes targeted panels of DNA or RNA data (including sequence data and / or expression level data) and targeted panels of protein data. By way of example, and not limitation, a targeted panel includes assays designed to evaluate or analyze only specific gene sequences, such as specific genes, portions of genes, or specific non-coding sequences (e.g., introns or promoter regions), or specific proteins, as opposed to whole genome RNA analysis. Molecular data may be obtained by methods well known in the art, and such methods are not intended to be limiting. By way of example, in some embodiments, the molecular data is derived from a multi-gene panel sequencing reaction and includes a plurality of nucleic acid sequences obtained from one or more of whole exome sequence data, mass array data, sequenced data from one or more introns, and sequence data from one or more gene regulatory regions.

[0094] For example, the methods and systems described herein can be used based on information generated from next-generation sequencing (NGS) technology. The field of NGS for genomics is new and faces significant challenges in managing the relationships between sequencing, bioinformatics, variant calling, analysis, and data reporting. NGS involves the use of specialized equipment, such as next-generation genetic sequencers, which are automated devices that determine the order of nucleotides in DNA and RNA. The devices report the sequence as a string of characters, called a read, which analysts can compare to one or more reference genomes of the same gene. The reference genomes can be compared to libraries of normal and variant gene sequences associated with several conditions. Because NGS standards have not been established, different NGS data providers and laboratories have different approaches to sequencing patient genomes and, based on their sequencing approaches, generate different types and amounts of genomics data to share with physicians, researchers, and patients. Disparate genomics datasets make the task of identifying meaningful genetic treatment effect insights difficult, and in some cases impossible, because the necessary data is not in a normalized format, not captured, or simply not generated at all. The systems and methods disclosed herein address this deficiency.

[0095] In an exemplary embodiment, DNA extracted from blood, saliva, biopsy, or other biological patient samples is single- or paired-end sequenced using an NGS platform, such as one provided by Illumina. The patient from whom the sample was collected may have been diagnosed with a psychiatric illness. The sequencing results (herein, "raw sequencing data") may be passed through a bioinformatics pipeline, where the raw sequencing data is analyzed. The raw sequencing data may relate to all exon and selected intron combinations in the human genome. After the sequencing information is passed through the bioinformatics pipeline, it may be evaluated for quality control, such as through an automated quality control system. If the sample does not pass the initial quality control step, it may be manually reviewed. If the sample passes the automated quality control system, or if it is manually passed, an alert may be issued to a message bus configured to listen for messages from the quality control system. This message may include an identifier for the sample and even the location of the BAM file. A BAM file (.bam) is a binary version of a SAM file. A SAM file (.sam) is a tab-delimited text file that contains sequence alignment data (e.g., raw sequencing data). When a message is received, a service can be triggered to evaluate the sequencing data for pharmacogenomic factors.

[0096] As used herein, the terms "BAM file" or "binary file containing an alignment map" refer to a file containing sequencing data aligned to a reference sequence (e.g., a reference genome or exome). In some embodiments, a BAM file is a compressed binary version of a SAM (sequence alignment map) file, which contains, for each of a plurality of unique sequence reads, an identifier for the sequence read, information about the nucleotide sequence, information about the alignment of the sequence to a reference sequence, and optionally metrics related to the quality of the sequence read and / or the quality of the sequence alignment. While a BAM file generally relates to a file having a particular format, for simplicity, the term herein simply refers to a file of any format that contains information about sequence alignments, unless otherwise specified.

[0097] BAM files can be generated by aligning raw molecular data to a reference genome. For example, the raw molecular data can be stored in BCL, FASTA, and / or FASTQ file formats. A suitable process can align the raw molecular data to a human reference sequence to generate aligned sequence reads. The aligned sequence reads can be stored in SAM and / or BAM file formats.

[0098] The bioinformatics pipeline may receive raw sequencing results and process them to identify genetic variants expressed in the patient's DNA or RNA, and may save this information in a variant call format file (.vcf). The identified variants may be referred to as variant calls. Once a variant has a sufficient number of reads from the raw sequencing results to qualify as a variant call, variant characterization may be performed on the variant call. In one example, variant characterization is the process of determining whether a variant is benign, linked to an increased risk of a specific disease, and / or likely to cause interactions with prescribed medications. Variant characterization may involve searching publicly available variant datasets that identify pharmacogenomically significant variants, searching FDA publications on therapies and their target variants, or comparing variant calls to an internally curated list of variants with pharmacogenomic significance. Variant calls with pharmacogenomic significance may be flagged for inclusion in a report, such as the report described in further detail below.

[0099] As used herein, the term "sequencing probe" refers to a molecule that binds to nucleic acids with affinity based on the predicted nucleotide sequence of RNA or DNA present at a locus.

[0100] As used herein, the term "targeted panel" or "targeted gene panel" refers to a combination of probes for sequencing (e.g., by next-generation sequencing) nucleic acids present in a biological sample (e.g., a saliva or blood sample) from a subject, selected to map to one or more loci of interest on one or more chromosomes. In some embodiments, the loci provide psychiatric disease diagnosis and / or drug metabolism information.

[0101] As used herein, the term "reference exome" refers to any sequenced or otherwise characterized exome, whether partial or complete, of any tissue from any organism or pathogen that can be used to reference identified sequences from a subject. Typically, the reference exome is derived from a subject of the same species as the subject whose sequence is being evaluated. Exemplary reference exomes used for human subjects, as well as many other organisms, are provided in the online genome browser hosted by the National Center for Biotechnology Information (NCBI). "Exome" refers to the complete transcriptional profile of an organism or pathogen, represented by nucleic acid sequences. As used herein, a reference sequence or reference exome is often an assembled or partially assembled exome sequence from an individual or multiple individuals. In some embodiments, a reference exome is an assembled or partially assembled exome sequence from one or more human individuals. A reference exome can be considered a representative set of expressed genes for a species. In some embodiments, a reference exome includes sequences assigned to chromosomes.

[0102] As used herein, the term "reference genome" refers to any sequenced or otherwise characterized genome, whether partial or complete, of any organism or pathogen that can be used to reference identified sequences from a subject. Typically, a reference genome is derived from a subject of the same species as the subject whose sequence is being evaluated. Exemplary reference genomes used for human subjects, as well as many other organisms, are provided in online genome browsers hosted by the National Center for Biotechnology Information (NCBI) or the University of California, Santa Cruz (UCSC). "Genome" refers to the complete genetic information of an organism or pathogen, represented by nucleic acid sequences. As used herein, a reference sequence or reference genome is often an assembled or partially assembled genome sequence from an individual or multiple individuals. In some embodiments, a reference genome is an assembled or partially assembled genome sequence from one or more human individuals. A reference genome can be considered a representative set of genes for a species. In some embodiments, a reference genome includes sequences assigned to chromosomes. Exemplary human reference genomes include, but are not limited to, NCBI build 34 (UCSC equivalent: hg16), NCBI build 35 (UCSC equivalent: hg17), NCBI build 36.1 (UCSC equivalent: hg18), GRCh37 (UCSC equivalent: hg19), and GRCh38 (UCSC equivalent: hg38). In a haploid genome, there can be only one nucleotide at each locus. In a diploid genome, heterozygous loci are identified, and each heterozygous locus can have two alleles, with either allele allowing for alignment to that locus.

[0103] As used herein, the terms "genomic alteration," "mutation," and "variant" refer to detectable changes in the genetic material of one or more cells. Genomic alteration, mutation, or variant may refer to various types of changes in the genetic material of a cell, including changes in the primary genomic sequence at single or multiple nucleotide positions, such as single nucleotide variants (SNVs), multinucleotide variants (MNVs), indels (e.g., nucleotide insertions or deletions), DNA rearrangements (e.g., chromosomal portions or chromosomal inversions or translocations), copy number changes (CNVs) of loci (e.g., exons, genes, large spans of chromosomes), partial or complete changes in cellular ploidy, and even altered DNA methylation patterns. In some embodiments, a mutation is a change in the genetic information of a cell relative to one or more "normal" alleles found in a particular reference genome or a population of a subject's species. Many loci in a species' reference genome are significantly represented in the subject's population and are associated with several variant alleles that are not associated with a pathology, e.g., that are not considered mutations.

[0104] As used herein, the term "pharmacokinetics" refers to the interaction between genetic polymorphisms (or downstream factors such as proteins) and the absorption, distribution, metabolism, and / or excretion characteristics of a drug or other therapy. Genes associated with the metabolism of a particular therapy are referred to as "metabolism-related genes." Genes associated with the absorption of a particular therapy are referred to as "absorption-related genes." Genes associated with the distribution of a particular therapy are referred to as "distribution-related genes." Genes associated with the excretion of a particular therapy are referred to as "excretion-related genes."

[0105] As used herein, the term "pharmacokinetics" refers to the interactions between genetic polymorphisms (or downstream elements such as proteins) and receptors, ion channels, enzymes, and the immune system associated with a drug or other therapy. Genes associated with immunogenicity are referred to as "immunogenic genes."

[0106] In some embodiments, molecular data, including sequence data, sometimes referred to as "sequence reads," may be aligned or compared to a reference sequence. As used herein, the term "sequence read" or "read" refers to a nucleotide sequence generated by any nucleic acid sequencing process described herein or known in the art. A sequence read may be aligned to a reference sequence, such as a reference genome, reference exome, or other reference construct prepared for a particular targeted panel sequencing reaction, using, for example, an alignment algorithm. For example, in some embodiments, individual sequence reads in electronic form (e.g., a FASTQ file) are aligned against a reference sequence construct for the subject's species (e.g., a reference human genome) by identifying the sequence within the reference sequence construct that best matches the sequence of nucleotides within the sequence read. In some embodiments, the sequence read is aligned to a reference exome or reference genome using methods known in the art, thereby determining alignment position information. The alignment position information may indicate the start and end positions of a region within the reference genome that corresponds to the starting and ending nucleotide bases of a given sequence read. The alignment position information may also include the sequence read length, which can be determined from the start position and end position. A region in the reference genome may be associated with a gene or a segment of a gene. Various alignment tools well known in the art can also be used for this task. In various embodiments, the alignment or comparison may identify genetic variants and other genetic features, including single nucleotide variants (SNVs), copy number variants (CNVs), gene rearrangements, etc. Methods and algorithms for identifying such variants and features are commercially available and well known in the art.

[0107] A knowledge database 40 can be generated to accumulate cohorts of patient molecular data, such as NGS results, and clinical information. The accumulated patient information can be analyzed to identify insights from the information, such as potential biomarkers or pharmacogenomic trends. The knowledge database 40 (KDB) can include therapeutic, diagnostic, and prognostic implications. The KDB 40 can include structured data on drug-gene interactions, including pharmacogenetic interactions, as well as precision medicine findings reported in the psychiatric and basic science literature. The KDB 40 can include clinically annotated pharmacogenomic classifications for key pharmacodynamic and pharmacokinetic outcomes related to the treatment of depression and other psychiatric disorders. The KDB40 of therapeutic and prognostic evidence, including treatment response and resistance information, may include information from a combination of external sources, such as CPIC guidelines, FDA labeling, PharmGKB, the Dutch Pharmacogenetics Working Group (DPWG), and / or other proprietary databases or information sources that are publicly available or available by subscription or upon request, as well as literature sources or novel findings derived from analyzing clinical and genetic, genomic, or other omic information repositories. The KDB40 may be maintained over time by individuals with experience, education, and training in the relevant field. In some embodiments, clinical actionability entries in the KDB40 are structured by both (1) the disease and / or drug-gene interaction to which the evidence applies and (2) the level or strength of the evidence.

[0108] Therapeutic actionability entries may be binned into tiers of evidence strength according to the patient's disease and / or drug-gene interaction match, as may be defined by professional society guidelines. Evidence-based treatment recommendations may be grouped into tiers by level of evidence strength, such as IA, IB, IIC, and IID. Briefly, tier IA evidence may be biomarkers that follow consensus guidelines and match the disease type. tier IB evidence may be biomarkers that follow clinical studies and match the disease indication. tier IIC evidence biomarkers may follow consensus guidelines or clinical studies for off-label use, or on- or off-label patient case studies. tier IID evidence biomarkers may follow preclinical evidence regardless of disease indication match. Patients may be matched to actionability entries by gene, specific variant, diagnosis, and level of evidence. The therapy options matched to a patient may represent the recommended therapy and do not reflect whether the patient was prescribed a specific recommendation by their treating physician.

[0109] KDB40 includes clinical data elements, imaging information, molecular data such as, but not limited to, DNA sequence information, single nucleotide variants (SNVs), insertion / deletions (indels), copy number variations (CNVs), fusion variants, RNA expression (including miRNA expression), microbiome information, haplotypes or alleles including star alleles, haplotype groups or diplotypes including star allele combinations, phenotypes of each haplotype or haplotype group, associated medications, associated risks, associated prognoses, associated diagnostic features, therapy classifications (including standard dosing regimens, dose adjustments, contraindications, etc.), It may include a data dictionary containing incidental germline findings, epigenetic values, proteomic values, analyses thereof (such as features extracted from images), including variants associated with additional health significance, and / or combinations of one or more of the data elements or data types included in KDB40 (e.g., CYP2D6 gene variants combined with CYP2C19 gene variants and associated phenotypes, or clinical data matched with molecular and / or image data, aggregated from multiple patient records, and analyzed to determine associations between data types). KDB40 may also include behavioral indicators including patient activity or mobility patterns, or related scores or indicators derived from images of the patient's face.

[0110] The knowledge database 40 may also include other types of information, such as image information. For example, the database may store images of patients' faces. The module may analyze the images and generate a set of information related to the images, which is also stored in the database. Other images include images created from MRI or CT scans.

[0111] The analytical capabilities of NGS are superior to traditional methods for processing genetic variants or alleles with pharmacogenetic significance. Because the entire normal human genome can be referenced for each target gene (as described in detail below), NGS can identify previously unobserved variant calls even if the variant was not targeted by the NGS panel. For example, if the normal genome is ATTACCA for a given region of a chromosome, but an untargeted and / or previously undocumented variant is present and a variant sequence is identified as ATTACCA in that same region, an allelic mismatch, indicating the detection of a new allele spanning that region, can be detected simply by the absence of an expected variant call. For example, an allele can be identified from a sequence of nucleotides that matches the normal sequence, a sequence of nucleotides that matches a sequence of a known allelic variation from the normal, or a new sequence that does not match any of the known alleles.

[0112] Furthermore, because NGS probe reads include not only the probe but also the sequence of the DNA molecule extending from each probe, probe reads from upstream DNA molecules, including non-target downstream variants, can be reported by the NGS sequencer. Confirmed detection of non-target variants can be based on new research or publicly available data after analysis in a bioinformatics pipeline. Additionally, genome-wide sequence coverage allows for research across aggregated sequencing results, enabling the identification of previously unknown new biomarkers. An exemplary system that provides the foundation for achieving these benefits and others is described below.

[0113] System Overview

[0114] In one example of a system that may be used to help healthcare providers make clinical decisions based on a combination of molecular and clinical data, the architecture of the present invention is designed to allow maximum system adaptability so that system processes are compartmentalized into loosely coupled, distinct microservices for defined subsets of system data and may generate new data products for consumption by other microservices, including other system resources, to quickly respond to new data types as well as treatment and research insights. Thus, small, autonomous teams of scientists and software engineers can develop new microservices with minimal system constraints that facilitate priority service development, because the microservices operate independently from other system resources to execute defined processes related to the system data consumed and the data products generated.

[0115] This system enables rapid changes to existing microservices and the development of new microservices to address any data handling and analysis need. For example, if a new record type is to be ingested into an existing system, a new record-ingestion microservice can be rapidly developed to add the new record in raw data format to the system database and generate a system alert to notify other system resources that the new record is available for consumption. Here, intra-microservice processes are independent of all other system processes and can therefore be developed as efficiently and quickly as possible to achieve service-specific goals. Alternatively, an existing record-ingestion microservice can be modified independently of other system processes to accommodate some aspects of the new record type. A microservice architecture allows many service development teams to work independently and develop many different microservices simultaneously, allowing many aspects of the overall system to be rapidly adapted and improved simultaneously.

[0116] A messaging gateway can receive data files and messages from microservices, glean metadata from those files and messages, and route those files and messages to other system components, including databases, other microservices, and various system applications. It allows microservices to poll their own messages as well as incoming transmissions (point-to-point) or bus transmissions (broadcast to all listeners on the bus) to identify messages that should start or stop a microservice.

[0117] Referring now to the figures accompanying this specification, and more particularly to FIG. 1 , the present disclosure will be described in the context of an exemplary disclosed system 10 in which data is received at a server 20 from many different data sources, such as databases 32, clinical records 24, and microservices (not shown). In some embodiments, the server 20 can store relevant data, such as in a database 34, shown to include empirical patient outcomes. The server 20 can manipulate and analyze the available data in many different ways via an analytics module 36. Additionally, the analytics module 36 can condition or “shape” the data to generate new provisional data or to structure the data in different structured formats for consumption by user application programs, which then drive user application programs that provide user interfaces via any of several different types of user interface devices. To simplify this description, a single server 20 and a single internal database 34 are shown in FIG. 1 ; however, it should be understood that in most cases, the system 10 will comprise multiple distributed servers and databases linked via local and / or wide area networks and / or the Internet or other types of communications infrastructure. An exemplary simplified communication network is labeled 18 in Figure 1. The network connection may be of any type, including wired, wireless, etc., and may operate according to any suitable communication protocol. Additionally, the network connection may include a communication / messaging gateway / bus that enables microservice file and message transfer according to the above system.

[0118] The disclosed system 10 allows many different system clients to securely link to the server 20 using various types of computing devices and access system application program interfaces optimized to facilitate the particular activities performed by those clients. For example, in Figure 1, providers 12 (such as physicians, researchers, laboratory technicians, etc.) are shown linking to the server 20 using display devices 16 (such as laptop computers, tablets, smartphones, etc.). In some embodiments, the display devices 16 may include virtual reality headsets, projectors, wearable devices (such as smart watches, etc.).

[0119] In at least some embodiments, when a physician, psychiatrist, psychologist, or other mental health professional or provider uses system 10, a physician user interface (such as on display device 16) is optimally designed to support typical physician activities supported by the system, including activities directed toward patient treatment plans. Similarly, when researchers (such as radiologists) use system 10, a user interface is provided that is optimally designed to support activities performed by those system clients. In other embodiments, the physician user interface, software, and one or more servers are implemented within one or more microservices. Additionally, each of the described systems and subsystems for implementing the embodiments described below may additionally be defined in one or more microsystems.

[0120] System specialists (e.g., employees who control / maintain the entire system 10) also use interfacing computing devices to link to server 20 to perform various processes and functions. For example, system specialists can include data abstractors, data sales specialists, and / or “general” specialists (e.g., “lab, modeling, radiology” specialists). Different specialists use system 10 to perform many different functions, and each specialist requires a specific skill set necessary to perform those functions. For example, data abstractor specialists are trained to ingest clinical records from various sources (e.g., clinical records 24) and transform that data into normalized, system-optimized, structured datasets. Laboratory specialists are trained to acquire and process patient and / or tissue samples, generate genomic data, grow tissue, treat tissue, and generate results. Other specialists are trained to evaluate treatment efficacy, perform data research to identify various types of new insights, and / or modify existing systems to accommodate new insights, new data types, etc. The system interfaces and toolsets available to provider specialists are optimized to suit the specific needs and tasks performed by those specialists.

