JP2022543240A - Data-based mental illness research and treatment systems and methods - Google Patents

Abstract

Disclosed herein is a system for personalized depression treatment. The system includes a server configured to communicate with existing healthcare resources and receive patient data corresponding to a patient, the server comprising an analysis module. The system further comprises 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 in communication 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 personalized depression treatment and personalized depression prediction based on empirical patient outcomes and patient data.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of US 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 a compact disc or as a text file via the office electronic filing system (efs-web).

This application contains tables that have been submitted in ASCII format via EFS-web and are hereby incorporated by reference in their entirety. Said ASCII copy, created on August 2, 2019, is named TABLE-1-List-of-Genes.txt and is 147,138 bytes in size.

The present invention acquires and employs data relating to clinical, physical, and genomic patient characteristics, as well as diagnosis, treatment, and therapeutic efficacy, to provide benefits to health care providers, researchers, and other interested parties. Systems and methods for providing stakeholders with a suite of tools that enable them to make clinical decisions, provide new psychiatric state-treatment-outcome insights, and improve overall patient care. It is about.

Over the past 40 years, more than 50 FDA-approved antipsychotics and antidepressants have become available, yet patients still undergo a trial-and-error approach to finding successful treatments. Despite the abundance of drugs available and clinical trials comparing efficacy between these commonly used antidepressants, more than 70% of patients still Unresponsive to initial therapy, 10 to 30% of patients do not respond to medication at all. Knowledge about treatment outcomes is often based on empirical data over long periods of time, decades or even longer, over which physicians and/or researchers have had many different experiences. It records treatment results for patients and reviews those results to identify generally successful disease-specific treatments. Researchers and physicians administer drugs or treat diseases in some other way to patients, observe the results, and if the results are favorable, use the therapy again for similar diseases. If treatment results are poor, they do not prescribe relevant treatment for the next similar illness encountered, instead trying some other treatment. Treatment results are sometimes published in medical journals and/or periodicals so that many physicians can benefit from the insights and treatment results of the treating physician.

In the treatment of at least some psychiatric disorders, such as depression, treatment and treatment 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, suggesting a causal link between patient condition and different treatment outcomes. There is no For example, two women of the same age and in good health who are diagnosed with the same depressive state (similar physical and psychological symptoms, BDI-II score, PHQ-9 score, etc.) There is Here, the first woman responds well to a depression treatment regimen, has few side effects, and may improve quickly, whereas the second woman, given the same treatment regimen, does not respond well to any number of symptoms. They suffer from some serious side effects and may never get relief from the initial depressive symptoms. Different treatment outcomes for seemingly similar depressive conditions adversely affect efforts to generate treatment and outcome datasets and prescribing activities.

Researchers and physicians, recognizing that in some cases virtually the same treatment can lead to different outcomes in different patients, base disease treatment on the particular patient's mental illness, such as depression. often create additional guidelines on how to optimize For example, a first treatment may be best for a young, relatively healthy woman with depression, while an older, weaker male with the same diagnosis of depression may have a second treatment with fewer side effects. A treatment of 2 may be optimal.

In these cases, unfortunately, there are factors (e.g., depression factors) involved in psychiatric disorders that have a causal relationship to specific treatment outcomes that are simply unknown at this time and are therefore not available at this time. It cannot be used to optimize specific patient treatment. Moreover, over 70% of patients do not respond to the first course of drug therapy. More than 43,800,000 American patients have mental health-related diagnoses, and there is a need to learn from first line medications that are over 70% ineffective to improve therapy selection on initial criteria.

Genetic testing has been investigated to some extent as another psychiatric disease (eg, depression) factor (such as different patient conditions) that may affect psychiatric disease (eg, depression, etc.) treatment efficacy. It is believed that there are many possible causal links between DNA and therapeutic outcomes that have yet to be discovered. One of the problems with genetic testing is that the tests are expensive, and in many cases the costs can be prohibitive--often insurance companies refuse to cover the costs.

Another problem with genetic testing for treatment regimens is that when genetic testing is performed, there is often no clear link between the resulting genetic factors and treatment efficacy. In other words, in most cases it is not fully known how the results of genetic testing can be used to prescribe better treatment regimens for patients, so there is little evidence of relevance to genetic testing in specific cases. The additional cost to do so cannot be justified. Thus, although promising, genetic testing as part of psychiatric treatment regimens has been minimal or sporadic at best.

For some psychiatric disorders (e.g., some depressive states), treatments and their associated outcomes are generally consistent and acceptable (minimum side effects or at least understood side effects, etc.). In other cases, however, treatment outcomes associated with other depressive conditions are unsatisfactory and unavailable for several reasons.

First, there are many factors that influence treatment efficacy, including many different types of patient conditions, and different conditions may favor some treatments for one patient more than others, or for others. It has a higher effect on one patient as opposed to a patient. It is not easy to pinpoint specific patient conditions that are or may be causal to treatment outcomes, and some causal conditions may never be evaluated and captured.

Second, for most depressive conditions, there are several different treatment options, and each general option can be customized for a specific psychiatric disorder (eg, a specific depressive condition) and patient condition set. Due to the large number of treatment modalities and customization options, there are often no clear standardized guidelines on how to capture this type of information, making it difficult to normalize and accurately capture treatment and outcome data. is.

Third, in most cases patient treatments and outcomes are not released for public consumption and are therefore not simple to be combined with other treatment and outcome data to provide an over-the-top data set. is not accessible to In this regard, many physicians see treatment results that are within the expected range of efficacy and may conclude that those results cannot be added to the overall depression treatment knowledge base, and those results are not published. There are many things. The problem here is that the expected range of effects may be large (20% of patients experienced marked relief of symptoms, 40% of patients experienced moderate relief of symptoms, 20% of patients experienced and 20% do not respond to the treatment regimen), thus all treatment outcomes are within the expected effect range, and nuances in treatment outcome are simply lost.

Fourth, there is currently no easy way to build and complement the many existing disease-treatment-outcome databases. As such, as more data are generated, new data and related results are added to existing databases as evidence of treatment efficacy, or efficacy cannot be rigorously examined. Thus, for example, if 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 data imputation over time, the consequences of treatment and outcomes cannot be examined, confirmed or investigated.

Fifth, the knowledge base around depression treatment is expanding with clinical trials in various stages around the world, so even if a physician's knowledge were current today, it would be within a few months. becomes outdated. Thousands of papers related to mental illness broadly, or depressive states specifically, are published each year, many of which are lengthy, informative, and particularly limited to absorbing new material and information. It is difficult for busy doctors with limited time to read and understand articles. Narrowing down the publications to those relevant to a particular physician's practice is a time consuming and often inexact task.

Sixth, in most cases, there is no clear incentive for physicians to memorize a complete set of treatment and outcome data, and indeed the time required to memorize such data greatly reduces the usefulness of that data. It can act as an obstacle to collecting in perfect form. For this reason, prescribing and treating physicians know what they know, and without getting anything in return (new insights, better prescribing treatment tools, etc.) For example, it can be perceived as a burden for physicians to laboriously capture a complete set of depression), treatment, and outcome data.

In addition to the problems associated with collecting and storing treatment and outcome data sets, there are also problems with digesting or consuming the recorded data to generate useful conclusions. For example, documented treatment and outcome data for psychiatric disorders (eg, depression) are often incomplete. In most cases, physicians are not researchers and do not follow well-defined research procedures to track all aspects of depression, treatment, and outcome. As a result, the data recorded are often associated with specific patient conditions that may be of current or future interest, why certain treatments were chosen and others rejected, specific outcomes, etc. missing important information. In many cases where a causal relationship exists between depressive factors and treatment outcomes, if the causative factors cannot be identified and recorded by the physician, those outcomes cannot be linked to existing causal datasets. , and therefore simply cannot be consumed and added to the overall depression knowledge dataset in a meaningful way.

Another obstacle to digesting the collected data is that physicians often use mental illness (e.g., depression), treatment, and outcome data to identify nuanced insights and Capturing in a form that makes it difficult, if not impossible, to process the collected information so that the data can be normalized to draw stronger conclusions and used with other data from similar patient care That is. For example, many physicians use pen and paper to track patient care and/or use personal shorthand or abbreviations to track different depressive state descriptions, patient conditions, treatments, outcomes, and even conclusions. prefer to use It is difficult at best to use software to glean precise information from handwritten notes, but the software may be able to generate personal abbreviations and shorthand representations of information that cannot be simply identified in the sense intended by the physician. The task becomes even more difficult when records are involved.

To be useful, psychiatric disease, treatment, and outcome data and conclusions based on them must be made accessible to physicians, researchers, and other parties. For example, in the case of depression treatment, where depression, treatments, outcomes, and conclusions are extremely complex and nuanced, physician and researcher interfaces present a vast amount of information and the inevitable need for a lot of data. should show the relevant results and relationships. When a vast amount of information is presented through an interface, the interface can often become overly complex and overwhelming, resulting in misunderstanding and underutilization.

Although treatments exist for many mental illnesses, such as depression, these are predominantly directed at alleviating symptoms and treating as opposed to "cure" the disease. there is In many cases, physicians turn to clinical trials because no treatment option has proven to be the best or even somewhat effective.

For example, depression research is advancing day by day in many hospitals and research institutions, and clinical trials are constantly being conducted to test new drugs and treatment regimens. Patients with depression who have no other effective treatment options should be treated if their depression meets clinical trial requirements and if clinical trials are not yet fully enrolled (e.g., clinical trials are often are limited in the number of patients who can participate in clinical trials), they may choose to participate in a clinical trial.

With thousands of clinical trials underway worldwide at any given time, identifying clinical trial options for a particular patient can be a daunting endeavor. Matching patient psychiatric disorders, such as depression, to ongoing clinical trial subsets is complex and time consuming. Reducing matching clinical trials to the best match given a given location, patient and physician requirements, and other factors makes the task of considering clinical trial participation even more difficult. In addition, considering whether to recommend a clinical trial for a particular patient may undermine the therapeutic efficacy of a clinical trial in which the treatment is experimental in nature, especially in light of the specific patient condition. When you think about it, it's a tedious task that most doctors don't take lightly.

One of the other problems with current depression treatment planning processes is the difficulty in integrating new relevant treatment factors, treatment effect data, and insights into existing planning databases. In this regard, known treatment planning databases have been developed with predefined sets of factors and insights, and modifying these databases often introduces new factors or insights into their It requires substantial effort on the part of software engineers to accept and integrate factors and insights in a meaningful way that correlates correctly with other known factors and insights. In some cases, the substantial effort required simply means that no new factors or insights are incorporated into the database or used to influence planning; , this means that new factors or insights are only added to the system with some lag time required to invest the effort.

One of the other problems with existing depression treatment effectiveness databases and systems is their inability to optimally support different types of system users. As such, 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 are often tempted to narrow treatment options, outcomes, and efficacy data to simple recommendations, while researchers seek new hypotheses related to the relationship between depression, treatment, and efficacy. Even more granular data access is often required. In known systems, data access, views, and interfaces are often developed with one consumer client in mind, for example, psychiatrists, general practitioners, radiologists, therapeutic researchers, etc. , is therefore optimized for that particular system's user type, which means that the system is not optimized for other user types.

Pharmacogenomics is the study of the role of the human genome in drug response. Aptly named for its combination of pharmacology and genomics, pharmacogenomics analyzes how an individual's genetic make-up affects their response to drugs. It is a genetic predictor of drug response in patients by correlating gene expression pharmacokinetics (drug absorption, distribution, metabolism, and excretion) with pharmacokinetics (drugs acting through their biological targets). Deal with mutation effects. Although both terms are concerned with drug response based on the influence of genes, pharmacogenomics focuses on single drug-gene interactions, whereas pharmacogenomics focuses on synonymous genes for drug response. Embracing broader genome-wide association approaches, incorporating genomics and epigenetics while addressing impact. One goal of pharmacogenomics is to develop rational means for optimizing drug therapy and ensuring maximum efficacy while minimizing side effects for a patient's genotype. Pharmacogenomics and pharmacogenetics may be used interchangeably throughout this disclosure.

The human genome consists of 23 pairs of chromosomes, each containing 46 to 250 million base pairs (approximately 3 billion base pairs total), each having complementary nucleotides (generally described by a double helix). match). For each chromosome, the base pair arrangement 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 the mother and the other copy from the father.

A conventional approach for incorporating pharmacogenomics into precision medicine for the treatment, diagnosis, and analysis of psychiatric disorders, such as depression, is the use of single nucleotide polymorphism (SNP) genotyping and detection methods (SNP chips). through, etc.). SNPs are one of the most common types of genetic variation. A SNP is a genetic variation that spans only a single base pair at a particular genetic locus. When individuals do not have the same nucleotide at a particular locus, a SNP can be defined for 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 a substitution of the nucleotide cytosine (C) by the nucleotide thymine (T) at a locus.

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

Although SNPs are commonly present throughout human DNA, they appear on average almost once every 1,000 nucleotides, which means that there are approximately 4 to 5 million SNPs in the human genome. means Over 100 million SNPs have been detected in populations worldwide. These mutations are usually found in DNA between genes (regions of DNA known as "introns") and help scientists identify genes linked to diseases (such as mental disorders). can serve as a target marker.

SNPs are not the only genetic variations possible within the human genome. Any deviation in a human genome sequence when compared to a normal reference genome sequence may be referred to as a variant. In some cases, a person's physical health may be affected by a single variant, while in other cases it may be affected only by a combination of specific variants located on the same chromosome. Variants within a gene are in the same allele of a gene when they are located on the same chromosome. An allele is defined as a contiguous sequence of bases of a region of a DNA molecule found within individuals, especially when the base sequence of that region has been shown to vary between individuals. Some genetic tests, such as NGS, when detecting multiple variants within a gene, are able to tell if the variants are within the same allele. Some genetic tests do not have this capability.

Several groups of variants that exist together on the same chromosome can form specific alleles that are known to alter human health. Sometimes a single allele cannot affect a person's health unless they also have a specific combination of alleles. Occasionally, an allele or allele combination is reported or published with health implications in databases or other records (e.g., those with the allele or allele combination outnumber humans). Fast Metabolizer, Intermediate Metabolizer, Poor Metabolizer). Exemplary records include records from the American College of Medical Genetics and Genomics (ACMG), the Association for Molecular Pathology (AMP), or the Clinical Pharmacogenetics Implementation Consortium (CPIC). Each of these published alleles may have a designated identifier, one category of identifier being the * (star) allele system. For example, for each gene, each star allele may be numbered *1, *2, *3, etc., where *1 is generally the reference or normal allele. As an example, the CYP2D6 gene has over 100 reported variant alleles.

Developed before next-generation sequencing (NGS), microarray assays were common genetic tests for detecting variants. Microarray assays use biochips that have DNA probes attached to the biochip 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 has attached to it a fluorescent dye or another type of dye. In many cases, the color of the dye is unique to the sample, which allows the assay to distinguish between two samples when multiple samples are simultaneously introduced to the biochip surface.

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 that is bound to the probe will fluoresce or produce another visually detectable signal. The location and sequence of each probe is known, so the location of the visually detectable signal indicates what bound complementary DNA sequences were present in the sample, and the color of the dye is Indicate from which sample the DNA sequence is derived. The probe sequences on the biochip each contain only one sequence, and the probes specifically bind to one complementary sequence in DNA, which is why most probes have one type of mutation or genetic variant. This means that it can only detect This also means that the microarray will not detect non-target sequences of the probes on the biochip. It cannot be used to find new variants. This is one of the reasons that next-generation sequencing is more useful than microarrays.

The fact that a probe detects only one specific DNA sequence does not mean that two detected variants are the same, unless the variant loci are close enough to allow a single probe to span both loci. It means that the microarray cannot determine whether it is an allele. In other words, the number of nucleotides between two variants plus the number of nucleotides in each variant must be less than the number of nucleotides in the probe, otherwise the microarray will detect that the two variants are identical. It cannot detect whether they are within a DNA strand, ie in the same allele.

Each probe also binds to complementary sequences within the unique temperature and concentration ranges of the components in the DNA solution introduced to each biochip. Since it is difficult to simultaneously achieve optimal binding conditions for all probes on a microarray (such as the microarrays used in SNP chips), any DNA from the sample should be fully integrated into the sample DNA sequence. have the potential to hybridize to non-complementary probes and cause inaccurate test results.

In addition, the drawbacks of microarrays are the limited number of probes targeting biomarkers due to the surface area of the biochip, the misclassification of non-probe-binding variants as normal genotypes, and the fact that patient including globally misclassifying the genotypes of Due to the limited processing efficiency of SNP chips, conventional microarray approaches are inefficient in detecting biomarkers and the many mutations involved.

TaqMan assays have similar limitations as microarrays. If the probe of the TaqMan assay is a perfect match for the complementary sequence within the DNA molecule from the sample, the DNA molecule is extended, similar to NGS. However, instead of reporting what sequence of each nucleotide type entered the DNA elongation, the assay only reports whether the elongation occurred. This poses the same limitations as the SNP chip. Other genetic tests such as dot blots and Southern blots have similar limitations.

Therefore, there is a need in the art to overcome the above drawbacks. With regard to psychiatric disorders such as depression, in many cases, patient conditions related to the disorder are piecemealized from clinical medical records via physical examination, genetic analysis, and/or patient interviews, and are used to identify individuals for specific illnesses. can be used to develop a customized treatment plan. Accordingly, 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 an optimal personalized treatment plan. .

In addition, what is needed is a well-designed interface that makes it easy to understand and digest complex datasets. For example, in the case of psychiatric disorders, treatments, and outcomes such as depression, it would be beneficial to provide an interface that would allow physicians to view anonymized patient data for many patients, the data being critical. specifically arranged to trigger therapeutic and outcome insights. It also allows physicians to use filters to access different treatment and outcome data sets, again triggering different insights, examining anomalies within the data sets, and making better treatment plans for their particular patients. It would be beneficial to make the interface interactive so that it can be thought out.

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

Therefore, what is needed is all treatment-related data, including factors associated with psychiatric disorders, e.g., depressive factors, treatment decisions, treatment effects, and exploratory factors (such as those that appear to be causally related to treatment effects). A system that can efficiently capture and structure data to optimally drive different system activities, including storage of data and treatment decisions, database analysis, and user applications and interfaces. In addition, the system is modified to absorb new data types and new therapeutic and research insights, as well as to enable the development of new user applications and interfaces optimized for specific user activities. It should be highly adaptable as quickly as possible.

One implementation of the present disclosure is a system for personalized psychiatric treatment. In one embodiment, the psychiatric disorder is a depressive disorder. The system includes a server configured to communicate with existing healthcare resources and receive patient data corresponding to a patient, the server comprising an analysis module. The system further comprises 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 in communication 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 personalized depression treatment and personalized depression prediction based on empirical patient outcomes and patient data.

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 psychosis to generate combined patient data. The method further includes comparing the combined patient data to a knowledge database to generate information. In addition, the method includes generating a clinical report containing information relevant to the comparison and providing the clinical report to the physician.

