Pharmacogenomic decision support for modulators of NMDA, glycine, and AMPA receptors

Abstract

Methods for identifying patients diagnosed with treatment-resistant or refractory depression, pain, or other clinical indications who are suitable for receiving N-methyl-D-aspartate receptor antagonists, glycine receptor beta (GLRB) modulators, or alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)-based therapies include determining appropriate medications, optimal doses for each patient, and determining which patients are not suitable for receiving the therapy. Pharmacogenomic clinical decision support assays include targeted single nucleotide polymorphisms and clinical values ​​or a combination of targeted single nucleotide polymorphisms, targeted ketamine-specific expansions and contractions of topologically associated domains, and clinical values. The methods described herein allow for more efficient determination of which patients will experience drug efficacy and which patients will experience adverse drug events. The methods provide personalized patient dosage recommendations, drug administration frequency, and drug selection recommendations.

Description

[0001] Citation of Related Applications

[0002] This application claims priority to and the benefit of (1) U.S. Provisional Application Serial No. 62 / 795,705, filed on January 23, 2019, entitled “Methods and Systems to Reconstruct Drug Spatial Networks from Pharmacogenomic Regulatory Interactions and Uses Thereof,” and (2) U.S. Provisional Application Serial No. 62 / 795,710, filed on January 23, 2019, entitled “Companion Diagnostic Assays for N-methyl-D-Aspartate Receptor Modulators,” the entire disclosure of each of which is expressly incorporated herein by reference. Technical Field

[0003] The technology described herein relates to pharmacogenomic clinical decision support assays that can be used to select therapies for depression (particularly treatment-resistant or refractory depression) and other clinical indications based on N-methyl-D-aspartate (NMDA) receptors, glycine receptors, and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, including anesthetic and analgesic / pain disorders, neuropsychiatric disorders, and neurological disorders using ketamine and its enantiomers as examples. More specifically, the technology relates to specific biomarkers, including genetic markers, clinical values, and patient-derived disease phenotypes to optimize drug selection that affects these receptor networks and individual patient dosages. Background Art

[0004] Existing antidepressant drugs are ineffective for many patients. A new class of antidepressant drugs is being developed that targets glutamate receptors in the human forebrain. Ketamine (RS-2-chlorophenyl-2-methylamino-cyclohexanone) is a non-competitive antagonist of glutamate N-methyl-d-aspartate receptors (NMDARs), approved by the U.S. Food and Drug Administration (FDA) for use as an anesthetic, and has shown promise as an antidepressant in patients with treatment-resistant depression (TRD). Although racemic formulations may have potent and undesirable psychotomimetic and other side effects depending on several variables, chemical analogs of ketamine exhibit reduced adverse events. In a patient population where conventional antidepressants targeting the known serotonin transporter (SLC6A4, also known as 5HTT or SERT1) are generally unresponsive, intravenous and oral formulations have demonstrated efficacy and tolerability in controlled trials and open-label studies, and the serotonin transporter includes a serotonin-norepinephrine reuptake inhibitor (SNRI). Evidence suggests that ketamine, its enantiomers, and ketamine analogs exert their mechanism of action primarily through modulation of NMDA receptors (NMDARs) and downstream receptors in this network in the human brain.

[0005] The pharmacodynamic (PD) target of ketamine-like drugs is the NMDAR, which is composed of GRIN1 and GRIN2 subunits, which bind glutamate and N-methyl-D-aspartate, binding sites for glycine and D-serine encoded by GLRB, and sites for polyamines, histamine, and cations. Antagonists, partial antagonists, and receptor modulators, such as ketamine and other NMDAR and glycine modulators, including phencyclidine, amantadine, dextromethorphan, tiletamine, riluzole, methoxetamine, methoxphenidine, and memantine, block the inward Ca 2+ transporter. +2Influx, thereby preventing postsynaptic depolarization. Neuroimaging studies have shown that intravenous infusion of ketamine causes a transient surge in glutamate levels observed in the prefrontal cortex, while having a rapid antidepressant effect. It has been shown that after NMDAR blockade, glutamate preferentially binds to the GRIA1, GRIA2, and GRIA4 subunits of the α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor. Several NMDAR antagonists and partial antagonists, GLRB modulators, and AMPAR agonists are being developed for the treatment of refractory depression, but they exhibit dissociative effects on patients. In addition, NMDAR antagonists, GLRB antagonists, and AMPAR modulators, partial antagonists, and receptor modulators that work in the same network as such drugs that failed in clinical trials may be used to change their use using the methods disclosed herein to increase their chances of success in clinical trials.

[0006] The three-dimensional (3D) architecture of the human regulatory epigenome plays an important role in determining human phenotypes. It is now well known that most of the important single nucleotide polymorphisms (SNPs) associated with disease risk, drug response and other human traits found in genome-wide association studies (GWAS) are located in enhancers, promoters and other non-coding regulatory elements. Coupled with the recent in-depth understanding of the functional 4D nuclear body organization, a large amount of biomedical "big data" has been obtained from open source and proprietary resources, enabling the reconstruction of drug regulatory pathways in the human genome. New methods are being applied to mining regulatory variants from genome-wide association studies (GWAS), phenotype-wide association studies (PheWAS) and mining electronic health record data and clinical trial data. It is now standard practice in the field to re-evaluate the single nucleotide polymorphism (SNP) trait associations from these data sources in the context of pathway analysis, and it has been found that the single nucleotide polymorphism trait associations are important because they are resolved to the same or related biological networks. Combining large amounts of biomedical data with innovative methods for mining biological networks lays the foundation for detecting genetic variants that may affect drug response variability (including adverse drug events). This approach promises to make great advances in specialty areas such as psychiatry, where lack of drug efficacy and a high number of adverse drug events have proven to be particularly problematic in patient care.

[0007] More than half of Americans will show symptoms of mental illness during their lifetime. The most common lifetime mental illnesses are stress and anxiety disorders, mood disorders, including major depressive disorder and bipolar disorder, impulse control disorders, substance use disorders and schizoaffective disorder. In the United States, the lifetime prevalence of any mental illness is 53%, while 28% suffer from two or more lifetime illnesses and 18% suffer from three or more, which shows that the comorbidity of mental illness is a significant medical challenge. Although some mental illnesses, such as bipolar disorder type 1, are inherited in families with a penetration rate of about 80%, other mental illnesses do not show obvious heritability. For major depressive disorder (MDD), as indicated by family, twin and adoption studies, genetic factors play an important role in the etiology of the disease. Twin studies have shown a heritability of 50%, and family studies indicate that the lifetime risk of developing MDD in first-degree relatives has increased by two to three times. In the study of the control group, several sociodemographic variables were significantly associated with the lifetime risk of mental illness. For example, women (biological sex) are at significantly higher risk for anxiety disorders and major depressive disorder than men, while men (biological sex) are at significantly higher risk for impulse control and substance abuse disorders than women. Non-Hispanic blacks and Hispanics are at significantly lower risk for anxiety disorders, mood disorders, and substance abuse disorders than non-Hispanic whites, and low education is associated with a high risk of substance abuse disorders. The data show that many factors, including environmental and social factors, biological sex, race, and family inheritance, are associated with the etiology of mental illness. In addition, the complexity of phenotypes within individual patients or patient groups, including comorbidities with other psychiatric disorders and stress-related diseases, requires a range of different algorithmic classification solutions, including machine learning, and a variety of statistical analyses, including linear regression, to accurately specify precise therapies beyond the currently available range.

[0008] Mental illness has a greater impact on human health than any other disease. For example, major depressive disorder (MDD) causes a greater burden of disability worldwide than any other medical condition (including cancer, heart disease, stroke, chronic obstructive pulmonary disease, and HIV / AIDS), but major depression remains the most undiagnosed, misdiagnosed, and untreated or poorly treated disease known to mankind. In 2013, the National Institutes of Health (NIH) provided 13 times more funding for oncology research than depression - about $5.3 billion, compared to $415 million. In the United States, adverse events related to prescription antidepressants accounted for more than 25,000 visits to the emergency room each year from 2009 to 2011, resulting in 30% of all prescription drug-related hospitalizations each year. Patients with major depression and comorbid medical conditions experience more severe depressive symptoms and have lower response and remission rates to antidepressant treatment than those without comorbid symptoms. Treatment-resistant depression (TRD) accounts for 30%-40% of all patients diagnosed with MDD and is defined as "failure to achieve remission after two well-established courses of antidepressant therapy known to be of evidence-based acceptable dose and duration."

[0009] Modern antidepressant drugs are ineffective for many patients, and in patients who do respond or remit, weeks to months of drug therapy are required before symptom relief is achieved. Therefore, newer and more effective antidepressant drugs are being developed. For example, the racemic mixture of ketamine and the S-enantiomer of ketamine are both examples of partial antagonists of the N-methyl-D-aspartate receptor (NMDAR) that have been approved by the U.S. Food and Drug Administration (FDA) for the treatment of TRD. As measured by the Montgomery-Asberg Depression Rating Scale ( Ketamine induces a rapid antidepressant response and concomitant increases in cortical glutamate levels in approximately 50% of patients with TRD, as measured by the total score of the MADRS. Although R,S-ketamine has been used for clinical indications such as chronic pain, perioperative analgesia, and sedation since the 1970s, adverse drug events (AEs) are common following ketamine treatment, and diversion has been limited by restricting its use to inpatient and outpatient treatment settings. For example, in a Phase III clinical trial of esketamine for TRD prior to submission to the FDA, almost a quarter of TRD patients experienced severe dissociative effects, 2 deaths were reported, and an additional 6.9% of TRD patients in the treatment group experienced severe psychotomimetic effects, including delirium, delusions, and suicidal ideation, as well as suicide attempts.

[0010] One of the challenges in psychiatry is the precise matching of drug therapy to accurately address the complex symptoms of individual patients. Psychiatric patients exhibit a wide range of comorbid disorders, and there are few objective biomarkers that can be used as diagnostic criteria to accurately tailor antidepressant, antipsychotic, and antimanic therapy for patients. Although diagnostic rating scales such as the Hamilton Scale for Depression (HAM-D) show good inter-rater reliability, psychiatric disorders such as depression present in a variety of different phenotypes. Non-drug therapies can show improved efficacy in patients with TRD or recurrent depression. For example, repetitive transcranial magnetic stimulation (rTMS) shows the prospect of being a non-drug treatment alternative for patients with TRD; however, the best results for TRD occur when rTMS is used as an adjunct to traditional antidepressant drug therapy, just as in the case of antidepressant drug classes, which contain NMDAR antagonists or partial antagonists, GLRB modulators, or AMPAR agonists, which can only be provided in a clinical setting for patients already taking another antidepressant drug.

[0011] rTMS therapy requires dozens of clinic visits, remission varies widely among patients with TRD, and rTMS is effective in only about 20%-40% of cases, with remission from depression lasting up to 1-2 years. Recent results from rTMS combined with neuroimaging demonstrate that patients with depression cluster specifically into 4 distinct phenotypes along the axes of anhedonia and anxiety based on differential rTMS array placement. These results provide substantial evidence that patients with psychosis can be stratified by phenotype based on activation of distinct brain connectivity pathways, networks that exhibit considerable inter-individual variability between patients, and greatly improve the chances of precisely matching the best therapy to individual patients.

[0012] Although rTMS offers hope for patients with TRD, its mechanism of action remained elusive until independent studies demonstrated that TMS first acts on the subgenual anterior cingulate cortex and significantly increases glutamate levels as well as biomarker modulation of the N-methyl-d-aspartate receptor (NMDAR).

[0013] These are remarkable findings because they demonstrate that the mechanism of rTMS brain activation is virtually indistinguishable from the pharmacological mechanism of action of ketamine. Thus, rTMS and ketamine exhibit similar mechanisms of action for relief of TRD, but approximately half of all TRD patients do not experience relief after treatment with either treatment option. In addition, both rTMS and ketamine exhibit transient but severe AEs, including dissociation (the putative basis for ketamine's analgesic efficacy), psychotomimetic, and neurocognitive impairment. This suggests that matching individual patients to one of these therapies or other antidepressant medications is critical if one can select which patients will benefit from these treatments and which patients will suffer severe AEs unnecessarily without adequate antidepressant efficacy.

[0014] Recent studies combining transcranial magnetic stimulation (TMS) to relieve depression followed by neuroimaging demonstrate that TRD patients can be clearly stratified into 4 subtypes based on their response to TMS device placement, with 4 different intrinsic neuroanatomical pathways activated, accompanied by distinctly different symptom clusters. As demonstrated in the present disclosure, these 4 subtypes can now be independently determined using a combination of clinical and molecular data, providing a paradigm for other psychiatric and stress-related disorders, where improved pharmacophenomic decision support will deliver better treatment options to patients. Similarly, NMDAR antagonist therapy can be used as an adjunct therapy for age-related degenerative medical conditions. Summary of the invention

[0015] Different methods can be used to accurately determine the precise treatment requirements of an individual patient phenotype or phenotype group. The present disclosure describes methods for configuring a pharmacophenomics assay for clinical decision support or a companion diagnostic for a psychotropic drug that optimizes the fit of therapeutic interventions to an individual patient or a group of patients diagnosed with a psychiatric disorder or related disorder, such as treatment-resistant depression, chronic pain, migraine, fibromyalgia, inflammatory disorders, and other conditions for which ketamine or one of its analogs comprises an effective therapeutic agent. In the context of the present disclosure, a patient's drug response and adverse event phenotype comprises multiple sets of variables as described herein, ranging from the intrinsic configuration of the patient's drug pharmacogenomic network, including its mutational profile configuration, to behavioral phenotypes that can be obtained from clinical data.

[0016] The methods for patient stratification in the present disclosure use different data sources, some of which may be incomplete, require data cleaning and / or governance, or may not exist. The different methods as described herein range from methods that can adapt to different combinations of limited data to more extensive computational solutions, or methods that can bridge missing data elements using probabilistic methods.