[0121] Referring again to FIG. 1 , server 20 is shown receiving data from multiple sources. According to some embodiments, clinical trial data may be provided to server 20 from database 32. In some embodiments, data not derived from a clinical trial (e.g., a cohort of subjects) may additionally or alternatively be provided to the server. Furthermore, patient data may be provided to server 20. As shown, patient 14 has corresponding data from multiple sources (e.g., test results 26 provided by a lab or technician, image data 28 provided by a radiologist, etc.). For simplicity, this is representatively shown in FIG. 1 as individual patient data 22. In some embodiments, individual patient data 22 includes clinical notes 24, test results 26, and / or image data 28. In some embodiments, clinical notes 24 may include treatment notes (e.g., notes written by a psychologist, psychiatrist, social worker, counselor, primary care physician / PCP, and / or nurse practitioner). Additionally, in some embodiments, the clinical record 24 can include applicable screening results, such as the patient's BDI-II score, PHQ-9 score, questionnaires customized for the particular patient, and the like. For example, the clinical record 24 can include reported scores regarding the patient's interest or pleasure in doing things, feelings of low mood, depression, or hopelessness, difficulty falling asleep or staying asleep, or sleeping too much, feeling tired or with little energy, loss of appetite or overeating, regret or feeling like a failure or a disappointment to oneself or one's family, difficulty concentrating on tasks such as reading the newspaper or watching television, moving or speaking so slowly that others might notice if they were interested, or moving or speaking at a rate that makes the patient appear fidgety or restless, thinking that death would be easier, or thoughts of self-harm. These reported scores can be outcomes, symptoms, or observations reported by the clinician, the patient, and / or a third party, including the patient's family.Clinical records may include time series data, which is data collected at multiple points in time during the course of a patient's treatment.

[0122] Individual patient data 22 may be provided to server 20, for example, by a data abstractor specialist (as described above). Alternatively, electronic records may be automatically transferred to server 20 from various facilities, practitioners, or third-party applications, if appropriate. As shown in FIG. 1 , patient data communicated to server 20 may include, but is not limited to, treatment data (such as current treatment information and outcome data), genetic data (such as RNA, DNA data), brain scans (such as PET scans, CT scans, MRI scans), and / or clinical records (such as biographical information, patient history, patient demographics, family history, comorbid disease conditions, treatment notes, etc.).

[0123] Still referring to FIG. 1 , server 20 is shown to include an analytics module 36 that can analyze data from database 34 (empirical patient outcomes) and individual patient data 22. Database 34 can store empirical patient outcomes for multiple patients suffering from the same or similar psychiatric disorder (e.g., depression) as patient 14. For example, “individual patient data” for multiple patients can be associated with each respective treatment and treatment outcome and then stored in database 34. Database 34 can be updated as new patient and / or treatment data becomes available. As an example, provider 12 can propose a particular treatment for patient 14, and then individual patient data 22 can be placed in database 34. Clinical and / or molecular data associated with patient 14 and generated after analytics module 36 is used to analyze database 34, and individual patient data 22 can be collected and stored in database 34. For example, provider 12 may suggest a particular treatment for patient 14, and patient 14's response to that particular treatment may be added to database 34 (e.g., collecting time-series patient information in database 34) with or without individual patient data 22. As shown, server 20 may include a knowledge database 40, a number of other databases 42, such as external databases 44 and / or third-party databases 46, and / or a number of internal databases 48.

[0124] The analysis module 36 can generally use available data to indicate a diagnosis, predict progression, predict treatment outcome, and / or suggest or select an optimized treatment plan (e.g., type of medication, available clinical trials, etc.) based on each patient's specific depression state, clinical data, behavioral data, and / or molecular data. For example, the indicated diagnosis may suggest that the patient does not suffer from depression. The patient may also likely have another diagnosis with similar symptoms, including, for example, a bipolar diagnosis or situational depression, or an entirely different health condition, including a thyroid condition with depression-like symptoms. In one example, antidepressants may not alleviate depression-like symptoms caused by a thyroid condition, and an indication that the patient may have a thyroid condition may guide the provider to prescribe thyroid treatment to the patient instead of an antidepressant.

[0125] The diagnostic prescription may be based on any portion of individual patient data 22 or aggregated data from multiple patients, including clinical data, behavioral data, and molecular data. In one example, individual patient data 22 is normalized, de-identified, and compiled into a database 34 to facilitate easy query access to the dataset as a whole, allowing healthcare providers to use system 10 to compare patient data, stratify patients, predict treatment outcomes, and generate new hypotheses. For example, system 10 may be used to stratify patients, discover biomarkers of various responses to therapy, and / or find cohorts and subcohorts of patients predicted to respond in a particular way to a particular therapy. Clinical data may include physician notes, imaging data, and behavioral data and may be generated from clinical records, hospital EMR systems, researchers, patients, and local physician practices. To generate standardized data supporting internal precision medicine initiatives, clinical data, including free-form text and / or handwritten notes, may be processed and structured into phenotypic data, treatment data, and outcome or patient response data by methods including open character recognition, natural language processing, and manual curation methods that may check data completeness, interpolate missing information, employ manual and / or automated quality assurance protocols, and store the data in FHIR-compliant data structures using industry-standard vocabularies for access by healthcare providers through system 10. Behavioral data may include patient-reported outcomes, wearable fitness tracker data, geographic location data indicating patient mobility trends, etc. Molecular data may include variants or other genetic alterations, DNA sequences, RNA sequences and expression levels, miRNA sequences, epigenetic data, protein levels, metabolic levels, etc.

[0126] In one example, datasets can be filtered by very specific criteria, including population filtering that reflects inclusion and exclusion criteria in study designs and clinical trials. These custom subsets can be saved and fed through an analytics module to generate predictive analytics. These quantitative predictive analytics include diagnostic and prognostic outcomes from the analysis of combined molecular, clinical, and imaging data and can be used to stratify patients into more granular, indicated disease subtypes. Understanding how patients relate at the molecular, metabolic, and phenotypic levels can facilitate the discovery of new targetable biomarkers for drug development and the expansion of indications for use of already approved drugs.

[0127] In one example, the system may include visualization and analysis tools that generate clinical and research insights regarding phenotypic, treatment, and outcome data. These tools enable healthcare providers to quickly explore and visualize clinical and molecular trends in a given patient population. Healthcare providers may use these tools to analyze the data in a semi-supervised or unsupervised manner to define clusters, separations, or stratifications among patients based on clinical, molecular, and treatment patterns through a series of interactive learnings from the data as a whole. New insights related to outcome, subtyping, and prognostic implications are generated by analyzing molecular and clinical attributes observed in a broader patient population.

[0128] In some embodiments, cohorts can be generated using gene weighting and other methods such as those described in U.S. Patent Application No. 16 / 671,165, filed October 31, 2019, entitled "User Interface, System, and Method For Cohort Analysis," which is incorporated herein by reference in its entirety. System 10 can process the actionable gene database to explicitly generate a weight for each gene from the evidence and its effect on mutations, alleles, haplotypes, diplotypes, or predicted phenotypes to assist clinicians in drug selection. Genes not in the actionable gene database have a weight of 0. Conversely, genes with a weight of 1 have the highest confidence that their variants affect the action of FDA-approved drugs for the cancer type in question. Other factors for adjusting gene weighting can include evidence of being a driver gene or having relevant drug interactions in metrics and evaluation of DNA mutations at the variant level, not just the gene level.

[0129] In some embodiments, gene weights may be determined based on evidence that a gene is useful for comparing patients within a cohort. For example, the presence of approved treatments or recommended treatment adjustments for a particular gene mutation may be used to modify the weight. In particular, the presence of an FDA-approved drug or known relevant drug interaction for a particular gene mutation for a particular drug or psychiatric disorder may result in an increased weight for that gene and / or set to "1." (Such weights may be gene-specific and psychiatric disorder-specific; the same drug may not be approved for the same gene mutation for a different psychiatric disorder, resulting in no change to the weight for that gene in such a situation.)

[0130] In some embodiments, the importance score can be calculated by following a rule set. For example, one rule set assigns a base weight of 0 to all genes. If a gene is not included in a gene-based panel, the weight remains 0 and the weight of the next gene is calculated. If the gene is included in a gene panel, information is extracted from the panel by starting with an initial gene base weight of 0. Such information can include whether there is an FDA-approved therapy targeting the gene mutation or whether there is a known drug interaction associated with the gene mutation. If such a therapy exists, the gene weight can be increased using metric c1. If no such therapy exists, a determination can be made as to whether the gene allele or allele combination has evidence for the drug or psychiatric disorder being queried. As before, if such evidence exists, the gene weight can be increased using metric c2, which is described below. The gene weight can then be increased based on the total level of evidence using a third metric c3. Finally, the gene weight can be rescaled, for example, using a maximum weight of 1 after this procedure has been performed for each gene under consideration.

[0131] As explained above, weights may be increased using a metric c1. This metric depends on the level of evidence that a particular gene / therapy combination should receive increased weight, with genes lower on the spectrum resulting in lower increases in weight and genes higher on the spectrum resulting in higher increases in weight. In particular, there may be levels of evidence for a therapy adjustment recommendation for a particular gene, e.g., from 1 to 7, with 1 being the best and 7 being the least informative. Such levels may be determined based on one or more factors, including, for example, the number of patients who receive therapy and experience favorable outcomes, the percentage of patients who experience relief after one, two, five, etc. years, a percentage reflecting the presence or absence of side effects, etc. In another embodiment, the presence of evidence that a gene allele or allele combination has a relevant drug interaction increases the weight of that gene, while gene alleles or allele combinations without a relevant drug interaction do not increase the weight of the gene.

[0132] Similarly, weights may be increased using metric c2. Similar to c1, this metric may depend on the level of evidence that a particular gene / drug interaction increases weight, with genes lower on the spectrum resulting in a lower increase in weight and genes higher on the spectrum resulting in a higher increase in weight. Similar to c1, different levels of evidence may exist for a particular cancer type, with stronger correlations being reflected in larger values ​​of c2. In another embodiment, the presence of evidence that a gene allele or allele combination is correlated with a drug interaction increases the gene's weight based on the level of correlation, while gene alleles or allele combinations that are not correlated with a drug interaction do not increase the gene's weight.

[0133] Other evidence may also be weighted. Such evidence may include gene alleles or allele combinations that have no known established correlation with some drug interactions, but some variants of the gene may hold a small correlation. Varying levels of c3 may be applied based on the strength of the correlation for each variant present.

[0134] After the weights c1, c2, and c3 are determined, it is possible that the sum of the weights c1+c2+c3 for a gene is greater than 1. Therefore, the gene weights may be normalized by dividing each particular c1+c2+c3 gene weight by the sum of the maximum values ​​for each metric, i.e., c1_max+c2_max+c3_max.

[0135] In addition to analyzing patients for similarity or commonality of at least one somatic mutation in common, DNA metrics also take into account when a pair of patients have no mutations in common (if they have several significant mutations), thereby separating potentially similar patients from those who are certainly very different.

[0136] If two patients have no common mutations, the metric for the pair may be 0. Conversely, if a pair of patients have at least one common mutation, the metric may be the sum of the gene importance scores between the mutated genes common to both patients, taking into account the gene weighting described above. Using gene importance scores may focus on highly important genes, while background important genes may be less affected.

[0137] The sum of gene importance scores can be rescaled by the geometric mean of the pair's patient similarities to themselves. This means that non-shared mutated genes are taken into account. For example, a patient who shares one mutation with a reference patient can be considered closer than a second patient who shares two mutations if the second patient has many more mutations that the reference patient does not have. When a pair of patients does not have common mutations, a metric can be generated from the sum of the scores of mutations that the first patient has but the second patient does not, and the sum of the scores of mutations that the second patient has but the first patient does not. The determined scores can then be used to group patients into cohorts based on the similarity of their scores for one or more genes.

[0138] In some embodiments, cohorts, such as smart cohorts, may be generated using the methods described in U.S. Patent No. 16 / 732,138, filed December 31, 2019, entitled "Method and Process for Predicting and Analyzing Patient Cohort Response, Progression, and Survival," which is incorporated by reference in its entirety.

[0139] In some embodiments, a predictive model can be developed that facilitates the identification of one or more smart cohorts of patients whose disease progression and / or survival chances are substantially different from expected, for example, significantly longer or shorter than expected. Information from these cohorts can then be examined to identify one or more key factors that may potentially contribute to the survival profile of the cohort. The identification of smart cohorts can be used to provide precision medicine results for specific patients, identify potential areas of interest for drug research, and / or identify unexpected potential opportunities for expanding drug patient coverage.

[0140] Cohorts may be generated by molecule type, treatment type, drug, drug dose, prior drug, prior treatment outcome, clinical information, and / or other suitable information.

[0141] An exemplary analysis using one or more analytics modules 36 may comprise one or more therapeutic engines capable of generating a report listing predicted drugs, predicted effective dosages for one or more drugs, potential drug side effects, and / or other treatment predictions that may be used to effectively treat a patient based on patient genetic data and real-world clinical data. The therapeutic engine may include a knowledge database 40. The knowledge database may include real-world clinical data, including numerous gene-drug interaction studies, cohort studies, U.S. Food and Drug Administration (FDA) label recommendations, in vitro studies on gene-drug interactions, information on primary metabolic enzymes, PD, and / or immune-related genes affecting a selected neuropsychiatric drug in the context of the genes included in the panel, and / or other suitable information on drug-gene interactions. In some embodiments, the report may be divided into two distinct sections: a test results section, which may include a phenotype associated with the nucleic acid sequence, and a supplemental section, which may include other information for the clinician to review. The supplemental section may include, for example, a listing of one or more drugs associated with the nucleic acid sequence, a classification for each drug in the listing, a view of the patient's clinical characteristics similar to those of the patient for whom the report was generated, such as clinical, phenotypic, genotypic, and / or morphological characteristics, such as those associated with prescribed or administered medications, patient outcomes, etc. When delivered in paper or PDF format, the test results portion may be separated from the supplemental section by being displayed on a different page. When delivered through a portal, the user may have to select a first hyperlink to view the test results and a second hyperlink to view the supplemental information. In other embodiments, the supplemental sections may be displayed to the user on the same page or through the same hyperlink, but may be distinguished using title, color, font size, or other graphical or stylistic features.

[0142] KBD40 can contain information about numerous drugs and their interactions with genes. In some embodiments, for a drug to be included in the knowledge database, the drug may be required to have at least one of instructions for use for a psychiatric condition or a black box PGx warning on the drug label. In some embodiments, the knowledge database can include drugs used to treat conditions for which the drug is not marked for treatment (i.e., off-FDA use). For off-FDA use, a medical professional may be required to verify that the drug is suitable for off-FDA use before including it in the knowledge database.

[0143] For drugs included in knowledge database 40, numerous resources regarding drug efficacy, drug-gene interactions, and other suitable literature may be curated by users, such as knowledge database experts. In some embodiments, resources may be curated from primary literature sources such as the Clinical Pharmacogenomics Implementation Consortium (CPIC), the Dutch Pharmacogenomics Working Group (DPWG), DailyMed Label resources (i.e., FDA), the Pharmacogenomics Knowledge Base (PharmGKB), PubMed, and / or ancillary resources such as the Psychoactive Drug Screening Program (PDSP Ki) Database.

[0144] The knowledge database expert can then generate clinical actionability entries based on the curated resources (e.g., using the system 10 and / or display device 16 of FIG. 1 ). As described above, the clinical actionability entries can be structured by both (1) the disease and / or drug-gene interaction to which the evidence applies and (2) the level or strength of the evidence. In some embodiments, the clinical actionability entries can include a disease, a metabolism-related gene, a phenotype, one or more alleles, a drug, a drug dosage, patient demographic information (e.g., gender, ethnicity, etc.), a level of evidence, and / or other suitable clinical actionability parameters. The clinical actionability entries can assist physicians in treating patients. For example, the therapeutic engine can receive patient information including molecular information, a disease diagnosis, and / or patient demographic information and identify one or more suitable clinical actionability entries based on the patient information.

[0145] As described above, the clinical actionability entries may be binned into hierarchies of strength of evidence by patient disease and / or drug-gene interaction match, as may be defined by professional society guidelines. A knowledge database expert can use system 10 to sort the clinical actionability entries into hierarchies.

[0146] A knowledge database expert can determine what evidence level should be assigned to a given clinical actionability entry based on a list of criteria. In some embodiments, the criteria can include the presence of sufficient evidence to determine that a given enzyme is the primary metabolic enzyme for a given drug, the presence of sufficient evidence to determine the effect of a pharmacogenomic variation in a gene on the pharmacokinetic parameters of the drug, the presence of sufficient evidence to determine the effect of a pharmacogenomic variation in a gene on the PD parameters of the drug, the presence of sufficient evidence to determine the clinical implications of the pharmacogenomic variation for the drug (e.g., dosing, efficacy, tolerability, adverse drug events), the presence of evidence that multiple genes are involved in the metabolism of the drug, and whether a multi-gene algorithm is likely to be required to best approximate the drug's pharmacokinetics and clinical outcome.

[0147] The treatment engine may also include one or more machine learning algorithms or neural networks. The machine learning algorithm (MLA) or neural network (NN) may be trained from a training dataset. For depression, an exemplary training dataset may include patient clinical and molecular details, such as those curated from electronic health records or gene sequencing reports. MLAs include supervised algorithms (e.g., algorithms where features / classifications in the dataset are annotated) using linear regression, logistic regression, decision trees, classification and regression trees, naive Bayes, nearest neighbor clustering, unsupervised algorithms (e.g., algorithms where features / classifications in the dataset are not annotated) using apriori, means clustering, principal component analysis, random forests, adaptive boosting, generative approaches (e.g., mixtures of Gaussian distributions, mixtures of multinomial distributions, hidden Markov models), sparse separation, graph-based approaches (e.g., mincut, harmonic functions, manifold regularization), heuristic approaches, or semi-supervised algorithms (e.g., algorithms where some features / classifications in the dataset are annotated) using support vector machines. The NN includes a conditional random field, a convolutional neural network, an attention-based neural network, a long short-term memory network, or other neural models, and the training data set includes a plurality of samples and the RNA expression data for each sample.Although MLA and neural network distinguish different approaches to machine learning, these terms may be used interchangeably herein.Therefore, a reference to an MLA may include the corresponding NN, or a reference to an NN may include the corresponding MLA.

[0148] Training may include identifying common clinical or genetic features that patients across a cohort or in a patient database may exhibit, labeling these features as they appear in patient records, and training the MLA to identify patterns in patient outcomes based on treatment as well as clinical and genetic information. Output from one or more analytics modules 36 may be provided to display device 16 via communications network 18. Additionally, provider 12 may input additional data (such as prescribed treatment) via display device 16, which may be transmitted to server 20.

[0149] The display device 16 can provide a graphical user interface (GUI) to the provider 12. The GUI, in some aspects, is interactive and can provide both comprehensive and concise data to the provider 12. In some embodiments, the provider 12 can include a physician 12a (e.g., a psychiatrist), a physician assistant, and / or other specialist 12c. In some embodiments, the provider 12 can be a knowledge database specialist, as described below. As an example, the GUI can include intuitive menu options, selectable features, color and / or highlighting to indicate the relative importance of data, and a sliding scale timeline for viewing disease progression. The GUI can be tailored to the type of provider or even customized for each individual user. For example, a physician can change the default GUI layout based on personal preferences. In addition, the GUI can be adjusted based on patient information. For example, the display components and / or the order of the components and the information contained within the components can be changed based on the patient's diagnosis.

[0150] Further aspects of the disclosed system are described in detail with respect to Figures 2-7D. In particular, an interactive GUI that may be displayed on display device 16 is shown and described.

[0151] Graphical User Interface

[0152] In some embodiments, a graphical user interface (GUI) may be included in the system 10. The GUI can assist providers in the prevention, diagnosis, treatment, and planning for patients with psychiatric disorders and / or psychosis. Advantageously, the GUI encompasses all necessary relevant data while providing a single source of information to the provider. This can ensure efficient personalized treatment for patients, including those suffering from depression. Exemplary GUIs are shown and described with respect to FIGS. 2-7D.