Another implementation of the present disclosure is to receive a list of clinical trial criteria from one or more clinical trials targeting psychiatric disorders, such as depression clinical trials, and from patient medical records from electronic health record systems. and receiving patient data from. 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. In addition, the method compares each patient metric to a list of clinical trial criteria and determines if the patient is eligible for one or more clinical trials if each criterion in the corresponding list of clinical trial criteria is met. and indicating that it has

In one aspect, the present disclosure provides a method for generating treatment information for a patient diagnosed with at least one psychosis. The method comprises multigene panel sequencing of samples from patients 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 reaction, the molecular data being whole-exome sequence data, mass array data, sequence data from one or more introns associated with metabolic-related genes, and sequence data associated with metabolic-related genes. obtaining molecular data comprising a plurality of nucleic acid sequences obtained from sequence data from one or more promoter regions that are associated with a patient; aligning the molecular data to a human reference sequence; providing a first set of clinical data, the first set of clinical data including a listing of pretreatment drugs and one or more diagnostic listings; and molecular data and clinical data. generating a first report from a therapy engine based on the first set of the report examining the report for each one of at least a portion of the plurality of nucleic acid sequences in the patient's molecular data The laboratory results section presents the phenotype associated with the nucleic acid sequence and the supplemental section of the report presents a listing of one or more drugs associated with the nucleic acid sequence and a classification for each drug in the listing,1 generating, at least in part, the listing of one or more drugs determined by the listing of pre-treatment drugs, the method comprising causing a report to be presented to a user; obtaining a second set of clinical data, the second set of clinical data describing clinical activity of the patient after presentation of the report; and updating in part.

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

In some embodiments, the clinical activity described in the second set of clinical data is among prescribed medication, drug dosage, patient compliance, and patient outcome after taking the prescribed medication. may include one or more of

In some embodiments, a patient may be diagnosed with multiple psychoses.

In some embodiments, the therapeutic engine can include a knowledge database, wherein the knowledge database provides information between a particular one or more drugs and one or more nucleic acid sequences associated with drug metabolism. Data related to interactions, primary drug metabolism pathway data, and a first cohort dataset derived at time 1 from a cohort of psychiatric subjects, wherein the first cohort dataset was used for treatment 1 Single or Multiple Drugs, Pretreatment Diagnosis, Treatment Outcomes, and Scientific Publications, U.S. Food and Drug Administration (FDA), Clinical Pharmacogenomics Implementation Consortium (CPIC), Dutch Pharmacogenomics Working Group (Dutch) Pharmacogenomics Working Group, DPWG), Pharmacogenomics Knowledge Base Review, and Psychoactive drug Screening Program Ki Database. and a first cohort dataset containing drug information data obtained from the drug information.

In some embodiments, cohort datasets may not be derived from clinical trial data. In some embodiments, at least a portion of the psychiatric subjects within the cohort may be diagnosed with multiple psychoses.

In some embodiments, the knowledge database can further include a second cohort dataset derived at time 2, wherein the second cohort dataset contains information from at least one of the first cohort subjects. can include In some embodiments, the knowledge database can further include an Nth cohort dataset derived at time N, the Nth cohort dataset including information from at least one of the previous cohort subjects. . In some embodiments, the method can further comprise providing an Nth set of clinical data associated with the patient, wherein the Nth set of clinical data is the (N-1)th It may be acquired at a time after the clinical data set is acquired. In some embodiments, each of the clinical data sets can describe the patient's clinical activity after presentation of the most recent report. In some embodiments, the method can further include updating the therapy engine with at least a portion of the Nth clinical data set.

In some embodiments, reports can further provide supporting information for classification. In some embodiments, the report can further provide hyperlinks to source documents or websites with information about drug classifications.

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.

In some embodiments, the clinical activity described in any of the Nth clinical data sets includes prescribed medication, medication dosage, patient compliance, and It can include one or more of patient outcomes.

In some embodiments, the method can further include generating a second report based on input from a second clinical data set.

In some embodiments, a listing of pre-treatment agents can include at least one drug dose.

In some embodiments, the listing of pretreatment drugs can include at least one patient response to a drug.

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

In some embodiments, the therapy engine can identify a recommended dose for each drug included in one or more drug listings and can provide that dose information in a report.

In some embodiments, the therapy engine may identify the next potential drug recommendation by excluding from the report at least one drug included in the prior drug listing.

In some embodiments, the treatment engine can include a classifier for identifying subtypes of depression, wherein the psychosis the patient has been diagnosed with is depression, and the subtype of depression is included in the report. can be listed.

In some embodiments, the therapeutic engine can include a classifier to identify drug resistance, which can be listed in reports.

In some embodiments, one or more drug listings may be determined based, at least in part, on at least one diagnosis.

In another aspect, the present disclosure provides a system for generating information regarding treatment for a patient diagnosed with psychosis. The system comprises at least one memory and at least one processor coupled to the at least one memory. The system causes at least one processor to execute instructions stored in at least one memory to obtain molecular data from a multigene panel sequencing reaction on samples from the patient, the molecular data comprising: Derived from whole exome sequence data, mass array data, sequence data from one or more introns associated with metabolic-related genes, and sequence data from one or more promoter regions associated with metabolic-related genes aligning the molecular data to a human reference sequence; and providing a first set of clinical data associated with the patient, comprising: generating a first report from a therapeutic engine based on the providing and the first set of molecular data and clinical data; wherein the report includes, for each one of at least some of the plurality of nucleic acid sequences in the patient molecular data, a phenotype associated with the nucleic acid sequence and one associated with the nucleic acid sequence or presenting a plurality of drug listings and a classification for each drug in the listing, the one or more drug listings being determined at least in part by the prior drug listings; and obtaining a second set of clinical data associated with the patient, the second set of clinical data describing clinical activity of the patient after presentation of the report; configured to perform the obtaining and updating the therapy engine with at least a portion of the second set of clinical data;

In some embodiments, the clinical activity described in the second set of clinical data is among prescribed medication, drug dosage, patient compliance, and patient outcome after taking the prescribed medication. may include one or more of

In some embodiments, cohort datasets may not be derived from clinical trial data.

In some embodiments, a subject may be diagnosed with multiple psychoses.

In some embodiments, at least a portion of the psychiatric subjects within the cohort may be diagnosed with multiple psychoses.

To the accomplishment of the foregoing and related ends, the invention then includes 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.

1 is a block diagram of a data-based therapy system, according to aspects of the present disclosure; FIG. 4 is an image of an exemplary graphical user interface (GUI), according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; 3 is another image of the example GUI of FIG. 2, according to aspects of the present disclosure; FIG. 11 illustrates an alternative embodiment of a patient report; FIG. 11 illustrates an alternative embodiment of a patient report; FIG. 11 illustrates an alternative embodiment of a patient report; FIG. 11 illustrates an alternative embodiment of a patient report; FIG. 11 illustrates an alternative embodiment of a patient report; FIG. 11 illustrates an alternative embodiment of a patient report; FIG. 11 illustrates an alternative embodiment of a patient report; FIG. 11 illustrates an alternative embodiment of a patient report; FIG. 11 is a portion of another alternative embodiment of a patient report; 11 is another portion of the patient report of FIG. 10; FIG. 11 is yet another portion of the patient report of FIG. 10; FIG. 11 is an additional portion of the patient report of FIG. 10; FIG. 11 is a diagram of a further portion of the patient report of FIG. 10; FIG. Figure 11 is a still further portion of the patient report of Figure 10; 11 is another additional portion of the patient report of FIG. 10; FIG. 11 is yet another additional portion of the patient report of FIG. 10; FIG. 11 is still another additional portion of the patient report of FIG. 10; FIG. FIG. 3 illustrates an exemplary process for generating treatment information for a patient diagnosed with psychosis;

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 will herein be described in detail. However, the description herein of particular embodiments is not intended to limit the invention to the particular form disclosed, but rather the present invention as defined in the appended claims. Covers all modifications, equivalents, and alternatives falling within the spirit and scope of

The invention will now be described with reference to the various accompanying drawings, wherein like reference numerals correspond to like elements throughout the several views. It should be understood, however, that the drawings and the following detailed description relating thereto 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.

The present disclosure is described in the context of systems related to the research, diagnosis, treatment, and outcome analysis of psychiatric disorders (depression, bipolar disorder, obsessive-compulsive disorder, borderline personality disorder, anxiety, etc.). More specifically, exemplary embodiments relating to depressive disorders (“depression”) are described herein. Nevertheless, the present disclosure is intended to teach concepts, features, and aspects that will be useful in many different health-related contexts, and thus the specification is specific for several system aspects. It should be understood that it should not be considered limited to depression-related systems unless specifically indicated. Additionally, the present disclosure is described in the context of systems related to research, diagnosis, therapy, and analysis of results from next-generation sequencing (NGS).

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 overall system disclosed herein, and in most cases runs servers, maintains databases, provides new data types, new medical practices and treatments. insights, as well as companies or other entities that employ people with the many different skill sets necessary to build, maintain, and adapt the disclosed systems to meet other needs. deaf. Exemplary provider employees may include researchers, data abstractors, neurologists, psychiatrists, data scientists, and many others with specialized skill sets.

The term "physician" includes, but is not limited to, any health care provider, including, but not limited to, primary care physicians, specialists, neurologists, psychiatrists, psychologists, nurses, and medical assistants. Used to refer generically.

The term "researcher" is used generally to refer to anyone conducting research, including but not limited to radiologists, neurologists, data scientists, or any other health care provider. be done. Some may be both physicians and researchers, while others may simply act in one of these qualifications.

The phrase "systems expert" generally refers to those who collect, develop, analyze, or otherwise process system data, tissue samples, or other information types (such as medical images) within the disclosed system. used to refer to a provider employee who works to process a form and produce an intermediate system work product or a final work product, the intermediate work product being the product of consumption by one or more other system professionals including any datasets, conclusions, tissue or other samples, or other information for the final work product to be documented in a final or definitive report for the system client or conducting the study , including data, conclusions, or other information operated within the system to adapt the system to changing needs, data types, or client requirements. For example, an "abstructor expert" consumes data available in the clinical record provided by a physician (such as a primary care physician or psychiatrist) and normalizes and structures it for use by other system experts. used to refer to the person who generated the data that was processed. The phrase "programming expert" is used to refer to a person who creates or modifies application program code to accommodate new data types and/or clinical insights and the like.

The phrase "system user" is used generically to refer to any person who uses the disclosed system to access or manipulate system data for any purpose, and thus Provided generally for the purpose of performing work for a patient or for other partner research institutions or systems professionals working for the donor including physicians and researchers who partner with

The term "depressive state" refers to diagnosed depression, mental and physical depressive symptoms, other patient conditions (such as age, sex, weight, race, habits (such as smoking, drinking, and eating)), and others. associated medical conditions (such as anxiety, hypertension, dry skin, other illnesses), medications, allergies, other associated medical history, current side effects of any depression treatments and other medications, etc. used to refer to the overall state of

The phrase "consume" is used to indicate whether the consumption is exhaustive (e.g., used only once, as in the case of non-reproducible saliva samples) or to ensure that data, samples, etc. persist for consumption by multiple entities. Any type of consideration, use, modification, or other Used to refer to activities. The term "consumer" includes professionals, physicians, researchers, clients consuming any system work product, and any automated system work product independent of any initiating human activity. is used to refer to any system entity that consumes any system data, samples, or other information in any manner, including each of the consuming software application programs or operational code.

The phrase “treatment planning process” refers to the processing of clinical data and other patient data and samples (such as saliva samples) into intermediate data deliverables and, ultimately, one or more final data deliverables provided to system clients. Used to refer to an entire process containing one or more subprocesses that produce a final work product in the form of a report. These processes typically involve various levels of exploration of treatment options for a patient's specific depressive state, but typically involve a more general exploration aimed at more general research efforts. Contrary to the treatment of specific patients. Therefore, treatment planning includes consideration of data generation and the processes used to generate that data, different treatment options and their effect on patient condition, etc., so that a particular patient's disease can be determined. A final regimen for treatment may be obtained.

A medical treatment prescription or plan typically describes the extent to which a particular treatment eradicates a disease, the duration of a particular treatment, the duration of the healing process associated with a particular treatment, and the typical treatment-specific Based on an understanding of the extent to which a treatment affects disease, including side effects (such as treatment outcomes). Ideally, treatment should result in complete resolution of the disease within a short period of time with minimal or no side effects. In some cases, cost is also a consideration when choosing a particular medical treatment for a particular disease.

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

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 superior to other aspects or designs.

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

Unless otherwise indicated, the disclosed system may be used for many different purposes (data collection, data analysis, data display, therapy, research, etc.), but for simplicity and consistency, the disclosed system is The system as a whole is hereinafter referred to as the "disclosed system".

As used herein, the term "clinical data" typically refers to the Refers to related information. Exemplary clinical data include, but are not limited to, physical characteristics (eg, gender, height, weight, age, general health, etc.), medical history, current and past diagnoses, current and past treatment regimens administered, patient Includes physician observations and notes regarding compliance, treatment outcomes, image analysis such as X-rays, CT scans, facial images, and body movement recordings, behavior, thought patterns, sleep cycles, physical state, changes, etc.

In one example, the invention disclosed herein may be a system, other class of device, and/or method that assists a healthcare provider in making clinical decisions based on a combination of molecular and clinical data; This may be done by combining patient molecular and clinical data into an aggregated dataset of molecular and/or clinical data from multiple patients (e.g., cohorts of subjects) and/or a knowledge database (KDB) of clinical genomics data. can include comparing with In addition, the invention disclosed herein captures, captures, cleanses, structures, and combines robust clinical and detailed molecular data to determine the significance of correlations, patterns, and trends. produce reports for physicians, analyze or confirm the accuracy of diagnoses, predict the likelihood of a patient responding to a particular treatment, recommend or withhold a particular treatment for a patient, biomarkers Support discovery, enhance clinical research work, oversee therapeutic and dosing decisions, expand indications of use for currently marketed treatments and clinical trials, and expedite federal or regulatory approval of therapeutic compounds can be used to In one example, the invention disclosed herein can be used in patients with any mental illness or disease, including, but not limited to, treatment-resistant depression, major depressive disorder, bipolar disorder, schizophrenia, and the like. can help academic medical centers, pharmaceutical companies, and community providers improve care options and treatment outcomes for patients experiencing In one example, one implementation of this system may be in the form of software.

As used herein, "drug metabolism" refers to the metabolic breakdown of drugs by the body, usually through specialized enzymatic systems. Genes encoding such enzymes, or genes encoding regulators of enzyme expression genes, are considered drug metabolism genes.

The terms "subject" and "patient" are used interchangeably herein. Although the subject is preferably a human subject, it is understood that the methods described herein are valid for all vertebrate species intended to be included in the term "subject." should. Accordingly, a "subject" is a human subject for medical purposes, such as treatment of an existing condition or disease, or prophylactic treatment to prevent the development of a condition or disease, or for medical, veterinary, or developmental purposes. animal subjects. Suitable animal subjects include, but are not limited to, primates such as monkeys, apes and the like, cattle such as cattle, bulls and the like, ovines such as sheep and the like. , goats, e.g., goats, and the like, pigs, e.g., small pigs, large pigs, and the like, horses, e.g., horses, donkeys, zebras, and the like, wild cats, domestic cats Includes felines, canines including dogs, lagomorphs including rabbits, hares and the like, mammals including rodents including mice, rats and the like. Additionally, a "subject" can include a patient diagnosed or suspected of having a condition or disease such as psychosis.

As used herein, the phrases "treatment" or "treating" refer to both prophylactic or preventative treatment, as well as curative or disease-modifying treatment, which may be including the treatment of patients at risk of contracting or suspected of being infected with a disease, as well as patients who are ill or have been diagnosed with a disease or have a medical condition; including suppression of The treatment is administered to a subject who has a medical disease or who may eventually acquire the disease, thereby preventing, curing, or developing one or more symptoms of the disease or recurring disease. may delay, lessen or ameliorate the severity of disease, or prolong survival of a subject beyond what would be expected in the absence of such therapy. "Treatment regimen" means a pattern of treatment for a disease such as psychosis, eg, a pattern of medications or other treatments (eg, counseling, group therapy, etc.) used during treatment.

As used herein, "control," "control sample," "reference," "reference sample," "normal," and "normal sample" describe samples from non-diseased tissue. is. In some embodiments, such samples are from subjects who do not have a particular condition (eg, a diagnosed psychiatric disorder). In other embodiments, such samples are, for example, internal controls from subjects who may or may not have a particular disease or disorder, and are from pre-treatment samples from subjects. . For example, if a blood or saliva sample was obtained from a subject diagnosed with one or more psychiatric disorders, an internal control sample may be obtained from the subject prior to any treatment. A pre-treatment sample may, for example, show elevated expression levels from one or more genes. After treatment, another sample can be analyzed to determine whether treatment alters expression levels. A reference sample can thus be obtained from a subject or from a database, eg, a second subject.

As used herein, "molecular data" refers to the sequence and/or quantity (e.g., expression level, or duplication/deletion information), etc. By way of example, but not limitation, in some embodiments, molecular data may include, but are not limited to, whole exome gene data, single nucleotide variants (SNVs), insertions/deletions (indels), copy number variations. (CNV), fusion variants, RNA expression data (including miRNA expression), microbiome information, haplotype or allele information including star alleles, haplotype groups or diplotypes including combinations of star alleles, mass array data, microarrays Contains DNA sequence information containing data. Whole-exome genetic data relating to any of the exons in the human genome can further include, for example, intron regions targeted by intron-specific probes spiked into the 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, but not limitation, targeted panels may include, for example, specific genes, portions of genes, or specific non-coding sequences (e.g., introns or promoter regions), as opposed to whole genome RNA analysis. specific gene sequences, or assays designed to assess or analyze only specific proteins. Molecular data may be obtained by methods well known in the art and such methods are not intended to be limiting. As an example, in some embodiments, the molecular data is derived from multigene panel sequencing reactions, including whole exome sequence data, mass array data, sequenced data from one or more introns, and one or comprising multiple nucleic acid sequences obtained from one or more of the sequence data from multiple gene regulatory regions.

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 using specialized instruments such as next-generation gene sequencers, automated machines that determine the order of nucleotides in DNA and RNA. The instrument reports sequences as strings of letters, called reads, which the analyst can compare to one or more reference genomes of the same gene. A reference genome can be compared to libraries of normal and variant gene sequences that have been associated with several conditions. Because NGS standards have not been established, different NGS data providers and laboratories have different approaches for sequencing patient genomes, and based on their sequencing approaches, physicians, researchers, and patient Generate different types and amounts of genomics data to share with Disparate genomics datasets make the task of identifying meaningful genetic therapeutic effect insights difficult because the required data were not in normalized form, were not captured, or were simply not generated at all. and in some cases make the task of identification impossible. The systems and methods disclosed herein address this deficiency.