[0017] The present disclosure includes a series of tandem and different methods to provide accurate drug phenotypic decision support for patients diagnosed with psychiatric disorders. The output provides a quantitative score for ranking therapeutic interventions, including suggestions such as drug selection and dosage, transcranial magnetic stimulation, electroconvulsive therapy, and behavioral interventions. The present disclosure includes drug phenotypic methods for classifying patients diagnosed with psychiatric disorders into subtypes to optimize therapeutic interventions. These methods can be used to configure diagnostics to recommend the best treatment match to individual patients. In another embodiment, these methods can be used to enhance the selection of patients based on drug phenotypic stratification before clinical trials. In another embodiment, these methods can be used to configure companion diagnostics for psychotropic drugs to ensure patient safety during drug development, listing, and post-marketing.

[0018] In another embodiment, clinical values ​​are obtained from an EHR or similar source, and SNPs in PD and PK genes are obtained from the patient's genotype, and these are input as quantitative values ​​into a regression equation (nomogram) to determine the drug dose of a drug, such as ketamine, for the individual patient. In this embodiment, the optimal therapeutic dose is developed in a stepwise regression model equation containing genetic and clinical values, and the regression model is retested and validated using a patient population to ensure the accuracy of the regression equation output, as can be judged by a receiver-operator (ROC) curve as the area under the curve (AUC).

[0019] In another embodiment, clinical values ​​are obtained from an EHR or similar source and SNPs in PD and PK genes are combined with disease risk SNPs obtained from genome-wide association studies (GWAS) to differentially annotate adverse event and efficacy-specific subnetworks of a drug-pharmacogenomic network (e.g., ketamine) to predict whether a patient will benefit from the drug and, if so, determine an appropriate dose for the patient.

[0020] In another embodiment, clinical values ​​are obtained from EHR or similar sources, and SNPs in PD and PK genes are combined with disease risk SNPs obtained from GWAS and PheWAS to differentially annotate adverse event and efficacy specific subnetworks of drug-pharmacogenomic networks (such as ketamine) to predict whether a patient will benefit from the drug, and if so, determine the appropriate dosage for the patient. In this embodiment, therapeutic drug monitoring by pharmaco-metabolomics is used to collect more accurate data on pre-existing prescription and over-the-counter drugs used by patients and their metabolites by analyzing biological samples (blood, cheek swabs, urine or other body fluids) obtained from patients or groups of patients.

[0021] In yet another embodiment, clinical values ​​are obtained from EHR or similar sources and SNPs in PD and PK genes are combined with disease risk SNPs obtained from GWAS and PheWAS to differentially annotate adverse event and efficacy specific subnetworks of a drug-pharmacogenomic network (such as ketamine), and these data are matched to 1 of 4 phenotypes that may or may not be derived from rTMS and neuroimaging data, determined using the Hamilton Depression Rating Scale (HAMD) score in the context of an antidepressant medication (such as ketamine).

[0022] In another embodiment, pharmacophenomic decision support is determined using inputs from a model comprising: (1) molecular profiling of drug-induced subnetworks in patients or patient cohorts, (2) clinical variables such as those derived from electronic health records or equivalent measurements performed by clinicians, (3) patient subtypes based on clinical variables and neuroimaging studies, and (4) PD and PK SNPs to stratify patients by drug response. Additionally, drug-drug and drug-gene interactions objectively measured using pharmaco-metabolomic approaches can be used to minimize adverse drug events for individual patients or patient cohorts.

[0023] Another embodiment of this system is the configuration of a companion diagnostic that can be used for patient selection in clinical trials, as well as during the marketing and post-marketing period of drugs, such as antidepressant drugs that are NMDAR antagonists, partial antagonists, GLRB modulators, and AMPAR agonists.

[0024] Another embodiment of this system is to identify and select therapeutic drugs to add to NMDAR modulators to improve outcomes.Another embodiment of the methods and systems described herein can be used to re-evaluate drugs for clinical trials and for drug repurposing. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1A A block diagram illustrating a computer network and system upon which an exemplary companion diagnostic system according to the presently described embodiments may operate;

[0026] Figure 1B According to the presently described embodiment, Figure 1A A block diagram of an exemplary medication and dosage decision server operating in a system of

[0027] Figure 1C According to the presently described embodiment, Figure 1A A block diagram of an exemplary client device operating in a system of;

[0028] Figure 1DA flow chart is presented representing an exemplary method for determining a drug and dosage to be administered to a patient suffering from depression or other neuropsychiatric disorder based on comparing data from a biological sample of the patient to a reference drug-specific pharmacogenomic network and a constituent subnetwork of a drug of interest;

[0029] Figure 2 presents example measurements collected from biological samples of patients using chromosome conformation capture, bioinformatics analysis, and / or similar measures;

[0030] Figure 3 A simple example is shown: how a SNP located within an enhancer in the network can disrupt the contact of the enhancer with one of its target gene promoters in the TAD, leading to adverse drug events in patients within the drug response cohort. Figure 3 A shows how different laboratory methods can be used to obtain measurements from the spatial interactome of chromatin in three dimensions and the data can be analyzed as 2D maps of enhancer-gene promoter interactions. Figure 3 B depicts how a SNP can disrupt the chromatin loop between an enhancer and one of the two gene promoters it regulates within a TAD. This disruption eliminates the spatial connection between the enhancer and gene promoter 1, resulting in dysregulation of gene 1, leading to adverse events in this patient and his cohort in response to administration of the specific drug of interest;

[0031] Figure 4 A flow chart and scoring system are presented representing an exemplary method for determining an appropriate drug and dosage to be administered to a patient suffering from depression or other neuropsychiatric disorder while avoiding adverse events (AEs) based on comparing data from a biological sample of the patient to a reference drug-specific pharmacogenomic network and a constituent subnetwork of the drug of interest;

[0032] Figure 5 presents a flow chart representing an exemplary method for securely prescribing a given drug at a given dose, continuing with caution, or discontinuing a recommendation to prescribe using a molecular phenotype quantitative assay generated from a drug subnetwork using efficacy and adverse event subnetworks derived from an individual patient's pharmacogenomics network;

[0033] Figure 6 We present 2 different approaches based on post hoc bioinformatics analysis and disease risk SNP annotation from GWAS to identify patients of a specific ancestry who should receive ketamine based on disease gene risk variant analysis and the presence of disease risk SNPs associated with ketamine adverse event subnetwork 3 and efficacy subnetwork 2;

[0034] Figure 7demonstrated how to fine-tune a range of subnetwork types across the range of human drug response phenotypes using machine learning for models created from a highly heterogeneous set of hierarchical biomedical and biological data types and elements after selecting a specific drug from a reference set database of drug-pharmacogenomics networks and their constituent subnetworks compared to patient input sample data;

[0035] Figure 8 A flowchart showing an exemplary method for matching a patient's drug pharmacodynamic efficacy and adverse events to those of a reference drug pharmacogenomic network using similarity scores to discover novel pharmacodynamic targets using computer vision algorithm-based TAD matrix rendering and deep learning to allow for the study of drug similarity using TAD patterns and / or the use of TAD matching methods in clinical trials studying drug TAD profiles;

[0036] Fig. 9A An exemplary model showing how the system integrates heterogeneous hierarchical biomedical and biological data and processes these multi-scale data using machine learning and deep learning for pharmacogenomic network topology and sub-network reconstruction. This strategy for mapping drug networks provides insights into mechanistic on-target and off-target effects. The discovered pharmacogenomic network topology provides a basis for advanced pharmacogenomic decision support, laying the foundation for refining this capability for subsequent preclinical and clinical studies, which can also be used for drug mechanism discovery and drug repurposing prediction;

[0037] Fig. 9B A flowchart representing a method for generating a reconstructed drug-pharmacogenomic network and corresponding sub-networks of drugs of interest is shown, including a human pharmacogenomics SNP input filter, a drug space network reconstruction engine, and an iterative gene set optimization engine. A reference set of these drug-pharmacogenomic networks, drug efficacy, and drug adverse event information can also be created;

[0038] Fig.10 presents a flowchart representing an exemplary method for integrating drug-gene set optimization to construct a pharmacogenomic network and determine its subnetworks;

[0039] Fig.11 A flowchart for post hoc validation of reconstructed drug-pharmacogenomics networks and sets of such networks using bioinformatics data / software and integrated pharmacoinformatics pipelines is presented;

[0040] Fig.12An example of comparison of significance test results for SNP rs12967143-G (located at an intragenic enhancer in the TCF4 gene) relative to other GWAS SNPs as described using the numerical outputs of six different machine learning algorithms used in the analysis and for various neural and non-neural cell types is shown;

[0041] Fig.13 Features of 2 distinct ketamine pharmacogenomics subnetworks are presented, as determined from post hoc validation of the ketamine pharmacogenomics network in the human brain. Fig.13 A is the gene enrichment of the ketamine pharmacogenomics subnetwork in the human brain mediating efficacy and neuroplasticity. Fig.13 B is the gene enrichment of the ketamine pharmacogenomic subnetwork in the human brain that mediates glutamate receptor signaling and adverse events in the human brain;

[0042] Fig.14A Graphical depictions of ketamine pharmacogenomic subnetworks in the human brain that mediate efficacy and neuroplasticity, as well as diseases and pathologies associated with efficacy and neuroplasticity, are presented;

[0043] Fig. 14B A graphical depiction of ketamine pharmacogenomic subnetworks in the human brain that mediate glutamate receptor signaling and adverse events in the human brain, as well as diseases and pathologies associated with this signaling and these adverse events, is presented;

[0044] Fig.15 Genes and regulatory RNAs localized in ketamine efficacy and neuroplasticity subnetworks are listed;

[0045] Fig.16 Genes localized in the glutamate-ketamine receptor signaling and adverse events subnetworks are listed;

[0046] Fig.17 Genes localized in the ketamine pharmacokinetic and hormonal regulatory subnetworks are listed;

[0047] Figures 18A-18B Example values ​​from linear regression analysis for ketamine dose determination and accuracy of ketamine dosing in the validation cohort are presented;

[0048] Fig.19 presents the definition of four subtypes of treatment-resistant depression (TRD) patients as determined by transcranial magnetic stimulation (TMS) coupled with neuroimaging of resting connectivity networks in the human brain;

[0049] Fig. 20 Example neural maps of four different subtypes of TRD patients with depression are shown;

[0050] Fig.21Example medication prescribing / non-prescribing recommendations and alternative medication options for each of the four different subtypes of TRD patients with depression are presented;

[0051] Fig. 22 Examples of beneficial combinatorial mechanisms and therapeutics mediated by synergistic histone modifications discovered using the methods described herein are presented, using valproic acid and ketamine for H3K9 acetylation and deacetylation, respectively, which jointly lead to neurogenesis and neural differentiation;

[0052] Fig.23 Shown Fig.23 A complementary pharmacogenomics network of valproic acid, and Fig.23 B Pharmacogenomic network of ketamine showing neurogenesis and neural differentiation, respectively; and

[0053] Fig.24 Combinatorial and biologically synergistic effects of valproate and ketamine pharmacogenomic networks in neurogenesis, neuronal proliferation, and terminal neuronal differentiation were demonstrated. DETAILED DESCRIPTION

[0054] Although the following text sets forth a detailed description of many different embodiments, it should be understood that the legal scope of this description is defined by the words of the claims set forth at the end of this disclosure. The detailed description should be construed as exemplary only and does not describe every possible embodiment, as describing every possible embodiment would be impractical, if not impossible. Numerous alternative embodiments may be implemented using current technology or technology developed after the filing date of this patent, which would still fall within the scope of the claims.

[0055] It should also be understood that unless a term is expressly defined in this patent using the sentence "As used herein, the term '______' is defined herein to mean ..." or similar sentences, there is no intention to limit the meaning of said term, either expressly or by implication, beyond its plain or ordinary meaning, and such term should not be construed as limited in scope based on any statement made in any section of this patent (except the language of the claims). To the extent that any term recited in the claims at the end of this patent is referenced in this patent in a manner consistent with a single meaning, this is done solely for clarity so as not to confuse the reader, and is not intended to limit such claim term by implication or otherwise to that single meaning. Finally, unless a claim element is defined by reference to the word "means" and the function of the recitation without any structure, it is not intended to interpret the scope of any claim element pursuant to the application of 35 U.S.C. § 112 (sixth paragraph).

[0056] The present disclosure includes systems and methods for stratifying patients or patient groups diagnosed with psychiatric disorders or requiring these drugs for other clinical indications to accurately select the dosage of drugs and NMDAR antagonists. Ketamine is used as an example, but these methods can be used for NMDAR antagonists or α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor modulators in clinical trials for clinical indications of refractory depression. For patients diagnosed with TRD or specific patient subgroups, ketamine and its enantiomers are new antidepressants that show greater efficacy and fewer side effects than other antidepressants. A pharmacogenomics decision support system can determine which patients diagnosed with TRD should receive ketamine or one of its enantiomers as an antidepressant drug, and if so, what the appropriate dose should be for individual patients or patient groups to maximize efficacy, maximize adverse drug events and drug-drug interactions, and reduce the harmful effects of drug-gene and drug-drug interactions. Adverse psychotropic drug side effects, including psychotomimetic and neurocognitive effects of glutamate receptor-targeted drugs used to alleviate depression, and the heterogeneity of the patient population that may exhibit treatment-resistant depression (TRD) are based on multiple variables, including demographic and sociological variables, trauma history, genotype, and clinical variables.

[0057] The system includes several methods that can be used to configure clinical decision support diagnostics for the selection and administration of ketamine or its enantiomers as an antidepressant for refractory depression and can be extended to other psychotropic drugs. One embodiment includes an integrated multi-scale measurement system, the integrated multi-scale measurement system including Figure 1D The components shown can be Figure 2 Extensive analysis of patient biological samples is shown and other relevant clinical data may also be obtained.

[0058] In some embodiments, the system is configured to analyze the minimum amount of results from the biological sample required to match a stored reference set of cohort subnetworks. In this case, a learning machine that has been pre-trained for a range of cohort responses for a specific drug is used to compare patient data to a reference set of drug subnetworks spanning the entire range of human drug response cohorts, and matching the reference set prompts a clinical decision recommendation. For example, for a specific drug, a reference set of drug subnetworks may include a set of reference drug pharmacodynamic efficacy subnetworks, reference drug pharmacodynamic adverse event subnetworks, reference chromatin remodeling subnetworks, and reference pharmacokinetic enzyme and hormone subnetworks.