[0153] FIG. 2 is a graphical user interface (GUI) 50 that may be implemented within system 10 to provide patient information for depression (or other psychiatric disorders or illnesses, including mood disorders, bipolar disorders, schizophrenia, personality disorders, etc.). As shown, a provider may log into a provided platform (e.g., account name 54). Here, the physician may view a patient summary table 52 containing a list view of each of their patients. In some embodiments, patient summary table 52 includes the patient name, attending physician, report type, report date, and tests, sequences, and / or patient status. Various report types may be generated for a patient. For example, a risk assessment report may include an analysis of patient risk factors (family history, genetics, etc.). Alternatively, a diagnostic report may include post-treatment test results, a prediction of disease progression, further subtyping of the patient's disease, identification of the patient's likelihood of misdiagnosis, prognostic implications, etc. Reports may be based on a single data type (e.g., a DNA report) or a combination of multiple data types (e.g., clinical and molecular data). The patient summary table 52 may include additional data not depicted in Figure 2. As shown, the provider may select an individual patient to view additional information. The corresponding table row may indicate the selection via highlighting or other means.

[0154] The GUI 50 can provide a secure, centralized, and user-friendly way to receive test status updates and results for patients. In some embodiments, the GUI 50 can provide services and / or other features including online ordering (e.g., placing test orders electronically and quickly through HIPAA-compliant forms), searching (e.g., fuzzy searching and / or filtering to efficiently find relevant patients and / or groups of patients), order status monitoring (e.g., viewing information about an order, including when the order is placed, when the sample is submitted, when processing on the sample has begun, when test results are expected to be delivered, and / or when the order is completed), and / or data insights (e.g., supplemental information on top of molecular information about a patient, such as patient-reported outcome (PRO) data, that allows clinicians to have a more holistic view of the patient, enabling them to make data-driven decisions regarding the patient's care and treatment). In some embodiments, the GUI 50 can provide options for clinicians to select the cadence at which they want to collect outcomes from patients and which assessment to assign to each patient.

[0155] The GUI 50 may be presented through a secure, centralized portal that allows clinicians to receive status updates and results for patients. The portal may allow clinicians to electronically order tests, use fuzzy search and / or filtering to find information about a patient or group of patients, and see when the order was placed, when the sample was submitted, when sample processing began, when the clinician can expect test results, and when the order is completed. In conjunction with molecular information about the patient, the GUI and / or reports may provide patient-reported outcome (PRO) data that allows clinicians to have a more holistic view of the patient, enabling them to make data-driven decisions about the patient's care and treatment. In some embodiments, clinicians may select the cadence in which they want to collect outcomes from patients and which assessments to assign to each patient. Figures 3A-3D illustrate further aspects of the GUI 50. In particular, the GUI 50 is shown displaying a patient identifier 56 (e.g., patient name), a menu 58, a summary section 60, a diagnosis tile 62, and a diagnosis timeline 64.

[0156] In some embodiments, menu 58 may comprise several selectable options grouped by topic. As shown, "Summary" 60 may include a Diagnosis tile 62, a Therapy tile 68, a Molecular Overview tile 76, and an Imaging Overview tile 78. Summary portion 60 may further include biographical information, patient history, patient demographics, family history, comorbidity status, treatment notes, etc. The "Summary" menu option is shown as selected, as indicated by a darkened area within menu 58. As shown by menu 58, "Reports" may include molecular and imaging information. Additionally, "Interventions" may include treatment and clinical trial information. Furthermore, menu 58 may include an Analytics portion. In some embodiments, menu 58 may have more (or fewer) selectable options, and the titles of the menu options may differ from the examples shown here.

[0157] The diagnosis tile 62 can display the patient's diagnosis (including major depressive disorder or another psychiatric illness) as well as related information. As shown, for example, the diagnosis tile 62 can include the age at diagnosis (e.g., 22 years old). In some embodiments, the diagnosis tile 62 can include the PHQ-9 score at diagnosis (e.g., 22: severity) or other diagnostic scoring results, such as the BDI-II score or the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) diagnostic criteria. The diagnosis tile 62 can further include the date the diagnosis was determined, the method used to generate the diagnosis, and clinical evaluations. As shown, the diagnosis tile 62 can include a diagnosis timeline 64. The diagnosis timeline 64 can visually depict patient progress over time. As shown, for example, the diagnosis timeline 64 can be stratified or otherwise separated to indicate depression severity levels, diagnostic changes, or disease progression. In some embodiments, the PHQ-9 score (or other severity metric) can present a severity range. For example, a patient was initially diagnosed with a moderate depressive disorder in June 2018. As time progresses, depression severity may change. As shown, the patient was diagnosed with major depressive disorder at a follow-up assessment in September 2018. In some embodiments, the diagnosis timeline 64 can include summarized treatment information. As an example, the patient began taking bupropion (Wellbutrin®) shortly after the initial diagnosis. In addition, the patient regularly participated in cognitive behavioral therapy (CBT) sessions as an additional form of treatment.The diagnostic timeline 64 may further include any treatments or interventions, start and / or end dates associated with each treatment or intervention, dosages and dosage changes associated with each treatment, the patient's response to each treatment or intervention, updated diagnostic or laboratory test results / method descriptions / dates, dates of assessments, adverse events and related details, dates of follow-up appointments, medical conditions observed at follow-up appointments, and other observation notes that may be collected from the treatment sessions and recorded along with the session dates.

[0158] The diagnostic timeline 64 not only assists providers in quantitatively assessing a patient's disease progression, but also shows the effectiveness of various treatments over time. As shown, the patient's depression worsened after using this particular medication. Therefore, the provider safely discontinued the drug treatment and subsequently performed genetic sequencing to determine whether the patient had a predisposition to risk or had problems metabolizing some medications (or classes of medications).

[0159] As shown in FIG. 3B, the provider may hover over (or select) a diagnosis point in the diagnosis timeline 64. An information box may then appear, which may include summary information for the diagnosis point. As shown, on September 3, 2018, the patient was diagnosed with major depressive disorder, had a PHQ-9 score of 22 (severe), and two structured clinical documents are available for viewing. In some embodiments, the provider can select "view structured clinical documents," and the GUI 50 can display the clinical documents associated with the diagnosis point.

[0160] 3C, summary portion 60 is shown to include a therapy tile 68 ("Tempus Insights"). The therapy tile 68 can include a summarized "Total Therapies" portion 70. In some embodiments, total therapies portion 70 can include a summary showing how an individual patient may respond to available therapies. As shown, three therapies are categorized as "Use as directed," six are categorized as "Dosage and Administration," ten are categorized as "Significant Implications," and three are categorized under "Resistance." In another example, "Use as directed" can be categorized under "Standard Administration," while categories covering "Significant Implications," "Resistance," or other medications with predicted side effects can be titled "Contraindicated."

[0161] According to the reported drug classification, a healthcare provider may consider initially treating a patient with one or more of the three therapies categorized as "use as directed." Additionally, providers may review the therapies for "dosage and administration" and "critical implications," learning details about commonly offered drug dosages, as well as possible administration methods, durations, frequencies, and drug combinations, along with possible implications for each of the therapies. For example, a subset of patients may respond well to a drug regimen below a certain dosage threshold and negatively respond to doses above a certain dosage threshold. Critical implications may be inferred from the drug regimen as a warning from an FDA guideline or report or a clinical trial review. Conversely, when treating a patient with the three therapies categorized as "resistant," a provider may consider closely monitoring the patient or avoid treating the patient with these therapies. In some embodiments, recommended therapies (e.g., "use as directed") may be identified with a unique marker and / or color (e.g., a check mark with a green background, as shown). Similarly, non-recommended therapies (such as "resistant") can be identified with a unique marker and / or color (such as an "X" on a red background, as shown). In this way, providers can efficiently identify which therapies to try and which to avoid.

[0162] The therapy tile 68 may further include a therapy list 72. The therapy list 72 may provide a detailed description of each therapy (e.g., primary use, side effects, etc.). Each of the therapies may be associated with a corresponding classification, allowing the provider to quickly find a recommended treatment. In some embodiments, each therapy may include a predicted response portion 74. As shown, the predicted response portion 74 may be displayed on the GUI 50 as a segmented line, with each segment and / or color corresponding to a different response. For example, one colored segment may represent a patient response defined by a PHQ-9 score reduction of more than 50%. Additionally, another colored segment may represent a patient response defined by remission (a PHQ-9 score less than 5 after three months of treatment). In some embodiments, the provider may select "view full results," and the GUI 50 may then display detailed therapy information. An "n" value may additionally be displayed near each predicted response portion 74. In some embodiments, the "n" value may indicate the total number of patients for whom response information is available. In other embodiments, the "n" value may indicate the total number of patients in a cohort associated with the patient who received the treatment identified in the list and for whom response information is available.

[0163] 3D , summary portion 60 is illustrated to include a molecular overview tile 76 and an image overview tile 78. In some embodiments, molecular overview tile 76 can include various genes and enzymes, as well as the patient's corresponding test results. For example, the genetic sequence of the CYP2D6 gene for this particular patient has been shown to cause the patient to be a poor metabolizer, while the genetic sequence of the HTR2A gene has been shown to result in normal activity. Molecular overview tile 76 can provide the provider with high-level information to use in determining medication type and / or dosage for the patient. In some embodiments, the provider can select "view full results," and GUI 50 can then display detailed molecular information. In some embodiments, molecular overview tile 76 includes genetic data, detected star alleles, and / or predicted phenotype for the patient, and the supplemental data section (e.g., detailed information 96) includes drug-gene interactions.

[0164] The predicted phenotype can be influenced by the variants, alleles, and / or combinations of alleles detected in a patient for specific genes, and / or by the combination of these data for all genes.

[0165] Still referring to FIG. 3D , image summary tile 78 can include imaging results corresponding to a patient. As shown, image summary tile 78 can include several display options (e.g., view menu 82), imaging results 80, and associated image data. Here, for example, the patient tested positive for white matter lesions, indicating that the patient may be resistant to escitalopram, sertraline, and / or nortriptyline. The conclusion that the patient is resistant may be based on a proprietary database of aggregated patient information and custom curation of published scientific studies and additional primary literature. Other associated image data can be displayed within image summary tile 78 based on the results for each individual patient. Results are not limited to white matter lesions and can include MRI, fMRI, CT scan, PET scan, or any other patient imaging technique. In some embodiments, the provider can select “view full results,” and GUI 50 can then display detailed molecular information.

[0166] 4A-4E, GUI 50 can display detailed molecular data for a patient. Menu 58 can again provide instructions for where the provider is within the platform. Molecular report 90 can include the test date (e.g., 08 / 15 / 2018), as well as a variation list 92, additional variants of interest 98, human leukocyte antigen (HLA) typing 100, treatment implications 102, and / or diagnostic implications 104.

[0167] As shown in FIG. 4A , the variation list 92 can include detailed information for various genes and / or enzymes for the patient. In some embodiments, each listed variation can have a selectable therapy icon 94. The provider can select a therapy icon 94, and the GUI 50 can then display detailed information 96 for the corresponding variation. In some embodiments, an information window can be overlaid on the molecular report 90. As shown, the detailed information 96 can include a list of known drug interactions corresponding to the variation. The detailed information 96 can include the drug name, drug class, drug type, level of evidence, and / or gene-drug interaction. The gene-drug interaction column can categorize the type of interaction (e.g., “use with caution,” “contraindicated,” etc.). In some embodiments, the severity of the interaction can be identified via a color indicator (e.g., yellow indicating moderate, red indicating severe, etc.).

[0168] As shown in FIG. 4C, the molecular report 90 can include additional variants of interest 98. The list can display the patient's genetic variants that are commonly expressed in patients with a particular diagnosis. As an example, the patient has a variant corresponding to the ANK3 gene. The ANK3 rs10994415 C variant can increase the risk of schizophrenia by affecting the expression level of ANK3. Furthermore, ANK3 is believed to be a specific susceptibility gene for bipolar disorder. Thus, the additional variants of interest 98 can provide the provider with information that can directly impact the patient's diagnosis and / or treatment.

[0169] In some embodiments, selectable reference icons may be provided, and the GUI 50 may display detailed information after selection. The molecular report 90 may further include HLA typing 100. HLA genotypes may be determined using RNA or DNA sequencing data and displayed via the GUI 50. The HLA typing 100 may be valuable to researchers or clinicians and for predicting pharmacogenetic effects. In one example of drug-gene interactions or pharmacogenetic effects, certain HLA haplotypes may be associated with the development of Stevens-Johnson syndrome and toxic epidermal necrolysis in some patients taking drugs such as carbamazepine. The molecular report 90 may also be based on custom curation and data management of published scientific studies and additional primary literature, for example, found in the KDB 40, as well as interpretation of this data selected for display in the molecular report 90. In some examples, the HLA typing 100 may include a disclaimer stating that the information is for research use only and should be verified with histology or other clinical pathology testing before use in clinical decision-making. In other instances, such as when HLA typing 100 may be used in clinical decision-making without validation by histology or other clinical pathology tests, the cautionary statement may be omitted.

[0170] As shown in FIG. 4D , the molecular report 90 can include therapeutic implications 102. In some embodiments, the therapeutic implications 102 can include a list of therapies as well as corresponding genetic variant data. For example, desvenlafaxine lists two applicable genetic variants, namely, CYP2C19 and CYP3A4. Based on the patient's genetic alterations, CYP2C19 is highlighted. Additionally, desvenlafaxine is identified as a potential treatment for the patient (e.g., "use as directed"). The molecular report 90 can further include diagnostic implications 104, if applicable. In some embodiments, the patient's molecular data can provide diagnostic insights, which can be identified and described within the diagnostic implications 104. Additional diagnostic insights can be obtained from FDA reports or guidelines, clinical trial reviews, or drug studies. In one example, the diagnostic insights or diagnostic implications can be based on additional variants of interest included in the molecular report 90.

[0171] 5A-5B, image data 106 is illustrated. A menu 58 may again provide instructions indicating where the provider is within the platform. In some embodiments, GUI 50 may display a graphical representation of the image data (such as image 108). In one example, GUI 50 and the graphical representation of the image data may be adjusted or customized based on the type of image 108 received by the patient. The type of image 108 may include a representation or image of the patient's face, an MRI, a CT scan, a PET scan, etc. Additionally, coloring of the image 108 may identify clinically significant features of the patient's brain. A view menu 110 may be provided, allowing the provider to select different viewing angles corresponding to the image data.

[0172] The image data 106 can include treatment implications 112. As shown, the system used the image data 106 to determine treatments to which the patient may be resistant. For example, nortriptyline lists two applicable genes containing variants: CYP2C19 and CYP3A4. Based on the patient's genetic alterations, CYP2C19 is highlighted. Furthermore, nortriptyline is identified as a potential resistant treatment for the patient (e.g., "resistant"). Such image data may include MRI or CT scans of the brain, which may allow additional insights to be observed, analyzed, and reported to the physician based on clinical information associated with the patient during the course of treatment. Additionally, the organoid lab may culture neurospheres or other organoids bearing a specific gene variant, allele of interest, or another phenotypic trait for imaging and testing to determine the effect on drugs and / or disease onset and progression that may be caused by a specific gene variant, allele of interest, phenotypic trait, or other condition in the patient that can be detected by analyzing molecular and / or omics data. In one example, the types of molecular and / or omics data include genomic, proteomic, metabolomic, or other data collected by studying emerging molecular or cellular activity in a patient. Brain testing and imaging may be combined to complement the insights that may be included in the report.

[0173] 6A-6E, GUI 50 may display a therapy portion 114. Menu 58 may again provide indication of where the provider is within the platform. In some embodiments, therapy portion 114 may include an intervention timeline 116 along with data 118. Additionally, therapy portion 114 may include display options 122 and potential therapy tiles 120. In some embodiments, display options 122 may include options for viewing PHQ-9 data, symptoms, and / or other qualitative and quantitative measures of disease onset and progression on intervention timeline 116. Additionally, various filters may be selected based on what type of treatment is desired by the provider. As illustrated by FIG. 6B, when "symptoms" is selected within display options 122, intervention timeline 116 may update to display symptom data 124. In some embodiments, intervention timeline 116 may display symptom data over time. In one example, symptoms may be reported by a third party, including a clinician, patient, and / or patient family member, and symptom data may be obtained through physician note curation, direct electronic medical record (EMR) integration, and / or integration with patient-, family-, or clinician-facing applications.

[0174] As shown in FIGS. 6C-6D , the intervention timeline 116 can display predicted symptoms, PHQ-9, and / or other measurement data 126 based on the selected therapy. For example, a provider may select selegiline from potential therapies 120, and the intervention timeline 116 can then display the predicted change if the medication is prescribed. In one example, each symptom may respond differently to a range of treatment options. Understanding which treatment is most likely to produce the desired outcome can help healthcare providers make decisions related to personalized medicine in psychiatric disorders. The predicted change may be used by the system 10 to calculate a predicted range of patient adherence, which may be displayed in the therapy portion 114. Patient adherence may be quantified, for example, as a percentage of the prescribed medication taken, as a measurable amount of the drug or drug metabolite to be detected in the patient's bloodstream, as a value on a scale based on how often the patient requires prescription refills and / or how accurately the patient follows the treatment regimen, or as another indicator of adherence. In some embodiments, GUI 50 can display dosage information 128. As shown, a provider can select different dosages of selegiline using a drop-down menu on intervention timeline 116. FIG. 6D includes predicted symptom and PHQ-9 data 126 based on a 225 mg / day selegiline dosage, while FIG. 6C includes predicted symptom and PHQ-9 data 126 based on 37.5-75 mg / day selegiline dosages. Thus, a provider can interact with GUI 50 to determine recommended treatments and even dosages for an individual patient.

[0175] 6E, therapy details 130 may be displayed based on which therapy is selected from potential therapy tiles 120 via GUI 50. In some embodiments, therapy details 130 may include dosing information, brand name, drug type, drug description, FDA evidence, Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines, and / or preliminary evidence based on patient data. In one example, therapy details 130 are searchable, which may allow a physician to easily select a drug to specifically address symptoms associated with that drug.

[0176] 7A-7D, cohort details 132 may be displayed via GUI 50. As shown, cohort details 132 may be displayed when the Analytics portion of menu 58 is selected. Therapy data 136 may be included within cohort details 132. Radar plots 134 may compare individual patients to other patients who share similarities. As shown, individual patients may be represented at the center of radar plot 134, and patients with similar or different metrics may be represented by additional dots around the central metric. Radar plot 134 may present two measures of similarity: radial distance for similarity between the center point and each plotted point, and angular distance for similarity between each plotted point. Similarities may include, but are not limited to, depression status, demographics, physical characteristics, genetic data, image data, and treatment history. FIG. 7A includes a radar plot 134 utilizing a set of patient data corresponding to the selected therapy selegiline. Conversely, FIG. 7B includes a radar plot 140 utilizing a set of patient data corresponding to the selected therapy, nortriptyline. In one example, symbols representing patients are color-coded according to the patient's response to therapy. Response to therapy can be measured in multiple ways. In the example, the delta (difference) between the PHQ-9 score at the start of treatment and the PHQ-9 score at the end of treatment is illustrated. Various response measures can be normalized to represent many response measures as a single endpoint, for example, by combining multiple response measures into a single score value. Metadata for each drug (which may include, but is not limited to, dose, diagnosis at time of prescription, likelihood of adherence or distribution, adverse events, etc.) can also be correlated to response.

[0177] In some embodiments, cohort details 132 can include a menu 138. Providers can view selections via the menu 138. Figures 7A-7B include cohort details 132, while Figures 7C-7D include cluster analysis 150. As shown by Figure 7C, providers can select from a list 142 of cluster patterns (including, but not limited to, original diagnosis, dose, molecular profile, and response). The displayed clusters 144 can compare individual patients to other patients. As shown, individual patients are represented by a "star" indicator, while other patients are represented by dots. Again, the dot distribution can indicate similarity. As shown, Figure 7C includes clusters 144 displayed based on original diagnosis. Conversely, Figure 7D includes clusters 146 displayed based on molecular profile. These clusters can be used to determine diagnostic, prognostic, and treatment implications, as well as studies such as diagnostic, subtyping diagnostic, therapeutic, and prognostic.