In one exemplary embodiment, DNA extracted from blood, saliva, biopsy, or other biological patient sample is subjected to single-end or paired-end sequencing using an NGS platform, such as those offered by Illumina. (single- or paired-end sequenced). The patient from whom the sample was taken may have been diagnosed with psychosis. Sequencing results (herein, “raw sequencing data”) can be passed through a bioinformatics pipeline where the raw sequencing data are analyzed. Raw sequencing data can be associated with every exon and selected intron combination within the human genome. After the sequencing information has been passed through the bioinformatics pipeline, it can be evaluated for quality control, such as through an automated quality control system. If the sample does not pass the initial quality control steps, it can be manually reviewed. If the sample passes the automated quality control system or passes manually, an alert may be issued to a message bus configured to listen for messages from the quality control system. This message may contain the identifier of the sample, as well as 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 containing sequence alignment data (such as raw sequencing data). When the message is received, a service can be triggered to evaluate the sequencing data for pharmacogenomic agents.

As used herein, the term "BAM file" or "binary file containing an alignment map" refers to a file containing sequencing data aligned to a reference sequence (e.g., a reference genome or exome). Point. 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, reference sequence and optionally metrics related to sequence read quality and/or sequence alignment quality. BAM files generally relate to files having a particular format, but for the sake of simplicity, this specification simply refers to files of any format that contain information about sequence alignments, unless otherwise specified. .

BAM files can be generated by aligning raw molecular data to a reference genome. For example, raw molecular data can be saved in BCL, FASTA, and/or FASTQ file formats. A suitable process can align raw molecular data to human reference sequences to generate aligned sequence reads. Aligned sequence reads can be saved in SAM and/or BAM file format.

A bioinformatics pipeline may receive raw sequencing results, process them to identify genetic variants expressed in a patient's DNA or RNA, and convert this information into a variant call format file (. vcf). An identified variant may be referred to as a variant call. Once a variant has a sufficient number of reads from raw sequencing results to qualify as a variant call, variant characterization can be performed on that variant call. In one example, variant characterization is the process of determining whether a variant is benign, linked to an increased risk of a particular disease, and/or likely to cause interactions with prescribed drugs. is. Variant characterization may involve searching published variant datasets that identify pharmacogenomically significant variants, searching FDA publications on therapeutics and their target variants, or calling variant calls for pharmacogenomics. This may include comparing to an internally curated list of variants with significance. Variant calls with pharmacogenomic significance may be flagged for inclusion in reports, such as those detailed further below.

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

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

As used herein, the term "reference exome" refers to a partial, partial, exome of any tissue from any organism or pathogen that can be used to refer to identified sequences from a subject. It refers to any sequenced or otherwise characterized exome, whether complete or complete. 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 US 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 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 representative of a species set of expressed genes. In some embodiments, the reference exome comprises a sequence assigned to a chromosome.

As used herein, the term "reference genome" refers to the complete or partial genome of any organism or pathogen that can be used to refer to identified sequences from a subject. Deaf refers to any sequenced or otherwise characterized genome. Typically, the 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 US National Center for Biotechnology Information (NCBI) or the University of California, Santa Cruz (UCSC). be. "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 genomic sequence from an individual or individuals. In some embodiments, the reference genome is an assembled or partially assembled genomic sequence from one or more human individuals. A reference genome can be considered representative of a species set of genes. In some embodiments, the reference genome comprises 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, either allele allowing a match for alignment to that locus. obtain.

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

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

As used herein, the term "pharmacokinetics" refers to genetic polymorphisms (or downstream components such as proteins) and receptors, ion channels, enzymes associated with drugs or other therapies. , and the interaction between the immune system. Genes that are associated with immunogenicity are referred to as "immunogenic genes."

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

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

Therapeutic feasibility entries may be binned into a hierarchy of strength of evidence by concordance of patient disease and/or drug-gene interaction, as may be defined by professional society guidelines. Evidence-based treatment recommendations may be grouped into hierarchies, eg, IA, IB, IIC, IID, by level of evidence strength. Briefly, Tier IA evidence may be biomarkers matched to disease type according to consensus guidelines. Tier IB evidence may be biomarkers consistent with disease indications according to clinical studies. Tier IIC-evidence biomarkers may follow consensus guidelines or off-label use in clinical studies, or case studies of on- or off-label patients. Hierarchical IID-evidence biomarkers may follow preclinical evidence regardless of disease indication concordance. Patients can be matched for feasibility entry by gene, specific variant, diagnosis, and level of evidence. Patient-matched therapy options may represent recommended therapy and do not reflect whether the patient has been prescribed a particular recommendation by the treating physician.

KDB40 contains clinical data elements, imaging information, molecular data such as but not limited to DNA sequence information, single nucleotide variants (SNVs), insertions/deletions (indels), copy number variations (CNVs), fusion variants, RNA expression ( miRNA expression), microbiome information, haplotypes or alleles including star alleles, haplotype groups or diplotypes including combinations of star alleles, phenotypes for each haplotype or haplotype group, associated drugs, associated risks, relevant prognosis, relevant diagnostic features, therapeutic classification (including standard dosing regimens, dose adjustments, contraindications, etc.), incidental germline findings, including variants associated with additional health implications, epigenetic value, Proteomic values, their analysis (such as features extracted from images), and/or combinations of one or more of the data elements or data types contained in KDB40 (e.g., combined with CYP2C19 gene variants and associated phenotypes) or clinical data that has been matched with molecular and/or imaging data, aggregated from multiple patient records, and analyzed to determine associations between data types). . KDB 40 may also include behavioral indicators, including patient activity or locomotion patterns, or related scores or indicators derived from images of the patient's face.

Knowledge database 40 may also contain other types of information, such as image information. For example, the database may store images of the patient's face. A module may analyze an image and generate a set of information related to the image, also stored in a database. Other images include images generated from MRI or CT scans.

The analytical capabilities of NGS are superior to conventional methods of processing genetic variants or alleles of pharmacogenetic significance. Since the entire normal human genome can be referenced for each of the target genes (described in detail below), NGS can discriminate previously unobserved variant calls even if the variant is not targeted by the NGS panel. . For example, the normal genome is ATTACCA for a given region of the chromosome, but there are untargeted and/or previously undocumented variants in which the variant sequence is identified as ATTACCA in that same region. In some cases, allelic mismatches indicating the detection of new alleles spanning the region can be detected simply from the absence of expected variant calls. For example, an allele can be a sequence of nucleotides that matches a normal sequence, a sequence of nucleotides that matches the sequence of a known allelic variation from normal, or a new sequence that does not match any of the known alleles. can be identified from

Furthermore, since NGS probe reads contain not only the probes, but also the sequence of the DNA molecule extending from each probe, probe reads from upstream within the DNA molecule that also include non-targeted downstream variants can be reported by NGS sequencers. . Confirmed detection of non-target variants can be based on new research or published data after analysis in the bioinformatics pipeline. In addition, the genome-wide sequence coverage enables studies across aggregated sequencing results, enabling the identification of novel, previously unknown biomarkers. Exemplary systems that provide the basis for obtaining the above advantages and others are described below.

System overview

In one example of a system that can 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 such that system processes are defined in system data. loosely coupled, compartmentalized into different microservices for a subset of the population, may generate new data products for consumption by other microservices, including other system resources, and new data types as well as therapeutic and research insights It is designed to allow maximum system adaptability to respond quickly to Therefore, microservices are a small resource for scientists and software engineers because their development constraints operate independently of other system resources to execute defined processes involving system data consumed and data products produced. Large-scale autonomous teams can develop new microservices with minimal system constraints that facilitate prioritized service development.

The system allows rapid modifications to existing microservices as well as the development of new microservices for arbitrary data handling and analysis needs. For example, when a new record type is to be ingested within an existing system, a new record ingestion microservice can be rapidly developed to add new records in raw data format to the system database and even new record types. A system alert can be generated to notify other system resources that a 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, existing record ingestion microservices can be modified independently of other system processes to accommodate some aspect of new record types. A microservices architecture allows many service development teams to work independently and develop many different microservices simultaneously so that many aspects of the overall system can be rapidly adapted and improved at the same time. .

A messaging gateway receives data files and messages from microservices, gleans metadata from those files and messages, and distributes those files and messages to other systems, including databases, other microservices, and various system applications. Can be routed to a component. This allows the microservice to poll its own messages as well as incoming transmissions (point-to-point) or bus transmissions (broadcast to all listeners on the bus) to identify messages that start or stop the microservice. make it possible.

Referring now to the figures accompanying this specification, and more specifically to FIG. 1, the present disclosure will be described in the context of an exemplary disclosure system 10, in which data are It is received at server 20 from data sources such as databases 32, clinical records 24, and microservices (not shown). In some embodiments, server 20 can store relevant data, such as in database 34, which has been shown to contain empirical patient outcomes. Server 20 can manipulate and analyze the data available via analytics module 36 in many different ways. Additionally, the analytics module 36 may structure the data in different structured formats to generate new interim data or for consumption by user application programs and then use any of several different types of user interface devices. Data can be conditioned, or "shaped," to drive a user application program that provides a user interface through To simplify this description, a single server 20 and a single internal database 34 are shown in FIG. 1, but in most cases the system 10 will be connected to local and/or wide area networks and/or the Internet. or multiple distributed servers and databases linked via any other type of communication infrastructure. An exemplary simplified communication network is labeled 18 in FIG. The network connection may be of any type, including wired, wireless, etc., and may operate according to any suitable communication protocol. Additionally, network connections may include communication/messaging gateways/buses that enable microservice file and message transfer in accordance with the above system.

The disclosed system 10 allows many different system clients, using various types of computing devices, to be securely linked to the server 20 to facilitate the specific activities performed by those clients. Allows access to optimized system application program interfaces. For example, in FIG. 1, a provider 12 (physician, researcher, laboratory technician, etc.) is shown using a display device 16 (laptop computer, tablet, smart phone, etc.) to link to a server 20. . In some aspects, display device 16 may include a virtual reality headset, projector, wearable device (such as a smartwatch).

In at least some embodiments, when the system 10 is used by a physician, psychiatrist, psychologist, or other mental health professional or provider, the physician user interface (such as on the display device 16) communicates with the patient. It is optimally designed to support typical physician activities that the system supports, including activities directed towards treatment planning. Similarly, when researchers (such as radiologists) use system 10, a user interface is provided that is optimally designed to support the activities performed by those system clients. In other embodiments, the physician's 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.

System specialists (such as employees who control/maintain the entire system 10) also link to server 20 using interface computing devices to perform various processes and functions. For example, system experts can include data abstractors, data sales experts, and/or "general" experts (such as "lab, modeling, radiology" experts). Different professionals use the system 10 to perform many different functions, and each professional requires a specific skill set necessary to perform those functions. For example, data abstractor specialists are trained in ingesting clinical records from a variety of sources (e.g., clinical records24) and transforming the data into normalized, system-optimized, structured datasets. . Laboratory professionals are trained in obtaining and processing patient and/or tissue samples, generating genomic data, growing tissues, treating tissues, and producing results. Others evaluate treatment efficacy, perform data research to identify new insights of various kinds, and/or modify existing systems to accommodate new insights, new data types, etc. trained about The system's interfaces and toolsets available to provider professionals are optimized for the specific needs and tasks performed by those professionals.

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 clinical trials (eg, cohorts of subjects) may additionally or alternatively be provided to the server. Additionally, patient data may be provided to server 20 . As shown, a patient 14 has corresponding data from multiple sources (eg, test results 26 may be provided by a laboratory or technician, image data 28 may be provided by a radiologist, and so on). etc.). For simplicity, this is represented representatively as individual patient data 22 in FIG. In some embodiments, individual patient data 22 includes clinical records 24, laboratory results 26, and/or imaging data 28. In some embodiments, the clinical record 24 can include treatment notes (eg, notes written by a psychologist, psychiatrist, social worker, counselor, primary care physician/PCP, and/or nurse practitioner). . Further, in some embodiments, the clinical record 24 includes applicable screening results such as the patient's BDI-II score, PHQ-9 score, questionnaires customized for a particular patient, and the like. can contain. For example, clinical records24 include reported scores on the patient's interest or pleasure in doing things, low mood, depression, or hopelessness, difficulty falling asleep or staying asleep, or oversleeping, fatigue. feeling depressed or having little energy, anorexia or overeating, regret or feeling that you have failed or disappointed yourself or your family, difficulty concentrating on things such as reading the newspaper or watching television moving or speaking so slowly that other people might have noticed if they wanted to, or moving at a speed that makes the patient appear fidgety or impatient or may include talking, thinking that the patient would be better off dead, or thoughts of self-harm. These reported scores may be outcomes, symptoms, or observations reported by third parties, including clinicians, patients, and/or family members of patients. A clinical record can include chronological data, which is data collected at multiple points in the course of a patient's treatment.

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

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

Analysis module 36 generally uses available data to indicate diagnosis, predict progression, predict treatment outcome, and/or identify each patient's specific depressive state, clinical data, behavioral data, and/or Alternatively, an optimized treatment regimen (drug class, available clinical trials, etc.) can be suggested or selected based on molecular data. For example, an indicated diagnosis may suggest that the patient is not depressed. For example, a patient may likely have a bipolar diagnosis or another diagnosis with similar symptoms, including situational depression, or an entirely different health condition, including a thyroid condition with depression-like symptoms. . In one example, antidepressants may not alleviate depressive-like symptoms caused by thyroid conditions, and an indication that a patient may have a thyroid condition encourages prescribing thyroid therapy to patients instead of antidepressants. can guide the provider to

A diagnostic prescription can be based on any part 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 stored together in a database 34 for health care providers to use system 10 to compare patient data, stratify patients, Facilitates easy query access to the dataset as a whole to allow predicting treatment outcomes and generating new hypotheses. For example, the system 10 stratifies patients, discovers various biomarkers of response to therapy, and/or finds cohorts and sub-cohorts of patients predicted to respond in a particular way to a particular therapy. can be used for 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 practice. Clinical data, including free-form text and/or handwritten notes, are publicly checked for character recognition, natural language processing, and data integrity, and missing data to generate standardized data to support internal precision medicine initiatives. Manual curation methods that may interpolate information, use manual and/or automated quality assurance protocols, and store data in FHIR compliant data structures using industry standard vocabularies for healthcare providers to access through the system 10 Phenotypic data, treatment data, and outcome or patient response data can be processed and structured by a method comprising: Behavioral data may include patient-reported outcomes, wearable fitness tracker data, geographic location data indicative of patient movement trends, and the like. Molecular data can include variants or other genetic alterations, DNA sequences, RNA sequences and expression levels, miRNA sequences, epigenetic data, protein levels, metabolic levels, and the like.

In one example, datasets can be filtered by very specific criteria, including population filtering, which reflects inclusion and exclusion criteria in study design and clinical trials. These custom subsets can be saved and fed through the 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 are used to stratify patients into finer-grained, indicated disease subtypes. can be Understanding how patients are related at the molecular, metabolic, and phenotypic level will enable the discovery of new targetable biomarkers for drug development and the use of already approved drugs. It can facilitate expansion of instructions.

In one example, the system can include visualization and analysis tools that generate clinical and research insights regarding phenotypic, therapeutic, and outcome data. These tools enable healthcare providers to rapidly investigate and visualize clinical and molecular trends in a given patient population. Providers can use these tools to analyze data in semi-supervised or unsupervised fashions and, as a whole, to discern between patients based on clinical, molecular, and treatment patterns through a series of interactive learnings from the data. It may define a cluster, segregation, or stratification. New knowledge relating to outcome, subtyping, and prognostic implications is generated by analyzing molecular and clinical attributes observed in broader patient populations.

In some embodiments, the cohorts are based on U.S. patent application Ser. No. 16/671,165, entitled "User Interface, System, and Method For can be generated using gene weighting and other methods as described in Cohort Analysis. The system 10 processes an actionable genetic database to provide evidence for mutations, alleles, haplotypes, diplotypes, or predicted phenotypes, and their impact, in order to assist clinicians in drug selection. , we can explicitly generate the importance for each gene. Genes not in the viable gene database have a weight of zero. Conversely, genes with a weight of 1 have the highest confidence that the variant affects the action of an FDA-approved drug against the cancer type in question. Other factors for adjusting gene weighting may include evidence of being a driver gene or having relevant drug interactions in the metric and assessment of DNA mutations at the variant level rather than just the gene level. .

In some embodiments, gene weights may be determined based on evidence that genes are useful for comparing patients within a cohort. For example, the existence of an approved treatment or recommended treatment adjustment for a particular genetic mutation can be used to modify the weightings. In particular, for mutations in a particular gene for a particular drug or psychiatric disorder, the presence of an FDA-approved drug or known relevant drug interactions results in increased weighting for that gene and/or "1 ”. (Such weighting may be both gene- and psychiatric disease-specific, and the same drug may not be approved for mutations in the same gene for different psychiatric disorders, and as a result, in such circumstances There may be no change in the weighting of that gene).

In some embodiments, importance scores may be calculated by following a set of rules. For example, in one rule set a base weight of 0 is assigned to all genes. If the gene is not included in the gene-based panel, the weight remains 0 and the next gene's weight is calculated. If genes are included in a gene panel, information is extracted from the panel by starting with an initial gene base weight of zero. Such information may include whether there are FDA-approved therapies that target the gene mutation or whether there are known drug interactions associated with the gene mutation. If such treatments exist, gene weights can be scaled using metric c1. In the absence of such therapy, a determination can be made as to whether the gene allele or combination of alleles has evidence for the drug or psychiatric disorder being referred to. As before, if such evidence exists, gene weights may be scaled using metric c2, which is described below. Gene weights can then be scaled based on the total level of evidence using a third metric, c3. Finally, the gene weights may be rescaled using a maximum weight of 1, for example after this procedure has been performed for each gene under consideration.

As explained above, the weight can be scaled using the metric c1. This metric is dependent on the level of evidence that a particular gene/therapy combination increases weights, with genes at lower positions on the spectrum resulting in lower increases in weights and Genes at higher positions result in higher increases in weight. In particular, there may be levels of evidence for therapy adjustment recommendations for a particular gene, eg, 1 to 7, with 1 being the best and 7 being the least informative. Such levels reflect, for example, the number of patients who received therapy and had a good outcome, the percentage of patients who were relieved after 1 year, 2 years, 5 years, etc., and the presence or absence of side effects. can be determined based on one or more factors, including the percentage of In another embodiment, the presence of evidence that a gene allele or combination of alleles has an associated drug interaction increases the weight of that gene and increases the weight of the gene allele or alleles that do not have an associated drug interaction. Combinations do not increase the weight of genes.

Similarly, the weight can be scaled using the 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 an increase in weight. Genes that are low on the spectrum and located high on the spectrum result in high increases to weights. As with c1, there may be different levels of evidence for particular cancer types, and stronger correlations may be reflected in higher values of c2. In another embodiment, the presence of evidence that a gene allele or combination of alleles is associated with drug interactions increases the weight of genes based on the level of Gene or allele combinations do not increase the weight of a gene.