[0059] Electronic encryption agents are first used to protect health information by de-identification and prevent patient identification. Biological samples (e.g., blood, cheek swabs, saliva, urine, or other body fluids) are obtained from patients or patient groups, with clinical data from medical records, such as electronic health records (EHR) or other sources. Initial drug metabolomics analysis is performed on small blood samples or plasma components collected from patients or patient groups to determine potential drug-drug and drug-gene interactions that may change subsequent pharmacogenomic decision support. These objective measurements increase self-reports, clinician reports, or other data contained in EHR or other patient records.

[0060] In general, techniques for determining whether to administer a drug, such as a glutamate NMDAR antagonist or partial antagonist, a GLRB modulator, or an AMPAR agonist, to a patient, and / or determining an appropriate dose of a drug to be administered to a patient can be implemented in one or more client devices, one or more network servers, or a system comprising a combination of these devices. However, for clarity, the examples below focus primarily on embodiments in which a healthcare professional obtains a biological sample from a patient and provides the biological sample to an assay laboratory for analysis.

[0061] The biological sample may comprise skin, blood, urine, sweat, lymph, bone marrow, cheek cells, saliva, cell lines, tissues, etc. of the subject. Cells are then extracted from the biological sample and reprogrammed into stem cells (such as induced pluripotent stem cells (iPSC)). The iPSC is then differentiated into various tissues such as neurons and cardiomyocytes, and assayed to obtain the patient's genomic data, chromosome data, metabolomics data, etc. In certain embodiments, iPSC may be assayed for loci associated with or causally associated with the phenotypic response of the drug of interest. iPSC includes a portion of a reference set for deriving a variable for assessing an individual patient.

[0062] The drug and dosage decision server relies on the drug and dosage decision support engine. This engine receives the representation such as Figure 2 The numerical score of overlap between the input patient sample shown and the relevant drug-specific reference pharmacogenomics network stored in a database of such references 154. The system uses a learning machine trained on the entire range of human drug response cohorts, consisting of a reference set of pharmacogenomics networks for a specific drug, covering the range of variation in human drug response to a specific drug, including the reference set matched to the input patient sample.

[0063] The drug and dosage decision server analyzes laboratory results to determine the subnetwork representation of the patient's drug gene set of NMDAR antagonists or partial antagonists, GLRB modulators or AMPAR agonists (such as ketamine). In addition, the drug and dosage decision server retrieves the reference pharmacogenomics network and the constituent reference subnetwork of NMDAR antagonists or partial antagonists, GLRB modulators or AMPAR agonists from, for example, a reference drug pharmacogenomics network database. Then, the drug and dosage decision server compares the patient's drug subnetwork representation with the reference pharmacogenomics network and the constituent reference subnetwork of the drug to determine whether to administer the drug to the patient. For example, the drug and dosage decision server can compare the patient's efficacy drug-specific (e.g., ketamine) subnetwork with the reference efficacy drug-specific (e.g., ketamine) subnetwork, and can compare the patient's adverse event drug-specific (e.g., ketamine) subnetwork with the reference adverse event drug-specific (e.g., ketamine) subnetwork. If the similarity between the patient's efficacy drug-specific subnetwork and the reference efficacy drug-specific subnetwork is greater than a threshold (indicating that the drug may be effective for the patient), the drug and dosage decision server can then determine that the drug should be administered to the patient. The medication and dosage decision server may also determine that a drug should be administered to the patient if the similarity between the patient's adverse event drug-specific subnetwork and the reference adverse event drug-specific subnetwork is below a threshold (indicating that the patient is unlikely to experience an adverse event) or based on some combination of the two.

[0064] Thus, the medication and dosage decision server can provide a recommendation to the client device of the healthcare professional indicating that the patient should receive the medication, so that the healthcare professional can administer the medication to the patient. Thus, the healthcare professional can administer the medication to the patient. In some embodiments, the medication and dosage decision server can determine the dosage of the medication administered to the patient based on a medication administration algorithm. The medication administration algorithm can be determined using machine learning techniques such as linear regression and can be based on the patient's demographic data, the patient's clinical data, the patient's biological data, etc.

[0065] The medication and dosage decision server can determine the dosage of medication to be administered to the patient and perform other methods described herein using various machine learning techniques, including but not limited to regression algorithms (e.g., ordinary least squares regression, linear regression, logistic regression, stepwise regression, multivariate adaptive regression splines, local estimated scatter plot smoothing, etc.), instance-based algorithms (e.g., k-nearest neighbors, learning vector quantization, self-organizing maps, local weighted learning, etc.), regularization algorithms (e.g., ridge regression, least absolute shrinkage and selection operators, elastic net, least angle regression, etc.), decision tree algorithms (e.g., classification and regression trees, iterative bisection3, C4.5, C5, chi-square automatic interaction detection, decision stumps, M5, conditional decision trees, etc.), clustering algorithms (e.g., k-means, k-median, period maximization, hierarchical clustering, spectral clustering, mean shift, density-based pharmacogenomic clustering with noise applications, sorting points for identifying clustering structures, etc.), association rule learning algorithms (e.g., prior algorithms, Eclat algorithms, etc.), Bayesian algorithms (e.g., naive Bayes, Gaussian naive Bayes, multinomial naive Bayes, average single dependency estimator, Bayesian belief network, Bayesian network, etc.), artificial neural networks (e.g., perceptron, Hopfield network, radial basis function network, etc.), deep learning algorithms (e.g., multilayer perceptron, deep Boltzmann machine, deep belief network, convolutional neural network, stacked autoencoder, generative adversarial network, etc.), dimensionality reduction algorithms (e.g., principal component analysis, principal component regression, partial least squares regression, Sammon mapping (Sammon mapping), multidimensional scaling, projection pursuit, linear discriminant analysis, hybrid discriminant analysis, quadratic discriminant analysis, flexible discriminant analysis, factor analysis, independent component entity analysis, non-negative matrix factorization, t-distributed random neighbor embedding, etc.), ensemble algorithms (e.g., boosting, bootstrap aggregation, AdaBoost, stacked generalization, gradient boosting machines, gradient boosted regression trees, random decision forests, etc.), reinforcement learning (e.g., temporal difference learning, Q-learning, learning automata, state-action-reward-state-action, etc.), support vector machines, hybrid models, evolutionary algorithms, probabilistic graphical models, etc.

[0066] refer to Figure 1A, an example pharmacogenomics decision support system 100 determines whether to administer a psychotropic drug to a patient suffering from depression, such as a patient suffering from TRD, and the appropriate dosage of the psychotropic drug. The pharmacogenomics decision support system 100 includes a drug and dosage decision server 102 and a plurality of client devices 106-116 that can be communicatively connected via a network 130, as described below. In one embodiment, the drug and dosage decision server 102 and the client devices 106-116 can communicate via a wireless signal 120 over a communication network 130, which can be any suitable local area network or wide area network, including a WiFi network, a Bluetooth network, a cellular network (such as 3G, 4G, long term evolution (LTE), 5G), the Internet, etc. In some cases, the client devices 106-116 can communicate with the communication network 130 via an intervening wireless or wired device 118, which can be a wireless router, a wireless repeater, a base station transceiver of a mobile phone provider, etc. For example, client devices 106-116 may include tablet computers 106, smart watches 107, web-enabled cell phones 108, wearable computing devices (such as Google Glass TM or 109), a personal digital assistant (PDA) 110, a mobile device smartphone 112 (also referred to herein as a “mobile device”), a laptop computer 114, a desktop computer 116, a wearable biosensor, a portable media player (not shown), a tablet phone, any device configured for wired or wireless RF (radio frequency) communication, etc. In addition, any other suitable client device that records patient clinical data can also communicate with the drug and dosage decision server 102.

[0067] Each of the client devices 106-116 may interact with the medication and dosage decision server 102 to receive recommendations regarding whether to administer a psychotropic medication to a patient and the dosage of the psychotropic medication. The client devices 106-116 may present the recommendations through a user interface for display to a healthcare professional.

[0068] In an example embodiment, the drug and dosage decision server 102 can be a cloud-based server, an application server, a network server, etc., and includes a memory 150, one or more processors (CPU) 142 (such as a microprocessor coupled to the memory 150), a network interface unit 144 and an I / O module 148, which can be, for example, a keyboard or a touch screen.

[0069] The drug and dosage decision server 102 may also be communicatively connected to a database 154 of reference drug pharmacogenomics networks and constituent sub-networks, such as efficacy and adverse event sub-networks of drugs.

[0070] The memory 150 may be a tangible, non-transitory memory and may include any type of suitable memory module, including random access memory (RAM), read-only memory (ROM), flash memory, other types of persistent memory, etc. The memory 150 may store, for example, instructions for an operating system (OS) 152 that can be executed on the processor 142, which may be any type of suitable operating system, such as a modern smartphone operating system. The memory 150 may also store, for example, instructions for a medication and dosage decision support engine 146 that can be executed on the processor 142. Figure 1B The medication and dosage decision server 102 is described in more detail. In some embodiments, the medication and dosage decision support engine 146 may be part of one or more of the client devices 106-116, the medication and dosage decision server 102, or a combination of the medication and dosage decision server 102 and the client devices 106-116.

[0071] In any case, the drug and dosage decision support engine 146 can obtain laboratory results from patient biological samples only when necessary to match one of the set of pharmacogenomics networks and its constituent subnetworks that define changes in human drug responses for a specific drug. These include molecular data that include genomic variations defined by SNPs in PD and PK genes, pharmacogenomics interactions between regulatory elements, genes in the patient genome that can be defined using chromosome conformation data (such as Hi-C) and / or direct topologically associated domain (TAD)-specific measurements, which include differential gene expression determined using RNA sequencing (RNA-Seq) or expression microarray profiling and patient-specific TAD contact group measurements in related or alternative cell types using chromosome conformation capture (e.g., 3C, 4C, 5C, Hi-C, ChIA-PET and GAM). Molecular data can be determined for loci associated or causally associated with the phenotypic response of the drug of interest. In addition, the drug and dosage decision support engine 146 can obtain reference pharmacogenomics networks and constituent reference subnetworks of the psychotropic drugs of interest (e.g., ketamine) from the reference drug pharmacogenomics network database 154.

[0072] The drug and dosage decision support engine 146 can then analyze the patient's laboratory results to determine the subnetwork representation of the psychotropic drug of interest, such as the efficacy subnetwork and the adverse event subnetwork. The drug and dosage decision support engine 146 can compare the patient's efficacy subnetwork and the adverse event subnetwork with the reference efficacy and adverse event subnetwork to determine whether to administer the psychotropic drug of interest to the patient. If the similarity between the patient's efficacy drug-specific subnetwork and the reference efficacy drug-specific subnetwork is greater than a threshold and / or the patient's adverse event drug-specific subnetwork and the reference adverse event drug-specific subnetwork are lower than a threshold, the drug and dosage decision support engine 146 can determine that the psychotropic drug of interest should be administered to the patient. The drug and dosage decision support engine 146 can then provide suggestions to the client devices 106-116 of healthcare professionals, and the suggestions indicate that the patient should receive the psychotropic drug of interest. In other ways, the drug and dosage decision support engine 146 can provide suggestions for another drug administered to the patient to treat depression. In addition, the drug and dosage decision support engine 146 can determine the dosage of the psychotropic drug of interest administered to the patient according to the administration algorithm. The medication and dosage decision support engine 146 may also provide a suggested dosage of the psychotropic medication of interest to the healthcare professional's client device 106 - 116 .

[0073] The medication and dosage decision server 102 may communicate with the client devices 106-116 via a network 130. The digital network 130 may be a private network, a secure public Internet, a virtual private network, and / or some other type of network, such as a dedicated access line, an ordinary conventional telephone line, a satellite link, a combination of these, etc. In the case where the digital network 130 includes the Internet, data communications may be conducted over the digital network 130 via Internet communication protocols.

[0074] Now go to Figure 1B , the drug and dosage decision server 102 may include a controller 224. The controller 224 may include a program memory 226, a microcontroller or microprocessor (MP) 228, a random access memory (RAM) 230, and / or input / output (I / O) circuits 234, all of which may be interconnected via an address / data bus 232. In some embodiments, the controller 224 may also include a database 239, or be otherwise communicatively connected to the database or other data storage mechanism (e.g., one or more hard drives, optical storage drives, solid-state storage devices, etc.). The database 239 may include data such as drug pharmacogenomics network reference data, drug recommendation display templates, web page templates and / or web pages, and other data necessary for interacting with a user via the network 130. The database 239 may include the same data as referenced above. Figure 1A Similar data is described in database 154 .

[0075] It should be understood that although Figure 1B Only one microprocessor 228 is depicted, but the controller 224 may include multiple microprocessors 228. Similarly, the memory of the controller 224 may include multiple RAMs 230 and / or multiple program memories 226. Although Figure 1B I / O circuitry 234 is depicted as a single block, but I / O circuitry 234 may include many different types of I / O circuitry. Controller 224 may implement, for example, RAM 230 and / or program memory 226 as semiconductor memory, magnetically readable memory, and / or optically readable memory.

[0076] like Figure 1B As shown, the program memory 226 and / or the RAM 230 can store various applications for execution by the microprocessor 228. For example, the user interface application 236 can provide a user interface to the drug and dosage decision server 102, which can, for example, allow a system administrator to configure, troubleshoot, or test various aspects of the server operation. The server application 238 can be operated to receive molecular data of a patient, analyze the molecular data to determine a subnetwork of the patient associated with a specific drug of interest, compare the subnetwork of the patient with a reference subnetwork of the specific drug of interest, determine to administer the specific drug of interest to the patient based on the comparison, and transmit a recommendation to the client device 106-116 to administer the specific drug of interest to the patient. The server application 238 can be a single module 238, such as the drug and dosage decision support engine 146, or multiple modules 238A, 238B.