[0178] 8A-8B illustrate further aspects of GUI 50, including patient report 800, which may be generated for display via GUI 50 as a report similar to molecules 90 and images 106, or exported as a PDF document for printing. In particular, GUI 50 is shown to display a patient identifier (e.g., patient name), such as previously shown at 56, a diagnostic tile, such as previously shown at 62, and other document identifiers, such as "Accession No." and / or an identifier "xG" for the type of NGS panel, in a first portion 810. Additional patient information, such as date of birth, gender, name of attending physician, and / or facility name and facility identification code, may be presented in a second portion 820. The second section 820 may further include information identifying the laboratory that generated the panel results and report ("Tempus"), a panel identifier and number of genes associated with the reported sequences for the patient ("xG 12 genes"); the nature of the report ("Psychotropic: Combinatorial pharmacogenetic test"); the type of specimen collected for the genetic test panel ("Blood"), as well as the date of collection by the ordering facility and the date of receipt by the laboratory. In one example, the "xG 12 genes" panel is a whole-exome panel with spike-in probes added to target and analyze gene sequences in all known exon regions plus some intronic regions of the human genome, with the panel including approximately 20,000 genes. In one example, the report may not display information for all of the genes analyzed by the gene panel.

[0179] The third section 830 may include relevant clinical information, such as notes regarding prior medications prescribed and / or taken by the patient that resulted in disease progression. The fourth section 840 may include a summary that includes most treatment-driven results from previous reports. For example, the fourth section may combine changes 92 from the molecular report 90 with therapies 94 with drug information 130 that are most likely to result in a favorable outcome for the patient according to the analytics module 36. The drug considerations may identify which therapies are associated with which detected genes and associated gene sequences in the patient, and may also provide a short summary regarding the interaction of each identified gene or gene sequence with the therapy.

[0180] The fifth section 850 may identify pharmacokinetic genes (FDA-approved, published, peer-reviewed, or identified by the analytics module 36) known to affect drug absorption and thus its expected metabolic absorption. Pharmacokinetics is the study of how drugs are affected in the body, including drug absorption, distribution, metabolism, and excretion. Differences in drug effectiveness between patients can be influenced by genetic differences in a patient's ability to respond to a drug. Numerous enzymes have been identified as important for drug metabolism. Different phenotypes are associated with deficient, reduced, normal, or increased activity of these enzymes, resulting in variable drug responses. Additionally, linear graphs are presented to visually identify expected metabolic absorption based on the identified genes along with an absorption rating such as ultrarapid, intermediate, normal, or poor metabolizer.

[0181] The sixth section 860 may identify pharmacodynamic genes known to have an effect on how a drug may affect a patient, as well as the expected effect the gene may have on a particular drug or therapy. Effects may include those manifested in the patient. Genes may be listed with expected drug effects, such as stimulating, depressing, blocking / antagonizing, stabilizing, replacing / substituting, or directly beneficial or adverse chemical reactions (grouped as S / S, G / G, Present, A / T, etc.). In alternative embodiments, alternative sections may be displayed, such as "Current Drugs," which may list the drugs the patient is currently taking for at-a-glance comparison with any drug being considered; "Genomic Results," which encapsulates the pharmacokinetic and pharmacodynamic genes described above along with "Metabolism Results"; and an "Adverse Events" field to describe the pharmacogenomic implications of genetic variants. In another example, alternative or additional field names may be used to group or describe these pharmacogenomic implications.

[0182] Figure 8B shows a seventh section 870 that may identify treatments including antidepressants such as desvenlafaxine and levomilnacipran. A brief summary is provided, including the class of antidepressant, such as selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), tricyclic antidepressants (TCAs), monoamine oxidase inhibitors (MAOIs), dopamine reuptake inhibitors, noradrenaline antagonists, and serotonin antagonist and reuptake inhibitors (SARIs). Antidepressants may be sorted according to expected pharmacogenomic or pharmacodynamic responses and the genes contributing to each response. Additionally, Reference IDs and PubMed IDs (PMIDs) may be linked to each antidepressant, along with notes regarding potential pharmacogenomic implications, such as increased or decreased prescription frequency or dosage intensity, side effects, or other outcomes. The Reference ID may be linked to internally curated drug databases, FDA-issued reports, CPIC guidelines, clinical trial reports, or reports from insurance companies or pharmaceutical companies. The PMID may link to PubMed publications related to the identified drug and / or gene that support the relationship between the gene and the therapy. Each identified therapy may also be accompanied by a field that identifies dosage guidelines or possible dosage adjustments based on the detected gene sequences of one or more genes alone or in combination with phenotypic or image data. For example, if the detected gene sequences of a gene encoding a particular enzyme indicate that the patient is a normal metabolizer, a standard dosing classification may be indicated for that therapy, which may not require adjustment, or a dosage adjustment may be indicated for poor metabolizers. If two gene sequences that adversely affect metabolism are detected, for example, if the detected sequence of one of the genes indicates that the patient is a poor metabolizer and the detected sequence of the other gene indicates that the patient is a normal metabolizer, a contraindication may be identified to alert the physician to potential adverse effects or harms, for which a dosage adjustment may be recommended, if applicable.

[0183] In another embodiment, the patient report 800 may be a printed report or a report in electronic format, such as a word document, PDF, or similar format. The report may be printed, emailed, faxed, downloaded, or saved from the GUI 50 and may further include any data displayed in the GUI 50.

[0184] A patient report may be associated with a patient and may include comparing the patient's genetic sequencing information and / or other types of profiles, including molecular profiles, to multiple sets of genetic sequencing information and / or other types of profiles each associated with another patient, particularly when each set relates to clinical features described in the patient's medical records and treatment notes. The patient report may display depictions or plots showing the relationships between multiple patients and the similarities between them based on genetic sequencing information and / or other types of profiles. These comparisons enable the patient report to provide clinical decision support to depressed patients and their healthcare providers in the context of other patient data. These comparisons may be direct, e.g., comparing one patient's clinical features to those of another patient and / or multiple patients, or indirect, i.e., involving comparisons utilized to predict patient response to treatment based on aggregated data associated with multiple patients.

[0185] The patient report may list drugs that are contraindicated for the patient, drugs and doses or other therapies for each drug that may be appropriate for the patient's therapeutic use, and warnings or information regarding potential drug-drug interactions. The drugs, doses, and other therapies selected to be listed in the patient report may be based on data associated with the patient, including the patient's genetic sequencing results, molecular profile including RNA or protein expression levels, the patient's medical records, treatment notes, patient-reported outcomes (PROs), similarity to other patients' sequencing information or other molecular profiles and / or reported successful treatment regimens of molecularly similar patients, assessment results based on the Patient Health Questionnaire (PHQ-9), General Anxiety Disorder (GAD) scale, Hamilton Depression (HAM-D) Rating Scale, Clinical Global Impression (CGI) score, Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), etc.

[0186] System 10 may interface with a mobile application that the patient uses to record patient-reported outcomes (PROs) and transfer information between system 10 and the mobile application. System 10 may generate or receive geographic location tracking information from the patient and calculate disposition metrics, including the amount of time spent outside the home and other metrics that may correlate with the severity of depression. The patient report may include the calculated disposition metrics and suggestions to the patient based on these metrics and / or PROs.

[0187] In some embodiments, a PRO may be any report of a patient's health status obtained directly from the patient, without interpretation of the patient's response by a clinician and / or others. This data may be used to measure the risks / benefits of treatment and to inform and guide patient-centered care and clinical decision-making. A mobile application that records a PRO may provide the data (e.g., patient data that may enable clinicians to automatically track patient progress and make informed decisions). In some embodiments, the mobile application may offer improved efficiency (e.g., a PRO may provide patients with personalized, evidence-based assessments, including scales such as the PHQ-9, with a proprietary daily check-in without interrupting the clinician's workflow). In some embodiments, the mobile application may automatically deliver, score, and chart results for the assessment, thus reducing clinicians' time filling out paperwork to track patient progress and outcomes. In some embodiments, the mobile application may offer patient insights, such as tracking patient progress and translating findings into personalized treatment decisions. In some embodiments, the mobile application can generate reports embedded with patient-specific insights derived from real-world evidence to provide clinicians with data to improve patient care. In some embodiments, the mobile application can provide reimbursement information. For example, each time a patient completes an assessment through the PRO app, clinicians may be eligible to receive reimbursement from insurance without having to spend additional time submitting claims.

[0188] In some embodiments, the mobile application can provide data for patients. In some embodiments, the mobile application can provide information for patients to monitor their mental health. In some embodiments, the mobile application can provide a complete and quick daily check-in to track positivity, energy, and symptoms. In some embodiments, the mobile application can provide insight into how sleep, exercise, and behavior patterns affect a patient's mood over time. In some embodiments, the mobile application can provide a way for patients to share symptoms and progress with clinicians, creating a more personalized care experience.

[0189] In one example, a PRO GUI may report a patient's health status directly from the patient. This data can be used to measure the risks / benefits of treatment and inform and guide patient-centered care and clinical decision-making. A mobile application used by the patient to record PROs may enable clinicians to automatically track patient progress, make measurement-based decisions, administer personalized evidence-based assessments to patients without interrupting clinician workflow, and combine scales like the PHQ-9 with regular check-in measures. The PRO GUI may automatically deliver, score, and chart the results of these assessments. The app may facilitate tracking of patient progress and translate findings into personalized treatment decisions. Reports may be embedded with patient-specific insights derived from real-world evidence, providing data to clinicians to improve patient care. For patients, the app may allow for quick daily check-ins for patients to monitor their mental health, track positivity, energy, and symptoms, understand how sleep, exercise, and movement patterns affect a patient's mood over time, and / or provide a means to share symptoms and progress with clinicians to create a more personalized care experience. The patient report may list clinical trials for which the patient meets the inclusion criteria and / or that are geographically close to the patient or do not require long-distance travel or relocation.

[0190] Specific examples of drugs that may be included in the patient report 800 for any reason include, but are not limited to, abacavir, allopurinol, alprazolam, amitriptyline, amoxapine, aripiprazole, armodafinil, asenapine, atomoxetine, brexpiprazole, bupropion, bupropion hydrobromide, buspirone, carbamazepine, cariprazine, chlordiazepoxide, chlorpromazine, citalopram, clomipramine, clonazepam, clopidogrel ... Razepate, clozapine, codeine, desipramine, desvenlafaxine, deutetrabenazine, dextromethorphan / quinidine, diazepam, doxypine, duloxetine, escitalopram, esketamine, eszopiclone, fluoxetine, fluphenazine, fluvoxamine, gabapentin, haloperidol, iloperidone, imipramine, isocarboxazid, lamotrigine, levomilnacipran, levothyroxine (thyroxine and / or (may contain T4), lithium, lorazepam, loxapine, lurasidone, maprotiline, milnacipran, mirtazapine, moclobemide, modafinil, nefazodone, nortriptyline, olanzapine, ondansetron, oxazepam, oxcarbazepine, paliperidone, paroxetine, perphenazine, phenelzine, phenytoin, pimavanserin, pimozide, pregabalin, propranolol, protriptyline, quetiapine, ramelteon, risperidone These may include fluconazole, selegiline (may be administered transdermally), sertraline, tamoxifen, temazepam, tetrabenazine, thioridazine, thiothixene, topiramate, tranylcypromine, trazodone, triazolam, trifluoperazine, trifluoperazine, trimipramine, tropisetron, valbenazine, valproate, venlafaxine, vilazodone, voriconazole, vortioxetine, warfarin, ziprasidone, zolpidem, zonisamide.

[0191] 9A-9H illustrate an alternative embodiment of a patient report 800. In addition to the sections described in Figures 8A and 8B, the patient report 800 may further include a summary 910, a legend 920, pharmacogenomic result details 930, and secondary finding details 940. The patient report 800 may further include other notes or instructions (not shown).

[0192] The summary 910 may indicate medications most likely to be successful in improving the patient's depressive or psychiatric symptoms based on individual and / or aggregated patient data, including clinical data, imaging data, behavioral data, molecular data, pharmacogenetic data, and other genetic sequences detected in the patient. The notes in the third section 830 may include a geneticist's note or a note regarding incidental germline findings. The legend 920 may include the classifications used in the seventh section 870 and a brief description of each classification to guide the healthcare provider in interpreting the classifications.

[0193] In the illustrated example, the seventh section 870 may be organized by treatment type, including "antidepressants," "antipsychotics," "anticonvulsants," "anti-anxiety medications," and "other medications." Additional treatment types may be added to identify other treatments and their respective pharmacogenomic implications. Additional treatment types may include therapies such as hypnotics, or even non-traditional therapies such as ayahuasca. Each treatment type may be organized by drug class, such as "SSRI," "SNRI," "tetracyclic," etc., and each class may be organized into treatment categories, including "standard dosing," "dose adjustment - escalation," "dose adjustment - reduction," "contraindications," "preferred alternative therapy - side effects," etc., and individual treatments may be listed under the category along with the rationale for assigning that category to the treatment.

[0194] The basis for classification may include pharmacogenetic results from sequencing the patient's genome. The pharmacogenetic results may include the gene name and the phenotype indicated by the gene sequence associated with that gene detected in the patient, or the allele name and a positive or negative result of detecting that allele in the patient. The pharmacogenetic results may include sources or references that provide evidence supporting the relationship between the pharmacogenetic results and treatment response, including published scientific research papers, databases, and published guidelines, including those from the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines, the DPWG, government agencies including the U.S. Food and Drug Administration (FDA), and others. In some embodiments, therapies that may be more effective for patients at non-standard doses may be labeled as "dose adjustment—increase or decrease" and / or unique markers and / or colors (e.g., arrows pointing up or down on a blue background, as shown) for efficient identification by healthcare providers. Similarly, other markers and / or colors may be used to label therapies under other classifications.

[0195] Similar to the fifth and sixth sections 850 and 860, the pharmacogenomic result details 930 may include one or more gene names, allele identifiers associated with that gene detected in the patient, or gene allele identifiers (in this example, HLA-A*13:01 or HLA-B*15:02), a presence / absence status indicating whether the alleles were detected in the patient, and a phenotype associated with each detected allele or combination of detected alleles for that gene. Examples of phenotypes include poor metabolizer, intermediate metabolizer, normal metabolizer, extensive metabolizer, ultra-rapid metabolizer, increased sensitivity, and increased risk. The pharmacogenomic result details 930 may also include an accession number identifying the location of the gene according to a particular reference sequence build of the human genome, including hg19 (in this example, the accession number is in the format "NM_...").

[0196] Secondary finding details 940 may describe germline variants detected in the patient that may indicate risk for or be associated with another disease. This section may include one or more gene names and may further include a description of the variant (c.4965C?G, p.Tyr1655*, stop gain), accession number, and genomic location (Chr13:32913457) associated with each described gene or variant. This section may further include a recommendation that the patient seek genetic counseling.

[0197] In conclusion, not only can system 10 be used to improve treatment and care for individual patients suffering from depression, but by combining key pillars of healthcare data, the research it catalyzes can help patients who are not yet affected.

[0198] In a first embodiment, a genetic testing panel (target panel) may analyze a patient sample for the presence of several gene sequences or gene variants, and the results may be presented to a physician, medical professional, or other individual via system 10, for example, in a molecular report 90. A knowledge database 40 may be provided to system 10 so that desired genes, gene variants, or mutations may be linked to one or more pharmacogenomic interactions. The system may then compare the listing of genes, variants, alleles, or gene variants from the sequencing report to the knowledge database 40 to generate the report. Additionally, knowledge database 40 may include information regarding any of the following panels: One or more panels may detect genetic variants in one or more genes involved in the metabolism, pharmacokinetics, and / or immunogenicity of one or more drugs.

[0199] One type of panel, such as a panel directed at detecting information relevant to pharmacogenomics across many areas of medicine, includes cyp2c19 (nm_000769.4), cyp2d6 (nm_000106.6), cyp1a2 (nm_000761.5), cyp2b6 (nm_000767.4), cyp2c9 (nm_000771.4), cyp3a4 (nm_017460. The present invention may detect genetic variants in patient samples in one or more of the following genes: HLA-A (NM_002116.8), HLA-B (NM_005514.8), HTR2A (NM_000621.4), SLC6A4 (NM_001045.5), UGT1A4 (NM_007120.2), and UGT2B15 (NM_001076.3). The arrangement of exons and / or introns associated with genes may be defined according to publicly available database entries. Database entries may be identified by accession numbers, examples of which are listed in parentheses after the name of each gene in the present disclosure. In this example, the arrangements listed in the entries associated with these accession numbers are according to the human reference genome hg19.

[0200] In another example, genetic testing panels can be used to target suspected genes of interest related to a particular medical field or a particular medical condition (such as depression). These panels include, in addition to the genes already listed, 5HT2C (NM_000868.3), ABCB1 (MDR1) (NM_000927.4), ABCG2 (NM_004827.2), ACE (NM_000789.3), ADRA2A (NM_001076.3), ADRB1 (NM_000684.2), ADRB2 (NM_000024.5), agt (nm_000029.4), ank3 (nm_020987.5), ankk1 (nm_178510.1), apoe (nm_0013 02688.1), bdnf(nm_001709.4), cacna1c(nm_199460.3), ces1(nm_001266.4), comt(nm_000754.3), cyp1a2(nm_000761.5), cyp2b6(nm_00 0767.4), cyp2c19(nm_000769.4), cyp2c9(nm_000771.4), cyp2d6(nm_000106.6), cyp3a4(nm_017460.5), cyp3a5(nm_000777.5), cyp4f2(n m_001082.4), dpyd(nm_000110.3), drd1(nm_000794.4), drd2(nm_000795.3), rd3(nm_000796.5), edn1(nm_001955.4), ercc1(nm_202001) .2), f2(nm_000506.4), f5(nm_000130.4), fcgr2a(nm_021642.3), fcgr3a(nm_000569.7), g6pd(nm_000402.4), gnb3(nm_002075.3), grik 1(nm_000830.4), grik4(nm_014619.4), gstp1(nm_000852.3), hla-a(nm_002116.8), hla-b(nm_005514.8), hnf4a(nm_178849.2), hsd3b1 (nm_000862.2), htr1a(nm_000524.3), htr2a(nm_000621.4), htr2c(nm_000868.3), ifnl3(il28b)(nm_001346937), ifnl3(nm_001346937.1) kcnip1(nm_001034837.2), kcnj11(nm_000525.3), kcnq1(nm_0 0218.2), LDLR(nm_000527.4), lipc(nm_000236.2), mc4r(nm_005912). .2) mthfr(nm_005957.4),mtrr(nm_002454.2),neurod1(β2)(nm_00 2500.4, nqo1(nm_000903.3), nr1h3(nm_00569.3), nudt15(nm_0182). 83.3, oprm1(nm_000914.4), pax4(nm_006193.2), polg(nm_002693.2), ppara(nm_002693.2), pparg(nm_015869.4), ppargc1a(nm_0072). 15.3) prkaa1(nm_006251.5), prkab2(nm_005399.4), ptprd(nm_005). 399.4, rbp4(nm_006744.3), ryr1(nm_000540.2), slc22a1(oct1)(n m_003057.2, slc22a2(oct2)(nm_003058.3), slc30a8(nm_173851.2), slc47a1(mate1)(nm_018242.2), slc47a2(mate2-k)(nm_152908). 3) slc49a4(pmat)(nm_032839.2);slc6a2(nm_001043.3) slc6a4(n m_001045.5), slco1b1(nm_006446.4), sod2(nm_000636.3), stk11(n m_000455.4), tcf7l2(nm_030756.4), tpmt(nm_000367.4), tyms(nm_ 000367.4, ucp2(nm_003355.2), UGT1A1(NM_000463.2), UGT1A4(NM_ 007120.2)、UGT1A9(NM_021027.3)、UGT2B15(NM_001076.3)、UMPS(NM _000373.3) and 1 of the 1 of VKORC1(NM_000373.3) contains a slice of the prefix.