Other evidence may also be weighted. Although such evidence has no known established correlations with some drug interactions, some variants of genes may retain faint correlations with gene alleles or alleles. It can contain combinations. Varying levels of c3 may be applied based on the strength of correlation for each variant present.

After the weights c1, c2, c3 are determined, the sum of the weights c1+c2+c3 for the genes can be greater than one. Gene weights can therefore be normalized by dividing each particular c1+c2+c3 gene weight by the sum of the maximum values for each metric, namely c1_max+c2_max+c3_max.

In addition to analyzing patients for similarity or commonality of at least one somatic mutation in common, the DNA metric is useful when a pair of patients have no mutations in common (if they have several significant mutations). mutations) are also taken into account, which can reliably separate potentially similar patients from very different patients.

If two patients have no mutations in common, the metric for the pair may be 0. Conversely, if a pair of patients have at least one mutation in common, the metric is the sum of the gene importance scores between the mutated genes common to the two, taking into account the gene weightings described above. good. Gene importance scores can be used to focus on highly important genes and leave background important genes less affected.

The sum of gene importance scores can be rescaled by the geometric mean of the similarity of the paired patients to themselves. This means that non-common mutated genes are considered. For example, a patient who shares one mutation with a reference patient has significantly more mutations that the reference patient does not have than a second patient who has two shared mutations. It can also be considered close if it has If a pair of patients have no mutations in common, the metric is the sum of the scores for mutations in the first patient but not in the second, and the scores for mutations in the second patient but not in the second. It can be generated from the sum of scores for mutations that are absent in one patient. Using the determined scores, patients can then be grouped into cohorts based on similarity of scores for one or more genes.

In some embodiments, cohorts, such as smart cohorts, are described in U.S. Patent No. 16/732,138, entitled "Method and Process for Predicting and Analyzing Patient Cohort Response, Progression, and Survival".

In some embodiments, facilitate identification of one or more smart cohorts of patients whose likelihood of disease progression and/or survival is substantially different than expected, e.g., significantly longer or shorter than expected Predictive models can be developed that Information from these cohorts can then be examined to identify one or more key factors that could potentially contribute to the survival profile of the cohorts. Identification of smart cohorts can provide precision medicine outcomes for specific patients, identify potential areas of interest for targeting medication research, and/or provide unexpected opportunities to expand patient coverage on medication. It can be used to help identify potential possibilities.

Cohorts may be generated by molecule type, therapy type, drug, drug dose, predecessor drug, predecessor treatment outcome, clinical information, and/or other suitable information.

An exemplary analysis using one or more analytics modules 36 is predictive drugs, one or more, that can be used to effectively treat a patient based on patient genetic data and real-world clinical data. One or more therapeutic engines may be provided that can generate reports listing predicted effective doses for drugs, potential drug side effects, and/or other therapeutic predictions. A therapy engine may include a knowledge database 40 . The knowledge database includes numerous gene-drug interaction studies, cohort studies, U.S. Food and Drug Administration (FDA) label recommendations, in vitro studies of gene-drug interactions, and effects on selected neuropsychiatric drugs in the context of the genes included in the panel. Real-world clinical data can be included, including information regarding primary metabolic enzymes, PD, and/or immune-related genes that are affected, and/or other suitable information regarding drug-gene interactions. In some embodiments, the report has two distinct parts, a test results portion, which may include phenotypes associated with the nucleic acid sequences, and a supplemental section, which may include other information for the clinician to consider. can be divided into The Supplementary Section includes, for example, a listing of one or more drugs associated with the nucleic acid sequence, a classification for each drug in the listing, clinical, phenotypic, such as those associated with prescribed or administered drugs, It may include a view of a patient's clinical characteristics similar to that of the patient for which the report was generated, such as genotype and/or morphological characteristics, patient outcomes, and the like. When delivered in paper or PDF format, the test results portion may be separated from the supplemental section by appearing on a different page. When delivered through the portal, the user may have to select a first hyperlink to view test results and a second hyperlink to view supplemental information. In other embodiments, the supplemental section may be displayed to the user on the same page or through the same hyperlink, but using titles, colors, font sizes, or other image or stylistic features. can be distinguished by

KBD40 can contain information about numerous drugs and interactions between drugs and genes. In some embodiments, in order for a drug to be included in the knowledge database, the drug may be required to have at least one of an indication of use for mental conditions or a black box PGx warning on the label of the drug. . In some embodiments, the knowledge database can include drugs that are used to treat medical conditions for which the drug is not marked for treatment (ie, non-FDA approved use). For FDA non-licensed use, prior to including a drug in the knowledge database, it may be necessary for healthcare professionals to verify that the drug is suitable for FDA non-licensed use.

For drugs contained 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, the resource is the Clinical Pharmacogenomics Implementation Consortium (CPIC), Dutch Pharmacogenomics Working Group (DPWG), DailyMed Label resource (i.e., FDA), Pharmacogenomics It can be curated from primary literature sources such as the Knowledge Base (PharmGKB), PubMed, and/or ancillary resources such as the Psychoactive Drug Screening Program (PDSP Ki) Database.

A knowledge database expert can then generate clinical feasibility entries based on the curated resources (eg, using system 10 and/or display device 16 of FIG. 1). As explained above, clinical feasibility 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, clinical feasibility entries include disease, metabolic-related gene, phenotype, one or more alleles, drug, drug dosage, patient demographic information (e.g., gender, ethnicity, etc.). , level of evidence, and/or other suitable clinical feasibility parameters. Clinical feasibility entries can assist physicians in treating patients. For example, the therapy engine may receive patient information including molecular information, disease diagnosis, and/or patient demographic information, and identify one or more preferred clinical feasibility entries based on the patient information. can.

As explained above, clinical feasibility entries are binned into a hierarchy of strength of evidence by patient disease and/or drug-gene interaction concordance, as may be defined by professional society guidelines. obtain. Knowledge database professionals can use the system 10 to sort the clinical feasibility entries into a hierarchy.

Based on the list of criteria, a knowledge database expert can decide what level of evidence should be assigned to a given clinical feasibility entry. In some embodiments, the criteria are the existence of sufficient evidence to determine that a given enzyme is the primary metabolizing enzyme for a given drug, intragenic pharmacogenomics for pharmacokinetic parameters of the drug Existence of sufficient evidence to determine the effects of pharmacogenomic variants on drug PD parameters Existence of sufficient evidence to determine the effects of intragenic pharmacogenomic variants on PD parameters of drugs existence of sufficient evidence to determine the relevant implications (e.g. dosing, efficacy, tolerability, adverse drug events); existence of evidence for the involvement of multiple genes in drug metabolism; It can include whether the algorithm is likely to be needed to best approximate the pharmacokinetics and clinical outcome of the drug.

A therapy engine may also include one or more machine learning algorithms or neural networks. A machine learning algorithm (MLA) or neural network (NN) may be trained from the training data set. For depressive conditions, an exemplary training data set may include patient clinical and molecular details, such as those curated from electronic health records or genetic sequencing reports. MLA includes linear regression, logistic regression, decision trees, classification and regression trees, naive Bayes, supervised algorithms using nearest neighbor clustering (such as algorithms where the features/classifications in the dataset are annotated), a priori, means clustering , principal component analysis, random forests, unsupervised algorithms using adaptive boosting (e.g. algorithms where the features/classifications in the dataset are not annotated), generative approaches (gaussian mixtures, multinomial mixtures, hidden markov model, etc.), sparse separation, graph-based approaches (mincut, harmonic functions, manifold regularization, etc.), heuristic approaches, or semi-supervised algorithms using support vector machines (several features/ algorithm whose classification is annotated). The NN includes conditional random fields, convolutional neural networks, attention-based neural networks, long short-term memory networks, or other neural models, and the training data set includes multiple samples and RNA expression data for each sample. MLA and neural networks identify different approaches to machine learning, but the terms may be used interchangeably herein. Thus, references to MLA may include corresponding NN, or references to NN may include corresponding MLA.

Training involves identifying common clinical or genetic characteristics that patients in an entire cohort or patient database may exhibit, labeling these characteristics as they occur in patient records, and training the MLA to identify patterns in patient outcomes based on 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 enter additional data (such as prescribed therapy) via display device 16 , which may be transmitted to server 20 .

Display device 16 may provide a graphical user interface (GUI) to provider 12 . The GUI can, in some embodiments, be interactive and provide both comprehensive and concise data to provider 12 . In some embodiments, providers 12 may include physicians 12a (eg, psychiatrists), physician assistants, and/or other professionals 12c. In some embodiments, provider 12 may be a knowledge database expert, as described below. As an example, the GUI offers intuitive menu options, selectable features, color and/or highlighting to indicate the relative importance of data, and a sliding scale timeline to view disease progression. can contain. The GUI can be tailored to the type of provider or even customized for each individual user. For example, a doctor can change the default GUI layout based on personal preference. Additionally, the GUI can be adjusted based on patient information. For example, the display components and/or the components and the order of information contained in the components may be changed based on the patient's diagnosis.

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

graphical user interface

In some embodiments, a graphical user interface (GUI) may be included with system 10 . The GUI can assist providers in prevention, diagnosis, treatment, and planning for mental illness and/or patients with psychosis. Advantageously, the GUI contains all relevant data needed while providing a single source of information for 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.

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

The GUI 50 can provide a secure, centralized, user-friendly way to receive test status updates and results for patients. In some embodiments, the GUI 50 facilitates online ordering (e.g., electronically and expeditiously placing test orders through HIPAA-compliant forms), searching (e.g., efficiently locating relevant patients and/or groups of patients). fuzzy search and/or filtering for ), order status monitoring (e.g. when an order is placed, when a sample is submitted, when processing has begun on a sample, when test results are expected to be sent , and/or viewing information about an order, including when the order will be completed), and/or data insights (e.g., patient monitoring, which enables clinicians to make data-driven decisions about patient care and treatment). Services and/or other features can be provided that include additional information in addition to molecular information about the patient, such as patient-reported outcome (PRO) data, that allow clinicians to have a more holistic view. In some embodiments, the GUI 50 may provide options for the physician to select the cadence at which they would like to collect outcomes from patients and which rating to assign to each patient.

The GUI 50 can be presented through a secure, centralized portal that allows clinicians to receive status updates and results for patients. The portal allows clinicians to electronically place test orders, use fuzzy searching and/or filtering to find information about patients or groups of patients, when orders were placed, when samples were submitted, and when samples were submitted. It may allow clinicians to see when processing has started, when test results can be expected, and when orders will be completed. In conjunction with molecular information about the patient, the GUI and/or reports enable clinicians to have a more holistic view of the patient that allows them to make data-driven decisions about patient care and treatment. It may provide enabling patient-reported outcome (PRO) data. In some embodiments, the physician may choose the cadence at which they would like to collect outcomes from patients and which assessment to assign to each patient. 3A-3D illustrate additional aspects of GUI 50. FIG. In particular, GUI 50 is shown displaying patient identifier 56 (eg, patient name), menu 58 , summary portion 60 , diagnostic tiles 62 , and diagnostic timeline 64 .

In some aspects, menu 58 may comprise a number of selectable options grouped by topic. As shown, a “summary” 60 can include a diagnosis tile 62, a therapy tile 68, a molecule overview tile 76, and an image overview tile 78. Summary portion 60 may further include biographical information, patient medical history, patient demographics, family history, co-morbid conditions, treatment notes, and the like. The “Summary” menu option is shown selected as indicated by the darkened area within menu 58 . As indicated by menu 58, "reports" can include molecular and image information. In addition, "intervention" may include therapeutic modalities and clinical trial information. Additionally, menu 58 may include an analytics portion. In some embodiments, menu 58 may have more (or fewer) selectable options, and menu option titles may differ from the examples shown here.

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

The diagnostic timeline 64 not only assists the provider in quantitatively assessing patient disease progression, but can also show the effectiveness of various treatments over time. As shown, the patient's depression worsened after using this particular drug. Therefore, providers can safely discontinue drug therapy and continue genetic sequencing to determine if a patient is predisposed to risk or has problems metabolizing some drugs (or drug types). did

The provider may hover over (or select) a diagnostic point within diagnostic timeline 64, as shown in FIG. 3B. An information box may then appear, which may contain summary information for the diagnostic point. As shown, on September 3, 2018, the patient was diagnosed with major depressive disorder, had a PHQ-9 score of 22 (severe), and had two structured clinical documents available. In some embodiments, the provider can select "view structured clinical documents" and the GUI 50 can display clinical documents related to diagnostic points.

Referring to FIG. 3C, summary portion 60 is shown to include therapy tile 68 (“Tempus Insights”). Therapy tile 68 may include a summarized “total therapy” portion 70 . In some embodiments, the overall therapy portion 70 can include a summary of how an individual patient may respond to available therapies. As illustrated, 3 therapies are categorized as "Use as directed", 6 therapies are categorized as "Dosage and Administration", 10 therapies are categorized as "Important", and 3 therapies are categorized as "Important". Therapies are classified under "resistant". Another example would be to classify "use as directed" into "standard dosing" and "contraindications" into the category of "serious," "resistant," or other drugs with expected side effects. can be titled.

According to the reported drug classification, health care organizations may consider initially treating patients with one or more of the three "use as directed" regimens. In addition, the provider considers therapy in terms of "dosage and method of administration" and "important implications," generally providing doses of the drug, as well as the method of administration, duration, frequency, and drug combinations, along with possible implications for each of the therapies. For example, a subset of patients may respond well to drug therapy below a certain dosage threshold and respond negatively to doses above a certain dosage threshold. Significant implications can be inferred from drug therapy as warnings from FDA guidelines or reports, or clinical trial reviews. Conversely, providers should consider closely monitoring patients when treating them with the three therapies classified as “resistant” or avoiding treating them with these therapies. may In some embodiments, recommended therapies (such as "use as directed") can be identified by unique markers and/or colors (check marks on green background, as shown). Similarly, non-recommended therapies (such as "resistant") can be identified with a unique marker and/or color ("X" on red background, as shown). In this way, the provider can efficiently identify which therapies to try and which therapies to avoid.

Therapy tile 68 may further include therapy list 72 . The therapy list 72 may provide detailed descriptions (primary use, side effects, etc.) for each therapy. Each of the therapies may be associated with a corresponding classification, allowing providers to quickly find recommended treatments. In some embodiments, each therapy can include a predicted response moiety 74. As shown, predicted response portion 74 may be displayed on GUI 50 as a segmented line, with each segment and/or color corresponding to a different response. For example, one colored segment can represent patient response defined by a decrease in PHQ-9 score of greater than 50%. In addition, another colored segment can represent patient response defined by remission (PHQ-9 score less than 5 after 3 months of treatment). In some embodiments, the provider can select "view full results" and the GUI 50 can 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 the cohort associated with the patient who received the therapy identified in the list and for whom response information is available.

Referring now to FIG. 3D, summary portion 60 is shown to include molecule overview tiles 76 and image overview tiles 78 . In some embodiments, molecular overview tiles 76 can include various genes and enzymes as well as corresponding test results for the patient. For example, the gene sequence of the CYP2D6 gene for this particular patient has been shown to render the patient a poor metabolizer, and the gene sequence of the HTR2A gene has been shown to result in normal activity. Molecular overview tiles 76 can provide providers with high-level information for use in determining drug types and/or dosages for patients. In some embodiments, the provider can select "view full results" and the GUI 50 can then display detailed molecular information. In some embodiments, the molecular overview tile 76 includes genetic data, detected star alleles, and/or predicted phenotypes for the patient, and the supplemental data section (e.g., detailed information 96) includes drug gene Including interactions.

The predicted phenotype may be influenced by the variants, alleles, and/or allele combinations detected in the patient for specific genes and/or the combination of these data for all genes .

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

4A-4E, GUI 50 can display detailed molecular data for a patient. Menu 58 may again provide an indication of where the provider is within the platform. Molecular reports 90 include test dates (e.g. 08/15/2018), as well as variation lists 92, additional variants of interest 98, human leukocyte antigen (HLA) typing 100, therapeutic implications 102, and/or diagnostic implications 104 can include

As shown in FIG. 4A, change list 92 can include detailed information for various genes and/or enzymes of the patient. In some embodiments, each listed variation can have a selectable therapy icon 94 . A provider can select a therapy icon 94 and the GUI 50 can then display detailed information 96 for the corresponding change. In some embodiments, an information window may be displayed overlaid on molecule report 90 . As shown, detailed information 96 can include a list of known drug interactions that correspond to the change. Detailed information 96 may include drug name, drug class, drug type, level of evidence, and/or gene drug interaction. The Gene Drug Interaction column allows classification of the type of interaction (“use with caution,” “contraindication,” etc.). In some embodiments, the severity of an interaction can be identified via a color indicator (yellow indicating moderate, red indicating severe, etc.).

As shown in FIG. 4C, molecular report 90 can include additional variants 98 of interest. The list can display patient genetic variants that are commonly expressed in patients with a particular diagnosis. As an example, a 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. In addition, ANK3 is believed to be a susceptibility gene specific to bipolar disorder. Accordingly, the additional variants of interest 98 can provide the provider with information that can directly affect patient diagnosis and/or treatment.

In some embodiments, a selectable reference icon may be provided and the GUI 50 may display detailed information after selection. Molecular report 90 can further include HLA typing 100 . HLA genotypes can be determined using RNA or DNA sequencing data and displayed via the GUI50. HLA typing 100 may be of value to researchers or clinicians and to predict 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 epidermolysis in some patients taking drugs such as carbamazepine. Molecular Reports 90 is also based on custom curation and data management of published scientific research and additional primary literature, for example in KDB40, and interpretation of these data selected for display in Molecular Reports 90. obtain. In some instances, HLA Typing 100 includes the caveat that the information is for research purposes only and should be verified with histology or other clinical pathology prior to use in clinical decision-making. can be described. In other instances where the HLA typing 100 may be used for clinical decision making without being verified by histology or other clinical pathology, the precautionary statements may be omitted.

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

5A-5B, image data 106 is illustrated. Menu 58 may again provide an indication of where the provider is within the platform. In some embodiments, GUI 50 can display graphical representations of image data (such as image 108). In one example, the GUI 50 and graphical representation of image data may be adjusted or customized based on the type of image 108 received by the patient. Types of images 108 may include patient facial representations or images, MRIs, CT scans, PET scans, and the like. Additionally, coloration of image 108 can identify clinically significant features of the patient's brain. A view menu 110 is provided which may allow the provider to select different viewing angles corresponding to the image data.