[0077] Despite Figure 1B 238 as including two modules 238A and 238B, the server application 238 may include any number of modules that perform tasks related to the implementation of the medication and dosage decision server 102. In addition, it should be understood that although the server application 238 is described in detail in FIG. Figure 1B Only one medication and dosage decision server 102 is depicted in the figure, but multiple medication and dosage decision servers 102 may be provided for distributing server load, serving different web pages, etc. These multiple medication and dosage decision servers 102 may include web servers, entity-specific servers (e.g., servers, etc.), servers located in retail or private networks, etc.

[0078] Reference now Figure 1C, the laptop computer 114 (or any of the client devices 106-116) may include a display 240, a communication unit 258, a user input device (not shown), and like the medication and dosage decision server 102, a controller 242. Similar to the controller 224, the controller 242 may include a program memory 246, a microcontroller or microprocessor (MP) 248, a random access memory (RAM) 250, and / or input / output (I / O) circuits 254, all of which may be interconnected via an address / data bus 252. The program memory 246 may include an operating system 260, a data storage device 262, a plurality of software applications 264, and / or a plurality of software routines 268. For example, the operating system 260 may include a Microsoft OS The data storage device 262 may include data such as application data of the plurality of applications 264, routine data of the plurality of routines 268, and / or other data necessary to interact with the medication and dosage decision server 102 via the digital network 130. In some embodiments, the controller 242 may also include other data storage mechanisms (e.g., one or more hard disk drives, optical storage drives, solid-state storage devices, etc.) residing within the laptop computer 114, or otherwise be communicatively connected to the other data storage mechanisms.

[0079] The communication unit 258 may communicate with the medication and dosage decision server 102 via any suitable wireless communication protocol network such as a wireless telephone network (e.g., GSM, CDMA, LTE, etc.), a Wi-Fi network (802.11 standard), a WiMAX network, a Bluetooth network, etc. The user input device (not shown) may include a "soft" keyboard displayed on the display 240 of the laptop computer 114, an external hardware keyboard (e.g., a Bluetooth keyboard) communicating via a wired or wireless connection, an external mouse, a microphone for receiving voice input, or any other suitable user input device. As discussed with reference to the controller 224, it should be understood that although Figure 1C Only one microprocessor 248 is depicted, but the controller 242 may include multiple microprocessors 248. Similarly, the memory of the controller 242 may include multiple RAMs 250 and / or multiple program memories 246. Although Figure 1C I / O circuitry 254 is depicted as a single block, but I / O circuitry 254 may include many different types of I / O circuitry. Controller 242 may implement one or more RAMs 250 and / or program memory 246 as, for example, semiconductor memory, magnetically readable memory, and / or optically readable memory.

[0080] The one or more processors 248 may be adapted and configured to execute, among other software applications, any one or more of a plurality of software applications 264 and / or any one or more of a plurality of software routines 268 resident in the program memory 246. One of the plurality of applications 264 may be a client application 266, which may be implemented as a series of machine-readable instructions for performing various tasks associated with receiving information at the laptop computer 114, displaying information on the laptop computer, and / or sending information from the laptop computer.

[0081] An application in the plurality of applications 264 may be a native application and / or a web browser 270 (eg, Apple's Google Chrome TM 、Microsoft Internet and Mozilla ), the local application and / or web browser may be implemented as a series of machine-readable instructions for receiving, interpreting and / or displaying web page information from the drug and dosage decision server 102, while also receiving input from a user such as a healthcare professional or researcher. Another application in the plurality of applications may include an embedded web browser 276, which may be implemented as a series of machine-readable instructions for receiving, interpreting and / or displaying web page information from the drug and dosage decision server 102.

[0082] One of the plurality of routines may include a medication suggestion display routine 272 that presents on the display 240 a suggestion and / or a suggested dosage of whether to administer a psychotropic medication of interest to the patient.

[0083] Preferably, the user can launch the client application 266 from a client device (such as one of the client devices 106-116) to communicate with the drug and dosage decision server 102 to implement the companion diagnostic system 100. In addition, the user can also launch or instantiate any other suitable user interface application (e.g., a native application or web browser 270, or any other application in the plurality of software applications 264) to access the drug and dosage decision server 102 to implement the companion diagnostic system 100.

[0084] Figure 1D A flow chart is shown representing an exemplary method 160 for determining a drug and dosage to administer to a patient suffering from depression based on comparing data from a biological sample of the patient to a reference pharmacogenomics network and a constituent subnetwork of drugs of interest. The method 160 may be performed by the drug and dosage decision server 102.

[0085] In some embodiments, Figure 1D Analysis of patient biological samples for personalized therapy is shown to quantify the relative activation of different pathways mediating the mechanism of action of ketamine in the human CNS, which pathways were determined using the methods described herein. Pharmaco-metabolomic assays can be used to analyze patient or patient cohort biological samples to determine drugs or metabolites in the samples that may cause unwanted drug-drug interactions, thereby affecting efficacy, adverse events, and dosing of pharmaceutical products. Biosample measurements include: (1) genotyping of pharmacokinetic SNPs, which in the ketamine paradigm consist of CYP2B6 gene mutations that have been shown to be determinants of metabolizer status (poor, subnormal, normal, or ultra-rapid subtypes), (2) pharmacodynamic SNP targeting as input to pharmacogenomic network and subnetwork profiling that is determinative of both efficacy and adverse events as analyzed using pharmacogenomic genomic classifiers and pharmacodynamic subnetwork profiling systems, (3) direct topologically associated domain (TAD)-specific measurements, which include differential gene expression determined using RNA sequencing (RNA-Seq) or expression microarray profiling and patient-specific TAD contactome measurements in relevant or surrogate cell types using chromosome conformation capture (e.g., 3C, 4C, 5C, Hi-C, ChIA-PET, and GAM), and (4) pharmaco-metabolomic analysis.

[0086] Figure 1D The data processing pipeline shown in includes parallel routes of analyzing biological samples using multiple methods and analyzing available clinical data for the same patient that may be collected from electronic health records (EHRs). This example demonstrates an analysis for determining whether an N-methyl-D-aspartate receptor (NMDAR) modulator should be administered to a particular patient. Adverse events associated with NMDAR modulators such as ketamine can be severe, including severe dissociation, hallucinations, and nightmares. Therefore, the first decision made from these parallel analyses is to ensure that the patient does not receive an NMDAR modulator, such as ketamine, if the system predicts that the individual will experience moderate to severe adverse events.

[0087] Figure 1D The data processing pipeline shown in may be based on the molecular network representation of the patient, certain disease risk SNPs that the patient may carry (such as Fig.23 shown), the patient's treatment-resistant depression phenotype (e.g. Fig. 20 and Fig. 22 The system determines a "no-go" decision to administer an NMDAR modulator (such as ketamine) based on the patient's drug metabolism profile and dosage adjustments. If the system determines that the patient can receive an NMDAR modulator, such as ketamine, then the dosage determination algorithm is initiated.

[0088] Method 160 is used as a clinical decision support diagnostic to determine whether a patient should be prescribed an NMDAR module as an antidepressant (block 162) and the optimal dose for the patient. In some embodiments, a patient biological sample (block 164a) is analyzed for personalized therapy to quantify the relative activation of different pathways mediating the mechanism of action of ketamine in the human CNS, the pathways being determined using the methods described herein. A patient or patient cohort biological sample may be analyzed using a drug metabolism assay to determine drugs or metabolites in the sample that may cause unwanted drug-drug interactions, thereby affecting efficacy, adverse events, and dosing of a drug product. At box 166, biological sample measurements include: (1) genotyping of pharmacokinetic SNPs, which in the ketamine paradigm consist of CYP2B6 gene mutations that have been shown to be determinants of metabolic status (poor, subnormal, normal, or ultra-rapid subtypes), (2) pharmacodynamic SNP targeting as input to pharmacogenomic network and subnetwork profiling that is determinative of both efficacy and adverse events as analyzed using a pharmacogenomic genomic classifier and a pharmacodynamic subnetwork profiling system, (3) direct topologically associated domain (TAD)-specific measurements that include differential gene expression determined using RNA sequencing (RNA-Seq) or expression microarray profiling and patient-specific TAD contactome measurements in relevant or surrogate cell types using chromosome conformation capture (e.g., 3C, 4C, 5C, Hi-C, ChIA-PET, and GAM), and (4) pharmaco-metabolomic analysis (box 164c).

[0089] As described above, biological sample measurement includes pharmacodynamic SNP targeting as input to pharmacogenomic network and subnetwork spectrum analysis (frame 168, 170b), which is decisive for both efficacy and adverse events as analyzed using pharmacogenomic genome classifier and pharmacodynamic subnetwork spectrum analysis system. At frame 170a, reference pharmacogenomic network and subnetwork for the drug of interest are retrieved from the reference database. Then the patient subnetwork of the particular drug of interest containing efficacy and adverse event subnetwork is compared with the reference pharmacogenomic network and subnetwork of the drug of interest (frame 172). In order to determine the similarity with the reference set, two pairs of different reference-patient indicators include an accurate measurement of similarity and an output similarity score for each in the efficacy and adverse event subnetwork. At frame 172, the efficacy and adverse event subnetwork similarity scores for the drug of interest can be used to determine whether the drug of interest is applied to the patient. For example, if the similarity score of the efficacy subnetwork is higher than the threshold similarity score, then method 160 can determine that the drug of interest should be applied to the patient (frame 176). Otherwise, the method 160 determines that a different drug is selected (block 174 ).

[0090] In addition to comparing the patient's subnetwork of the drug of interest to the reference subnetwork of the drug of interest, clinical data of the patient is collected and analyzed at block 164b to determine whether to administer the drug to the patient and / or the dosage of the drug. More specifically, the patient's HAMD score and / or patient symptoms may be analyzed to classify the patient into one of four TRD patient subtypes (block 180). Figure 15-17 TRD patient subtypes are described in more detail. If the patient is classified as TRD subtype 3 (box 182), the method 160 determines to select a different drug (box 184). Other clinical data may also be analyzed, such as the patient's drug-drug interactions, age, weight, biological sex, body mass index, race, family history, patient substance abuse history, diagnosis codes, hospitalization history, drug-gene interactions, psychiatric history, whether the patient smokes or uses nicotine, etc. (box 186).

[0091] The dose of the drug of interest to be administered to the patient is then determined (block 178). The dose may be determined based on a dosing algorithm with predetermined constants to apply to each of several patient characteristics, such as biological characteristics, demographic characteristics, clinical characteristics, etc. In other embodiments, a dosing algorithm may be generated using machine learning techniques. The patient characteristics used in the dosing algorithm may include biological data, such as SNPs reported to stratify responses to ketamine in humans. The patient characteristics may also include demographic data of the patient, such as the patient's gender, height and weight, age, and race. In addition, the patient characteristics may include clinical data, such as family history, drug-drug interactions, history of psychiatric illness, whether the patient smokes or uses nicotine, and Hamilton Depression Rating Scale (HAM-D) score.

[0092] Figure 2 Various actions that can be taken based on the patient's biological sample are shown. In one embodiment, blood and buccal swab samples are obtained and processed. For ketamine and other drugs that undergo first-pass metabolism by the protein encoded by the super-inducible and hyper-variable CYP2B6 gene, targeted SNP genotyping is performed using a 4-SNP panel, but most importantly, the splice variant SNP rs3745274 is preferentially examined because it is relatively common in the population (>10% frequency) and carriers of this SNP include ultra-poor metabolizers of any drug that is primarily metabolized by this enzyme and are likely to experience adverse drug events from NMDAR modulators such as ketamine.

[0093] exist Figure 1D The methods presented and Figure 2 Among the measurements collected, pharmaco-metabolomics analysis of the patient’s blood is recommended to exclude the possibility of any negative drug-drug or drug-gene interactions.

[0094] Figure 2 Analysis is presented that can be performed on a patient's biological sample to obtain the minimum information required to allow a machine or deep learning algorithm to match drug-specific patterns to a reference set of comprehensive subnetworks that include activation of topologically associated domains (TADs) contained in database 154. These methods can include measuring TAD-specific changes in gene expression using RNA-seq or expression microarrays, pharmacogenomic contacts between genes using chromosome conformation capture analysis (such as Hi-C), and / or targeted genotyping following drug-induced geometric changes in the pharmacogenomic genome, either after a particular drug is administered to a patient or before a decision is made as to whether the drug should be administered, by actively analyzing buccal swabs obtained from the patient.

[0095] Figure 2 The biological sample measurements in include genotyping of targeted pharmacokinetic and pharmacodynamic SNPs (box 292), direct TAD-specific measurements including differential gene expression and pharmacogenomic contacts (box 294), and pharmaco-metabolomics (box 296). Targeted pharmacokinetic and pharmacodynamic SNP genotyping includes identifying mutations in the CYP2B6 gene that have been shown to determine metabolizer status (box 282), and classifying efficacy and adverse event subnetworks based on pharmacogenomic network and subnetwork profile analysis (box 284). Direct chromatin contact-specific measurements include differential gene expression determined using limited RNA sequencing (RNA-Seq) (box 286), and limited chromosome conformation capture analysis for identifying pharmacogenomic contacts (box 288). In addition, pharmaco-metabolomics analysis is used to identify pre-existing drugs and metabolites in patient biological samples to evaluate, for example, potential drug-drug interactions (box 290), and can be used to regularly monitor patient drug-drug interactions and drug compliance.

[0096] Figure 3 A simple example is shown: how a SNP located within an enhancer in the network can disrupt the contact of an enhancer or super enhancer with one of its target gene promoters in a TAD, leading to adverse drug events in patients within the drug response cohort. Figure 3 A shows how different laboratory approaches can be used to obtain measurements from the chromatin pharmacogenomic interactome in three dimensions and analyze the data as 2D maps of enhancer-gene promoter interactions. Figure 3 B depicts how a SNP can disrupt the chromatin loop between an enhancer and one of the two gene promoters it regulates within a TAD. This disruption eliminates the pharmacogenomic connection between the enhancer and gene promoter 1, resulting in dysregulation of gene 1, leading to adverse events in this patient and his cohort in response to administration of the specific drug of interest.

[0097] Figure 4 The first of several embodiments for matching reference data to patient input for clinical decision support is presented. In this example, a pharmacogenomics drug subnetwork reference set for a specific drug can be combined with a biological sample input of sparse results from a patient, and a combined co-training approach is used to derive a drug efficacy score for decision making.