[0201] Still other genetic testing panels may be utilized to develop large datasets of genetic information for research purposes, such as identifying unknown biomarkers for identifying a patient's pharmacogenetic response, or biomarkers for susceptibility to conditions such as depression. Such panels may include one or more of the genes in Table 1 in addition to those already listed, and may generate whole-exome sequencing data that includes gene sequences from all exon regions in the human genome, in addition to selected intron regions. Genetic variants detected in a patient may be matched with entries in the KDB40 to predict the patient's response to various therapies, which may be displayed in the patient report 800 and / or GUI50, as described above.

[0202] Specific genetic variants (e.g., SNPs at identified loci) at specific genomic locations associated with genes detected by these genetic testing panels, sequence analysis, microarrays, or another method can be identified according to the reference SNP cluster (rs) ID and / or genomic location of the genetic variant. In this example, the genomic location is based on human genome build GRCh37 / hg19. In addition to coding exons that may be analyzed by exome sequencing or another genetic sequence analysis method, specific intronic regions associated with genes analyzed by these genetic testing panels, sequence analysis, or another method can be identified according to their nucleotide location on a given chromosome.

[0203] Some genes, including CYP2C19, CYP2D6, CYP1A2, CYP2B6, CYP2C9, CYP3A4, HLA-A, HLA-B, UGT1A4, and UGT2B15, use a special nomenclature known as star alleles to define their genetic variants. Star alleles are a group of variants that define an allele and predict the functional outcome of that allele. In one example, *1 refers to star allele 1, which may be the normal reference allele, and *1xN refers to the gain of N copies of allele 1 compared to the normal reference genome. The copy number gain of any star allele can be written as *#xN, where # is the star allele number and N is the number of copies obtained. In another example, specific alleles of specific genes, combinations of two alleles of specific genes (e.g., that may determine or influence a patient's phenotype), and changes (gains or losses) in the copy number of these alleles detected by a genetic test panel, sequence analysis, or another method, may be defined and / or referenced by standardized identifiers, including, but not limited to, star allele nomenclature.

[0204] For example, specific genetic variants associated with the CYP2C19 gene include variants located within the exons of the gene as well as within introns, with rs IDs, i.e., rs2860840, rs1326830, rs11188072, rs11316681, rs111490789, rs17878739, rs7902257, rs11568732, rs12248560, rs4986894, rs367543001, rs17885098, rs12768009, rs17884832, and rs791664. 9, rs17878649, rs12769205, rs17879992, rs7088784, rs72558186, rs12571421, rs12767583, rs4494250, rs4417205, rs7915414, rs28399513, rs3758581, rs4917623, and rs55640102. Specific CYP2C19 alleles can include the following CYP2C19 star alleles: *1, *1A, *1B, *1C, *2, *2A, *2B, *2C, *2D, *2E, *2F, *2G, *2H, *2J, *3, *3A, *3B, *3C, *4A, *4B, *5, *6, *7, *8, *9, *10, *12, *17, *27, *28, *34, and *35.

[0205] The specific intron regions associated with the CYP2C19 gene are located at nucleotide positions on chromosome 10, i.e., positions 96495132 to 96612771, and more specifically at positions on chromosome 10, i.e., 96495132 to 96495332, 96495693 to 96495893, 96518961 to 96519161, and 9652033 3 to 96520533, 96520343 to 96520543, 96520924 to 96521124, 96521322 to 96521522, 96521474 to 96521674, 96521557 to 96521757, 96522265 to 96522465, 96522350 to 96522550, 96522461 to 96522661, 96522550 to 96522662 6525765 to 96525965, 96534375 to 96534575, 96534484 to 96534684, 96534668 to 96534868, 96535024 to 96535224, 96535528 to 96535728, 96541273 to 96541473, 96541656 to 96541856, 96541882 to 9654 2082, 96547363 to 96547563, 96563657 to 96563857, 96580102 to 96580302, 96599410 to 96599610, 96602298 to 96602498, 96602523 to 96602723, 96609468 to 96609668, and 96612571 to 96612771.

[0206] For example, specific genetic variants associated with the CYP2D6 gene can be identified by their location within the exons of the gene and their rs IDs, i.e., rs12169962, rs28371738, rs267608322, rs28371729, rs4987144, rs267608322, rs28371730, rs2004511, rs267608292, rs28371729, rs1985842, rs267608291, rs28371725, rs28371721, rs188062577, rs79738337, rs2676083 00, rs267608290, rs2267447, rs3892097, rs267608305, rs67497403, rs267608306, rs267608289, rs201377835, rs28 371702, rs28371701, rs575159870, rs28371699, rs267608273, rs1081000, rs28695233, rs29001518, rs1080998, rs1 080997, rs1080996, rs1080995, rs75085559, rs267608272, rs1080993, rs35481113, rs35023634, rs34894147, rs350 46171, rs34898711, rs35534760, rs34167214, rs530422334, rs566383351, rs28633410, rs534009571, rs28624811, r The variants may include those identified by rs536645539, rs1080990, rs1080989, rs59360719, rs544534350, rs375413467, rs267608271, rs28735595, rs59099247, rs28588594, rs76210340, rs1080985, rs576829306, rs545591749, rs58188898, and rs1080983.Additional specific genetic variants associated with the CYP2D6 gene can include genetic variants at genomic locations Chr22:42525917, Chr22:42525946, Chr22:42527060, Chr22:42527068, Chr22:42527114, Chr22:42527115, Chr22:42527122, Chr22:42527127, and Chr22:42527422.

[0207] Specific CYP2D6 alleles are known as CYP2D6 star alleles, *1, *1A, *1B, *1C, *1D, *1E, *1xN, *2, *2A, *2B, *2C, *2D, *2E, *2F, *2G, *2H, *2K, *2L, *2M, *2xN, *3, *3A, *3B, *3xN, *4, *4A, *4B, *4C, *4D, *4E, *4F, *4G, *4H, *4J, *4K, *4L, *4M, *4N, *4P, *4xN, *5, *6, *6A, *6B, *6C, *6D, *6xN, *7, *8, *9, *9xN, *10, * 10A, *10B, *10D, *10xN, *11, *11A, *11B, *12, *13, *14A, *14B, *15, *17, *17xN, *21A, *21B, *31, *35A, *35B, *36, *36xN, *41, *41xN, *45B, *45A, *46A, *46B, *47, *49, *51, *52, *53, *56A, *56B, *58, *59, *60, *64, *68, *69, *70, *71, *72, *73, *74, *84, *85, *91, *99, *100, *101, *109, and It may include the specific combination of *36 and *10.

[0208] Specific intron regions associated with the CYP2D6 gene are nucleotide positions on chromosome 22, i.e., positions 42522212 to 42528668, and more specifically, positions on chromosome 22, i.e., 42522212 to 42522412, 42522292 to 42522492, 42523084 to 42523284, 42523258 to 42523458, 42522903 to 42523103, 42523084 to 42523284, 42523109 to 42523309, and 42523111 to 42523311. , 42523202 to 42523402, 42523258 to 42523458, 42523309 to 42523509, 42523663 to 42523863, 42523705 to 42523905, 42524032 to 42524232, 42524385 to 42524585, 42524390 to 42524590, 42524402 to 42524602, 42524564 to 42524764, 42524596 to 42524796, 42524847 to 42525047, 42525095 to 42525295 , 42525180 to 42525380, 42525199 to 42525399, 42525633 to 42525833, 42525812 to 42526012, 42525817 to 42526017, 42525846 to 42526046, 42525852 to 42526052, 42525949 to 42526149, 42526088 to 42526288, 42526384 to 42526584, 42526414 to 42526614, 42526449 to 42526649, 42526461 to 42526661 , 42526462 to 42526662, 42526467 to 42526667, 42526471 to 42526671, 42526473 to 42526673, 42526480 to 42526680, 42526736 to 42526936, 42526831 to 42527031, 42526869 to 42527069, 42526918 to 42527118, 42526925 to 42527125, 42526960 to 42527160, 42526964 to 42527164, 42526968 to 42527168,42526968 to 42527168, 42527014 to 42527214, 42527015 to 42527215, 42527020 to 42527220, 42527022 to 42527222, 42527024 to 42527224, 42527027 to 42527227, 42 527047 to 42527247, 42527124 to 42527324, 42527322 to 42527522, 42527371 to 42527571, 42527385 to 42527585, 42527433 to 42527633, 42527442 to 42527642, 4252 7653 to 42527853, 42527693 to 42527893, 42527704 to 42527904, 42527686 to 42527986, 42527802 to 42528002, 42527928 to 42528128, 42527996 to 42528196, 425281 24 to 42528324, 42528241 to 42528441, 42528282 to 42528482, 42528298 to 42528498, 42528299 to 42528499, 42528438 to 42528638, and 42528468 to 42528668.

[0209] For example, specific genetic variants associated with the CYP1A2 gene can include variants located within the exons of the gene as well as variants identified by the rs IDs: rs2472299, rs2069514, rs35694136, rs2069526, rs12720461, rs762551, rs2069526, rs28399417, rs12720461, rs183165301, rs762551, rs4646425, rs2472304, rs3743484, rs56107638, and rs4646427. Additional specific genetic variants associated with the CYP1A2 gene can include genetic variants at genomic locations Chr15:75038486, Chr15:75038967, Chr15:75039027, Chr15:75039272, Chr15:75039413, Chr15:75041341, Chr15:75041713, Chr15:75042757, Chr15:75044104, and Chr15:75047284. Specific CYP1A2 alleles can include CYP1A2 star alleles, *1, *1E, *1F, *1G, *1J, *1K, *1L, *1M, *1N, *1P, *1Q, *1R, *1V, *1W, *3, *4, *6, *7, *8, *11, *15, *16, *17, and *21.

[0210] Specific intron regions associated with the CYP1A2 gene are located at nucleotide positions on chromosome 15, i.e., 75033300 to 75047384, and more specifically, at positions on chromosome 15, i.e., 75033300 to 75033500, 75038120 to 75038320, 75038386 to 75038 586, 75038867 to 75039067, 75038927 to 75039127, 75039172 to 75039372, 75039313 to 75039513, 75039513 to 75039713, 75041241 to 75041441, 75041241 to 75041441, 75041251 to 75 041451, 75041613 to 75041813, 75041817 to 75042017, 75041241 to 75041441, 75041247 to 75041447, 75041251 to 75041451, 75041613 to 75041813, 75041817 to 75042017, 75042657 to 75042857, 75043181 to 75043381, 75044004 to 75044204, 75044138 to 75044338, 75044300 to 75044500, 75045512 to 75045712, 75045592 to 75045792, 75047184 to 75047384.

[0211] For example, specific genetic variants associated with the CYP2B6 gene can be identified by their location within the exons of the gene and their rs IDs, i.e., rs1962261, rs2054675, rs4802101, rs34223104, rs28399484, rs3786547, rs2279342, rs28969408, rs4803418, rs28399487, rs28399488, rs28399490, rs4803419, rs28399491, rs35266616, rs28399492, rs The variants may include those identified in rs202050252, rs34155858, rs2279344, rs2279345, rs8192718, rs12721649, rs28399498, rs35622401, rs8192719, rs7260329, rs8109848, rs28399502, rs28969419, rs28969420, and rs12979898.

[0212] Additional specific genetic variants associated with the CYP2B6 gene can include genetic variants at genomic locations Chr19:41494891, Chr19:41495363, Chr19:41495433, Chr19:41495633, Chr19:41495987, Chr19:41496025, Chr19:41496410, Chr19:41496454, and Chr19:41496620.

[0213] Specific CYP2B6 alleles can include CYP2B6 star alleles, *1, *1A, *1B, *1C, *1D, *1E, *1F, *1G, *1H, *1J, *1K, *1L, *1M, *1N, *4, *4A, *4B, *4C, *4D, *5B, *6, *6A, *6B, *6C, *7B, *9, *11B, *12, *13A, *13B, *14, *15A, *15B, *17A, *17B, *18, *19, *20, *21, *22, *27, *28, *34, *35, *36, *38.

[0214] Specific intron regions associated with the CYP2B6 gene are located at nucleotide positions on chromosome 19, i.e., 41494791 to 41524232, and more specifically, at positions on chromosome 19, i.e., 41494791 to 41494991, 41495139 to 41495339, 41495263 to 41495463, 41495333 to 41495533, 41495533 to 41495733, 41495655 to 41495855, 414958 87 to 41496087, 41495925 to 41496125, 41496310 to 41496510, 41496354 to 41496554, 41496361 to 41496561, 41496520 to 41496720, 41497029 to 41497229, 41497407 to 41497607, 41506091 to 41506291, 41510027 to 41510227, 41510308 to 41510508, 41511703 to 415 11903, 41512524 to 41512724, 41512525 to 41512725, 41512687 to 41512887, 41512692 to 41512892, 41512904 to 41513104, 41512947 to 41513147, 41514632 to 41514832, 41514918 to 41515118, 41515276 to 41515476, 41515383 to 41515583, 41515602 to 41515802, 4 1515714 to 41515914, 41515737 to 41515937, 41515784 to 41515984, 41516022 to 41516222, 41518673 to 41518873, 41521538 to 41521738, 41521969 to 41522169, 41522770 to 41522970, 41523257 to 41523457, 41523704 to 41523904, and 41524032 to 41524232.

[0215] For example, specific genetic variants associated with the CYP2C9 gene can be identified by their location within the exons of the gene and their rs IDs, i.e., rs146705863, rs9332092, rs9332093, rs9332094, rs9332096, rs61604699, rs4918758, rs4917636, rs9332098, rs9332100, rs9332101, rs9332102, rs9332104, rs12772884, rs9332116, rs9332119, rs9332120, rs2860905, rs28371675, rs28371676, rs28371677, rs28371679, rs rs28371680, rs28371681, rs28371682, rs4086116, rs9332127, rs28371683, rs9332129, rs28371684, rs4917639, rs9332172, rs9332174, rs10509680, rs9332197, rs17847029, rs9332230, rs9332232, rs2298037, rs9332238, rs1934969, rs146139873, and rs57749228. Additional specific genetic variants associated with the CYP2C9 gene can include a genetic variant at genomic location Chr10:96709253. Specific CYP2C9 alleles can include the CYP2C9 star alleles, *1, *2, *3, *4, *5, and *6.

[0216] Specific intron regions associated with the CYP2C9 gene are located at nucleotide positions on chromosome 10, i.e., 96695677 to 96748620, and more specifically, at positions on chromosome 10, i.e., 96695677 to 96695877, 96696429 to 96696629, 96696455 to 96696555, 96696574 to 96696774, 96696775 to 96696975, 96696803 to 96697003, 96697152 to 96697352, 966972 44 to 96697444, 96697359 to 96697559, 96697720 to 96697920, 96697855 to 96698055, 96697856 to 96698056, 96698590 to 96698790, 96700530 to 96700730, 96700679 to 96700879, 96701501 to 96701701, 96701750 to 96701950, 96702195 to 96702395, 96702237 to 96702437, 96702263 to 96 702463, 96702372 to 96702572, 96702496 to 96702696, 96702648 to 96702848, 96702967 to 96703167, 96703009 to 96703209, 96707102 to 96707302, 96707371 to 96707571, 96707408 to 96707608, 96708650 to 96708850, 96709021 to 96709221, 96709153 to 96709353, 96725435 to 96725635 , 96731688 to 96731888, 96731997 to 96732197, 96734239 to 96734439, 96740808 to 96741008, 96741065 to 96741265, 96745884 to 96746084, 96745932 to 96746132, 96745978 to 96746178, 96748392 to 96748592, 96748395 to 96748595, 96748405 to 96748605, 96748420 to 96748620.

[0217] For example, specific genetic variants associated with the CYP3A4 gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs59715127, rs72552794, rs3735451, rs6956344, rs4646440, rs2242480, rs56153749, rs4646437, rs2246709, rs2687116, rs55808838, rs35599367, rs68106838, rs72552800, rs12721636, and rs2740574. Additional specific genetic variants associated with the CYP3A4 gene are located at genomic locations Chr7:99364921, Chr7:99365083, Chr7:99365719, Chr7:99365887, Chr7:99365943, Chr7:99365969, Chr7:99366316, Chr7:99367496, Chr7:9937 5629, Chr7:99381766, Chr7:99381766, Chr7:99381860, Chr7:99382073, Chr7:99382096, Chr7:99382148, Chr7:99382334, Chr7:99382359, Chr7:99382451, and Chr7:99382539. Specific CYP3A4 alleles can include the CYP3A4 star alleles, *1, *1B, *13, *15A, *15B, *22, *23, and *24.

[0218] Specific intron regions associated with the CYP3A4 gene are located at nucleotide positions on chromosome 7, i.e., 99355390 to 99382640, and more specifically at positions on chromosome 7, i.e., 99355390 to 99355590, 99355583 to 99355783, and 99355875 to 9935607. 5, 99359051 to 99359251, 99360770 to 99360970, 99361366 to 99361566, 99363780 to 99363980, 99364821 to 99365021, 99364983 to 99365183, 99365619 to 99365819, 99365787 to 993 65987, 99365843 to 99366043, 99365869 to 99366069, 99366216 to 99366416, 99367396 to 99367596, 99375529 to 99375729, 99381666 to 99381866, 99381666 to 99381866, 99381760 et 99381960, 99381973 to 99382173, 99381996 to 99382196, 99382048 to 99382248, 99382234 to 99382434, 99382259 to 99382459, 99382351 to 99382551, 99382440 to 99382640.

[0219] For example, specific genetic variants associated with the HLA-A gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs3823339, rs1061235, and rs2499. Specific intronic regions associated with the HLA-A gene can include nucleotide positions on chromosome 6, i.e., 29912868 to 29913642, and more specifically, positions on chromosome 6, i.e., 29912868 to 29913068, 29913198 to 29913398, and 29913442 to 29913642.

[0220] For example, specific genetic variants associated with the HLA-B gene can be identified by their location within the exons of the gene and their rs IDs, i.e., rs13203895, rs2074488, rs2524074, rs2524084, rs2844613, rs28498059, rs13218306, rs12199223, rs9366775, rs9357121, rs4361609, rs2524082, rs12111032, rs2844619, rs2524078, rs3134745, rs6913377, rs7759127, rs6923313, rs10456057, rs12189871, rs12191 877, rs16899160, rs16899166, rs16899168, rs2394963, rs2524043, rs2524044, rs2524048, rs2524051, rs2524057, rs2524070, rs2524156 , rs2853929, rs2853933, rs2853935, rs2853939, rs3873374, rs3873375, rs6457372, rs6906846, rs7382297, rs7754443, rs9366776, rs937 8228, rs9380236, rs9461684, rs9468920, rs9468922, rs9468925, rs9468926, rs2844599, rs9348859, rs2853934, rs2524049, rs2524069, r s9391714, rs2853948, rs7381988, rs9348862, rs2524163, rs2524040, rs10081114, rs10484554, rs12664384, rs16899178, rs16899203, rs 16899205, rs16899207, rs16899208, rs2243868, rs2246954, rs2247056, rs2394967, rs2508004, rs2524066, rs2524089, rs2524095, rs252 4115, rs2524123, rs2524132, rs2524145, rs2524168, rs2853923, rs2853925, rs2853926, rs28894983, rs28894990, rs3094682, rs3094691,rs364415, rs3873379, rs3873386, rs3905495, rs396038, rs396243, rs396337, rs4406273, rs4 523128, rs4543367, rs6905036, rs6918048, rs7750269, rs7760988, rs7761965, rs9264848, rs9 264850, rs9264869, rs9264899, rs9264916, rs9264917, rs9264942, rs9357123, rs9366778, rs9 368677, rs9380238, rs9380240, rs9461685, rs9468942, rs2894207, rs3915971, rs9295970, rs9 264904, rs6457375, rs28732109, rs9348863, rs9468929, rs16899202, rs9394054, rs3873385, r s9368673, rs9368680, rs1634761, rs9264951, rs2524229, rs9264902, rs6457374, rs16867947, These may include variants identified as rs28894993, rs9295976, rs7755852, rs2156875, rs2507997, rs2596501, rs2596503, rs3134792, rs4394275, rs4540292, rs9295984, rs2844586, rs4394274, and rs2523619.