Image data 106 may include therapeutic implications 112 . As shown, the system used the image data 106 to determine treatments that the patient may be resistant to. For example, nortriptyline lists two applicable genes containing variants, namely CYP2C19 and CYP3A4. Based on patient genetic alterations, CYP2C19 is highlighted. Additionally, nortriptyline is identified as a potential refractory treatment for patients (eg, "resistant"). Such imaging data may include MRI or CT scans of the brain, which are observed, analyzed, and reported to physicians for additional insights based on clinical information associated with the patient during the course of treatment. can allow it to be done. In addition, organoid labs are investigating drugs and drugs that may be caused by specific genetic variants, alleles of interest, phenotypic traits, or other conditions in patients detectable by analyzing molecular and/or omics data. /or Neurospheres or other organoids with genetic variants, alleles of interest, or other phenotypic traits may be cultured for imaging and testing to determine effects on disease initiation and progression. In one example, types of molecular and/or omics data include genomics, proteomics, metabolomics, or other data collected by studying molecular or cellular activity presented in patients. Brain examination and imaging may be combined to complement the insights that may be included in the report.

6A-6E, GUI 50 can display therapy portion 114. FIG. Menu 58 may again provide an indication of where the provider is within the platform. In some embodiments, therapy portion 114 can include 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. can. Additionally, various filters may be selected based on what type of therapy is desired by the provider. As illustrated by FIG. 6B, intervention timeline 116 may be updated to display symptom data 124 when “symptoms” is selected within display option 122 . In some embodiments, intervention timeline 116 can display symptom data over time. In one example, symptoms may be reported by third parties, including clinicians, patients, and/or family members of patients, and symptom data may be collected through the curation of physician notes, direct electronic medical record (EMR) integration, and/or or obtained through integration with patient, family, or clinician facing applications.

As shown in FIGS. 6C-6D, intervention timeline 116 can display predicted symptoms, PHQ-9, and/or other measured data 126 based on the therapy selected. For example, the provider has selected selegiline from the potential therapies 120 and the intervention timeline 116 can display expected changes if the drug is subsequently prescribed. In one example, each symptom may respond differently to a range of treatment options. Understanding which treatments are most likely to produce the desired outcome can help providers make decisions related to personalized medicine in mental illness. The predicted change may be used by system 10 to calculate a predicted range of patient adherence, which may be displayed in therapy section 114 . Patient adherence is defined, for example, as a percentage of prescription medication taken, as a measurable amount of a drug or drug metabolite to be detected in the patient's bloodstream, as the frequency of prescription medication supplementation required by the patient and/or as the patient It can be quantified as a scaled value based on the degree of accuracy in following the treatment regimen, or as another indicator of adherence. In some embodiments, GUI 50 can display dosage information 128 . As shown, the provider can select different dosages of selegiline using drop-down menus on intervention timeline 116 . FIG. 6D contains predicted symptoms and PHQ-9 data126 based on a selegiline dose of 225 mg/day, while FIG. Contains data 126. Thus, the provider can interact with the GUI 50 to determine recommended treatments and even recommended dosages for individual patients.

Referring to FIG. 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 can 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 the symptoms associated with that drug.

7A-7D, cohort details 132 may be displayed via GUI 50. FIG. 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 can compare individual patients with other patients that share similarities. As shown, individual patients may be represented in the center of the radar plot 134, and patients with similar or different indices may be represented by additional dots around the central index. Radar plot 134 has two measures for depicting similarity: radial distance for similarity between the center point and each plotted point, and distance for similarity between each plotted point. Angular distances can be presented. Similarities can include, but are not limited to, depression, demographics, physical characteristics, genetic data, imaging data, and medical history. FIG. 7A includes a radar plot 134 utilizing a patient data set corresponding to the selected therapy selegiline. Conversely, FIG. 7B includes a radar plot 140 utilizing a patient data set 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 by several methods. 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. Multiple response measures may be normalized to represent many response measures as a single endpoint, for example, by integrating multiple response measures into a single score value. Metadata for each drug (which may include, but is not limited to, dose, prescribing diagnosis, likelihood of adherence or distribution, adverse events, etc.) can also be correlated to response.

In some embodiments, cohort details 132 may include menu 138 . The provider can view selections via menu 138 . 7A-7B include cohort details 132, while FIGS. 7C-7D include cluster analysis 150. FIG. As illustrated by FIG. 7C, the donor can select from a list 142 of cluster patterns (including but not limited to original diagnosis, dose, molecular profile, response). The displayed clusters 144 allow individual patients to be compared to other patients. As shown, individual patients are represented by "star" indicators and other patients are represented by dots. Again, the dot distribution can show similarities. As shown, FIG. 7C includes clusters 144 displayed based on the original diagnosis. Conversely, FIG. 7D includes clusters 146 displayed based on molecular profiles. These clusters can be used to determine diagnostic, prognostic, therapeutic implications, and studies such as diagnostic, subtyping diagnostic, therapeutic, prognostic.

8A-8B include a patient report 800 that can be generated for display via GUI 50 as a report similar to molecules 90 and images 106, or exported as a PDF document so that it can be printed. 2 shows a further aspect of In particular, the GUI 50 displays patient identifiers (such as patient name), such as 56 previously indicated, diagnostic tiles, such as 62 previously indicated, and an identifier "Accession No." and/or an identifier "xG" for the type of NGS panel. Other document identifiers, such as , are shown displayed in the 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 second portion 820 . The second part 820 contains information "Tempus" identifying the laboratory that generated the panel results and report, the panel identifier and number of genes associated with the sequence being reported for the patient "xG 12 genes", the nature of the report. It can further include "Psychotropic: Combinatorial pharmacogenetic test", the type of specimen collected for the genetic testing panel "Blood", as well as the date of collection by the ordering institution and the date of receipt by the laboratory. In one example, the "xG 12 genes" panel was designed to target and analyze gene sequences in several intronic regions of the human genome, in addition to all known exonic regions, comprising approximately 20,000 genes. Whole exome panel with spike-in probes added to . In one example, the report may not display information for all of the genes analyzed by the gene panel.

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

Fifth portion 850 is known to affect drug absorption as well as its expected metabolic absorption (FDA approved, published, peer reviewed, or identified by analytics module 36). Pharmacokinetic genes can be identified. Pharmacokinetics is the study of how drugs are affected in the body, including drug absorption, distribution, metabolism, and excretion. Differences in drug efficacy between patients can be influenced by genetic differences in patients' ability to respond to drugs. There are many enzymes that have been identified as important for drug metabolism. Different phenotypes have been associated with defective, decreased, normal, or increased activity of these enzymes leading to variable drug responses. In addition, linear graphs are presented to visually identify the expected metabolic absorption based on identified genes along with absorption ratings such as ultrafast, intermediate, normal, or poor metabolism.

The sixth portion 860 is known to have an effect on how the drug may affect the patient as well as the expected effect the gene may have on a particular drug or therapy. can identify pharmacokinetic genes that are Effects can include those manifested in the patient. Genes are stimulated, depressive, blocking/antagonistic, stabilizing, exchange/replacement, directly beneficial or adverse chemical reactions (collected as S/S, G/G, Present, A/T, etc.) etc., can be listed together with possible drug effects. In an alternative embodiment, the pharmacokinetics described above with "Current Drugs," "Metabolism Results," which may list drugs currently being taken by the patient for at-a-glance comparison with any drug being studied. and pharmacokinetics genes, and alternative sections such as the "Adverse Events" field to describe the pharmacogenomic implications of the gene variant. In another example, alternative or additional field names may be used to group or describe their pharmacogenomic implications.

FIG. 8B shows a seventh portion 870 that may identify treatments including antidepressants such as desvenlafaxine, levomilnacipran, selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors. Antibiotics such as uptake inhibitors (SNRIs), tricyclic antidepressants (TCAs), monoamine oxidase inhibitors (MAOIs), dopamine reuptake inhibitors, noradrenergic antagonists, serotonin antagonists and reuptake inhibitors (SARIs) A short summary including classes of depressants is presented. Antidepressants can be sorted according to the expected pharmacogenomic or pharmacokinetic response and the genes that contribute to each response. In addition, Reference ID and PubMed ID (PMID) should be identified for each antidepressant along with notes on what potential pharmacogenomic implications may require, such as increased or decreased prescribing frequency or dose intensity, side effects, or other outcomes. can be linked to Reference IDs can be linked to internally curated drug databases, FDA published reports, CPIC guidelines, clinical trial reports, or reports from insurance or pharmaceutical companies. PMIDs may link to PubMed publications related to identified drugs and/or genes that support the relationship between genes and therapies. Each identified therapy is also accompanied by a field that identifies dosage guidelines or possible dosage adjustments, based either alone on the detected gene sequence of one or more genes, or in combination with phenotypic or imaging data. obtain. For example, if the detected gene sequence of a gene encoding a particular enzyme indicates that the patient is a normal metabolizer, then a standard dosing classification for that treatment is indicated, which may require adjustment. or dosing adjustments may be indicated for poor metabolizers. If two gene sequences are detected that adversely affect metabolism, for example, the detected sequence for one of the genes indicates that the patient is a poor metabolizer, and the detected sequence for the other gene indicates that the patient is a poor metabolizer. Contraindications can be identified to alert the physician to potential adverse effects or harms that may recommend dose adjustments, if applicable, in the indication of a normal metabolizer.

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

A patient report may be associated with a patient and may contain other types of profiles, including genetic sequencing information and/or molecular profiles of the patient, particularly when related to clinical characteristics described in the patient's medical records and treatment notes. In addition, many sets of genetic sequencing information and/or other types of profiles each associated with a different patient, particularly each set relating to clinical characteristics described in the other patient's medical records and treatment notes. may include comparing when doing. A patient report may display a depiction or plot showing relationships between multiple patients and similarities between patients based on genetic sequencing information and/or other types of profiles. These comparisons allow patient reports 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 clinical characteristics of one patient with characteristics of another patient and/or multiple patients, or indirect, i.e., multiple patients It may include comparisons that are leveraged to predict patient response to treatment based on associated aggregated data.

The patient report may list drugs that are contraindicated for the patient, drugs that may be suitable for the patient's therapeutic use and doses or other regimens for each drug, and warnings or information regarding potential drug-drug interactions. Drugs, doses, and other therapies selected for listing in the patient report are collected from the patient's genetic sequencing results, molecular profile, including RNA or protein expression levels, patient medical records, treatment notes, and patient-reported outcomes (PRO ), sequencing information or other molecular profiles of other patients and/or similarity to reported successful treatment regimens in molecularly similar patients, 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. data-based.

System 10 may interface with mobile applications that patients use to record patient-reported outcomes (PROs) and transmit information between system 10 and mobile applications. The system 10 generates or receives geolocation tracking information from the patient and calculates placement metrics, including length of time spent outside the home and other metrics that may be correlated with severity of depression. can. A patient report may include calculated placement metrics and recommendations to the patient based on these metrics and/or PROs.

In some embodiments, a PRO may be any report on 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 can be used to measure treatment risks/benefits and to inform and guide patient-centered care and clinical decision-making. Mobile applications that record PROs can provide data (eg, patient data that can enable clinicians to automatically track patient progress and make informed decisions about measurements). In some embodiments, mobile applications can provide improved efficiencies (e.g., the PRO can provide PHQ-9 can provide patients with personalized evidence-based assessments, including scales such as In some embodiments, the mobile application may automatically deliver, score, and chart results for assessments, so clinicians can submit paperwork to track patient progress and outcomes. You can spend less time writing. In some embodiments, mobile applications can provide 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 that improves patient care. In some embodiments, the mobile application can provide reimbursement information. For example, every time a patient completes an assessment through the PRO app, a clinician may be eligible to receive reimbursement from insurance without wasting time filing a claim.

In some embodiments, the mobile application can provide data to the patient. 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 activity, energy, and symptoms. In some embodiments, mobile applications can provide insight into how sleep, exercise, and movement patterns affect a patient's mood over time. In some embodiments, mobile applications can provide a way for patients to share symptoms and progress with clinicians to create a more personalized care experience.

In one example, the PRO GUI may report the patient's health status obtained directly from the patient. This data can be used to measure treatment risks/benefits and to inform and guide patient-centered care and clinical decision-making. The mobile application used by patients to record PROs allows clinicians to automatically track patient progress, make measurement-informed decisions, and make personalized, evidence-based decisions without interrupting the clinician's workflow. An assessment may be administered to the patient, allowing a scale such as the PHQ-9 to be combined with regular check-in measures. The PRO GUI may automatically deliver, score, and chart the results for these assessments. The app may facilitate tracking patient progress and translate findings into personalized treatment decisions. Reports may be embedded with patient-specific insights derived from real-world evidence to provide data to clinicians so they can improve patient care. For patients, the app enables quick daily check-ins for patients to monitor their mental health, track positivity, energy, and symptoms, and track their sleep, exercise, and behavior patterns over time. and/or provide a means for sharing symptoms and progression with clinicians to create a more personalized care experience. Patient reports should list clinical trials in which patients meet the trial inclusion criteria and/or are geographically close to the patient or do not require long-distance travel or relocation may be

Specific examples of drugs that may be included in patient report 800 for any reason include, but are not limited to, abacavir, allopurinol, alprazolam, amitriptyline, amoxapine, aripiprazole, armodafinil, asenapine, atomoxetine, brexpiprazole, bupropion, bupropion odor. Hydrochloride, buspirone, carbamazepine, cariprazine, chlordiazepoxide, chlorpromazine, citalopram, clomipramine, clonazepam, clopidogrel, clorazepate, clozapine, codeine, desipramine, desvenlafaxine, detetrabenazine, dextromethorphan/quinidine, diazepam, doxypine , duloxetine, escitalopram, esketamine, eszopiclone, fluoxetine, fluphenazine, fluvoxamine, fluvoxamine, gabapentin, haloperidol, iloperidone, imipramine, isocarboxazid, lamotrigine, levomilnacipran, levothyroxine (containing thyroxine and/or 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, selegiline (may be administered transdermally), sertraline, tamoxifen, temazepam, tetrabenazine, thioridazine, thiothixene (thiothixene), topiramate, tranylcypromine , trazodone, triazolam, trifluoperazine, trifluoperazineea, trimipramine, tropisetron, valbenazine, valproate, venlafaxine, vilazodone, voriconazole, vortioxetine, warfarin, ziprasidone, zolpidem, zonisamide.

9A-9H illustrate an alternative embodiment of patient report 800. FIG. 8A and 8B, patient report 800 may further include summary 910, legend 920, pharmacogenomic outcome details 930, and secondary findings details 940. Patient report 800 may further include other notes or instructions (not shown).

Summary 910 is based on individual and/or aggregated patient data, including clinical data, imaging data, behavioral data, molecular data, pharmacogenetic data, and other gene sequences detected in the patient's body. Or it may point to the drugs most likely to be successful in ameliorating psychiatric symptoms. The notes of the third portion 830 may include notes of the geneticist or notes regarding incidental germline findings. A legend 920 may include the classifications used in the seventh portion 870 and a brief description of each classification to guide a healthcare provider in interpreting the classifications.

In the illustrated example, the seventh portion 870 is organized by treatment type, including "Antidepressants," "Antipsychotics," "Anticonvulsants," "Anxiolytics," and "Other Medications." can be 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 is organized by drug class such as "SSRI", "SNRI", "tetracyclic", and each class is classified as "standard dose", "adjusted dose-increase", "adjusted dose-decrease", Organized into treatment categories, including "Contraindications", "Preferred Alternative Therapy - Side Effects", etc., individual treatments may be listed under the category along with the rationale for assigning that category to that treatment.

Basis for classification may include pharmacogenetic results from sequencing the patient's genome. Pharmacogenetic results are the gene name and the phenotype indicated by the gene sequence associated with that gene detected in the patient, or the allele name and the positive or negative detection of that allele in the patient. may contain the results of Pharmacogenetic results include published scientific research papers, databases, and published guidelines, including Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines, DPWG, and government agencies, including the U.S. Food and Drug Administration (FDA). , may include sources or references that provide evidence to support the relationship between pharmacogenetic outcomes and effects on treatment. In some embodiments, therapies that may be more effective for patients at non-standard doses are "dose adjusted-increase or decrease" and/or specific marker and/or may be labeled as color (arrows pointing up and down on a blue background, as shown). Similarly, other markers and/or colors can be used to label therapies under other classifications.

Similar to the fifth and sixth portions 850 and 860, the pharmacogenomic outcome details 930 may include one or more gene names, allele identifiers associated with that gene detected in the patient, or gene Allele identifier (in this example, HLA-A*13:01 or HLA-B*15:02), and presence/absence status indicating whether that allele was detected in the patient, as well as each detected It can include phenotypes associated with alleles or combinations of detected alleles. Examples of phenotypes include poor metabolizers, intermediate metabolizers, normal metabolizers, extensive metabolizers, ultrafast metabolizers, increased sensitivity, and increased risk. The pharmacogenomic results details 930 may also include accession numbers that identify the placement of genes according to a particular reference sequence build of the human genome, including hg19 (in this example, the accession numbers are "NM_..."). is of the form).

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 contains one or more gene names plus variants (c.4965C?G, p.Tyr1655*, stop gain), accession numbers, and genomes associated with each gene or variant listed A description of the arrangement (Chr13:32913457) may be included. This section may further include recommendations for the patient to obtain genetic counseling.

Taken together, the system 10 can not only be used to improve treatment and care for individual patients with depression, but by combining key pillars of healthcare data, it can be used to catalyze research. can help patients who are not yet ill.

In a first embodiment, a genetic testing panel (targeted panel) may analyze patient samples for the presence of several gene sequences or genetic variants, the results of which are transmitted via the system 10, e.g. In report 90, it may be presented to a physician, medical professional, or other individual. Knowledge database 40 may be provided to system 10 such that desired genes, gene variants, or mutations may be linked to one or more pharmacogenomic interactions. The system may then compare the gene, variant, allele, or gene variant listings from the sequencing report to the knowledge database 40 to generate the report. In addition, knowledge database 40 may contain information regarding any of the following panels. One or more panels may detect genetic variants in one or more genes involved in metabolism, pharmacokinetics, and/or immunogenicity of one or more drugs.

One type of panel, such as a panel directed to detecting information relevant to pharmacogenomics across many fields of medicine, is cyp2c19 (nm_000769.4), cyp2d6 (nm_000106.6), cyp1a2 (nm_000761. 5), cyp2b6 (nm_000767.4), cyp2c9 (nm_000771.4), cyp3a4 (nm_017460.5), HLA-A (NM_002116.8), HLA-B (NM_005514.8), HTR2A (NM_000621.4), SLC6A4 (NM_001045.5), UGT1A4 (NM_007120.2), UGT2B15 (NM_001076.3). The arrangement of exons and/or introns associated with a gene can be defined according to published database entries. Database entries may be identified by an accession number, examples of which are listed in parentheses after the name of each gene of this disclosure. In this example, the alignments listed in the entries associated with these accession numbers follow the human reference genome hg19.