[0098] For example, Figure 5 As shown, the drug and dosage decision server 102 compares the efficacy and adverse event subnetwork of the psychotropic drug of interest to the patient with the reference efficacy and adverse event subnetwork of the psychotropic drug of interest. In this example, reference subnetwork 1 (reference number 502) and reference subnetwork 2 (reference number 504) are adverse event subnetworks, and reference subnetwork 3 (reference number 506) is an efficacy subnetwork. The subnetwork 510 of patient X is different from the adverse event subnetwork (reference subnetworks 1 and 2 (reference numbers 502, 504)) and is similar to the efficacy subnetwork (reference subnetwork 3 (reference number 506)). Therefore, the drug and dosage decision server 102 determines that the psychotropic drug of interest should be applied to the patient 520. The subnetwork 512 of patient Y is different from an adverse event subnetwork (reference subnetwork 1 (reference number 502)) in the adverse event subnetwork, and is similar to another adverse event subnetwork and efficacy subnetwork (reference subnetworks 2 and 3 (reference numbers 504, 506)). Due to the adverse event in subnetwork 2 (reference number 504), the medication and dosage decision server 102 determines that the psychotropic drug of interest should be administered to the patient but at a reduced dose 522. The subnetwork 514 of patient Z is similar to the adverse event subnetwork (reference subnetworks 1 and 2 (reference numbers 502, 504)) and different from the efficacy subnetwork (reference subnetwork 3 (reference number 506)), and therefore, the medication and dosage decision server 102 determines that the psychotropic drug of interest should not be administered to the patient 524.

[0099] Figure 5 An example is provided of how bioinformatics analysis can be used to match a reference set of antidepressant efficacy of a drug against adverse event signatures from a gene set optimizer to signatures of input patient biosamples. Figure 5 In the example above, ketamine can be administered to patient X based on the antidepressant efficacy from subnetwork 2 of the reference pharmacogenomics network matching the antidepressant efficacy from the pharmacogenomics network that the individual will experience and the subnetwork 3 of adverse events matching the antidepressant efficacy from the pharmacogenomics network that the individual will experience. However, in patient Z, since this individual carries some of the disease risk SNPs found in the GWAS in the subnetworks of the pharmacogenomics network, the ketamine dose for patient Z should be adjusted.

[0100] Figure 6We show how we can perform post hoc bioinformatics analysis or disease risk SNP annotations from GWAS and derive two different approaches based thereon to determine that patients should not receive ketamine based on disease gene risk variant analysis and the presence of disease risk SNPs associated with the ketamine adverse event subnetwork and efficacy subnetwork of the ketamine pharmacogenomics drug network. These simple and preliminary approaches can be used to initially screen patients for potential negative consequences of ketamine administration.

[0101] Biological samples 2002, 2004 are collected from patient A and patient B. The biological sample 2002 of patient A is analyzed to perform pharmacodynamic SNP targeting as input to pharmacogenomic network and subnetwork profiling to determine the efficacy and adverse event subnetworks for patient A (box 2006). The biological sample 2004 of patient B is analyzed to identify pharmacokinetic SNPs associated with ketamine response (box 2008). The efficacy and adverse event subnetworks for patient A are then compared to a reference pharmacogenomic network and a reference efficacy and adverse event subnetwork for ketamine (box 2010). The SNPs of patient B are compared to SNPs contained in a reference pharmacogenomic network and a reference efficacy and adverse event subnetwork for ketamine (box 2012). Figure 6 In the example, the dose of patient A must be adjusted before ketamine administration based on the match of the ketamine subnetwork mediating adverse events with the antidepressant efficacy from the pharmacogenomics network that the individual will experience and the reduced match of the antidepressant efficacy subnetwork of the reference pharmacogenomics network (box 2014). In addition, in patient B, since this individual carries many of the disease risk SNPs from GWAS found in the subnetwork of the pharmacogenomics network, the ketamine dose administered to patient B should be adjusted (box 2016).

[0102] Figure 7 A comprehensive strategy is presented in which a library of phenotypes spanning human drug response cohorts stored in a reference database is used to make an initial “go or no-go” decision as to whether ketamine should be administered to a particular patient and how other clinical data from the specific patient can be fine-tuned to make an informed clinical decision.

[0103] Figure 8 A flowchart is presented that uses a well-known pattern matching algorithm from deep learning for computer vision to match a patient's drug efficacy and adverse events with those of a reference drug pharmacogenomics network using a similarity score. Figure 8 It is also demonstrated how this strategy can be used to discover new similar drugs.

[0104] Fig. 9A An overview of the integrated, multiscale data analysis used in the system is presented. Fig. 9BA flow chart representing an exemplary method for generating a reconstructed drug-pharmacogenomics network and corresponding sub-networks of drugs of interest is shown, comprising a human pharmacogenomics SNP input filter, a drug-pharmacogenomics network reconstruction engine, and an iterative gene set optimization engine.

[0105] As described above, biological sample measurements including pharmacodynamic SNP targeting are used as inputs to pharmacogenomic network and subnetwork profiling, which determine both efficacy and adverse events, as analyzed using a pharmacogenomic genomic classifier and a pharmacodynamic subnetwork profiling system. The patient's subnetwork for a specific drug of interest is then compared to a reference pharmacogenomic network and its constituent subnetworks for the drug of interest. Fig. 9B A method 300 for identifying a reference pharmacogenomics network and constituent subnetworks for a particular drug of interest (e.g., ketamine) is shown. In some embodiments, the drug and dosage decision server 102 performs the method 300 to identify a pharmacogenomics network and constituent subnetworks for a particular drug of interest, and stores the pharmacogenomics network and constituent subnetworks in the reference pharmacogenomics network database 154. In other embodiments, the method 300 is performed by another computing device and the output of the method is provided to the drug and dosage decision server 102 and stored in the reference pharmacogenomics network database 154.

[0106] Selecting SNPs

[0107] In any case, SNPs are obtained from human clinical studies that have been shown to be significantly associated with responses and adverse events to the drug of interest at box 302. Since the positions of SNPs associated with the trait under study have in most cases been inaccurately assigned to the nearest gene or nearby candidate gene in the published literature and GWAS based on the linear sequence assembled from the reference human genome, accurate positioning using imputation and annotation techniques is used to determine the actual position of the reported SNPs.

[0108] The new study has several important implications for drug pharmacogenomic network identification. First, novel drug target mechanisms can be identified by collecting pharmacogenomic network outputs in a training set using computer vision-based TAD matching, validated using deep learning (machine learning), and using correspondence with genome-wide TAD matrices induced by known drugs. Second, clustering novel drug target mechanisms in previously defined but incompletely informed biological pathways will increase the likelihood of success. Third, insights gained from identifying drug targets from pharmacogenomic GWAS using three-dimensional (3D) genomic architectures will generate the next generation of drug candidates and greatly improve the accuracy of pharmacogenomic clinical decision support diagnostics.

[0109] At box 304, pharmacodynamics, pharmacokinetics and other SNPs are evaluated using a pharmacogenomic informatics pipeline. The pharmacogenomic informatics pipeline uses lead SNPs reported from GWAS and candidate gene studies to discover genetically linked licensed candidate SNPs using TAD boundaries rather than linkage disequilibrium measures. These SNPs are evaluated using two independent workflows: an enhancer-regulated workflow for regulatory SNPs and a coding SNP workflow. The enhancer-regulated SNP workflow uses tissue-specific omics data sets to evaluate DNA methylation, transcription factor binding, histone marks, DNase I hypersensitivity, chromatin state, quantitative trait loci (QTLs) and transcription factor binding site destruction of licensed candidate SNPs in disease-related tissues. The coding SNP workflow finds common non-synonymous coding SNPs in a licensed candidate SNP pool, and then checks its exclusion of histone modifications containing exon enhancer SNPs. Both groups of SNPs are mapped back to their TADs and host genes, and screened for expression in relevant tissues. The final output SNPs are then evaluated using an open source machine learning algorithm to determine whether the SNP has a causal relationship (box 306), and the causal variants are retained for further analysis in the workflow (box 308). Exonic SNPs are also evaluated as splice donors or splice acceptors using the Altrans algorithm. If they are found to be involved in alternative splicing, they are stored as such.

[0110] Interrogation using incidental enhancer SNPs

[0111] At box 310, enhancer SNPs are used as probes to determine target genes within the same TAD as the enhancer, and pharmacogenomic trans interactions with other TADs are determined using Hi-C chromosome conformation capture and ChIA-PET data sets (box 314), which are generated from cell types and tissues in which the drug of interest works. If the TAD has strong boundaries and significant associations with super enhancers as predicted by the number of CTCF binding (box 312), genes containing other functional elements (such as long non-coding RNAs) are located in the same TAD, which is a target of enhancers that significantly change the drug response of the population selected by the drug pharmacogenomic network. In the TADs that include the first three statistically significant pharmacogenomic contacts of the first group of pharmacogenomic TADs in the same cell and / or tissue type in which the drug of interest works, an evaluation is then performed, and if the genes in these "trans TADs" are controlled by the same cell and / or tissue-specific enhancers in which the drug of interest works, the genes are selected (box 316).

[0112] At block 318, the interconnectivity of the combined gene set is evaluated, wherein the combined gene set is selected from the first group of TADs carrying the pharmacogenomics SNPs and the genes selected from the "trans-TADs", including genes that are consistently controlled by the first group of TAD genes. For example, third-party software such as Ingenuity Pathway Analysis TM , used to check the connectivity of the combined gene set. Using Fisher's right-hand exact test, if there is significant interconnectivity within the combined gene set based on published literature, the gene is placed into a preliminary gene set for a pharmacogenomics network that includes the drug of interest. Any gene that does not form a connected network is discarded as a non-candidate gene for a pharmacogenomics network (box 320).

[0113] Revision of a knowledge-based preliminary pharmacogenomic network of drug-specific interconnected genes

[0114] Then at box 322, each gene in the gene set comprising the preliminary drug-pharmacogenomic network is manually, semi-automatically or automatically curated, or a combination thereof, to remove genes whose functions are not relevant to the drug of interest in the cell and / or tissue type in which they act or to add other genes that are not part of the preliminary set of drug-pharmacogenomic networks, and if the genes are judged to be specifically affected by the drug of interest in the cell and / or tissue type in which they act, they should be added to the set. The interrogation step involves defining the function of a single gene, the phenotypic consequences of gene mutation damage, and the human cells and tissues expressing the gene to determine whether it is a candidate for becoming a member of the pharmacogenomics network for a particular drug of interest.

[0115] In one embodiment, manual, semi-automatic or automated strategies can be used to combine the management of each gene, its mutation spectrum and its expression location in human tissue to carry out these determinations. These are realized by various web-based search tools, comprising gene definition, genome browser annotation, GWAS catalog and other bioinformatics resources. For example, an application programming interface (API) may have an executable file written in R, Python, PERL or other programming languages ​​to promote data access, data cleaning and data analysis. This embodiment is an enhanced model for manual management, but if there are many genes in the gene set of the drug pharmacogenomics network or the gene subset of the subnetwork, and particularly in the case of functional genomic elements may include regulatory RNA or functional RNA, as in the case of long non-coding RNA or if the function of gene is little known, then it can become time-constrained. Listing and analyzing the mutation situation of a given gene (± 10Kb upstream and downstream) is the simplest in 3 inquiry steps to be performed, because these databases are the most comprehensive. There are other resources for analyzing the tissue distribution of gene expression patterns. Results from imaging modalities, including from radiological studies, light microscopy analysis in pathology, and even more complex methods can be analyzed in comparing these patterns to sites where a particular drug of interest acts. In some embodiments, this analysis is performed using machine learning techniques, such as neural networks.

[0116] In another embodiment, a Bayesian probability classifier based on machine learning or using Bayesian probability calculation can be used. Automated methods can be used to reduce the complexity of data analyzed from different data resources, where the functional knowledge profile of the gene, its mutation situation and its tissue expression map are input into a learning machine that has been trained on many such instances, and independently tested on another set of instances to determine accuracy. The predictive features selected by the trained neural network can be implemented on a support vector machine classifier to build a functional and mutation prediction model for the gene, where the subsequent machine state determines the adequacy of the statistical fit of the drug pharmacogenomics network.

[0117] In some cases, machine learning suffers from overfitting, outputting false positives or false negatives. In another embodiment, machine learning can be used in parallel to perform semi-automatic and naive Bayes classification to improve the accuracy of the final output.

[0118] Knowledge-based governance can be performed by following the following steps. First, the gene definition is checked from multiple databases to see if it is specifically but not generally affected by the drug of interest. Additionally, a knowledge-based approach is used to identify the gene in question using, for example, Google Scholar. TMAfter a thorough Internet search of , and / or PubMed, published literature (containing text strings containing gene names or precursor gene names or equivalent protein names and any functions associated with the drug of interest) is evaluated. These may include binding affinity studies that can reproducibly find molecules whose binding affinity is within 10 times the binding affinity of the drug of interest to the same pharmacodynamic target. Secondly, the drug and dosage decision server 102 checks all mutations of each gene, including SNPs, variable number of tandem repeats, duplications, and all other known mutation changes, extending in linear sequence ± 10kb from the transcription start site and stop codon of the gene of interest in a genome browser (such as the UCSC genome browser or the Ensembl genome browser). If any of these mutations are found in published literature or sources such as unpublished clinical trial data, and they are involved in the effects of the drug of interest, including efficacy, adverse events, or first-pass metabolism, they are added to the preliminary gene set (box 324) including the pharmacogenomic network. Third, particularly for complex tissues such as the brain, skin, and cardiovascular system, the drug and dose decision server 102 qualitatively performs a consistency plot to compare the expression of all genes in this final set with the location where the drug of interest exerts its action, if known. Genes whose expression does not match the pharmacodynamic substrates of the drug of interest are discarded (block 324). Finally, third-party software such as Ingenuity Pathway Analysis TM This gene set is checked for connectivity (block 326). Using the Fisher's right-side exact test, if the drug and dose decision server 102 determines that there is significant interconnectivity based on published literature, they are placed into a preliminary gene set for the pharmacogenomic network of the drug of interest. Any genes that do not form a connected network are discarded as non-candidate genes for the drug pharmacogenomic network (block 328).