[0221] Specific intron regions associated with the HLA-B gene are located at nucleotide positions on chromosome 6, i.e., 31243982 to 31318244, and more specifically, at positions on chromosome 6, i.e., 31243982 to 31244182, 31240331 to 31240531, 31243921 to 31244121, 31241539 to 31241739, 31243746 to 31243946, 31240253 to 31240453, 31240832 to 31241032, 31242631 to 31242831, 31242931 to 31242831, 31242946 to 31242953, 31242953 to 3124296, 3124297 to 3124298, 3124298 to 3124299 239996 to 31240196, 31240379 to 31240579, 31240535 to 31240735, 31241661 to 31241861, 31242091 to 31242291, 31242123 to 31242323, 31242549 to 31242749, 31242662 to 31242862, 31243395 to 31243595, 31240888 to 31241088, 31241270 to 31241470, 31245434 to 31245634, 31251824 to 31252024, 3 1252825 to 31253025, 31256567 to 31256767, 31257996 to 31258196, 31258587 to 31258787, 31251362 to 31251562, 31256912 to 31257112, 31256653 to 31256853, 31256461 to 31256661, 31255400 to 31255600, 31251795 to 31251995, 31244420 to 31244620, 31260297 to 31260497, 31255334 to 31255534, 31253988 to 31254188, 31253778 to 31253978, 31250542 to 31250742, 31251211 to 31251411, 31251260 to 31251460, 31247021 to 31247221, 31245636 to 31245836, 31246967 to 31247167, 31254163 to 31254363, 31256530 to 31256730, 31246271 to 31246471, 31254564 to 31254764, 31253344 to 31253544,31254836 to 31255036, 31255186 to 31255386, 31258737 to 31258937, 31260118 to 31260318, 31255905 to 31256105, 31249877 to 31250077, 31253828 to 31254028, 31255853 to 31256053, 31244689 to 31244889, 31244980 to 31245180, 31245473 to 31245673, 31246603 to 31246803, 31252647 to 31252847, 31259479 to 31259679, 31257525 to 31257725, 31271369 to 31271569, 31274455 to 31274655, 31272792 to 31272992, 31261037 to 31261237, 31266235 to 31266435, 31266261 to 31266461, 31266287 to 31266487, 31267396 to 31267596, 31261176 to 31261376, 31265162 to 31265362, 31265390 to 31265590, 31269029 to 31269229, 31273495 to 31273695, 31269054 to 31269254, 31266422 to 31266622, 31266017 to 31266217, 31265454 to 31265654, 31265214 to 31265414, 31264812 to 31265012, 31268647 to 31268847, 31275063 to 31275263, 31265637 to 31265837, 31264822 to 31265022, 31262951 to 31263151, 31263540 to 31263740, 31264219 to 31264419, 31264361 to 31264561, 31274593 to 31274793, 31273124 to 31273324, 31262069 to 31262269, 31273645 to 31273845, 31265439 to 31265639, 31272880 to 31273080, 31275074 to 31275274, 31272815 to 31273015, 31265990 to 31266190, 31269282 to 31269482,31266667 to 31266867, 31273046 to 31273246, 31268329 to 31268529, 31271057 to 31271257, 31272930 to 31273130, 31273395 to 31273595, 31271095 to 31271295, 31271140 to 31271340, 31271530 to 31271730, 31272321 to 31272521, 31272674 to 31272874, 31272708 to 31272908, 31274280 to 31274480, 31262769 to 31262969, 31269073 to 31269273, 31272221 to 31272421, 31267518 to 31267718, 31268732 to 31268932, 31265255 to 31265455, 31274341 to 31274541, 31263651 to 31263851, 31269248 to 31269448, 31269422 to 31269622, 31272453 to 31272653, 31272512 to 31272712, 31263197 to 31263397, 31262361 to 31262561, 31263116 to 31263316, 31266199 to 31266399, 31268480 to 31268680, 31269208 to 31269408, 31271657 to 31271857, 31272744 to 31272944, 31273927 to 31274127, 31275000 to 31275200, 31275131 to 31275331, 31272406 to 31272606, 31272161 to 31272361, 31280723 to 31280923, 31289614 to 31289814, 31281670 to 31281870, 31277888 to 31278088, 31317247 to 31317447, 31314681 to 31314881, 31321111 to 31321311, 31320710 to 31320910, 31312226 to 31312426, 31318077 to 31318277, 31317082 to 31317282, 31317597 to 31317797, 31317924 to 31318124, 31318064 to 31318264,31318044 to 31318244.

[0222] For example, specific genetic variants associated with the HTR2A gene can include variants located within the exons of the gene as well as variants identified by the rs IDs: rs9567733, rs7997012, rs2274639, rs9316233, rs659734, rs1928040, rs9567746, rs17288723, rs6312, and rs6311. Specific HTR2A alleles can include the reference allele, -1438A>G, and 102T>C. Specific intron regions associated with the HTR2A gene can include nucleotide positions on chromosome 13, i.e., 47401235 to 47471578, and more specifically, positions on chromosome 13, i.e., 47401235 to 47401435, 47411885 to 47412085, 47430163 to 47430363, 47433255 to 47433455, 47435183 to 47435383, 47447136 to 47447336, 47456448 to 47456648, 47457593 to 47457793, 47470724 to 47470924, 47471378 to 47471578.

[0223] For example, specific genetic variants associated with the SLC6A4 gene can include variants located within the exons of the gene as well as variants identified by the rs IDs: rs774676466, rs1042173, rs140700, rs57098334, rs2020933, and rs25531. Specific SLC6A4 alleles can include long (L) and short (S). Particular intron regions associated with the SLC6A4 gene can include nucleotide positions on chromosome 17, i.e., 28564227 to 28564446, and more particularly, positions on chromosome 17, i.e., 28564227 to 28564427, 28524911 to 28525111, 28543289 to 28543489, 28548496 to 28548696, 28561655 to 28561855, 28564246 to 28564446.

[0224] For example, specific genetic variants associated with the UGT1A4 gene can be identified by their location within the exons of the gene and their rs IDs, i.e., rs3732219, rs3732218, rs2011219, rs1983023, rs45507691, rs3806596, rs3806597, rs2008595, rs10929302, rs2003569, rs60469444, rs34531096, rs76063448, rs4124874, rs375531 9, rs11568318, rs11568316, rs1976391, rs4148327, rs873478, rs3213726, rs2302538, rs887829, rs34650714, rs8175347, rs10929303, rs1042640, rs8330, and rs34942353. Additional specific genetic variants associated with the UGT1A4 gene can include genetic variants at genomic locations Chr2:234656479, Chr2:234675628, Chr2:234675608. Specific UGT1A4 alleles can include the UGT1A4 star alleles, *1a, *3a, and *3b.

[0225] Specific intron regions associated with the UGT1A4 gene are located at nucleotide positions on chromosome 2, i.e., 234627148 to 234681724, and more specifically, at positions on chromosome 2, i.e., 234627148 to 234627348, 234627204 to 234627404, 234628276 to 234628476, 234636922 to 234637122, 234637120 to 2346 637320, 234637607 to 234637807, 234637469 to 234637669, 234637092 to 234637292, 234665682 to 234665882, 234667837 to 234668037, 234652540 to 234652740, 234665491 to 234665691, 234656379 to 234656579, 234652542 to 2346527 42, 234665559 to 234665759, 234667482 to 234667682, 234665398 to 234665598, 234665437 to 234665637, 234665883 to 234666083, 234675726 to 234675926, 234675528 to 234675728, 234668770 to 234668970, 234675423 to 234675623, 2 34675508 to 234675708, 234676313 to 234676513, 234668470 to 234668670, 234675729 to 234675929, 234668781 to 234668981, 234681316 to 234681516, 234681444 to 234681644, 234681545 to 234681745, 234681524 to 234681724.

[0226] For example, specific genetic variants associated with the UGT2B15 gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs4148271, rs72551389, and rs3100. Specific UGT2B15 alleles can include the UGT2B15 star alleles, *1 and *2. Specific intronic regions associated with the UGT2B15 gene can include nucleotide positions on chromosome 4, i.e., 69512537 to 69512754, and more specifically, positions on chromosome 4, i.e., 69512537 to 69512737, 69512591 to 69512791, and 69512554 to 69512754.

[0227] For example, specific genetic variants associated with the ANK3 gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs12357206 and rs7911953. Specific intronic regions associated with the ANK3 gene can include nucleotide positions on chromosome 10, i.e., 61790283 to 61791139, and more specifically, positions on chromosome 10, i.e., 61790283 to 61790483, 61790939 to 61791139.

[0228] For example, specific genetic variants associated with the ADRA2A gene can include variants located within the exons of the gene, as well as variants identified by rs ID, i.e., rs1800545. A specific intronic region associated with the ADRA2A gene can include nucleotide positions 112837438 to 112837638 on chromosome 10.

[0229] For example, specific genetic variants associated with the BDNF gene can include variants located within the exons of the gene as well as variants identified by rs IDs: rs7124442, rs11030104, rs7103411, rs962369, and rs7934165. Specific intronic regions associated with the BDNF gene can include nucleotide positions on chromosome 11: 27676941 to 27732083, and more specifically, positions on chromosome 11: 27676941 to 27677141, 27684417 to 27684617, 27700025 to 27700225, 27734320 to 27734520, and 27731883 to 27732083.

[0230] For example, specific genetic variants associated with the CACNA1C gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs10848615, rs2238032, rs10848635, rs1006737, rs2239050, rs216013, and rs2239128. Specific intron regions associated with the CACNA1C gene can include nucleotide positions on chromosome 12, i.e., 2218995 to 2757869, and more specifically, positions on chromosome 12, i.e., 2218995 to 2219195, 2222632 to 2222832, 2316095 to 2316295, 2345195 to 2345395, 2447314 to 2447514, 2729532 to 2729732, 2757669 to 2757869.

[0231] For example, specific genetic variants associated with the CES1 gene can include variants located within the exons of the gene and also the variant identified by rs ID, i.e., rs3815583. A specific intronic region associated with the CES1 gene can include nucleotide positions on chromosome 16, i.e., 55866942 to 55867142.

[0232] For example, specific genetic variants associated with the COMT gene can include variants located within the exons of the gene as well as variants identified by the rs IDs: rs6269, rs2239393, rs933271, rs737865, rs737866, rs4646312, rs5746849, rs4646316, rs9332377, rs174699, rs740603, rs165599, and rs165728. Specific intron regions associated with the COMT gene are nucleotide positions on chromosome 22, i.e., 19949852 to 19957123, and more specifically, positions on chromosome 22, i.e., 19949852 to 19950052, 19950328 to 19950528, 19931307 to 19931507, 19930021 to 19930221, 19930 009 to 19930209, 19948237 to 19948437, 19942897 to 19943097, 19952032 to 19952232, 19955592 to 19955792, 19954358 to 19954558, 19945077 to 19945277, 19956681 to 19956881, and 19956923 to 19957123.

[0233] For example, specific genetic variants associated with the CYP3A5 gene can include variants located within the exons of the gene as well as variants identified by the rs IDs, i.e., rs15524, rs28365094, rs17161788, rs4646453, rs55965422, rs776746, rs28365095, rs28371764, rs2740565, rs55798860, and rs28451617. Specific intron regions associated with the CYP3A5 gene are located at nucleotide positions on chromosome 7, i.e., 99245814 to 99332865, and more specifically at positions on chromosome 7, i.e., 99245814 to 99246014, 99250375 to 99250575, 99245809 to 99246009, 99260 262 to 99260462, 99264473 to 99264673, 99270439 to 99270639, 99277505 to 99277705, 99277493 to 99277693, 99293375 to 99293575, 99332707 to 99332907, and 99332665 to 99332865.

[0234] For example, specific genetic variants associated with the CYP4F2 gene can include variants located within the exons of the gene, as well as the variant identified by rs ID, i.e., rs3093158. A specific intronic region associated with the CYP4F2 gene can include nucleotide positions on chromosome 19, i.e., 16000066 to 16000266.

[0235] For example, specific genetic variants associated with the DPYD gene can include variants located within the exons of the gene as well as variants identified by the rs IDs: rs1760217, rs1413241, rs72728438, rs12022243, rs7548189, rs3918290, rs17116806, rs75017182, rs115632870, and rs4970722. Specific intron regions associated with the DPYD gene can include nucleotide positions on chromosome 1, i.e., 97602894 to 98352153, and more specifically, positions on chromosome 1, i.e., 97602894 to 97603094, 97722572 to 97722772, 97847774 to 97847974, 97862680 to 97862880, 97867613 to 97867813, 97915514 to 97915714, 97973152 to 97973352, 98045349 to 98045549, 98293721 to 98294921, and 98351953 to 98352153.

[0236] For example, specific genetic variants associated with the DRD1 gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs265981, rs686, and rs4532. Specific intronic regions associated with the DRD1 gene can include nucleotide positions on chromosome 5, i.e., 174870802 to 174870250, and more specifically, positions on chromosome 5, i.e., 174870802 to 174871002, 174868600 to 174868800, and 174870050 to 174870250.

[0237] For example, specific genetic variants associated with the DRD2 gene can include variants located within the exons of the gene as well as variants identified by the rs IDs: rs1124493, rs2283265, rs2440390, rs2734841, rs1076560, rs1110976, rs12363125, rs6279, rs2734833, rs1125394, rs1079598, rs4436578, rs4460839, rs4648317, rs1799978, and rs1799732. Specific intron regions associated with the DRD2 gene are located at nucleotide positions on chromosome 11, i.e., 113282195 to 113346351, and more specifically, at positions on chromosome 11, i.e., 113282195 to 113282395, 113285436 to 113285636, 113286778 to 113286978, 113281676 to 113281876, 113283588 to 113283788, and 113284419 to 11328461. 9, 113285816 to 113286016, 113280973 to 113281173, 113292820 to 113293020, 113297085 to 113297285, 113296174 to 113296374, 113306665 to 113306865, 113321696 to 113321896, 113331432 to 113331632, 113346251 to 113346451, and 113346151 to 113346351.

[0238] For example, specific genetic variants associated with the DRD3 gene can include variants located within the exons of the gene as well as variants identified by the rs IDs, i.e., rs963468, rs167771, rs167770, and rs324026. Specific intronic regions associated with the DRD3 gene can include nucleotide positions on chromosome 3, i.e., 113862787 to 113891142, and more specifically, positions on chromosome 3, i.e., 113862787 to 113862987, 113876175 to 113876375, 113879462 to 113879662, and 113890942 to 113891142.

[0239] For example, specific genetic variants associated with the ERCC1 gene can include variants located within the exons of the gene as well as variants identified by the rs IDs, i.e., rs3212948, rs2276469, and rs2276470. Particular intronic regions associated with the ERCC1 gene can include nucleotide positions on chromosome 19, i.e., 45924262 to 45974768, and more specifically, positions on chromosome 19, i.e., 45924262 to 45924462, 45974493 to 45974693, and 45974568 to 45974768.

[0240] For example, specific genetic variants associated with the F2 gene may include variants located within the exons of the gene, as well as the variant identified by rs ID, i.e., rs1799963. A specific intronic region associated with the F2 gene may include nucleotide positions on chromosome 11, i.e., 46760955 to 46761155.

[0241] For example, a particular genetic variant associated with the F5 gene may include a variant located within an exon of the gene, as well as the variant identified by rs ID, i.e., rs3766117. A specific intronic region associated with the F5 gene may include nucleotide positions on chromosome 1, i.e., 169527756 to 169527956.

[0242] For example, specific genetic variants associated with the GNB3 gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs11064426 and rs2301339. Specific intronic regions associated with the GNB3 gene can include nucleotide positions on chromosome 12, i.e., 6953157 to 6954724, and more specifically, positions on chromosome 12, i.e., 6953157 to 6953357, 6954524 to 6954724.

[0243] For example, specific genetic variants associated with the GRIK1 gene can include variants located within the exons of the gene, as well as the variant identified by rs ID, i.e., rs2832407. A specific intronic region associated with the GRIK1 gene can include nucleotide positions on chromosome 21, i.e., 30967408 to 30967608.

[0244] For example, specific genetic variants associated with the GRIK4 gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs11601979, rs1954787, and rs12800734. Specific intronic regions associated with the GRIK4 gene can include nucleotide positions on chromosome 11, i.e., 120476890 to 120836854, and more specifically, positions on chromosome 11, i.e., 120476890 to 120477090, 120663263 to 120663463, and 120836654 to 120836854.

[0245] For example, specific genetic variants associated with the GSTP1 gene may include variants located within the exons of the gene as well as the variant identified by rs ID, i.e., rs8191439. A specific intronic region associated with the GSTP1 gene may include nucleotide positions on chromosome 11, i.e., 67351197 to 67351397.

[0246] For example, specific genetic variants associated with the HNF4A gene can include variants located within the exons of the gene as well as variants identified by the rs IDs: rs4812831, rs1800963, rs2071197, rs3212198, rs3212200, rs2273618, rs6103731, rs6130615, rs3818247, and rs3746574. Specific intron regions associated with the HNF4A gene can include nucleotide positions on chromosome 20, i.e., 43018160 to 43058118, and more specifically, positions on chromosome 20, i.e., 43018160 to 43018360, 43029185 to 43029385, 43030335 to 43030535, 43044262 to 43044462, 43046829 to 43047029, 43052470 to 43052670, 43047193 to 43047393, 43059337 to 43059537, 43057380 to 43057580, 43057918 to 43058118.

[0247] For example, specific genetic variants associated with the HTR1A gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs1364043, rs1423691, rs6295, and rs10042486. Specific intronic regions associated with the HTR1A gene can include nucleotide positions on chromosome 5, i.e., 63250751 to 63261429, and more specifically, positions on chromosome 5, i.e., 63250751 to 63250951, 63251562 to 63251762, 63258465 to 63258665, and 63261229 to 63261429.

[0248] For example, specific genetic variants associated with the HTR2C gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs3813928, rs3813929, rs518147, and rs1414334. Specific intronic regions associated with the HTR2C gene can include nucleotide positions on the X chromosome, i.e., 113818182 to 114138244, and more specifically, positions on the X chromosome, i.e., 113818182 to 113818382, 113818420 to 113818620, 113818482 to 113818682, and 114138044 to 114138244.

[0249] For example, specific genetic variants associated with the IFNL3 gene may include variants located within the exons of the gene, as well as the variant identified by rs ID, i.e., rs11881222. A specific intronic region associated with the IFNL3 gene may include nucleotide positions on chromosome 19, i.e., 39734823 to 39735023.

[0250] For example, specific genetic variants associated with the IFNL4 gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs12979860 and rs8099917. Specific intronic regions associated with the IFNL4 gene can include nucleotide positions on chromosome 19, i.e., 39738687 to 39743265, and more specifically, positions on chromosome 19, i.e., 39738687 to 39738887 and 39743065 to 39743265.

[0251] For example, specific genetic variants associated with the KCNQ1 gene can include variants located within the exons of the gene as well as variants identified by rs IDs: rs757092, rs58762055, rs2237892, and rs2237895. Specific intronic regions associated with the KCNQ1 gene can include nucleotide positions on chromosome 11: 2499078 to 2857294, and more specifically, positions on chromosome 11: 2499078 to 2499278, 2738847 to 2739047, 2839651 to 2839851, and 2857094 to 2857294.

[0252] For example, specific genetic variants associated with the LDLR gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs6511720, rs14158, rs7254521, rs5742911, rs2569537, and rs1433099. Particular intron regions associated with the LDLR gene can include nucleotide positions on chromosome 19, i.e., 11202206 to 11242758, and more particularly, positions on chromosome 19, i.e., 11202206 to 11202406, 11241944 to 11242144, 11243322 to 11243522, 11243345 to 11243545, 11239953 to 11240153, and 11242558 to 11242758.