In another example, genetic test panels can be used to target suspect genes of interest associated with a particular medical field or a particular medical condition (such as depression). These panels, in addition to the genes already listed, included 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_001302688.1), bdnf (nm_001709.4) ), cacna1c (nm_199460.3), ces1 (nm_001266.4), comt (nm_000754.3), cyp1a2 (nm_000761.5), cyp2b6 (nm_000767.4), cyp2c19 (nm_000769.4), cyp2c9 (nm_000771.4) , cyp2d6 (nm_000106.6), cyp3a4 (nm_017460.5), cyp3a5 (nm_000777.5), cyp4f2 (nm_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), grik1 (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_001134) ), kcnip1(nm_001034837.2), kcnj11(nm_000525.3), kcnq1(nm_000218.2), LDLR( nm_000527.4), lipc(nm_000236.2), mc4r(nm_005912.2), mthfr(nm_005957.4), mtrr(nm_002454.2), neurod1(β2)(nm_002500.4), nqo1(nm_000903.3), nr1h3(nm_00569.3), nudt15(nm_018283.3), oprm1(nm_000914.4), pax4(nm_006193.2), polg(nm_002693.2), ppara(nm_002693.2), pparg(nm_015869.4), ppargc1a (nm_007215.3), prkaa1(nm_006251.5), prkab2(nm_005399.4), ptprd(nm_005399.4), rbp4(nm_006744.3), ryr1(nm_000540.2), slc22a1(oct1)(nm_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(nm_001045.5), slco1b1(nm_006446.4), sod2(nm_000636.3), stk11(nm_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 VKORC1 (NM_000373.3) may include one or more additional genes.

Still other genetic testing panels use large datasets of genetic information for research purposes, such as identifying unknown biomarkers that identify a patient's pharmacogenetic response, or biomarkers of susceptibility to medical conditions such as depression. can be used for the purpose of developing Such a panel would include one or more of the genes in Table 1, in addition to the genes already listed, and provide gene sequences for all exonic regions within the human genome, in addition to selected intronic regions. can generate whole exome sequencing data containing Genetic variants detected in patients are matched with entries in KDB40 to predict patient response to various therapies, which can be displayed in patient report 800 and/or GUI 50, as described above.

Specific genetic variants (such as SNPs at identified loci) at specific genomic locations associated with genes detected by these genetic testing panels, sequence analyses, microarrays, or other methods are identified as reference SNP clusters ( rs) can be identified according to the ID and/or the genomic location of the gene variant. In this example, genomic locations are 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 test panels, sequence analysis, or another method are They can be distinguished according to their nucleotide position on a given chromosome.

Some genes, including CYP2C19, CYP2D6, CYP1A2, CYP2B6, CYP2C9, CYP3A4, HLA-A, HLA-B, UGT1A4, and UGT2B15, have special genes, known as star alleles, to define their genetic variants. Use proper nomenclature. A star allele is a group of variants that define an allele and predict the functional outcome of that allele. In one example, *1 refers to the star allele 1, which can be the normal reference allele, and *1xN refers to a gain of N copies of allele 1 compared to the normal reference genome. The copy number gain for 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, a particular allele of a particular gene, a combination of two alleles of a particular gene (which can, for example, determine or affect a patient's phenotype), and genetic test panels of these. Allele copy number alterations (gains or losses) detected by, sequence analysis, or another method are defined by standardized identifiers, including but not limited to star allele nomenclature, and/or can be referenced.

For example, certain genetic variants associated with the CYP2C19 gene include variants located within exons of the gene as well as those located within introns, rs IDs namely rs2860840, rs1326830, rs11188072, rs11316681, rs111490789, rs17878739. 、rs7902257、rs11568732、rs12248560、rs4986894、rs367543001、rs17885098、rs12768009、rs17884832、rs7916649、rs17878649、rs12769205、rs17879992、rs7088784、rs72558186、rs12571421、rs12767583、rs4494250、rs4417205、rs7915414、rs28399513、rs3758581、rs4917623、およびrs55640102で識別されmay contain variants that Specific CYP2C19 alleles are *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 CYP2C19 star alleles.

Particular intronic regions associated with the CYP2C19 gene are located at nucleotide positions on chromosome 10, namely positions 96495132 to 96612771, and more specifically positions on chromosome 10, namely positions 96495132 to 96495332, 96495693 to 96495893. 、96518961から96519161、96520333から96520533、96520343から96520543、96520924から96521124、96521322から96521522、96521474から96521674、96521557から96521757、96522265から96522465、96522350から96522550、96522461から96522661、96525765から96525965、96534375から96534575、96534484から96534684、96534668から96534868、96535024から96535224、96535528から96535728、96541273から96541473、96541656から96541856、96541882から96542082、96547363から96547563、96563657から96563857、96580102から96580302、96599410から96599610、96602298から96602498、96602523から96602723 , 96609468 to 96609668, and 96612571 to 96612771.

For example, a particular genetic variant associated with the CYP2D6 gene has variants located within exons of the gene as well as rs IDs, namely rs12169962, rs28371738, rs267608322, rs28371729, rs4987144, rs267608322, rs28371730, rs2004511, rs267608292, rs28371729、rs1985842、rs267608291、rs28371725、rs28371721、rs188062577、rs79738337、rs267608300、rs267608290、rs2267447、rs3892097、rs267608305、rs67497403、rs267608306、rs267608289、rs201377835、rs28371702、rs28371701、rs575159870、rs28371699、rs267608273、rs1081000、rs28695233、rs29001518、rs1080998、 rs1080997、rs1080996、rs1080995、rs75085559、rs267608272、rs1080993、rs35481113、rs35023634、rs34894147、rs35046171、rs34898711、rs35534760、rs34167214、rs530422334、rs566383351、rs28633410、rs534009571、rs28624811、rs536645539、rs1080990、rs1080989、rs59360719、rs544534350、rs375413467、rs267608271、 It may include variants identified by rs28735595, rs59099247, rs28588594, rs76210340, rs1080985, rs576829306, rs545591749, rs58188898, and rs1080983. Additional specific gene variants that have been associated with the CYP2D6 gene are located at genomic locations Chr22:42525917, Chr22:42525946, Chr22:42527060, Chr22:42527068, Chr22:42527114, Chr22:42527115, Chr22:42527122, Chr22:4722, Chr22:42525946 and genetic variants of Chr22:42527422.

Specific CYP2D6 alleles are 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 specific combinations of *36 and *10.

Specific intron regions associated with the CYP2D6 gene are located at nucleotide positions on chromosome 22, namely positions 42522212 to 42528668, and more specifically at positions on chromosome 22, namely positions 42522212 to 42522412, 42522292 to 42522492, 42523084から42523284、42523258から42523458、42522903から42523103、42523084から42523284、42523109から42523309、42523111から42523311、42523202から42523402、42523258から42523458、42523309から42523509、42523663から42523863、42523705から42523905、42524032から42524232、42524385から42524585、42524390から42524590、42524402から42524602、42524564から42524764、42524596から42524796、42524847から42525047、42525095から42525295、42525180から42525380、42525199から42525399、42525633から42525833、42525812から42526012、42525817から42526017、42525846から42526046、 42525852から42526052、42525949から42526149、42526088から42526288、42526384から42526584、42526414から42526614、42526449から42526649、42526461から42526661、42526462から42526662、42526467から42526667、42526471から42526671、42526473から42526673、42526480から42526680、42526736から42526936, 42526831 to 42527031, 42526869 to 42527069, 42526918 to 42527118, 42526925 to 42527125, 42526960 to 42527160, 42526964 to 42527164, 42526964648642768 2527168、42527014から42527214、42527015から42527215、42527020から42527220、42527022から42527222、42527024から42527224、42527027から42527227、42527047から42527247、42527124から42527324、42527322から42527522、42527371から42527571、42527385から42527585、42527433から42527633、 42527442から42527642、42527653から42527853、42527693から42527893、42527704から42527904、42527686から42527986、42527802から42528002、42527928から42528128、42527996から42528196、42528124から42528324、42528241から42528441、42528282から42528482、42528298から42528498、42528299から42528499, 42528438 to 42528638, and 42528468 to 42528668.

For example, a particular genetic variant associated with the CYP1A2 gene is a variant located within an exon of the gene as well as the rs IDs, i. It may include variants identified by rs183165301, rs762551, rs4646425, rs2472304, rs3743484, rs56107638, and rs4646427. Additional specific gene variants that have been associated with the CYP1A2 gene are located at the following genomic locations: Chr15:75038486, Chr15:75038967, Chr15:75039027, Chr15:75039272, Chr15:75039413, Chr15:75041341, Chr15:75041713, Chr15:75:75:75038967 Chr15:75044104 and Chr15:75047284 gene variants may be included. Specific CYP1A2 alleles are 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.

Specific intronic regions associated with the CYP1A2 gene are nucleotide positions on chromosome 15, namely 75033300 to 75047384, and more specifically positions on chromosome 15, namely 75033300 to 75033500, 75038120 to 75038320, 75038386から75038586、75038867から75039067、75038927から75039127、75039172から75039372、75039313から75039513、75039513から75039713、75041241から75041441、75041241から75041441、75041251から75041451、75041613から75041813、75041817から75042017、75041241から75041441、75041247から75041447、75041251から75041451、75041613から75041813、75041817から75042017、75042657から75042857、75043181から75043381、75044004から75044204、75044138から75044338、75044300から75044500、75045512から75045712、75045592から75045792、75047184から75047384を含み得る。

For example, a particular genetic variant associated with the CYP2B6 gene is a variant located within an exon of the gene as well as the rs IDs, i. rs28399487、rs28399488、rs28399490、rs4803419、rs28399491、rs35266616、rs28399492、rs202050252、rs34155858、rs2279344、rs2279345、rs8192718、rs12721649、rs28399498、rs35622401、rs8192719、rs7260329、rs8109848、rs28399502、rs28969419、rs28969420、およびrs12979898で識別されるバリアントをcan contain.

Additional specific gene variants that have been associated with the CYP2B6 gene are located at genomic locations Chr19:41494891, Chr19:41495363, Chr19:41495433, Chr19:41495633, Chr19:41495987, Chr19:41496025, Chr19:41496410, Chr19:4149644 and genetic variants of Chr19:41496620.

Specific CYP2B6 alleles are 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 can contain.

Specific intronic regions associated with the CYP2B6 gene are nucleotide positions on chromosome 19, namely 41494791 to 41524232, and more specifically positions on chromosome 19, namely 41494791 to 41494991, 41495139 to 41495339, 41495263から41495463、41495333から41495533、41495533から41495733、41495655から41495855、41495887から41496087、41495925から41496125、41496310から41496510、41496354から41496554、41496361から41496561、41496520から41496720、41497029から41497229、41497407から41497607、41506091から41506291、41510027から41510227、41510308から41510508、41511703から41511903、41512524から41512724、41512525から41512725、41512687から41512887、41512692から41512892、41512904から41513104、41512947から41513147、41514632から41514832、41514918から41515118、41515276から41515476、 41515383から41515583、41515602から41515802、41515714から41515914、41515737から41515937、41515784から41515984、41516022から41516222、41518673から41518873、41521538から41521738、41521969から41522169、41522770から41522970、41523257から41523457、41523704から41523904、および41524032 from 41524232.

For example, the specific genetic variants associated with the CYP2C9 gene are variants located within exons of the gene as well as rs IDs, namely rs146705863, rs9332092, rs9332093, rs9332094, rs9332096, rs61604699, rs4918758, rs4917636, rs9332098, rs9332100、rs9332101、rs9332102、rs9332104、rs12772884、rs9332116、rs9332119、rs9332120、rs2860905、rs28371675、rs28371676、rs28371677、rs28371679、rs28371680、rs28371681、rs28371682、rs4086116、rs9332127、rs28371683、rs9332129、rs28371684、rs4917639、rs9332172、rs9332174、rs10509680、 It may include variants identified by rs9332197, rs17847029, rs9332230, rs9332232, rs2298037, rs9332238, rs1934969, rs146139873, and rs57749228. Additional specific genetic variants that have been associated with the CYP2C9 gene may include a genetic variant at genomic location Chr10:96709253. Particular CYP2C9 alleles may include the CYP2C9 star alleles *1, *2, *3, *4, *5, and *6.

Specific intronic regions associated with the CYP2C9 gene are nucleotide positions on chromosome 10, i.e., 96695677 to 96748620, and more specifically positions on chromosome 10, i.e., 96695677 to 96695877, 96696429 to 96696629, 96696455から96696555、96696574から96696774、96696775から96696975、96696803から96697003、96697152から96697352、96697244から96697444、96697359から96697559、96697720から96697920、96697855から96698055、96697856から96698056、96698590から96698790、96700530から96700730、96700679から96700879、96701501から96701701、96701750から96701950、96702195から96702395、96702237から96702437、96702263から96702463、96702372から96702572、96702496から96702696、96702648から96702848、96702967から96703167、96703009から96703209、96707102から96707302、96707371から96707571、 96707408から96707608、96708650から96708850、96709021から96709221、96709153から96709353、96725435から96725635、96731688から96731888、96731997から96732197、96734239から96734439、96740808から96741008、96741065から96741265、96745884から96746084、96745932から96746132、96745978から96746178, 96748392 to 96748592, 96748395 to 96748595, 96748405 to 96748605, 96748420 to 96748620.

For example, a particular genetic variant associated with the CYP3A4 gene is a variant located within an exon of the gene as well as the rs IDs, i. It may include variants identified by rs2687116, rs55808838, rs35599367, rs68106838, rs72552800, rs12721636, and rs2740574. Additional specific gene variants that have been 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, Chr7:99375629、Chr7:99381766、Chr7:99381766、Chr7:99381860、Chr7:99382073、Chr7:99382096、Chr7:99382148、Chr7:99382334、Chr7:99382359、Chr7:99382451、およびChr7:99382539の遺伝子バリアントを含み得る. Particular CYP3A4 alleles may include the CYP3A4 star alleles *1, *1B, *13, *15A, *15B, *22, *23, and *24.

Specific intron regions associated with the CYP3A4 gene are nucleotide positions on chromosome 7, namely 99355390 to 99382640, and more specifically positions on chromosome 7, namely 99355390 to 99355590, 99355583 to 99355783, 99355875から99356075、99359051から99359251、99360770から99360970、99361366から99361566、99363780から99363980、99364821から99365021、99364983から99365183、99365619から99365819、99365787から99365987、99365843から99366043、99365869から99366069、99366216から99366416、99367396から99367596、99375529から99375729、99381666から99381866、99381666から99381866、99381760から99381960、99381973から99382173、99381996から99382196、99382048から99382248、99382234から99382434、99382259から99382459、99382351から99382551、99382440から99382640を含み得る。

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

For example, a particular gene variant associated with the HLA-B gene is a variant located within an exon of the gene and also the rs IDs, i. rs9366775、rs9357121、rs4361609、rs2524082、rs12111032、rs2844619、rs2524078、rs3134745、rs6913377、rs7759127、rs6923313、rs10456057、rs12189871、rs12191877、rs16899160、rs16899166、rs16899168、rs2394963、rs2524043、rs2524044、rs2524048、rs2524051、rs2524057、rs2524070、rs2524156、 rs2853929、rs2853933、rs2853935、rs2853939、rs3873374、rs3873375、rs6457372、rs6906846、rs7382297、rs7754443、rs9366776、rs9378228、rs9380236、rs9461684、rs9468920、rs9468922、rs9468925、rs9468926、rs2844599、rs9348859、rs2853934、rs2524049、rs2524069、rs9391714、rs2853948、 rs7381988、rs9348862、rs2524163、rs2524040、rs10081114、rs10484554、rs12664384、rs16899178、rs16899203、rs16899205、rs16899207、rs16899208、rs2243868、rs2246954、rs2247056、rs2394967、rs2508004、rs2524066、rs2524089、rs2524095、rs2524115、rs2524123、rs2524132、rs2524145、rs2524168、 rs2853923, rs2853925, rs2853926, rs28894983, rs28894990, rs3094682, rs3094691, rs3644 15、rs3873379、rs3873386、rs3905495、rs396038、rs396243、rs396337、rs4406273、rs4523128、rs4543367、rs6905036、rs6918048、rs7750269、rs7760988、rs7761965、rs9264848、rs9264850、rs9264869、rs9264899、rs9264916、rs9264917、rs9264942、rs9357123、rs9366778、rs9368677、 rs9380238、rs9380240、rs9461685、rs9468942、rs2894207、rs3915971、rs9295970、rs9264904、rs6457375、rs28732109、rs9348863、rs9468929、rs16899202、rs9394054、rs3873385、rs9368673、rs9368680、rs1634761、rs9264951、rs2524229、rs9264902、rs6457374、rs16867947、rs28894993、rs9295976、 It may include variants identified by rs7755852, rs2156875, rs2507997, rs2596501, rs2596503, rs3134792, rs4394275, rs4540292, rs9295984, rs2844586, rs4394274, and rs2523619.

Specific intronic 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から31244121、31241539から31241739、31243746から31243946、31240253から31240453、31240832から31241032、31242631から31242831、31239996から31240196、31240379から31240579、31240535から31240735、31241661から31241861、31242091から31242291、31242123から31242323、 31242549から31242749、31242662から31242862、31243395から31243595、31240888から31241088、31241270から31241470、31245434から31245634、31251824から31252024、31252825から31253025、31256567から31256767、31257996から31258196、31258587から31258787、31251362から31251562、31256912から31257112、31256653から31256853、31256461から31256661、31255400から31255600、31251795から31251995、31244420から31244620、31260297から31260497、31255334から31255534、31253988から31254188、31253778から31253978、31250542から31250742、31251211から31251411、31251260から31251460、 31247021から31247221、31245636から31245836、31246967から31247167、31254163から31254363、31256530から31256730、31246271から31246471、31254564から31254764、31253344から31253544、31254836から31255 036、31255186から31255386、31258737から31258937、31260118から31260318、31255905から31256105、31249877から31250077、31253828から31254028、31255853から31256053、31244689から31244889、31244980から31245180、31245473から31245673、31246603から31246803、31252647から31252847、 31259479から31259679、31257525から31257725、31271369から31271569、31274455から31274655、31272792から31272992、31261037から31261237、31266235から31266435、31266261から31266461、31266287から31266487、31267396から31267596、31261176から31261376、31265162から31265362、31265390から31265590、31269029から31269229、31273495から31273695、31269054から31269254、31266422から31266622、31266017から31266217、31265454から31265654、31265214から31265414、31264812から31265012、31268647から31268847、31275063から31275263、31265637から31265837、31264822から31265022、 31262951から31263151、31263540から31263740、31264219から31264419、31264361から31264561、31274593から31274793、31273124から31273324、31262069から31262269、31273645から31273845、31265439から31265639、31272880から31273080、31275074から31275274、31272815から31273015、31265990から31266190, 31269282-31269482, 31266667-31266867, 31273046から31273246、31268329から31268529、31271057から31271257、31272930から31273130、31273395から31273595、31271095から31271295、31271140から31271340、31271530から31271730、31272321から31272521、31272674から31272874、31272708から31272908、31274280から31274480、31262769から31262969 、31269073から31269273、31272221から31272421、31267518から31267718、31268732から31268932、31265255から31265455、31274341から31274541、31263651から31263851、31269248から31269448、31269422から31269622、31272453から31272653、31272512から31272712、31263197から31263397、31262361から31262561、31263116から31263316、31266199から31266399、31268480から31268680、31269208から31269408、31271657から31271857、31272744から31272944、31273927から31274127、31275000から31275200、31275131から31275331、31272406から31272606、31272161から31272361、31280723から31280923 、31289614から31289814、31281670から31281870、31277888から31278088、31317247から31317447、31314681から31314881、31321111から31321311、31320710から31320910、31312226から31312426、31318077から31318277、31317082から31317282、31317597から31317797、31317924から31318124、31318064 from 31318264, 31318044 to 31318244.