[0119] Iterative gene set optimization

[0120] As at block 330, and Fig.10 As shown in more detail in , iterative gene set optimization is performed on the candidate gene sets identified in the pharmacogenomics network for a particular drug of interest. Fig.10The flowchart of shows an example method 400 for iterative gene set optimization for deconstructing a drug-pharmacogenomics network into subnetworks. Iterative gene set optimization can be performed to identify subnetworks of a drug-pharmacogenomics network. More specifically, iterative gene set optimization includes using its API to convert all input molecular terms into gene or long non-coding RNA names, such as from the Human Gene Nomenclature Committee (HGNC) name (box 402). Iterative gene set optimization is different from gene set enrichment methods in that it not only combines multiple statistical methods, but also does not rank genes in a hierarchical manner as in threshold-dependent methods, and iterative gene set optimization does not rely on comparison of experimental results, such as in full distribution tests. Instead, iterative gene set optimization uses Jaccard distance to group genes or long non-coding RNAs from a drug-pharmacogenomics drug-pharmacogenomics network (box 404) to first measure the similarity between two genes or long non-coding RNAs based on the non-similarity of the terms selected by the user, where the Jaccard distance is the ratio of the size of the symmetric difference gene AΔgene B=A∩BA∪B to the union (box 406). This can be expanded to clusters of related different gene names. The drug and dosage decision server 102 then uses a minimum entropy sorting algorithm, such as the COOLCAT algorithm, to automatically sort these sets or use a user-defined number of clusters to sort into subsets of clustered subsets of functionally related genes (box 408). After gene subset optimization using entropy minimization, manual governance can be used to assign efficacy, adverse events, or functional mechanism subnetworks based on known properties of the drug mechanism of action under consideration (boxes 410, 412).

[0121] Post hoc validation using third-party bioinformatics tools

[0122] In order to scientifically validate the deconstruction of the drug-pharmacogenomic network into a mechanistic sub-network based on the optimization of functional gene subsets, each sub-network of the drug-pharmacogenomic network is evaluated post hoc for top gene ontology terms (molecular function and biological process), such as top canonical pathways, as determined using other proprietary or open source pathway analysis software, such as disease risk gene variant analysis, as determined using other proprietary or open source pathway analysis software, and upstream xenobiotic regulators (frame 332) determined using different bioinformatics resources. In addition, the GWAS catalogs of the European Bioinformatics Institute, the National Human Genome Research Institute, and the National Institutes of Health can be searched to find the significant SNP-trait associations of each gene in each sub-network gene set. By providing examples of statistically significant SNPs from GWAS, additional evidence can be provided that the mutational damage of the genes contained in each sub-network provides insights into the normal, unimpaired function of the sub-network.

[0123] In some embodiments, after performing post-validation, such as Fig.11As shown, the generated pharmacogenomic network and the constituent subnetworks of a particular drug of interest are stored in, for example, a database 154, such as Figure 1A As shown.

[0124] For example, to map the causal SNPs that discretize ketamine response in a population, their target genes within their TADs, and the pharmacogenomic contactomes of these TADs, Hi-C chromosome conformation capture data can be used from publicly available datasets mapped to the A735 astrocyte cell line, the H1 neuronal cell line, the SK-N-SH cell line, and postmortem human brain samples.

[0125] Fig.12 Shown is an example of a comparison of results from eight different algorithms testing the predicted causal relationship of the GWAS SNP rs12967143-G (localized to an intragenic enhancer in the TCF4 gene, a member of the ketamine pharmacogenomic efficacy subnetwork) relative to other GWAS SNPs, as described using the numerical output of the machine learning algorithm used in the analysis (*p≤0.05; **p≤0.01; ANOVA).

[0126] Using H-GREEN, a user-adjustable binning software package, trans-TADs are mapped to overlaps between different data sources. The genome-wide top 3 trans-TAD pharmacogenomics contacts can be selected for each originating causal SNP TAD locus. Using prior knowledge of ketamine as an anesthetic and analgesic, and recent studies and clinical trials of ketamine and other glutamate receptor modulators as antidepressants, the methods described herein can be used to score the top trans-TAD contacts, and for each causal SNP the top 3 can be selected for inclusion in the pharmacogenomics network. The recent availability of a database of validated enhancers and their targets can be used for the originating and targeted TADs in this workflow to reconstruct the ketamine pharmacogenomics network.

[0127] Intra-TAD and trans-TAD gene sets can be used as seeds to initiate pathway analyses. Filters and thresholds that eliminate genes expressed in cell types, neurons and astrocytes, and brain regions where ketamine exerts its mechanism of action can be applied. These contain not only PD genes, but also PK genes, the latter recently shown to be expressed at high levels in relevant human brain regions where ketamine acts, and in the case of the CYP2B6 gene, induced to higher expression levels by this psychotropic drug compared to the liver, gastrointestinal tract, or kidney.

[0128] Following the output of the automated pathway analysis, the drug-pharmacogenomics network gene set is evaluated for plausibility and genes not selected by the pathway analysis program can be added to the pathway. Genes are added back from earlier studies of binding affinity using molecular pharmacology approaches whose products exhibit 10-fold affinity for NMDAR over racemic R,S-ketamine or the enantiomer and demonstrate molecular interconnectivity. Additional expression studies and studies of ketamine metabolism may yield additional genes that are added back to the ketamine pharmacogenomics network.

[0129] The ketamine pharmacogenomics network was analyzed into three subnetworks by gene set optimization, two of which were subsets of genes and regulatory RNAs that were significantly different using iterative analysis. The three subnetworks included: (1) antidepressant efficacy and neuroplasticity, (2) glutamate receptor signaling, chromatin remodeling, and adverse events, and (3) pharmacokinetics and hormonal regulation associated with the drug. The second subnetwork (2) glutamate receptor signaling, chromatin remodeling, and adverse events may contain two independent subnetworks: the chromatin remodeling subnetwork and the drug pharmacodynamic adverse events subnetwork. In order to understand and validate the pharmacogenomics network and its mechanistic subnetworks, four types of additional analyses were performed. First, the pharmacogenomics network and the genes in each subnetwork were interrogated for the presence of enhancer SNPs associated with the relevant traits in GWAS. Second, pathway enrichment containing biological processes and molecular functions was performed using gene ontology criteria to determine the most significant top pathways for these gene sets. Third, disease gene risk variant analysis was performed, which analyzed each superset and subset of genes for the importance of the overall mutational contribution of these sets in humans to appropriately assign to both the parental pathway and its constituent subnetworks, and to assign top diseases (most important, Fisher exact test) to the superset and subnetwork sets. Fourth, the top (most important, Fisher exact test) xenobiotic drugs that regulate the superset of genes that comprise the pharmacogenomics network were determined. In the last case, the pharmacogenomics network gene set should be regulated by drugs that mediate the pharmacogenomics network mechanism, but for some of the subnetworks, drugs that are more relevant to a particular subnetwork of the pharmacogenomics network mechanism may be most significantly associated, depending on the mechanistic properties of the network.

[0130] Fig.13 The top gene ontology terms for two significantly different ketamine pharmacogenomics subnetworks in the human brain are shown. More specifically, Fig.13 A shows the subnetwork mediating efficacy and neuroplasticity. Fig.13 B shows the ketamine subnetwork mediating glutamate receptor signaling and adverse events in the human CNS.

[0131] Fig.14AGraphical depiction of ketamine pharmacogenomics subnetworks in the human brain mediating efficacy and neuroplasticity is shown. Fig. 14B A graphical depiction of the pharmacogenomics subnetworks of ketamine in the human brain that mediate glutamate receptor signaling and adverse events in the human brain is presented. More specifically, as Fig.14A and 15The genes and regulatory RNAs located in the ketamine efficacy and neuroplasticity subnetworks shown include one or more of the following: activity-regulated cytoskeleton-associated protein (ARC) gene, bHLH transcription factor 1 (ASCL1) gene, brain-derived neurotrophic factor (BDNF) gene, BDNF antisense RNA (BDNF-AS) gene, calcium / calmodulin-dependent protein kinase IIα (CAMK2A) gene, cyclin-dependent kinase inhibitor 1A (CDKN1A) gene, cAMP response element regulator (CREM) gene, Cut like homeobox 2 (Cut like homeobox 2, CUX2) gene, DCC spindle protein 1 receptor (DCC) gene, dopamine receptor D2 (DRD2) gene, eukaryotic translation elongation factor 2 kinase (EEF2K) gene, fragile X mental retardation 1 (FMR1) gene, ganglioside-induced differentiation-associated protein 1-like 1 (GDAP1L1) gene, glutamate metabotropic receptor 5 (GRM5) gene, Homer scaffold protein 1 (HOMER1) gene, serotonin receptor 1B (HTR1B) gene, serotonin receptor 2A (HTR2A) gene, Kruppel-like factor 6 (KLF6) gene gene, Lin-7 homolog C, lin7C long noncoding RNA, LOC105379109 long noncoding RNA, myocyte enhancer factor 2D (MEF2D) gene, myosin VI (MYO6) gene, myelin transcription factor 1-like (MYT1L) gene, neuronal differentiation 1 (NEUROD1) gene, neuronal differentiation 2 (NEUROD2) gene, nonsense helix-loop-helix 2 (NHLH2) gene, neuromedin B (NMB) gene, NMDA receptor synaptic nuclear signaling and neuronal migration factor (NMDA) gene. receptor synaptonuclear signaling and neuronal migration factor, NSMF) gene, neurotrophic receptor tyrosine kinase 2 (NTRK2) gene, phosphatase and tensin homolog (PTEN) gene, prostaglandin-endoperoxide synthase 2 (PTGS2) gene, Rac family small GTPase 1 (RAC1) gene, Ras protein-specific guanine nucleotide release factor 2 (RASGRF2) gene, Ras homolog family member A (RHOA) gene, Roundabout guidance receptor 2 (ROBO2) gene, RP11_360A181 long noncoding RNA, Semaphorin 3A (Semaphorin 3A,SEMA3A) gene, SH3 and multiple ankyrin repeat domains 1 (SHANK1) gene, SH3 and multiple ankyrin repeat domains 2 (SHANK2) gene, SH3 and multiple ankyrin repeat domains 3 (SHANK3) gene, solute carrier family 22 member 15 (SLC22A15) gene, solute carrier family 6 member 2 (SLC6A2) gene, slit guiding ligand 1 (SLIT1) gene, slit guiding ligand 2 (SLIT2) gene, synaptosome associated protein 25 (SNAP25) gene, synapsin I (SYN1) gene, synapsin II (SYN2) gene, synapsin III (SYN3) gene, T box, brain 1 (TBR1) gene or transcription factor 4 (TCF4) gene.

[0132] In addition, if Fig. 14B and 16The genes and regulatory RNAs located in the ketamine glutamate receptor signaling and adverse events subnetwork shown include one or more of the following: acetylcholinesterase (ACHE) gene, activating transcription factor 7 interacting protein (ATF7IP) gene, activating transcription factor 7 interacting protein 2 (ATF7IP2) gene, ATPase Na+ / K+ transporter subunit alpha 1 (ATP1A1) gene, BLOC-1 related complex unit 7 (BORCS7) gene, bromodomain-containing 4 (BRD4) gene, calcium voltage-gated channel subunit alpha 1C (CAC NA1C) gene, calcium voltage-gated channel auxiliary subunit β1 (CACNB1) gene, calcium voltage-gated channel auxiliary subunit β2 (CACNB2) gene, calcium voltage-gated channel auxiliary subunit γ2 (CACNG2) gene, cholinergic receptor muscarinic 2 (CHRM2) gene, cholinergic receptor nicotinic α3 subunit (CHRNA3) gene, cholinergic receptor nicotinic α5 subunit (CHRNA5) gene, cholinergic receptor nicotinic α7 subunit (CHRNA7) gene, cannabinoid receptor 1 (CNR1) gene, Disks large homolog 3 (Disks large homolog 3,DLG3) gene, disc large homolog 4 (DLG4) gene, DNA methyltransferase 1 (DNMT1) gene, euchromatic histone lysine methyltransferase 1 (EHMT1) gene, gamma-aminobutyric acid type A receptor alpha 2 subunit (GABRA2) gene, gamma-aminobutyric acid type A receptor alpha 5 subunit (GABRA5) gene, glutamate decarboxylase 1 (GAD1) ​​gene, glycine receptor alpha 1 (GLRA1) gene, glycine receptor alpha 2 (GLRA2) gene, glycine receptor beta (GLRB) gene, glutamate ionotropic receptor AMPA type subunit 1 ( GRIA1) gene, glutamate ionotropic receptor AMPA subunit 2 (GRIA2) gene, glutamate ionotropic receptor AMPA subunit 1 (GRIA4) gene, glutamate ionotropic receptor NMDA subunit 1 (GRIN1) gene, glutamate ionotropic receptor NMDA subunit 2A (GRIN2A) gene, glutamate ionotropic receptor NMDA subunit 2B (GRIN2B) gene, glutamate ionotropic receptor NMDA subunit 2C (GRIN2C) gene, glutamate ionotropic receptor NMDA subunit 2D (GRIN2D ) gene, glutamate ionotropic receptor NMDA-type subunit 3A (GRIN3A) gene, glutamate ionotropic receptor NMDA-type subunit 3B (GRIN3B) gene, hyperpolarization-activated cyclic nucleotide-gated potassium channel 1 (HCN1) gene, histone deacetylase 5 (HDAC5) gene, methyl-CpG binding domain protein 1 (MBD1) gene, M-phase phosphoprotein 8 (MPHOSPH8) gene, neural cell adhesion molecule 1 (NCAM1) gene, nitrate synthase 1 (NOS1) gene, nitrate synthase 1 (NOS2) gene, nitrate synthase 2 (NOS 3) gene, NAD(P)H quinone dehydrogenase 1 (NQO1) gene, opioid receptor kappa 1 (OPRK1) gene, opioid receptor μ1 (OPRM1) gene, ring-guided receptor 2 (ROBO2) gene, SET domain bifurcation 1 (SETDB1) gene, SH3 and multiple ankyrin repeat domains 2 (SHANK2) gene, σ non-opioid intracellular receptor 1 (SIGMAR1) gene, solute carrier family 6 member 9 (SLC6A9) gene, transcriptional activation inhibitor (TASOR) gene, axonemal microtubule TOG array regulator of axonemal microtubules 2 (TOGORAM2) gene, triple motif 28 (TRIM28) gene or zinc finger protein 274 (ZNF274) gene.