[0253] For example, specific genetic variants associated with the MTHFR gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs786204005, rs1476413, rs17421511, and rs17367504. Specific intronic regions associated with the MTHFR gene can include nucleotide positions on chromosome 1, i.e., 11852200 to 11862878, and more specifically, positions on chromosome 1, i.e., 11852200 to 11852400, 11857688 to 11857888, 11862678 to 11862878, and 11863113 to 11863313.

[0254] For example, specific genetic variants associated with the NQO1 gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs1050873, rs3191214, rs10517, and rs1063556. Specific intronic regions associated with the NQO1 gene can include nucleotide positions on chromosome 16, i.e., 69744674 to 69744847, and more specifically, positions on chromosome 16, i.e., 69744674 to 69744874, 69744755 to 69744955, 69743660 to 69743860, and 69744647 to 69744847.

[0255] For example, specific genetic variants associated with the NR1H3 gene can include variants located within the exons of the gene and also the variant identified by rs ID, i.e., rs11039149. A specific intronic region associated with the NR1H3 gene can include nucleotide positions on chromosome 11, i.e., 47276575 to 47276775.

[0256] For example, specific genetic variants associated with the OPRM1 gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs510769, rs3778151, rs9479757, rs558025, rs544093, and rs2281617. Specific intron regions associated with the OPRM1 gene can include nucleotide positions on chromosome 6, i.e., 154361919 to 154487521, and more specifically, positions on chromosome 6, i.e., 154361919 to 154362119, 154393580 to 154393780, 154411244 to 154411444, 154441865 to 154442065, 154457393 to 154457593, and 154487321 to 154487521.

[0257] For example, specific genetic variants associated with the PPARA gene can include variants located within the exons of the gene, as well as variants identified by rs IDs, i.e., rs4253728, rs4823613, rs9626730, rs135550, and rs4253778. Specific intronic regions associated with the PPARA gene can include nucleotide positions on chromosome 22, i.e., 46609967 to 46630734, and more specifically, positions on chromosome 22, i.e., 46609967 to 46610167, 46598207 to 46598407, 46562083 to 46562283, 46553134 to 46553334, and 46630534 to 46630734.

[0258] For example, specific genetic variants associated with the PPARG gene can include variants located within the exons of the gene and the variant identified by rs ID, i.e., rs7627605. A specific intronic region associated with the PPARG gene can include nucleotide positions 12399170 to 12399370 on chromosome 3.

[0259] For example, specific genetic variants associated with the PTPRD gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs1500318, rs10977204, rs77455504, rs1333111, rs10816196, rs12004295, rs424301, rs439467, and rs1535661. Specific intron regions associated with the PTPRD gene can include nucleotide positions on chromosome 9, i.e., 8361345 to 10369468, and more specifically, positions on chromosome 9, i.e., 8361345 to 8361545, 8561442 to 8561642, 8821581 to 8821781, 9416862 to 9417062, 9829154 to 9829354, 9926419 to 9926619, 10098443 to 10098643, 10102803 to 10103003, and 10369268 to 10369468.

[0260] For example, specific genetic variants associated with the SLC22A1 gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs461473, rs35854239, and rs622342. Specific intronic regions associated with the SLC22A1 gene can include nucleotide positions on chromosome 6, i.e., 160543462 to 160572966, and more specifically, positions on chromosome 6, i.e., 160543462 to 160543662, 160560808 to 160561008, and 160572766 to 160572966.

[0261] For example, specific genetic variants associated with the SLC47A1 gene can include variants located within the exons of the gene, as well as variants identified by rs ID, i.e., rs2289669. A specific intronic region associated with the SLC47A1 gene can include nucleotide positions 19463243 to 19463443 on chromosome 17.

[0262] For example, specific genetic variants associated with the SLC47A2 gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs12943590 and rs34834489. Specific intronic regions associated with the SLC47A2 gene can include nucleotide positions on chromosome 17, i.e., 19619898 to 19620364, and more specifically, positions on chromosome 17, i.e., 19619898 to 19620098 and 19620164 to 19620364.

[0263] For example, specific genetic variants associated with the SLC6A2 gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs3785143 and rs12708954. Specific intronic regions associated with the SLC6A2 gene can include nucleotide positions on chromosome 16, i.e., 55695006 to 55731699, and more specifically, positions on chromosome 16, i.e., 55695006 to 55695206 and 55731499 to 55731699.

[0264] For example, specific genetic variants associated with the SLCO1B1 gene can include variants located within the exons of the gene as well as variants identified by the rs IDs, i.e., rs2291073, rs4149036, rs77271279, rs4149032, rs11045821, rs11045872, rs4363657, rs4149081, rs11045879, and rs4149015. Specific intron regions associated with the SLCO1B1 gene can include nucleotide positions on chromosome 12, i.e., 21325714 to 21283422, and more specifically, positions on chromosome 12, i.e., 21325714 to 21325914, 21327640 to 21327840, 21329732 to 21329932, 21317691 to 21317891, 21332323 to 21332523, 21372244 to 21372444, 21368622 to 21368822, 21377921 to 21378121, 21382519 to 21382719, and 21283222 to 21283422.

[0265] For example, specific genetic variants associated with the TCF7L2 gene can include variants located within the exons of the gene as well as variants identified by the rs IDs: rs7917983, rs4132670, rs4506565, rs7903146, rs12243326, rs12255372, rs290487, and rs1056877. Particular intron regions associated with the TCF7L2 gene can include nucleotide positions on chromosome 10, i.e., 114732782 to 114925858, and more particularly, positions on chromosome 10, i.e., 114732782 to 114732982, 114767671 to 114767871, 114755941 to 114756141, 114758249 to 114758449, 114788715 to 114788915, 114808802 to 114809002, 114909631 to 114909831, and 114925658 to 114925858.

[0266] For example, specific genetic variants associated with the TPMT gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs9333570, rs1800584, rs12201199, rs3931660, rs2518463, and rs12529220. Specific intron regions associated with the TPMT gene can include nucleotide positions on chromosome 6, i.e., 18134021 to 18148347, and more specifically, positions on chromosome 6, i.e., 18134021 to 18134221, 18130912 to 18131112, 18139702 to 18139902, 18149005 to 18149205, 18143669 to 18143869, and 18148147 to 18148347.

[0267] For example, particular genetic variants associated with the TYMS gene can include variants located within the exons of the gene as well as variants identified by rs IDs, i.e., rs2847153 and rs151264360. Particular intronic regions associated with the TYMS gene can include nucleotide positions on chromosome 18, i.e., 661547 to 673544, and more specifically, positions on chromosome 18, i.e., 661547 to 661747 and 673344 to 673544.

[0268] For example, specific genetic variants associated with the UGT1A9 gene can be identified by their location within the exons of the gene and their rs IDs, i.e., rs2741048, rs2741047, rs4663871, rs4261716, rs7586110, rs3732218, rs3732219, rs1983023, rs45507691, rs3806596, rs3806597, rs2008595, rs10929302, rs2003569, rs60469444, rs34531096, rs7606344 8, rs4124874, rs3755319, rs11568318, rs11568316, rs1976391, rs4148327, rs873478, rs3213726, rs2302538, rs887829, rs34650714, rs8175347, rs10929303, rs1042640, rs8330, and rs34942353. Additional specific genetic variants associated with the UGT1A9 gene can include genetic variants at genomic locations Chr2:234581625, Chr2:234656479, Chr2:234675628, and Chr2:234675608.

[0269] Specific intronic regions associated with the UGT1A9 gene are located at nucleotide positions on chromosome 2, i.e., 234581648 to 234681724, and more specifically, at positions on chromosome 2, i.e., 234581648 to 234581848, 234581554 to 234581754, 234581487 to 234581687, 234581525 to 234581725, 234593017 to 234593217, and 234590427 to 234590627. , 234627204 to 234627404, 234627148 to 234627348, 234636922 to 234637122, 234637120 to 234637320, 234637607 to 234637807, 234637469 to 234637669, 234637092 to 234637292, 234665682 to 234665882, 234667837 to 234668037, 234652540 to 234652740, 234665491 to 234665691, 234656379 to 234656579, 234652542 to 234652742, 234665559 to 234665759, 234667482 to 234667682, 234665398 to 234665598, 234665437 to 234665637, 234665883 to 234666083, 234675726 to 234675926, 234675528 to 234675728, 234668770 to 23466897 0, 234675423 to 234675623, 234675508 to 234675708, 234676313 to 234676513, 234668470 to 234668670, 234675729 to 234675929, 234668781 to 234668981, 234681316 to 234681516, 234681444 to 234681644, 234681545 to 234681745, and 234681524 to 234681724.

[0270] For example, specific genetic variants associated with the UGT1A1 gene can include variants located within the exons of the gene as well as variants identified by the rs IDs: rs4148323, rs4148327, rs3213726, rs2302538, rs34650714, rs10929303, rs1042640, rs8330, and rs34942353. Additional specific genetic variants associated with the UGT1A1 gene can include genetic variants at genomic locations Chr2:234675628 and Chr2:234675608. Specific UGT1A1 alleles can include the UGT1A1 star alleles, *82 and *83.

[0271] Specific intron regions associated with the UGT1A1 gene are located at nucleotide positions on chromosome 2, i.e., 234669044 to 234681724, and more specifically, at positions on chromosome 2, i.e., 234669044 to 234669244, 234675726 to 234675926, 234675528 to 234675728, 234675423 to 234675623, 234675508 to 234675708, 234676313 to 234676513, 234675729 to 234675929, 234681316 to 234681516, 234681444 to 234681644, 234681545 to 234681745, and 234681524 to 234681724.

[0272] For example, specific genetic variants associated with the UMPS gene may include variants located within the exons of the gene and even the variant identified by rs ID, i.e., rs3772810. A specific intronic region associated with the UMPS gene may include nucleotide positions on chromosome 3, i.e., 124462859 to 124463059.

[0273] For example, specific genetic variants associated with the VKORC1 gene can include variants located within the exons of the gene as well as variants identified by the rs IDs: rs17886199, rs17884982, rs17884850, rs8050894, rs17708472, rs104894542, rs2359612, rs2884737, rs9934438, rs11540137, rs7294, rs7200749, rs13336384, rs13337470, rs72547528, and rs9923231. Specific intronic regions associated with the VKORC1 gene are located at nucleotide positions on chromosome 16, i.e., 31104347 to 31107789, and more specifically, at positions on chromosome 16, i.e., 31104347 to 31104547, 31103445 to 31103645, 31104302 to 31104502, 31104409 to 31104609, 31105253 to 31105453, 31102464 to 31102464 02664, 31103696 to 31103896, 31105454 to 31105654, 31104778 to 31104978, 31102224 to 31102424, 31102221 to 31102421, 31102489 to 31102689, 31105071 to 31105271, 31105292 to 31105492, 31102555 to 31102755, and 31107589 to 31107789.

[0274] The design of a panel for detecting the above-mentioned gene and variant list can be based on published research information, FDA-approved therapies, and / or independent analyses of the pharmacogenomic effects of several variants and / or alleles in drug treatments of any nature (the pharmacogenomic effects can persist across different medical fields, so the nature of the treatment cannot be limited to only one field or disease state). Current methods for gene sequencing can rely on the technology used to isolate several regions of interest in the genome, such as the genome of interest or the portion of the genome to be analyzed. In one embodiment, the region of interest can be isolated using one or more specific probes that bind to complementary sections of DNA / RNA in the sample. This detection is made possible by the probes included in the manufactured panel. A probe is a small stretch of DNA or RNA that serves as a starting point for DNA synthesis, allowing the detection of nucleic acid sequences complementary to the probe's sequence. The probe hybridizes (binds) to complementary nucleotides in the template or target DNA, amplifying the DNA and creating millions of copies of the DNA molecule. Each probe can be single-stranded DNA and is designed to match a specific fragment of the template DNA. This specificity arises from the fact that each DNA base can only pair with one other DNA base; i.e., adenine (A) pairs only with thymine (T) in DNA and uracil (U) in RNA, and guanine (G) pairs only with cytosine (C). For a copy to be made, the probe binds to the correct piece of DNA and the bases match. If a match occurs, DNA polymerase (the enzyme that copies DNA) can bind and amplify the DNA. If the probe does not match the DNA sequence, DNA polymerase will not bind and no copy will be made. To precisely obtain the correct order of A, G, T, and C, a panel can be designed and ordered to include probes containing the desired sequence of nucleotides.

[0275] Probes may be selected to target each of the identified genes and / or variants, and if the panel of probes is not sufficient to target each of the identified genes or variants, one or more spike-in probes targeting additional genes and / or variants may be added to the panel to supplement the panel's detection capabilities. A spike-in is a target probe that can be added to a probe panel to target additional genes or variants for detection in sequencing.

[0276] Panel design is an iterative process in which a base panel is selected, spike-ins that complement the panel are identified to enable detection of additional target genes and / or variants, probes are manufactured according to the panel and additional spike-ins, different concentrations of probes are optimally selected during titration testing by using different concentrations of each probe to identify the amount that produces the most reliable detection for each variant, gene sequencing runs are performed on test samples with known sequences to confirm the accuracy of the panel, and results obtained by the panel are compared to the known sequences to confirm that the panel is optimized to detect the intended target genes and variants.

[0277] Furthermore, some NGS panels may only include probes for detecting exon regions (coding regions) of DNA or RNA. It may be advantageous to expand the base panel to generate sequencing results for intronic regions and identify other biomarkers within the human genome for both research and pharmacogenomic analysis. A spike-in may be added to the panel, thereby targeting at least one intronic region, to address this deficiency.

[0278] FIG. 10 illustrates another embodiment of a patient report 1000. Specifically, FIG. 10 illustrates a molecular results portion of the patient report 1000 associated with a patient. The molecular results portion can include a pharmacogenomic results portion that includes a list of genes 1004, some of which include metabolic genes, and a list of phenotypes 1008 of the list of genes 1004. The list of phenotypes 1008 can include poor metabolizer, intermediate metabolizer, normal metabolizer, extensive metabolizer, ultrarapid metabolizer, increased sensitivity, and / or increased risk phenotypes based on the molecular data associated with the patient. The genes 1004 can be displayed due to their involvement in the metabolism, pharmacokinetics, and / or immunogenicity of a selected drug.

[0279] 10 and further referring to FIG. 11, the patient report 1000 can include a classification section 1012 that includes information about different classifications (e.g., gene-drug classifications) that can be included in the report. In some embodiments, the classifications can include standard administration, dosing considerations, additional risks to consider, and / or contraindications.

[0280] The patient report 1000 may also include information regarding one or more medication types. In some embodiments, medication types may include antidepressants, antipsychotics, anticonvulsants, antianxiety medications, mood stabilizers, antimanics, hypnotics, VMAT2 inhibitors, ADHS medications, and / or other medication types that do not fall into one of the previous categories. Within each medication type, the patient report 1000 may further organize medications by medication subtype. As shown, a number of selective serotonin reuptake inhibitors (SSRIs) 1016 may be included in the antidepressant portion 1020 included in the patient report 1000.

[0281] The patient report 1000 may order medications by classification type. Drugs with a single medication classification may be listed first within each medication type and / or subtype. For example, an SSRI 1016 may include a medication 1024 classified as a standard medication. As shown, vilazodone is classified as a standard medication for the patient. The medication 1024 (e.g., vilazodone) may be listed with a link 1032 (e.g., a hyperlink) to a source document and / or website with information about the gene and / or phenotype 1028 (e.g., CYP3A4 and / or normal metabolizer) associated with the medication and / or the medication classification.

[0282] For drugs with conflicting evidence 1036 (e.g., citalopram), each drug classification may be listed along with a link 1044 (e.g., a hyperlink) to the gene and / or phenotype 1040 associated with the drug (e.g., CYP2C19 and / or normal metabolizer), and / or source document and / or website with information about the drug classification.

[0283] In some embodiments, for any drug associated with an "increased risk phenotype," the patient report 1000 may include supplemental information 1048 regarding the increased risk and / or one or more links 1052 (e.g., hyperlinks) to source documents and / or websites having information regarding the increased risk.

[0284] 10-11 and further to FIG. 12 and FIG. 13, the patient report 1000 can include multiple SNRIs 1056 that can be included in the antidepressant portion 1020. Referring now to FIG. 10-13 and further to FIG. 14, the patient report 1000 can include an antipsychotic portion 1060. Referring now to FIG. 10-14 and further to FIG. 15, the patient report 1000 can include an anticonvulsant portion 1064. Referring now to FIG. 10-15 and further to FIG. 16, the patient report 1000 can include an anxiolytic portion 1068 and / or a mood stabilizer portion 1072. Referring now to FIG. 10-16 and further to FIG. 17, the patient report 1000 can include an antimanic portion 1076, a hypnotic portion 1080, a VMAT2 inhibitor portion 1084, and / or an ADHD medication portion 1088. 10-17 and further to FIG. 18, the patient report 1000 can include an other medication section 1092.

[0285] 19, an exemplary process 1900 for generating treatment information for a patient diagnosed with a psychosis is shown. In some embodiments, process 1900 may be stored on a non-transitory computer-readable medium. In some embodiments, process 1900 may be stored as executable instructions in a non-transitory computer-readable medium (e.g., at least one memory) and executed by at least one processor coupled to the computer-readable medium. In some embodiments, process 1900 may be implemented in system 10 of FIG. 1.

[0286] At 1904, process 1900 may receive molecular data associated with the patient. In some embodiments, the patient may be diagnosed with at least one psychiatric illness (e.g., depression) as described above. In some embodiments, the molecular data may include a plurality of nucleic acid sequences. At least some of the plurality of nucleic acid sequences may be associated with metabolism-related genes. In some embodiments, the molecular data may be generated based on a multigene panel sequencing reaction on a sample from the patient. In some embodiments, the molecular data may include a plurality of nucleic acid sequences obtained from whole exome sequence data, mass array data, sequence data from one or more introns associated with metabolism-related genes, and / or sequence data from one or more promoter regions associated with metabolism-related genes.

[0287] In some embodiments, process 1900 can align the molecular data to a human reference sequence at 1904. In some embodiments, process 1900 can receive raw molecular data (e.g., stored in BCL, FASTA, and / or FASTQ file formats) and align the raw molecular data to a human reference sequence. In some embodiments, process 1900 can generate aligned molecular data and save the data in SAM and / or BAM file formats. In some embodiments, process 1900 can receive molecular data including pre-aligned molecular data.

[0288] At 1908, process 1900 can receive clinical data associated with the patient. The clinical data can include a listing of prior medications taken by the patient and / or a listing of one or more diagnoses. The listing of prior medications can include one or more medication names, medication dosages, and / or patient response to the medications. The one or more diagnoses can include a recent set of diagnoses and / or a list of prior diagnoses the patient has had in the past. In some embodiments, the clinical activity described in the second set of clinical data can include one or more of prescribed medications, medication dosages, patient compliance, and patient outcomes after taking the prescribed medications.

[0289] At 1912, process 1900 can generate a report based on the molecular and clinical data associated with the patient. Process 1900 can generate the report using a therapeutic engine. The therapeutic engine can include a knowledge database, such as KDB40 described above. The knowledge database can include structured data on drug-gene interactions, including pharmacogenetic interactions, and precision medicine findings reported in the psychiatric and basic science literature. The knowledge database can include clinically annotated pharmacogenomic classifications for key pharmacodynamic and pharmacokinetic outcomes related to the treatment of depression and other psychiatric disorders. The knowledge database of treatment and prognostic evidence, including treatment response and resistance information, can include information from a combination of external sources, such as CPIC guidelines, FDA labeling, PharmGKB, or other proprietary databases publicly available or available by subscription or upon request, as well as literature sources or novel findings obtained from analyzing clinical and genetic, genomic, or other omic information repositories. The knowledge database can be maintained over time by individuals with experience, education, and training in the relevant field. In some embodiments, clinical actionability entries in the knowledge database are structured by both (1) the disease and / or drug-gene interaction to which the evidence applies, and (2) the level or strength of the evidence.