For example, the specific genetic variants associated with the HTR2A gene are variants located within the exons of the gene as well as the rs IDs, i. and variants identified by rs6311. Particular HTR2A alleles may include the reference alleles -1438A>G and 102T>C. Specific intronic regions associated with the HTR2A gene are located at nucleotide positions on chromosome 13, i.e., 47401235 to 47471578, and more specifically at 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 47457593, 47470724 to 47474784.

For example, the specific genetic variant associated with the SLC6A4 gene includes variants located within exons of the gene as well as variants identified by rs IDs, i. can contain. A particular SLC6A4 allele may include Long (L) and Short (S). Specific intron regions associated with the SLC6A4 gene are located at nucleotide positions on chromosome 17, i.e., 28564227 to 28564446, and more specifically at positions on chromosome 17, i.e., 28564227 to 28564427, 28524911 to 28525111, 28543289 to 28543489, 28548496 to 28548696, 28561655 to 28561855, 28564246 to 28564446.

For example, the specific genetic variants associated with the UGT1A4 gene are the variants located within the exons of the gene and also the rs IDs, i. rs2003569、rs60469444、rs34531096、rs76063448、rs4124874、rs3755319、rs11568318、rs11568316、rs1976391、rs4148327、rs873478、rs3213726、rs2302538、rs887829、rs34650714、rs8175347、rs10929303、rs1042640、rs8330、およびrs34942353で識別されるバリアントを含み得る。 Additional specific genetic variants associated with the UGT1A4 gene may include genetic variants at genomic locations Chr2:234656479, Chr2:234675628, Chr2:234675608. Particular UGT1A4 alleles may include the UGT1A4 star alleles, *1a, *3a, and *3b.

Specific intronic 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から234628476、234636922から234637122、234637120から234637320、234637607から234637807、234637469から234637669、234637092から234637292、234665682から234665882、234667837から234668037、234652540から234652740、234665491から234665691、234656379から234656579、234652542から234652742、234665559から234665759、234667482から234667682、234665398から234665598、234665437から234665637、234665883から234666083、234675726から234675926、234675528から234675728、234668770から234668970、234675423から234675623、234675508から234675708、234676313から234676513、234668470から234668670、234675729から234675929、 234668781 to 234668981, 234681316 to 234681516, 234681444 to 234681644, 234681545 to 234681745, 234681524 to 234681724.

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

For example, specific genetic variants associated with the ANK3 gene may include variants located within exons of the gene as well as variants identified by rs IDs, ie rs12357206 and rs7911953. Specific intron regions associated with the ANK3 gene 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. can contain.

For example, a particular genetic variant associated with the ADRA2A gene may include variants located within an exon of the gene as well as the variant identified by the rs ID, ie rs1800545. A specific intron region associated with the ADRA2A gene may include nucleotide positions 112837438 to 112837638 on chromosome 10.

For example, specific genetic variants associated with the BDNF gene may include variants located within exons of the gene as well as variants identified by rs IDs, i. . Specific intronic regions associated with the BDNF gene are nucleotide positions on chromosome 11, namely 27676941 to 27732083, and more specifically positions on chromosome 11, namely 27676941 to 27677141, 27684417 to 27684617, 27700025 to 27700225, 27734320 to 27734520, 27731883 to 27732083.

For example, specific genetic variants associated with the CACNA1C gene are identified by variants located within exons of the gene as well as rs IDs: rs10848615, rs2238032, rs10848635, rs1006737, rs2239050, rs216013, and rs2239128. may contain variants. Specific intron regions associated with the CACNA1C gene are located at nucleotide positions on chromosome 12, i.e., 2218995 to 2757869, and more specifically at 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.

For example, a particular genetic variant associated with the CES1 gene may include variants located within an exon of the gene as well as the variant identified by the rs ID, ie rs3815583. A specific intron region associated with the CES1 gene may include nucleotide positions 55866942 to 55867142 on chromosome 16.

For example, certain genetic variants associated with the COMT gene are variants located within exons of the gene as well as rs IDs, i.e., It may include variants identified by rs174699, rs740603, rs165599, and rs165728. Specific intron regions associated with the COMT gene are located at nucleotide positions on chromosome 22, i.e., 19949852 to 19957123, and more specifically at positions on chromosome 22, i.e., 19949852 to 19950052, 19950328 to 19950528, 19931307から19931507、19930021から19930221、19930009から19930209、19948237から19948437、19942897から19943097、19952032から19952232、19955592から19955792、19954358から19954558、19945077から19945277、19956681から19956881、および19956923から19957123を含み得る。

For example, certain genetic variants associated with the CYP3A5 gene are variants located within exons of the gene and also rs IDs, namely rs15524, rs28365094, rs17161788, rs4646453, rs55965422, rs776746, rs28365095, rs28371764, rs2740565, May include variants identified by rs55798860, and rs28451617. Specific intronic regions associated with the CYP3A5 gene are nucleotide positions on chromosome 7, i.e., 99245814 to 99332865, and more specifically positions on chromosome 7, i.e., 99245814 to 99246014, 99250375 to 99250575, 99245809から99246009、99260262から99260462、99264473から99264673、99270439から99270639、99277505から99277705、99277493から99277693、99293375から99293575、99332707から99332907、および99332665から99332865を含み得る。

For example, a particular genetic variant associated with the CYP4F2 gene may include variants located within an exon of the gene as well as the variant identified by the rs ID, ie rs3093158. A specific intron region associated with the CYP4F2 gene may include nucleotide positions 16000066 to 16000266 on chromosome 19.

For example, the specific genetic variants associated with the DPYD gene are variants located within the exons of the gene as well as the rs IDs, i. and variants identified by rs4970722. Specific intron regions associated with the DPYD gene are located at nucleotide positions on chromosome 1, i.e., 97602894 to 98352153, and more specifically at positions on chromosome 1, i.e., 97602894 to 97603094, 97722572 to 97722772, 97847774から97847974、97862680から97862880、97867613から97867813、97915514から97915714、97973152から97973352、98045349から98045549、98293721から98294921、および98351953から98352153を含み得る。

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

For example, certain genetic variants associated with the DRD2 gene are variants located within the exons of the gene as well as the rs IDs, i. It may include variants identified by rs1125394, rs1079598, rs4436578, rs4460839, rs4648317, rs1799978, and rs1799732. Specific intronic 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から113286978、113281676から113281876、113283588から113283788、113284419から113284619、113285816から113286016、113280973から113281173、113292820から113293020、113297085から113297285、113296174から113296374、113306665から113306865、113321696から113321896、113331432から113331632、113346251から113346451, and 113346151 to 113346351.

For example, specific genetic variants associated with the DRD3 gene may include variants located within exons of the gene as well as variants identified by rs IDs, namely rs963468, rs167771, rs167770 and rs324026. Specific intron regions associated with the DRD3 gene are located at nucleotide positions on chromosome 3, namely 113862787 to 113891142, and more specifically at positions on chromosome 3, namely 113862787 to 113862987, 113876175 to 113876375, 113879462 to 113879662, 113890942 to 113891142.

For example, specific genetic variants associated with the ERCC1 gene may include variants located within exons of the gene as well as variants identified by rs IDs, namely rs3212948, rs2276469 and rs2276470. Specific intronic regions associated with the ERCC1 gene are nucleotide positions on chromosome 19 i.e. 45924262 to 45974768, and more specifically positions on chromosome 19 i.e. and 45974568 to 45974768.

For example, a particular genetic variant associated with the F2 gene may include variants located within an exon of the gene as well as the variant identified by the rs ID, ie rs1799963. A specific intron region associated with the F2 gene may include nucleotide positions 46760955 to 46761155 on chromosome 11.

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

For example, specific genetic variants associated with the GNB3 gene may include variants located within exons of the gene as well as variants identified by rs IDs, ie rs11064426 and rs2301339. Specific intronic regions associated with the GNB3 gene 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. can contain.

For example, a particular genetic variant associated with the GRIK1 gene may include variants located within an exon of the gene as well as the variant identified by the rs ID, ie rs2832407. A specific intron region associated with the GRIK1 gene may include nucleotide positions 30967408 to 30967608 on chromosome 21.

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

For example, a particular genetic variant associated with the GSTP1 gene may include variants located within an exon of the gene as well as the variant identified by the rs ID, ie rs8191439. A specific intronic region associated with the GSTP1 gene may include nucleotide positions 67351197 to 67351397 on chromosome 11.

For example, certain genetic variants associated with the HNF4A gene are variants located within exons of the gene as well as rs IDs, i. and variants identified by rs3746574. Specific intronic regions associated with the HNF4A gene are located at nucleotide positions on chromosome 20, i.e., 43018160 to 43058118, and more specifically at 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 43057537, 43057380 to 43057580.

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

For example, specific genetic variants associated with the HTR2C gene may include variants located within exons of the gene as well as variants identified by rs IDs, namely rs3813928, rs3813929, rs518147 and rs1414334. Specific intron regions associated with the HTR2C gene are located at nucleotide positions on the X chromosome, namely from 113818182 to 114138244, and more specifically from positions on the X chromosome, namely from 113818182 to 113818382, 113818420 to 113818620, 113818682, 114138044 to 114138244.

For example, a particular genetic variant associated with the IFNL3 gene may include variants located within an exon of the gene as well as the variant identified by the rs ID, ie rs11881222. A specific intron region associated with the IFNL3 gene may include nucleotide positions 39734823 to 39735023 on chromosome 19.

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

For example, specific genetic variants associated with the KCNQ1 gene may include variants located within exons of the gene as well as variants identified by rs IDs, namely rs757092, rs58762055, rs2237892, and rs2237895. Specific intron regions associated with the KCNQ1 gene are located at nucleotide positions on chromosome 11, i.e., 2499078 to 2857294, and more specifically at positions on chromosome 11, i.e., 2499078 to 2499278, 2738847 to 2739047, 2839651 to 2839851, and 2857094 to 2857294.

For example, certain genetic variants associated with the LDLR gene include variants located within exons of the gene as well as variants identified by rs IDs, i. can contain. Specific intronic regions associated with the LDLR gene are located at nucleotide positions on chromosome 19, i.e., 11202206 to 11242758, and more specifically at 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.

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

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

For example, a particular genetic variant associated with the NR1H3 gene may include variants located within exons of the gene as well as variants identified by rs ID, ie, rs11039149. A specific intronic region associated with the NR1H3 gene may include nucleotide positions 47276575 to 47276775 on chromosome 11.

For example, specific genetic variants associated with the OPRM1 gene include variants located within exons of the gene as well as variants identified by rs IDs, i. can contain. Specific intronic regions associated with the OPRM1 gene are located at nucleotide positions on chromosome 6, i.e., 154361919 to 154487521, and more specifically at 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.

For example, specific genetic variants associated with the PPARA gene may include variants located within exons of the gene as well as variants identified by rs IDs, i. . Specific intronic regions associated with the PPARA gene are located at nucleotide positions on chromosome 22, i.e., 46609967 to 46630734, and more specifically at positions on chromosome 22, i.e., 46609967 to 46610167, 46598207 to 46598407, 46562083 to 46562283, 46553134 to 46553334, and 46630534 to 46630734.

For example, a particular genetic variant associated with the PPARG gene may include variants located within an exon of the gene as well as the variant identified by the rs ID, ie rs7627605. A specific intron region associated with the PPARG gene may include nucleotide positions 12399170 to 12399370 on chromosome 3.

For example, the specific genetic variants associated with the PTPRD gene are variants located within the exons of the gene as well as the rs IDs: may include variants identified in Specific intronic regions associated with the PTPRD gene are located at nucleotide positions on chromosome 9 i.e. 8361345 to 10369468, and more specifically at positions on chromosome 9 i.e. 8821581 to 8821781, 9416862 to 9417062, 9829154 to 9829354, 9926419 to 9926619, 10098443 to 10098643, 10102803 to 10103003, and 10369268 to 10369468.

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

For example, a particular genetic variant associated with the SLC47A1 gene may include variants located within an exon of the gene as well as the variant identified by the rs ID, ie rs2289669. A specific intron region associated with the SLC47A1 gene may include nucleotide positions 19463243 to 19463443 on chromosome 17.

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

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

For example, a particular genetic variant associated with the SLCO1B1 gene includes variants located within the exons of the gene and also the rs IDs, i. and variants identified by rs4149015. Specific intron regions associated with the SLCO1B1 gene are located at nucleotide positions on chromosome 12, i.e., 21325714 to 21283422, and more specifically at positions on chromosome 12, i.e., 21325714 to 21325914, 21327640 to 21327840, 21329732から21329932、21317691から21317891、21332323から21332523、21372244から21372444、21368622から21368822、21377921から21378121、21382519から21382719、および21283222から21283422を含み得る。

For example, specific genetic variants associated with the TCF7L2 gene are identified by variants located within exons of the gene as well as by rs IDs: rs7917983, rs4132670, rs4506565, rs7903146, rs12243326, rs12255372, rs290487, and rs1056877. may include variants that are Specific intronic regions associated with the TCF7L2 gene are located at nucleotide positions on chromosome 10, i.e., 114732782 to 114925858, and more specifically at 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.

For example, specific genetic variants associated with the TPMT gene include variants located within exons of the gene as well as variants identified by rs IDs, i. can contain. Specific intronic regions associated with the TPMT gene are nucleotide positions on chromosome 6, namely 18134021 to 18148347, and more specifically positions on chromosome 6, namely 18134021 to 18134221, 18130912 to 18131112, 18139702 to 18139902, 18149005 to 18149205, 18143669 to 18143869, and 18148147 to 18148347.

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

For example, the specific genetic variants associated with the UGT1A9 gene are variants located within exons of the gene as well as the rs IDs, i.e. rs3806596、rs3806597、rs2008595、rs10929302、rs2003569、rs60469444、rs34531096、rs76063448、rs4124874、rs3755319、rs11568318、rs11568316、rs1976391、rs4148327、rs873478、rs3213726、rs2302538、rs887829、rs34650714、rs8175347、rs10929303、rs1042640、rs8330、およびrs34942353で識別may include variants that are Additional specific genetic variants associated with the UGT1A9 gene may include genetic variants at genomic locations Chr2:234581625, Chr2:234656479, Chr2:234675628, and Chr2:234675608.

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から234581687、234581525から234581725、234593017から234593217、234590427から234590627、234627204から234627404、234627148から234627348、234636922から234637122、234637120から234637320、234637607から234637807、234637469から234637669、234637092から234637292、234665682から234665882、234667837から234668037、234652540から234652740、234665491から234665691、234656379から234656579、234652542から234652742、234665559から234665759、234667482から234667682、234665398から234665598、234665437から234665637、234665883から234666083、234675726から234675926、234675528から234675728、234668770から234668970、 234675423から234675623、234675508から234675708、234676313から234676513、234668470から234668670、234675729から234675929、234668781から234668981、234681316から234681516、234681444から234681644、234681545から234681745、および234681524から234681724を含み得る。

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

Specific intronic 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から234675728、234675423から234675623、234675508から234675708、234676313から234676513、234675729から234675929、234681316から234681516、234681444から234681644、234681545から234681745、および234681524から234681724を含み得る。

For example, a particular genetic variant associated with the UMPS gene may include variants located within an exon of the gene as well as the variant identified by the rs ID, ie rs3772810. A specific intronic region associated with the UMPS gene may include nucleotide positions 124462859 to 124463059 on chromosome 3.

For example, the specific genetic variants associated with the VKORC1 gene are variants located within the exons of the gene as well as the rs IDs, i. It may include variants identified by 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から31104502、31104409から31104609、31105253から31105453、31102464から31102664、31103696から31103896、31105454から31105654、31104778から31104978、31102224から31102424、31102221から31102421、31102489から31102689、31105071から31105271、31105292から31105492、31102555から31102755, and 31107589 to 31107789.

The design of panels to detect the genes and variant lists described above is based on published research information, FDA-approved therapies, and/or genomic pharmacological studies of several variants and/or alleles in drug treatments of any nature. It can be based on an independent analysis of efficacy (the nature of the treatment cannot be limited to only one area or condition, as pharmacogenomic effects can persist across different medical areas). Current methods for gene sequencing can rely on techniques used to isolate several regions of the genome of interest, such as the genome of interest or the portion of the genome to be analyzed. In one embodiment, regions 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 probes included in the manufactured panels. A probe is a small stretch of DNA or RNA that serves as a starting point for DNA synthesis and allows the detection of nucleic acid sequences complementary to the sequence of the probe. The probes hybridize (bind) to complementary nucleotides in the template or target DNA and amplify the DNA to produce millions of copies of the DNA molecule. Each probe may be single-stranded DNA and is designed to match a specific segment of template DNA. This specificity arises from the fact that each DNA base can only pair with one other DNA base: adenine (A) pairs with thymine (T) in DNA and uracil (U) in RNA. Guanine (G) pairs only with Cytosine (C). To make a copy, the probe binds to the correct piece of DNA and the bases match. If a match appears, DNA polymerase (an enzyme that copies DNA) can bind and amplify the DNA. If the probe does not match the DNA sequence, the DNA polymerase will not bind and no copies will be made. To precisely obtain the correct order of A, G, T, and C, a panel can be designed and ordered to contain probes containing the desired sequence of nucleotides.

Probes may be selected to target each of the identified genes and/or variants, and additional genes if the panel of probes is not sufficient to target each of the identified genes or variants. and/or one or more spike-in probes targeting the variant may be added to the panel to supplement the detection capabilities of the panel. Spike-ins are target probes that can be added to probe panels to target additional genes or variants for detection in sequencing.

Panel design is such that a base panel is selected, spike-ins that complement the panel to allow detection of additional target genes and/or variants are identified, probes are manufactured according to the panel and additional spike-ins, and probes are Different concentrations were optimally selected during the titration test by using different concentrations of each probe to identify the amount that produced the most reliable detection for each variant, and the gene sequencing run was tested by a panel. The results obtained by the panel are processed on test samples with known sequences to confirm the accuracy of the test, ensuring that the panel is optimized to detect the intended target genes and variants. It is an iterative process in which sequences are compared to known sequences for confirmation.

Additionally, some NGS panels may only contain probes to detect exon regions (coding regions) of DNA or RNA. It may be advantageous to extend the base panel to generate sequencing results for intronic regions to 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 remedy this defect.