[0133] like Fig.17The genes and regulatory RNAs shown, located in the pharmacokinetic enzyme and hormone subnetwork, include one or more of the following: anaphase promoting complex subunit 2 (ANAPC2) gene, cytochrome P450 family 2 subfamily A member 6 (CYP2A6) gene, cytochrome P450 family 2 subfamily B member 6 (CYP2B6) gene, cytochrome P450 family 3 subfamily A member 4 (CYP3A4) gene, disc large homolog 4 (DLG4), eukaryotic elongation factor 2 kinase (EEF2K) gene, estrogen receptor 1 (ESR 1) gene, glutamate ionotropic receptor AMPA-type subunit 1 (GRIA1) gene, glutamate ionotropic receptor AMPA-type subunit 2 (GRIA4) gene, glutamate ionotropic receptor NMDA-type subunit 1 (GRIN1) gene, glutamate ionotropic receptor NMDA-type subunit 2B (GRIN2B) gene, myosin VI (MYO6) gene, ring-shaped guidance receptor 2 (ROBO2) gene, SH3 and multiple ankyrin repeat domains 2 (SHANK2) gene and transcription elongation regulator 1 (TCERG1) gene.

[0134] The automated iterative gene set optimization of the psychotropic pharmacogenomics network into subnetworks may be limited by the user to investigate other features of the pharmacogenomics network. Fig.10 As described, the ketamine pharmacogenomics network was iteratively deconstructed until certain specific genes were not significantly associated with any subnetwork or were associated with several subnetworks deconstructed from the pharmacogenomics network. The gene ESR1 encodes the nuclear hormone receptor for estrogen, and it regulates the expression of several genes in the ketamine pharmacogenomics network. It is well known that estrogen greatly induces the CYP2B6 gene in the human brain and elsewhere.

[0135] The learning architecture for training the pattern matching subnetwork includes a pre-trained reference set (reference number 710). More specifically, at box 704, the drug and dosage decision server 102 develops a patient pattern matching subnetwork derived from a patient input biological sample, and jointly develops a separate training pattern metric (box 712) containing the features of the efficacy and adverse event subnetworks to a joint feature representation metric. In order to determine the similarity with the reference set (boxes 706, 708), two pairs of different reference-patient indicators contain an accurate measurement of similarity and an output similarity score (box 714, 716) for each of the efficacy and adverse event subnetworks. At box 702, a biological sample obtained from a patient, which can be a cheek swab, saliva, blood or urine sample, undergoes targeted enhancer SNP genotyping, as well as combined chromosome conformation capture and RNA-seq. Then at box 704, the drug and dosage decision server 102 performs the necessary analysis to construct an input patient-specific graph of the efficacy and adverse event subnetworks for the specific drug of interest. These patient-specific, drug-induced subnetwork patterns can be further processed using Bayesian probability calculations to fill in sparse or missing data. When new patients are fed in as input, the pre-trained reference set of drug-specific efficacy and adverse event subnetworks used for pattern matching is again optimized for subsequent patients, resulting in a more accurate measurement of pharmacogenomics variability in humans, thereby enhancing clinical utility. This matching task assumes that patches undergo the same feature encoding before calculating and outputting similarity scores, greatly improving efficiency while reducing computational requirements.

[0136] Therefore, each set of inputs (reference set (reference number 710) and patient set (reference number 720)) is constructed differently through feature set extraction and sparse data reasoning using Bayesian distribution-based probability calculations to increase the accuracy of the reference and patient maps. The trained feature network is based on a "Siamese" network approach, where the constraint is that the two groups must share the same parameters. Upon completion, the patient's drug-induced training pattern network is coupled with those obtained from the reference database, paired efficacy feature set pairs, and adverse event feature set pairs. These provide the basis for developing trained efficacy metrics and trained adverse event metrics that attempt to match all features from the patient and the reference set of the drug of interest. These pairwise matching scores produce separate efficacy and adverse event similarity scores between the reference and the patient.

[0137] In further embodiments, a reference pattern match set can be developed for each patient, which can be used to create a patient-specific database of such reference maps and updated regularly as additional biological samples are obtained from the patient in a longitudinal manner over time in a clinical setting or outpatient pharmacy.

[0138] In any case, the drug and dosage decision server 102 can then use the similarity scores of the efficacy and adverse event subnetworks of the psychotropic drug of interest generated by method 700 to determine whether to administer the psychotropic drug of interest to the patient. For example, the similarity score of the efficacy subnetwork can be compared with a threshold similarity score. If the similarity score of the efficacy subnetwork is higher than the threshold similarity score, the drug and dosage decision server 102 can determine that the psychotropic drug of interest should be administered to the patient. The similarity score of the adverse event subnetwork can also be compared with the threshold similarity score. If the similarity score of the adverse event subnetwork is lower than the threshold similarity score, the drug and dosage decision server 102 can determine that the psychotropic drug of interest should be administered to the patient. In another embodiment, the similarity scores of the efficacy subnetwork and the adverse event subnetwork can be combined or aggregated in any suitable manner. For example, the similarity score of the adverse event subnetwork can be subtracted from the similarity score of the efficacy subnetwork. If the combined score is greater than the threshold similarity score, the drug and dosage decision server 102 can determine that the psychotropic drug of interest should be administered to the patient.

[0139] In other ways, the medication and dosage decision server 102 may determine not to administer the psychotropic medication of concern, and may provide a recommendation to the healthcare professional's client device 106 - 116 to administer another medication to treat the patient's depression.

[0140] In some embodiments, the drug and dosage decision server 102 can determine whether to administer the psychotropic drug of interest to the patient by generating a machine learning model based on training data of drug responses from patients who were previously prescribed the psychotropic drug of interest. The machine learning model can be generated based on several features of the previous patient, including drug-induced subnetworks of the previous patient, PD and PK SNPs that stratify patients by drug response, neuroimaging data, direct TAD-specific measurements containing differential gene expression, and clinical variables of the previous patient, such as age, weight, biological sex, body mass index, race, family history, patient substance abuse history, diagnosis code, hospitalization history, drug-drug interactions, history of psychiatric illness, whether the patient smokes or uses nicotine, and Hamilton Depression Rating Scale (HAM-D) score. The drug and dosage decision server 102 can obtain the same features of the current patient, including molecular and clinical data, and apply the features of the current patient to the generated machine learning model to determine whether to administer the psychotropic drug of interest to the patient.

[0141] In addition to determining whether to administer a psychotropic drug of interest to a patient, the medication and dosage decision server 102 also determines the dosage to administer to the patient. Fig.18A and 18BAn analysis of an independent cohort data set is presented for developing a regression model for ketamine dose estimation. Published literature and other sources provide both pharmacokinetic SNP data and clinical values ​​that aid in determining dose based on CYP2B6 SNP and clinical data. As can be seen from the differential analysis, the largest contribution to ketamine dose is the presence or absence of the poor metabolizer phenotype rs3745274, a variant that results in exon skipping and ketamine super-induced loss of first-pass metabolism of CYP2B6. This model derived a quantitative intranasal ketamine dose range of 0.4 mg / kg to 0.8 mg / kg in the cohort. The drug and dose decision server 102 can generate a model based on published ketamine clinical trial results obtained from clinicaltrials.org. The following equation shows a general example based on the sum of the values ​​of the regression variables that can be included in the ketamine dosing algorithm:

[0142] Dose = exp[2.00 × rs3745274 + 0.25 × female (biological sex) + 0.22 × rs3786547 + 0.22 × clopidogrel + 0.19 × rs11083595 + 0.11 × BSA + 0.20 × smoking + 0.17 × suicide attempt history + 0.07 × age per decade + 0.09 × non-Hispanic white race + 0.20 × ticlopidine + 0.15 × previous psychiatric hospitalization]

[0143] More specifically, the medication and dosage decision server 102 may generate a dosing algorithm based on published literature and may have predetermined constants to apply to each of several patient characteristics, such as biological characteristics, demographic characteristics, clinical characteristics, etc., as in the equation above. In other embodiments, the medication and dosage decision server 102 may generate a dosing algorithm using machine learning techniques. For example, the medication and dosage decision server 102 may collect dosage information about patients who were previously prescribed ketamine as training data. The dosing information may include the dosage prescribed to each patient and whether the patient's dosage was adjusted during treatment and / or whether the patient experienced an adverse event. The medication and dosage decision server 102 may then analyze the training data to generate a machine learning model (e.g., a neural network, a decision tree, a hyperplane, a regression model, etc.) to determine the dosage of a new patient based on the biological characteristics, demographic characteristics, and clinical characteristics of the new patient. The patient characteristics used in the dosing algorithm may include biological data, such as SNPs reported to stratify responses to ketamine in humans. Patient characteristics may also include demographic data of the patient, such as the patient's gender, height and weight, age, and race. Additionally, patient characteristics can include clinical data such as family history, drug-drug interactions, history of psychiatric illness, whether the patient smokes or uses nicotine, and Hamilton Rating Scale for Depression (HAM-D) scores.

[0144] In any case, the medication and dosage decision server 102 applies the patient's characteristics to a dosing algorithm to determine the dosage of ketamine to administer to the patient. The medication and dosage decision server 102 then provides a recommended dosage to the healthcare professional's client device 106-116.

[0145] Although regression analysis for patient-specific dose optimization cannot account for almost half of the pharmacogenomics and clinical variables required for accuracy, few published studies have reported the inclusion of variables used in algorithmic determination of antidepressant selection and dose estimation. Clinical values ​​obtained from medical records are also critical for determining reductions in ketamine administration, such as Fig.13 These include individuals with a body mass index (BMI) over 30, family history of alcohol use disorder (first degree), history of suicide attempt, prior psychiatric hospitalization, female biological sex (premenopausal), non-Hispanic white race, and smoking. As indicated, these values ​​contribute to the potential for substantial ketamine administration, but do not preclude the use of this drug.

[0146] In addition to comparing the patient's subnetwork of psychotropic drugs of interest to a reference subnetwork of psychotropic drugs of interest, the medication and dosage decision server 102 also analyzes the patient's clinical data and neuroimaging data to determine whether to administer the drug to the patient. For example, the medication and dosage decision server 102 may analyze the patient's HAMD score and / or the patient's symptoms to classify the patient into one of four TRD patient subtypes.

[0147] Fig.19 Four subtypes of TRD patients as determined by transcranial magnetic stimulation (TMS) coupled with neuroimaging of resting connectivity networks in the human brain are shown. In patients with TRD, subtypes have been identified and replicated using transcranial magnetic stimulation (TMS) and neuroimaging studies that discretize subtypes by differential activation and inhibition of resting state connectivity networks in the human brain. TMS has four different anatomical locations on the lateral side of the human head that can activate different neuroanatomical structures that are part of the limbic-cortical circuit and include a portion of the following: resting state connectivity network, default mode network, defense response involving amygdala, reward circuits located in the basal forebrain including nucleus accumbens (NA) or orbitofrontal cortex (OFC) in depression subtype 3, sensory stimulation gating via thalamus to cortex, cortical area S1 and insula, prefrontal cortex inhibition of dorsolateral impulses, and inhibition of dorsomedial prefrontal cortex activation of limbic cortex amygdala, memory consolidation in the entire anterior cingulate cortex and hippocampal formation.

[0148] Fig.19We also demonstrated that TRD subtypes can be identified from notes using clinical data obtained from EHRs or other clinical records, using structured values ​​or using natural language processing.

[0149] Fig. 20 Brain regions that are consistently activated during the antidepressant response to ketamine are shown, and how they map differentially to the four TRD depression subtypes. Despite the involvement of brain regions, these are consistent with the different depression subtypes as defined by TMS and clinical values, as well as with the results of neuroimaging studies examining ketamine-induced activation and inhibition.

[0150] Fig.21 Based on the definition of HAMD-17 rating (Hamilton Depression Rating Scale), neuroimaging meta-analysis, such as Fig.19 The associated clinical values ​​shown, along with available information from the American Psychiatric Association on the efficacy, indications, and recommendations of currently available antidepressants and professional guidelines, provide recommendations for switching from ketamine for the four different TRD subtypes.

[0151] Fig.21 Example medication recommendations and alternative drug options are shown for each of the four different subtypes of TRD patients with depression.

[0152] To identify which depression subtypes should or should not be provided with ketamine and ketamine analogs, 24 publicly available neuroimaging datasets were analyzed to determine the neuroanatomical regions activated by ketamine and its analogs, depression and TRD patients, and healthy controls. Since TRD subtype 3 patients consistently exhibited hyperactive subgenual anterior cingulate cortex (sgACC), dorsolateral and dorsomedial prefrontal (executive) cortex (dlPFC, dmPFC), and hyperactive orbitofrontal cortex (OFC), it is recommended that these patients not receive ketamine pharmacotherapy because this patient group will not respond or remit, but instead may experience exaggerated psychotropic adverse drug events. Independent analysis of the neuroanatomical localization of all genes found in the ketamine pharmacogenomic network showed that they were all expressed at high levels in the anterior cingulate, prefrontal cortex, supplementary motor cortex, orbitofrontal cortex, temporal cortex, amygdala, hippocampal formation, anterior caudate nucleus, and nucleus accumbens, but not in other cortical regions, hypothalamus, or brainstem. This is the same pattern as found in 24 functional neuroimaging studies that examined where ketamine first acts in the human brain to exert its antidepressant effects (Table 1).

[0153]

[0154]

[0155]

[0156]

[0157] Table 1. Functional neuroimaging studies reveal human CNS substrates in which ketamine and other NMDAR modulators act and in which all genes in the ketamine pharmacogenomic network are expressed at their highest levels.

[0158] Neuroimaging modality (MOD.):

[0159] 1H-MRS: 4-T 1H proton magnetic resonance spectroscopy

[0160] fMRI: functional magnetic resonance imaging.