[0290] The knowledge database can include data related to interactions between one or more specific drugs and one or more nucleic acid sequences associated with drug metabolism, primary drug metabolic pathway data, and a cohort dataset previously derived from a cohort of psychiatric subjects, the cohort dataset including one or more drugs used in treatment, pretreatment diagnosis, and / or treatment outcomes for patients in the cohort. In some embodiments, the cohort data is not derived from clinical trial data. In some embodiments, the cohort can include patients diagnosed with multiple psychiatric disorders. In some embodiments, the knowledge database can include a second cohort dataset derived at a different time than the first cohort set. The second cohort dataset can include information from at least one patient in a previously generated cohort. In some embodiments, the knowledge database can include a private cohort updated using treatment outcome information from a large number of patients. The knowledge database can store a portion of the clinical data from at least some of the patients and the second set of clinical data. The clinical data can be, for example, de-identified, stored in a limited dataset, anonymized, or pseudo-anonymized.

[0291] The therapeutic engine can identify relevant drug-gene interactions based on the molecular data and the clinical data. More specifically, in some embodiments, process 1900 can identify relevant drug-gene interactions based at least in part on nucleic acid sequences included in the molecular data. In some embodiments, the therapeutic engine can identify resources related to the phenotype included in the molecular data and one or more of the psychiatric diseases with which the patient has been diagnosed. For example, the therapeutic engine can match the patient with documents related to studies on patients with the same diagnosis and phenotype as the patient. Medications given to the patient in the study can also be matched with the patient. In some embodiments, the knowledge database can include multiple drug-gene pairs, each including a drug and a phenotype and associated with a disease. Process 1900 can search the knowledge database for drug-gene pairs that have the same phenotype as the patient and are associated with the same psychiatric disease as the patient.

[0292] In some embodiments, process 1900 can compile a list of all drugs included in the drug-gene pair. The list of drugs can be associated with at least a portion of the nucleic acid sequences included in the molecular data. Each drug in the list of drugs can be associated with a classification. As described above, the classification can include standard dosing, dose adjustments and / or administration considerations, contraindications, and / or additional risks to consider. The classification can be predetermined based on the results of a study associated with the drug-gene interaction in which the drug is included. For example, if a study determines that drug X is effective for disease Y in patients with phenotype Z, the drug can be classified as standard dosing. Some studies can provide information about the most effective dose given other factors, such as other diagnoses and / or patient information. The therapeutic engine can determine what the recommended dosage is based on clinical data associated with the patient.

[0293] In some embodiments, the classification may be generated based on a listing of prior medications. In some embodiments, the treatment engine can determine whether some drugs are more effective and / or pose risks to the patient based on what medications the patient has taken, what dosages were used, and / or what the outcomes of prior treatments have been. Some studies may be associated with particular cohorts of patients for whom some drugs were ineffective in treating a given psychiatric illness and / or for which some drugs pose potential risks (e.g., of side effects).

[0294] Conflicting evidence may result in some drugs being classified as "contraindicated." For example, the therapeutic engine may find a first source recommending drug X for phenotype Y for a first gene, while another source recommends not using drug X to treat phenotype Z for the first gene and / or another gene. The therapeutic engine may output source documents and / or links to source documents for any sources relevant to the patient. In particular, the sources used to support the drug-gene interaction and / or classification may be included in the report.

[0295] In some embodiments, the therapeutic engine can output likely side effects of at least one drug included in the list of drugs. In some embodiments, the patient is diagnosed with depression and the therapeutic engine can determine the subtype of depression the patient has. In some embodiments, the therapeutic engine can identify drug resistance associated with the patient based on molecular and / or clinical data.

[0296] In some embodiments, the process 1900 can determine the list of drugs based on a listing of prior medications the patient has received. In some embodiments, the therapeutic engine can identify research into alternative drugs to take after other drugs have proven ineffective for some patients, such as patients with certain phenotypes.

[0297] In some embodiments, the therapeutic engine can generate a list of drugs based on time series data associated with a cohort of patients similar to the patient. Cohorts can be generated as described above. Using time series information to generate a list of drugs can help diagnose patients based on what drugs or drug doses have proven effective for similar patients in the past. For example, a cohort of patients with one or more of the same phenotypes as the patient can be created. The therapeutic engine can then determine what drugs may be effective for the patient based on what drugs were effective for the cohort. For example, the therapeutic engine can determine that drug Z was effective 80% of the time in the cohort when drug X was ineffective for patients with phenotype Y. Thus, cohorts can provide real-world information about patient diagnoses that is not available in public clinical documentation and / or research. It is understood that cohorts can be created based on other factors, such as phenotype and / or genotype, therapy, geographic location, poverty level, gender, insurance status, and / or a combination of factors.

[0298] The process 1900 can generate a report based on any of the information received from the treatment engine, such as molecular and / or clinical information associated with the patient, such as drug-gene interactions, drug classifications, dosages, potential side effects, depression subtypes, source documents, links to source documents (e.g., hyperlinks to websites or source documents), and / or other suitable information, as well as phenotypes and / or medication history. In some embodiments, the report can include at least a portion of the patient report 1000 of FIGS. 10-18.

[0299] At 1916, process 1900 may cause the report to be output. In some embodiments, process 1900 may cause the report to be output to at least one of a display (e.g., display device 16 of FIG. 1 ) or memory. In some embodiments, process 1900 may cause the report to be presented to a user (e.g., using display device 16). In some embodiments, the user may be a medical professional treating the patient. Then, in some embodiments, process 1900 may end.

[0300] At 1920, process 1900 may receive a second set of clinical data associated with the patient. The second set of clinical data may include a listing of prior medications taken by the patient and / or a listing of one or more diagnoses. Importantly, the second set of clinical data may include the clinical data received at 1908 along with updated information associated with the patient. Specifically, the second set of clinical data may include the patient's clinical activity subsequent to the presentation of the report. In some embodiments, the second set of clinical data may include one or more of prescribed medications, medication dosages, patient compliance, and patient outcomes after taking the prescribed medications. The second set of clinical data may aid in treating the patient with different medications and / or dosages. The one or more diagnoses may include a recent set of diagnoses and / or a list of prior diagnoses the patient has previously received. In some embodiments, the clinical activity described in the second set of clinical data may include one or more of prescribed medications, medication dosages, patient compliance, and patient outcomes after taking the prescribed medications. In some embodiments, at 1920, the process 1900 can update the therapy engine, including the knowledge database, based on the second set of clinical data.

[0301] At 1924, process 1900 can generate a second report based on the second set of clinical data. Process 1900 may generate the second report in a manner similar to the report generated at 1912, albeit with updated clinical information for the patient, which can help find an effective treatment plan for the patient. In some embodiments, process 1900 can perform at least a portion of 1912 at 1924.

[0302] At 1928, process 1900 may cause the second report to be output. In some embodiments, process 1900 may cause the second report to be output to at least one of a display (e.g., display device 16 of FIG. 1 ) or memory. In some embodiments, process 1900 may cause the second report to be presented to a user (e.g., using display device 16). In some embodiments, the user may be a medical professional treating the patient. Then, in some embodiments, process 1900 may end. In some embodiments, process 1900 may proceed to 1920.

[0303] All references cited herein are incorporated by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes.

[0304] The present invention can be implemented as a computer program product comprising a computer program mechanism embedded in a non-transitory computer-readable storage medium, and these program modules can be stored on a CD-ROM, DVD, magnetic disk storage product, USB key, or any other non-transitory computer-readable data or program storage product.

[0305] Many modifications and variations of this invention can be made without departing from the spirit and scope of the invention, as will be apparent to those skilled in the art. The specific embodiments described herein are provided by way of example only. The embodiments have been chosen and described to best explain the principles of the technology and its practical application, so that those skilled in the art can best utilize the invention and various embodiments, with various modifications as may be suited to the particular uses contemplated. The present invention is limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled.

[0306] Table 1 JPEG2025176712000003.jpg243170JPEG2025176712000004.jpg245170JPEG2025176712000005.jpg243170JPEG2025176712000006.jpg246170JPEG2025176712000007.jpg244170JPEG2025176712000008.jpg243170JPEG2025176712000009.jpg243170JPEG2025176712000010.jpg243170JPEG2025176712000011.jpg245170JPEG2025176712000012.jpg243170JPEG2025176712000013.jpg243170JPEG2025176712000014.jpg243170JPEG2025176712000015.jpg244170JPEG2025176712000016.jpg245170JPEG2025176712000017.jpg244170JPEG2025176712000018.jpg246170JPEG2025176712000019.jpg244170JPEG2025176712000020.jpg244170JPEG2025176712000021.jpg246170JPEG2025176712000022.jpg244170JPEG2025176712000023.jpg245170JPEG2025176712000024.jpg244170JPEG2025176712000025.jpg243170JPEG2025176712000026.jpg244170JPEG2025176712000027.jpg244170JPEG2025176712000028.jpg244170JPEG2025176712000029.jpg245170JPEG2025176712000030.jpg244170JPEG2025176712000031.jpg244170JPEG2025176712000032.jpg243170JPEG2025176712000033.jpg244170JPEG2025176712000034.jpg243170JPEG2025176712000035.jpg243170JPEG2025176712000036.jpg243170JPEG2025176712000037.jpg243170JPEG2025176712000038.jpg243170JPEG2025176712000039.jpg243170JPEG2025176712000040.jpg243170JPEG2025176712000041.jpg245170JPEG2025176712000042.jpg243170JPEG2025176712000043.jpg243170JPEG2025176712000044.jpg245170JPEG2025176712000045.jpg243170JPEG2025176712000046.jpg246170JPEG2025176712000047.jpg243170JPEG2025176712000048.jpg243170JPEG2025176712000049.jpg243170JPEG2025176712000050.jpg243170JPEG2025176712000051.jpg243170JPEG2025176712000052.jpg243170JPEG2025176712000053.jpg243170JPEG2025176712000054.jpg243170JPEG2025176712000055.jpg243170JPEG2025176712000056.jpg243170JPEG2025176712000057.jpg243170JPEG2025176712000058.jpg244170JPEG2025176712000059.jpg243170JPEG2025176712000060.jpg243170JPEG2025176712000061.jpg243170JPEG2025176712000062.jpg243170JPEG2025176712000063.jpg243170JPEG2025176712000064.jpg243170JPEG2025176712000065.jpg244170JPEG2025176712000066.jpg243170JPEG2025176712000067.jpg243170JPEG2025176712000068.jpg243170JPEG2025176712000069.jpg243170JPEG2025176712000070.jpg243170JPEG2025176712000071.jpg243170JPEG2025176712000072.jpg24 4170JPEG2025176712000073.jpg246170JPEG2025176712000074.jpg243170JPEG2025176712000075.jpg243170JPEG2025176712000076.jpg190170. [Explanation of symbols]

[0307] 10 Systems 12 Provider 12a Doctor 12c Other Experts 14 patients 16 Display Devices 18. Communication Networks 20 servers 22 Patient Data 24 Clinical Records 26 Test Results 28 Image data 32 databases 34 Databases 36 Analytics Module 40 Knowledge Database 42 databases 44 External Databases 46 Third-Party Databases 48 Internal Database 50 Graphical User Interface (GUI) 52 Patient Summary Table 54 Account Name 56 Patient Identifiers 58 Menu 60 Summary section 62 Diagnostic Tiles 64 Diagnostic Timeline 68 Therapy Tile 70 Summarized "whole treatment" section 74 predicted reactive moieties 76 Molecular Outline Tiles 78 Image Overview Tiles 90 Molecular Report 92 Change List 94 selectable therapy icons 96 More Information 98 variants 100 Human Leukocyte Antigen (HLA) Typing 102 Treatment Implications 104 Diagnostic Implications 106 Image Data 108 images 112 Treatment Implications 114 Therapy part 116 Intervention Timeline 118 Data 120 Potential Therapy Tile 122 Display Options 124 Symptom Data 126 measurement data 130 Therapy Details 132 Cohort Details 134 Radar Plot 136 Therapy Data 138 Menu 140 Radar Plot 146 clusters 150 Cluster Analysis 800 patient reports 810 First Part 830 Third Part 840 Fourth Part 850 Fifth Part 860 Sixth Part 870 Seventh Part 910 Summary 920 Legend 930 Pharmacogenomics Results Details 940 Secondary findings details 1000 Patient Reports List of 1004 genes List of 1008 phenotypes 1012 Classification part 1016 Selective serotonin reuptake inhibitors (SSRIs) 1020 Antidepressant section 1024 Drugs 1028 Phenotype 1032 Links 1036 Conflicting Evidence 1040 Phenotype 1044 Links 1048 Supplementary Information 1052 Links 1056 SNRI 1060 Antipsychotic drug portion 1064 Anticonvulsant portion 1068 Anti-anxiety drug portion 1072 Mood stabilizer portion 1076 Antimanic drug portion 1080 Hypnotic Part 1084 VMAT2 inhibitor moiety 1088 ADHD medication portion 1092 Other drug parts 1900 processes

Claims

1. 1. A method for generating treatment information for a patient diagnosed with at least one psychiatric illness, comprising: a computer system having one or more processors and a memory storing one or more programs for execution by the one or more processors; a. obtaining molecular data from a multi-gene panel sequencing reaction on a sample from the patient, the molecular data comprising a plurality of nucleic acid sequences obtained from whole exome sequence data, mass array data, sequence data from one or more introns associated with metabolism-related genes, and sequence data from one or more promoter regions associated with the metabolism-related genes; b. aligning the molecular data to a human reference sequence; c. providing a first set of clinical data associated with the patient, the first set of clinical data including a listing of prior therapeutic medications and a listing of the one or more diagnoses; d. generating a first report from a therapeutic engine based on the first set of molecular data and clinical data, The report includes, for each one of the at least a portion of the plurality of nucleic acid sequences in the patient's molecular data: (1) in the laboratory results section of the report, a phenotype associated with the nucleic acid sequence; and (2) providing, in a supplemental section of the report, a listing of one or more drugs associated with the nucleic acid sequence, and a classification for each drug in the listing, the listing of the one or more drugs being determined at least in part by the listing of prior therapeutic drugs; e. causing a user to present said report; f. obtaining a second set of clinical data associated with the patient, the second set of clinical data describing clinical activity of the patient subsequent to presentation of the report; g. updating said therapy engine with at least a portion of said second set of clinical data; A method comprising:

2. 10. The method of claim 1, wherein the classification relates to one or more of drug administration, drug risk, and contraindications.

3. 10. The method of claim 1, wherein the clinical activity described in the second set of clinical data includes one or more of prescribed medications, medication dosages, patient compliance, and patient outcomes after taking the prescribed medications.

4. 10. The method of claim 1, wherein the patient has been diagnosed with multiple psychiatric illnesses.

5. The therapeutic engine comprises a knowledge database, the knowledge database comprising i. data relating to the interaction between a particular drug or drugs and one or more nucleic acid sequences associated with drug metabolism; ii. Primary drug metabolism pathway data; and iii. a first cohort dataset derived at time 1 from a cohort of psychiatric subjects, the first cohort dataset including one or more medications used in treatment, diagnoses prior to the treatment, and treatment outcomes; iv. Drug information data collected from one or more of the following sources: scientific publications, the U.S. Food and Drug Administration (FDA), the Clinical Pharmacogenomics Practice Association (CPIC), the Netherlands Pharmacogenomics Working Group (DPWG), Pharmacogenomics Knowledge Base Reviews, and the Psychotropic Drug Screening Program Ki database; 2. The method of claim 1, comprising:

6. 6. The method of claim 5, wherein the cohort dataset is not derived from clinical trial data.

7. 6. The method of claim 5, wherein at least a portion of the psychiatric subjects in the cohort have been diagnosed with multiple psychiatric illnesses.

8. 6. The method of claim 5, wherein the knowledge database further comprises a second cohort data set derived at time 2, the second cohort data set comprising information from at least one of the first cohort subjects.

9. 9. The method of claim 8, wherein the knowledge database further comprises an Nth cohort dataset derived at time N, the Nth cohort dataset including information from at least one previous cohort subject.

10. 10. The method of claim 9, further comprising providing an Nth set of clinical data associated with the patient, the Nth set of clinical data being acquired at a time after an (N-1)th clinical data set is acquired.

11. The method of claim 10 , wherein each of the clinical datasets describes the patient's clinical activity since the presentation of a previous report.

12. 12. The method of claim 11, further comprising updating the therapy engine with at least a portion of the N clinical datasets.

13. The method of claim 1 , wherein the report further provides supporting information for the classification.

14. 14. The method of claim 13, wherein the report further provides a hyperlink to a source document or website having information about the drug classification.

15. 10. The method of claim 1, wherein the report further provides a listing of drugs that are associated with the patient's diagnosis but that do not have a known nucleic acid association.

16. 11. The method of claim 10, wherein the clinical activity described in any of the N clinical datasets includes one or more of prescribed medications, medication dosages, patient compliance, and patient outcomes after taking the prescribed medications.

17. The method of claim 1 , further comprising generating a second report based on input from a second clinical dataset.

18. 10. The method of claim 1, wherein the listing of prior therapeutic agents includes at least one drug dosage.

19. 10. The method of claim 1, wherein the listing of prior therapeutic agents includes at least one patient response to the agent.

20. The method of claim 1 , wherein the treatment engine identifies possible side effects of the listed drugs and provides the side effect information in a report.

21. The method of claim 1 , wherein the therapeutic engine identifies a recommended dosage for each drug included in the listing of one or more drugs and provides that dosage information in the report.

22. The method of claim 1 , wherein the therapeutic engine identifies a next potential drug recommendation by excluding from the report at least one drug included in the listing of prior therapeutic drugs.

23. 10. The method of claim 1, wherein the treatment engine comprises a classifier for identifying a subtype of depression, the psychiatric illness for which the patient has been diagnosed is depression, and the subtype of depression is listed in the report.

24. The method of claim 1 , wherein the therapy engine comprises a classifier for identifying drug resistance, and the drug resistance is listed in the report.

25. 10. The method of claim 1, wherein the listing of the one or more drugs is determined, at least in part, based on at least one diagnosis.

26. 1. A system for generating information regarding treatment for a patient diagnosed with a psychosis, comprising: a. at least one memory; b. at least one processor coupled to said at least one memory; The system causes the at least one processor to execute instructions stored in the at least one memory, i. obtaining molecular data from a multi-gene panel sequencing reaction on a sample from the patient, the molecular data comprising a plurality of nucleic acid sequences obtained from whole exome sequence data, mass array data, sequence data from one or more introns associated with metabolism-related genes, and sequence data from one or more promoter regions associated with the metabolism-related genes; ii. aligning the molecular data to a human reference sequence; iii. providing a first set of clinical data associated with the patient, the first set of clinical data including a listing of prior therapeutic medications and a listing of the one or more diagnoses; iv. generating a first report from a therapeutic engine based on the first set of molecular data and clinical data; generating the report, for each one of at least a portion of the plurality of nucleic acid sequences in the patient's molecular data, providing: (1) a phenotype associated with the nucleic acid sequence; (2) a listing of one or more drugs associated with the nucleic acid sequence; and (3) a classification for each drug in the listing, the listing of the one or more drugs being determined at least in part by the listing of prior therapeutic drugs; v. Having the user submit said report; vi. obtaining a second set of clinical data associated with the patient, the second set of clinical data describing clinical activity of the patient subsequent to the presentation of the report; vii. updating said therapy engine with at least a portion of said second set of clinical data; A system configured to:

27. 27. The system of claim 26, wherein the clinical activity described in the second set of clinical data includes one or more of prescribed medications, medication dosages, patient compliance, and patient outcomes after taking the prescribed medications.

28. 27. The system of claim 26, wherein the cohort dataset is not derived from clinical trial data.

29. 27. The system of claim 26, wherein the subject has been diagnosed with multiple psychiatric illnesses.

30. 27. The system of claim 26, wherein at least a portion of the psychiatric subjects in the cohort have been diagnosed with multiple psychiatric illnesses.

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