FIG. 10 shows another embodiment of a patient report 1000. FIG. Specifically, FIG. 10 shows the molecular results portion of the patient report 1000 associated with the 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. A list of phenotypes 1008 based on the molecular data associated with the patient, poor metabolizer, intermediate metabolizer, normal metabolizer, extensive metabolizer, ultrafast metabolizer, increased sensitivity, and/or increased risk Phenotype can be included. Gene 1004 may be represented due to its involvement in the metabolism, pharmacokinetics, and/or immunogenicity of the selected drug.

10 and 11, the patient report 1000 can include a classification portion 1012 that contains information regarding different classifications (eg, gene drug classifications) that can be included in the report. In some embodiments, classifications can include standard dosing, dosing considerations, additional risks to consider, and/or contraindications.

Patient report 1000 may also include information regarding one or more drug types. In some embodiments, the drug type is antidepressants, antipsychotics, anticonvulsants, anxiolytics, mood stabilizers, antimanic drugs, hypnotics, VMAT2 inhibitors, ADHS drugs, and/or Other drug types that do not fall into one of the categories can be included. Within each drug type, patient report 1000 may further organize drugs by drug subtype. As shown, a number of selective serotonin reuptake inhibitors (SSRIs) 1016 may be included in the antidepressants portion 1020 included in the patient report 1000. FIG.

The patient report 1000 may order drugs by classification type. Drugs with a single drug category may be listed first within each drug type and/or subtype. For example, SSRI 1016 may contain drugs 1024 classified as standard doses. As shown, vilazodone is classified as standard dosing for patients. Drug 1024 (e.g., vilazodone) is a source document and/or website with information on the gene and/or phenotype 1028 (e.g., CYP3A4 and/or normal metabolizer) associated with the drug, and/or drug classification may be listed with a link 1032 (eg, a hyperlink) to.

For drugs with conflicting evidence1036 (e.g., citalopram), each drug class should include genes and/or phenotypes1040 associated with the drug (e.g., CYP2C19 and/or normal metabolizers), and/or information on the drug class. may be listed with links 1044 (eg, hyperlinks) to source documents and/or websites having

In some embodiments, for any drug associated with an “increased risk phenotype,” the patient report 1000 includes supplemental information 1048 regarding increased risk, and/or source documents with information regarding increased risk and /or may include one or more links 1052 (eg, hyperlinks) to websites.

10-11 and also FIGS. 12 and 13, the patient report 1000 can include a number of SNRIs 1056 that can be included in the antidepressant portion 1020. FIG. 10-13 and also to FIG. 14, the patient report 1000 can include an antipsychotic portion 1060. FIG. 10-14 and 15, the patient report 1000 can include an anticonvulsant portion 1064. FIG. 10-15 and also to FIG. 16, the patient report 1000 can include an anxiolytic portion 1068 and/or a mood stabilizer portion 1072. FIG. 10-16 and also to FIG. 17, the patient report 1000 can include an antimanic drug portion 1076, a hypnotic drug portion 1080, a VMAT2 inhibitor portion 1084, and/or an ADHD drug portion 1088. 10-17 and also to FIG. 18, the patient report 1000 can include an Other Medications portion 1092. As shown in FIG.

Referring now to FIG. 19, an exemplary process 1900 for generating treatment information for a patient diagnosed with psychosis is shown. In some embodiments, process 1900 may be stored in non-transitory computer-readable media. In some embodiments, process 1900 may be stored as executable instructions in a non-transitory computer-readable medium (eg, 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.

At 1904, process 1900 can receive molecular data associated with the patient. In some embodiments, the patient can be diagnosed with at least one psychosis (eg, depression) as described above. In some embodiments, molecular data can include multiple nucleic acid sequences. At least a portion of the plurality of nucleic acid sequences can be associated with metabolic-related genes. In some embodiments, molecular data can be generated based on multigene panel sequencing reactions on samples from patients. In some embodiments, the molecular data are whole-exome sequence data, mass array data, sequence data from one or more introns associated with metabolic-related genes, and/or associated with metabolic-related genes It can include multiple nucleic acid sequences obtained from sequence data from one or more promoter regions.

In some embodiments, 1904 the process 1900 can align molecular data to human reference sequences. In some embodiments, process 1900 can receive raw molecular data (eg, stored in BCL, FASTA, and/or FASTQ file formats) and align the raw molecular data to human reference sequences. can. 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.

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. A listing of pre-treatment drugs may include one or more drug names, drug doses, and/or patient response to the drug. The one or more diagnoses can include a recent set of diagnoses and/or a list of previous diagnoses that the patient has had in the past. In some embodiments, the clinical activity described in the second set of clinical data is among prescribed medication, drug dosage, patient compliance, and patient outcome after taking the prescribed medication. including one or more of

At 1912, process 1900 can generate a report based on the molecular and clinical data associated with the patient. Process 1900 can generate a report using the therapy engine. A therapy engine can include a knowledge database such as KDB40 described above. The knowledge database may contain structured data on drug-gene interactions, including pharmacogenetic interactions, and precision medicine findings reported in the psychiatric and basic science literature. The knowledge database may contain clinically annotated pharmacogenomic classifications for key pharmacokinetic and pharmacokinetic outcomes relevant to the treatment of depression and other psychoses. Knowledge databases of therapeutic and prognostic evidence, including therapeutic response and resistance information, may include information from a combination of external sources, including CPIC guidelines, FDA labeling, PharmGKB, or published or by subscription. or other proprietary databases that are available upon request, as well as sources such as literature sources or novel findings derived from analyzing repositories of clinical and genetic, genomic, or other omic information. obtain. Knowledge databases can be maintained over time by individuals with experience, education, and training in the relevant fields. In some embodiments, the clinical feasibility 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. be.

The knowledge database contains data relating to interactions between specific drug(s) and one or more nucleic acid sequences involved in drug metabolism, primary drug metabolism pathway data, and data from cohorts of psychiatric subjects. a previously derived cohort dataset comprising one or more drugs used in treatment, pre-treatment diagnosis, and/or treatment outcome for patients in the cohort; can be done. In some embodiments, cohort data are not derived from clinical trial data. In some embodiments, a cohort can include patients diagnosed with multiple psychotic disorders. In some embodiments, the knowledge database can include a second cohort data set derived at a different time than the first cohort set. The second cohort dataset can include information from at least one patient in the 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. A knowledge database can store a portion of clinical data and a second set of clinical data from at least a portion of the patients. Clinical data may be de-identified, stored in the form of limited data sets, anonymized, or pseudo-anonymized, for example.

A therapeutic engine can identify relevant drug-gene interactions based on molecular and clinical data. More specifically, in some embodiments, process 1900 can identify relevant drug-gene interactions based at least in part on the nucleic acid sequences contained in the molecular data. In some embodiments, the therapy engine can identify resources related to including one or more of the phenotypes included in the molecular data and the patient's diagnosed psychosis. For example, the therapy engine may match a patient with documents relating to research on patients who have the same diagnosis and phenotype as the patient. Drugs given to patients in a study can also be matched to patients. In some embodiments, the knowledge database can contain a large number of drug-gene pairs, each drug-gene pair containing a drug and a phenotype and associated with a disease. Process 1900 can search for drug gene pairs in the knowledge database that have the same phenotype as the patient and are associated with the same psychosis as the patient.

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

In some embodiments, a classification may be generated based on a listing of prior therapies. In some embodiments, the treatment engine is based on what drugs the patient has taken, what dosages have been used, and/or what the outcome of previous treatments has been. drugs are more effective and/or pose a risk to the patient. Some studies have identified patients in whom some drugs had no effect in treating a given psychosis and/or where some drugs pose potential risks (e.g., of side effects). may be associated with cohorts of

Due to conflicting evidence, some drugs may be classified as 'contraindicated'. For example, a therapy engine finds a first source that recommends drug X for a first gene phenotype Y, while another source recommends a first gene and/or another gene phenotype Z It is recommended not to use drug X for the treatment of The therapy engine can output source documents and/or links to source documents of any source associated with the patient. In particular, sources used to support drug-gene interactions and/or classification can be included in the report.

In some embodiments, the therapy engine can output the likely side effects of at least one drug included in the list of drugs. In some embodiments, a patient is diagnosed with depression and the treatment 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.

In some embodiments, the process 1900 can determine the list of drugs based on a list of prior medications that the patient has received. In some embodiments, the therapeutic engine directs research into alternative drugs to be taken after other drugs have proven ineffective in some patients, such as those with some phenotypes. can be identified.

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

Process 1900 may include drug-gene interactions, drug classes, dosages, potential side effects, depression subtypes, source documents, links to source documents (e.g., hyperlinks to websites or source documents), and/or Generate reports based on any other suitable information, as well as information received from the therapeutic engine, such as molecular and/or clinical information associated with the patient, such as phenotype, and/or medication history be able to. In some embodiments, the report can include at least a portion of the patient report 1000 of FIGS. 10-18.

At 1916, process 1900 can cause the report to be output. In some embodiments, process 1900 can cause the report to be output to at least one of a display (eg, display device 16 of FIG. 1) or memory. In some embodiments, process 1900 can cause the report to be presented to the user (eg, using display device 16). In some embodiments, the user can be a healthcare professional treating a patient. Process 1900 may then end in some embodiments.

At 1920, process 1900 can 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 after presentation of the report. In some embodiments, the second set of clinical data includes one or more of prescribed medication, dosage of medication, patient compliance, and patient outcome after taking the prescribed medication. be able to. A second set of clinical data may help treat the patient with another drug and/or dosage. The one or more diagnoses can include a recent set of diagnoses and/or a list of previous diagnoses that the patient has had in the past. In some embodiments, the clinical activity described in the second set of clinical data is among prescribed medication, drug dosage, patient compliance, and patient outcome after taking the prescribed medication. may include one or more of In some embodiments, at 1920 the process 1900 can update therapy engines, including knowledge databases, based on the second set of clinical data.

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

At 1928, process 1900 can cause a second report to be output. In some embodiments, process 1900 can cause the second report to be output to at least one of a display (eg, display device 16 of FIG. 1) or memory. In some embodiments, process 1900 can cause the second report to be presented to the user (eg, using display device 16). In some embodiments, the user can be a healthcare professional treating a patient. Process 1900 may then end in some embodiments. Process 1900 may continue to 1920 in some embodiments.

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

The present invention can be implemented as a computer program product, comprising computer program mechanisms embedded in a non-transitory computer-readable storage medium. These program modules may 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.

Many modifications and variations of this invention can be made without departing from its spirit and scope, 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 in order to best explain the principles of the technology and its practical application, thereby enabling those skilled in the art to make various modifications as appropriate for the particular applications contemplated. The present invention and various embodiments can be best utilized. The invention is to be limited only with respect to the appended claims, along with the full scope of equivalents to which such claims are entitled.

10 systems
12 Donors
12a doctor
12c Other professionals
14 patients
16 display devices
18 Communication Network
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 database
46 Third Party Databases
48 internal databases
50 Graphical User Interface (GUI)
52 Patient Summary Table
54 account name
56 Patient Identifier
58 menu
60 Summary part
62 diagnostic tiles
64 Diagnostic Timeline
68 Therapy Tile
70 Summarized “All Therapies” Section
74 Predicted reaction part
76 molecule outline tiles
78 Image Overview Tile
90 molecule report
92 change list
94 selectable therapy icons
96 Further information
98 variants
100 Human Leukocyte Antigen (HLA) Typing
102 Implications of treatment
104 Diagnosis Implications
106 image data
108 images
112 Implications of treatment
114 therapy part
116 Intervention Timeline
118 data
120 Potential Healing 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 5th part
860 6th part
870 7th part
910 Summary
920 Legend
930 Pharmacogenomic Results Details
940 Secondary Finding Details
1000 patient reports
List of 1004 genes
List of 1008 phenotypes
1012 Classification part
1016 Selective serotonin reuptake inhibitors (SSRIs)
1020 Antidepressant portion
1024 drugs
1028 Phenotype
1032 links
1036 Conflicting Evidence
1040 Phenotype
1044 links
1048 Supplementary Information
1052 links
1056 SNRI
1060 Antipsychotic part
1064 Anticonvulsant parts
1068 Anxiolytic parts
1072 Mood stabilizer parts
1076 Antimanic part
1080 hypnotic part
1084 VMAT2 inhibitor moieties
1088 ADHD drug part
1092 Other pharmaceutical parts
1900 processes

Claims (30)

A method for generating treatment information for a patient diagnosed with at least one psychosis comprising one or more processors and one or more programs for execution by said one or more processors In a computer system having a memory for storing
a. obtaining molecular data from a multigene panel sequencing reaction on samples from said patient, said molecular data being one of whole exome sequence data, mass array data, metabolic related genes associated with or a plurality of nucleic acid sequences obtained from sequence data from a plurality of introns and sequence data from one or more promoter regions associated with said metabolic-related genes;
b. aligning said molecular data to a human reference sequence;
c. providing a first set of clinical data associated with said patient, said first set of clinical data comprising a listing of prior therapies and a listing of said one or more diagnoses; , step and
d. generating a first report from a therapy engine based on said first set of molecular and clinical data, comprising:
the report comprising, for each one of at least a portion of the plurality of nucleic acid sequences within the patient's molecular data:
(1) in the laboratory results section of said report, a phenotype associated with said nucleic acid sequence;
(2) providing, in a supplemental section of said report, a listing of one or more drugs associated with said nucleic acid sequence and a classification for each drug in said listing; is determined at least in part by said listing of pre-treatment agents;
e. having said report presented to a user;
f. obtaining a second set of clinical data associated with said patient, said second set of clinical data describing clinical activity of said patient after presentation of said report; ,
g. updating said therapy engine with at least a portion of said second set of clinical data. 2. The method of claim 1, wherein the classification relates to one or more of drug administration, drug risks, and contraindications. said clinical activity described in said second set of clinical data is one of prescribed medication, medication dosage, patient compliance, and patient outcome after taking said prescribed medication; or 2. The method of claim 1, comprising a plurality. 2. The method of claim 1, wherein the patient has been diagnosed with multiple psychoses. The therapy engine comprises a knowledge database, the knowledge database comprising:
i. data relating to interactions between a particular drug or drugs and one or more nucleic acid sequences associated with drug metabolism;
ii. primary drug metabolism pathway data;
iii. A first cohort data set derived from a cohort of psychiatric subjects at time 1, wherein said first cohort data set comprises one or more drugs used for treatment and a first cohort dataset comprising a diagnosis and a treatment outcome;
iv. Scientific publications, US Food and Drug Administration (FDA), Council for Clinical Pharmacogenomics Practice (CPIC), Dutch Pharmacogenomics Working Group (DPWG), Pharmacogenomics Knowledge Base Review, and Psychotropic Drug Screening Program Ki Database 2. The method of claim 1, comprising drug information data collected from one or more sources of: 6. The method of claim 5, wherein said cohort dataset is not derived from clinical trial data. 6. The method of claim 5, wherein at least some of the psychiatric subjects within said cohort are diagnosed with multiple psychoses. 6. The knowledge database of claim 5, wherein the knowledge database further comprises a second cohort dataset derived at time 2, the second cohort dataset comprising information from at least one of the first cohort subjects. the method of. 9. The knowledge database of claim 8, wherein the knowledge database further comprises an Nth cohort dataset derived at time N, the Nth cohort dataset comprising information from at least one of the previous cohort subjects. Method. further comprising providing an Nth set of clinical data associated with the patient, the Nth set of clinical data at a time after the (N-1)th clinical data set is acquired; 10. The method of claim 9, obtained. 11. The method of claim 10, wherein each of said clinical data sets describes the patient's clinical activity after presentation of a previous report. 12. The method of claim 11, further comprising updating the therapy engine with at least a portion of the Nth clinical data set. 2. The method of claim 1, wherein the report further provides supporting information for the classification. 14. The method of claim 13, wherein the report further provides hyperlinks to source documents or websites having information regarding the classification of the drug. 2. The method of claim 1, wherein the report further provides a listing of drugs associated with the patient's diagnosis but without a known nucleic acid association. The clinical activity described in any of the Nth clinical data sets is one of prescribed medication, medication dosage, patient compliance, and patient outcome after taking the prescribed medication. 11. The method of claim 10, comprising one or more. 2. The method of claim 1, comprising generating a second report based on input from a second clinical data set. 2. The method of claim 1, wherein said listing of pre-treatment agents comprises at least one drug dose. 2. The method of claim 1, wherein the listing of pre-treatment drugs includes at least one patient response to a drug. 2. The method of claim 1, wherein the therapy engine identifies possible side effects of the listed drugs and provides the side effect information in a report. 2. The method of claim 1, wherein the therapy engine identifies a recommended dose for each drug included in the listing of one or more drugs and provides that dose information in the report. 2. The method of claim 1, wherein the therapy engine identifies subsequent potential drug recommendations by excluding from the report at least one drug included in the listing of prior therapy drugs. wherein said treatment engine comprises a classifier for identifying subtypes of depression, said psychosis with which said patient has been diagnosed is depression, said subtypes of depression being listed in said report. The method of paragraph 1. 2. The method of claim 1, wherein the therapy engine comprises a classifier for identifying drug resistance, and wherein the drug resistance is listed in the report. 2. The method of claim 1, wherein said listing of said one or more drugs is determined, at least in part, based on at least one diagnosis. 1. A system for generating information regarding treatment for a patient diagnosed with 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 multigene panel sequencing reaction on samples from said patient, said molecular data being one or more associated with whole exome sequence data, mass array data, metabolic-related genes; said obtaining comprising a plurality of nucleic acid sequences obtained from sequence data from a plurality of introns and sequence data from one or more promoter regions associated with said metabolic-related gene;
ii. aligning said molecular data to a human reference sequence;
iii. providing a first set of clinical data associated with said patient, said first set of clinical data including a listing of prior therapies and a listing of said one or more diagnoses; , said providing;
iv. generating a first report from a therapeutic engine based on said first set of molecular data and clinical data;
The report includes, for each one of at least a portion of the plurality of nucleic acid sequences in the patient's molecular data, (1) a phenotype associated with the nucleic acid sequence; (3) a classification for each drug in said listing, wherein said listing of said one or more drugs is at least partially associated with said drug; generating, as determined by the listing;
v. having the report presented to a user;
vi. obtaining a second set of clinical data associated with said patient, said second set of clinical data describing clinical activity of said patient after presentation of said report; and
vii. A system configured to: update said therapy engine with at least a portion of said second set of clinical data. said clinical activity described in said second set of clinical data is one of prescribed medication, medication dosage, patient compliance, and patient outcome after taking said prescribed medication; or 27. The system of claim 26, comprising a plurality. 27. The system of Claim 26, wherein the cohort dataset is not derived from clinical trial data. 27. The system of claim 26, wherein the subject has been diagnosed with multiple psychoses. 27. The system of claim 26, wherein at least some of the psychiatric subjects within the cohort are diagnosed with multiple psychoses.

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