[0161] MEG: magnetoencephalography;

[0162] PET: FDG positron emission tomography;

[0163] phMRI; pharmacological magnetic resonance imaging.

[0164] rs-fcMRI: resting-state functional connectivity magnetic resonance imaging.

[0165] In another embodiment, disease risk and pharmacogenomics SNPs that distinguish between two significantly different ketamine subnetworks in the human brain can be used to determine patient responses and adverse events when treated with ketamine. Table 2A lists enhancer and super-enhancer SNPs found in the ketamine efficacy subnetwork, which can be used to determine the representation of mutations that are significantly associated with an effective response to ketamine. In contrast, Table 2B lists enhancer and super-enhancer SNPs found in the ketamine adverse event subnetwork, which can be used to determine the representation of mutations that are significantly associated with adverse CNS events in response to ketamine.

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[0170] Table 2A, Part 1. Enhancer and super-enhancer SNPs found in the ketamine efficacy subnetwork.

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[0188] Table 2A, Part 2. Enhancer and super-enhancer SNPs associated with regulatory elements found in the ketamine efficacy subnetwork.

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[0193] Table 2A, section 3. Chromatin interactions of enhancer and super-enhancer SNPs found in the ketamine efficacy subnetwork.

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[0200] Table 2B, Part 1. Enhancer and super-enhancer SNPs found in the ketamine adverse event subnetwork.

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[0220] Table 2B, Part 2. Enhancer and super-enhancer SNPs associated with regulatory elements found in the ketamine adverse event subnetwork.

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[0226] Table 2B, section 3. Chromatin interactions of enhancer and super-enhancer SNPs found in the ketamine adverse events subnetwork.

[0227] In another embodiment of the method of the present disclosure, generalization of this method can be used to reveal combinations of FDA-approved drugs that can be used to enhance the treatment of neuropsychiatric disorders through their corresponding network or sub-network mechanisms in biology. Fig. 22 An example of how the valproate pharmacogenomics network and the ketamine pharmacogenomics network work in a complementary manner to support neurogenesis is shown. Valproate induces the conversion of neural progenitor cells into committed neural progenitor cells via the npBAF complex (1, top), and ketamine can act on committed neural progenitor cells via the human silencing complex (HUSH) to convert the progenitor cells into differentiated neurons (1, bottom).

[0228] Fig.23 Shown Fig. 22 The process shown in Figure 1 is how the valproate pharmacogenomic network induces the stepwise deacetylation of the histone 3 lysine 9 (H3K9) moiety. Fig.23 A) and acetylation of H3K9 after activation of ketamine pharmacogenomics network ( Fig.23 B).

[0229] Fig.24 Demonstrated how a complementary pharmacogenomics network of valproate and ketamine primes neural progenitor cells to become mature, differentiated neurons.

[0230] In another embodiment of the present disclosure, these methods can be used for other antidepressant drugs targeting the NMDAR network. For example, other NMDAR partial antagonists including AVP-786 and GLYX-13 (Rapastinel) are being clinically tested as antidepressants. In addition, GLRB blockers on NMDAR are also being developed as antidepressants, including AV101 and D-cycloserine (silicycin). Selective antagonists of GRIN2B of NMDAR are also being developed as antidepressants, for example, including EVT103, CP101 and MK-0657. Downstream of this pathway is AMPAR, and several antidepressants are being developed as agonists at GRIA1 and GRIA2, such as ORG 265576.

[0231] In another embodiment, these methods can be used to optimize drug selection for other antidepressants, which provides greater power than commercially available pharmacogenomic clinical decision support assays that rely solely on coding SNPs for stratifying patients to drugs. The technology disclosed herein encompasses methods that exploit knowledge of the pharmacogenomic epigenome, including its organization into TADs and TAD-TAD pharmacogenomic connections, which provides deep insights into CNS drug mechanisms. Additionally, these methods allow for objective monitoring of drug-drug interactions and dosage, as well as measurement of parent drugs and their metabolites, such as in the case of S-ketamine and its active metabolite norketamine, from serum to provide additional insight into individual metabolizer subtypes.

[0232] Throughout the entire specification, multiple instances can implement the parts, operations or structures described as single instances. Although the individual operations of one or more methods are shown and described as individual operations, one or more of the individual operations can be performed simultaneously, and do not need to perform the operations in the order shown. The structure and function presented as independent components in the example configuration can be implemented as a combined structure or assembly. Similarly, the structure and function presented as a single component can be implemented as a separate component. These and other changes, modifications, additions and improvements all fall within the scope of this paper theme.

[0233] In addition, some embodiments are described herein as including logic or multiple routines, subroutines, applications, or instructions. It can constitute software (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware. In hardware, routines, etc. are tangible units that can perform certain operations and can be configured or arranged in a certain way. In an example embodiment, one or more computer systems (e.g., independent client or server computer systems) or one or more hardware modules of a computer system (e.g., a processor or a processor group) can be configured by software (e.g., an application or an application portion) to operate to perform certain operations as described herein. Hardware module.

[0234] In various embodiments, hardware modules may be implemented mechanically or electronically. For example, a hardware module may include dedicated circuits or logic that are permanently configured to perform certain operations (e.g., a dedicated processor such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC)). A hardware module may also include programmable logic or circuits that are temporarily configured to perform certain operations by software (e.g., as contained in a dedicated processor or other programmable processor). It should be understood that the decision to implement a hardware module mechanically in a dedicated and permanently configured circuit or in a temporarily configured circuit (e.g., configured by software) may be driven by cost and time considerations.

[0235] Thus, the term "hardware module" should be understood to encompass tangible entities, referring to entities that are physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. In view of embodiments in which hardware modules are temporarily configured (e.g., programmed), it is not necessary to configure or instantiate each hardware module at any one time. For example, where the hardware modules include a general-purpose processor configured using software, the general-purpose processor can be configured into corresponding different hardware modules at different times. Thus, software can configure a processor, for example, to constitute a particular module at one time and to constitute a different module at a different time.

[0236] A hardware module can provide information to other hardware modules, or receive information from other hardware modules. Therefore, the hardware module can be considered to be communicatively coupled. In the case of multiple such hardware modules being present at the same time, communication can be achieved by connecting the signal transmission of the hardware modules (e.g., by appropriate circuits and buses). In an embodiment where multiple hardware modules are configured or instantiated at different times, communication between such hardware modules can be achieved, for example, by storing and retrieving information in a memory structure accessible to multiple hardware modules. For example, a hardware module can perform an operation and store the output of such operation in a memory device to which it is communicatively coupled. Then, another hardware module can access this memory device at a later time to retrieve and process the stored output. A hardware module can also initiate communication with an input or output device, and can operate on a resource (e.g., a collection of information).

[0237] The various operations of the example methods described herein may be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Such processors, whether temporarily or permanently configured, may constitute processor-implemented modules that operate to perform one or more operations or functions. In some example embodiments, the modules referred to herein may include processor-implemented modules.

[0238] Similarly, the methods or routines described herein may be implemented at least in part by a processor. For example, at least some operations of a method may be performed by one or more processors or a hardware module implemented by the processor. Some of the executions in the operation may be distributed between one or more processors that not only reside in a single machine but also across multiple machine deployments. In some example embodiments, one or more processors may be located at a single location (e.g., in a home environment, in an office environment, or as a server farm), but in other embodiments, the processor may be distributed across multiple locations.

[0239] The execution of some of the operations may be distributed between one or more processors that reside not only in a single machine but also deployed across multiple machines. In some exemplary embodiments, one or more processors or processor-implemented modules may be located in a single geographic location (e.g., in a home environment, an office environment, or a server farm). In other exemplary embodiments, one or more processors or processor-implemented modules may be distributed in multiple geographic locations.

[0240] Unless otherwise expressly stated, discussions herein using terms such as "processing," "computing," "calculating," "determining," "presenting," "displaying," and the like may refer to the action or process of a machine (e.g., a computer) to manipulate or transform data represented as physical (e.g., electrical, magnetic, or optical) quantities in one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information.

[0241] As used herein, any reference to "one embodiment" or "an embodiment" means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The phrase "in one embodiment" appearing in various places in the specification does not necessarily all refer to the same embodiment.

[0242] For example, some embodiments may be described using the term "coupled" to indicate that two or more elements are in direct physical contact or electrical contact. For example, some embodiments may be described using the term "coupled" to indicate that two or more elements are in direct physical contact or electrical contact. However, the term "coupled" may also refer to two or more elements that are not in direct contact with each other, but still cooperate or interact with each other. The embodiments are not limited in this context.

[0243] As used herein, the terms "comprises / comprising", "includes / including", "has / having" or any other variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, article, or apparatus that includes a list of elements is not necessarily limited to only those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, "or" refers to an inclusive or, not an exclusive or. For example, any of the following satisfies condition A or B: A is true (or exists) and B is false (or does not exist), A is false (or does not exist) and B is true (or exists), and both A and B are true (or exist).

[0244] In addition, "a / an" is used to describe elements and components of the embodiments herein. This is merely for convenience and to give a general description. This description and the appended claims should be understood to include one or at least one, and unless otherwise clearly indicated, the singular also includes the plural. This detailed description should be interpreted as providing only examples, and does not describe every possible embodiment, because it would be impractical, if not impossible, to describe every possible embodiment. Many alternative embodiments may be implemented using current technology or technology developed after the filing date of this application.

Claims

1. A computing device for determining a medication to be administered to a patient suffering from a neuropsychiatric disorder, the computing device comprising: Communications networks; one or more processors; as well as a non-transitory computer-readable memory coupled to the one or more processors and storing thereon instructions that, when executed by the one or more processors, cause the computing device to: obtaining biological samples from patients; For a drug selected from glutamate N-methyl- d -aspartate receptor (NMDAR) antagonists or partial antagonists, obtaining a reference drug pharmacogenomic network representation and constituent subnetworks from a reference database, which at least includes a reference drug pharmacodynamic efficacy subnetwork and a reference drug pharmacodynamic adverse event subnetwork; analyzing the biological sample in view of the reference drug pharmacogenomic network representation and constituent subnetworks based on differential activation and repression of topologically associated domains (TADs) indicative of drug efficacy and adverse drug events in the patient using one or more of: targeted single nucleotide polymorphism (SNP) genotyping, RNA sequencing, and chromatin conformation capture; determining a patient pharmacogenomics network representation for the drug based on the analyzing, wherein the patient pharmacogenomics network representation includes a patient drug pharmacodynamic efficacy subnetwork and a patient drug pharmacodynamic adverse event subnetwork; Determine a similarity score based on at least one of the following: Comparison of the patient drug pharmacodynamic efficacy subnetwork with the reference drug pharmacodynamic efficacy subnetwork, or Comparison of the pharmacodynamic adverse event subnetwork of the patient drug with the pharmacodynamic adverse event subnetwork of the reference drug; Determining to administer the drug to the patient based on the similarity score; and The medicament is administered to the patient.

2. The computing device of claim 1, wherein: The patient pharmacogenomic network representation for the drug includes a patient drug pharmacodynamic efficacy subnetwork, a patient drug pharmacodynamic adverse event subnetwork, a chromatin remodeling subnetwork, and a pharmacokinetic enzyme and hormone subnetwork, and the constituent subnetworks from the reference database for the drug include a reference drug pharmacodynamic efficacy subnetwork, a reference drug pharmacodynamic adverse event subnetwork, a reference chromatin remodeling subnetwork, and a reference pharmacokinetic enzyme and hormone subnetwork that spans human drug response variation, and To determine the similarity score, the instructions cause the computing device to: assigning a first score to the patient drug pharmacodynamic efficacy subnetwork based on an amount of similarity between the patient drug pharmacodynamic efficacy subnetwork and the reference drug pharmacodynamic efficacy subnetwork; A second score is assigned to the patient-drug adverse pharmacodynamic event subnetwork based on an amount of similarity between the patient-drug adverse pharmacodynamic event subnetwork and the reference-drug adverse pharmacodynamic event subnetwork.

3. The computing device of claim 2, wherein to determine to administer the drug to the patient based on the similarity score, the instructions cause the computing device to: Administration of the drug to the patient is determined when the first score is above a first threshold score or the second score is below a second threshold score.

4. The computing device of claim 3, wherein to determine to administer the drug to the patient when the first score is above a first threshold score or the second score is below a second threshold score, the instructions cause the computing device to: combining the first score and the second score to determine the similarity score; and Administration of the drug to the patient is determined when the similarity score is higher than a third threshold score.

5. The computing device of any one of claims 1 to 4, wherein the instructions further cause the computing device to: determining a dose of the drug for administration to the patient based on the similarity score; and The determined dose of the drug is administered to the patient.

6. The computing device of claim 5, wherein to determine a dosage of the drug for administration to the patient, the instructions cause the computing device to: The dose is determined using a regression model based on a combination of two or more of the following: the patient's gender, the patient's age, whether the patient smokes, the patient's race, the patient's height, the patient's weight, and the patient's history of psychiatric illness.

7. The computing device of claim 5, wherein the instructions further cause the computing device to: obtaining clinical data of the patient; analyzing the biological sample using pharmaco-metabolomics to determine pre-existing drugs and metabolites of the pre-existing drugs in the patient; and A determination is made whether to administer the drug to the patient or a dosage to administer based at least in part on a drug-gene or drug-drug interaction between the pre-existing drug in the patient and the drug.

8. The computing device according to any one of claims 1 to 7, wherein the reference drug pharmacogenomics network for the drug from the reference database is a ketamine pharmacogenomics network and comprises one or more of the following: activity-regulated cytoskeleton-associated protein (ARC) gene, bHLH transcription factor 1 (ASCL1) gene, brain-derived neurotrophic factor (BDNF) gene, BDNF antisense RNA (BDNF-AS) gene, calcium / calmodulin-dependent protein kinase IIα (CAMK2A) gene, cyclin-dependent kinase inhibitor 1A (CDKN1A) gene, cAMP response element regulator (CREM) gene, Cut like homeobox 2 (Cut like homeobox 2, CUX2) gene, DCC spindle protein 1 receptor (DCC) gene, dopamine receptor D2 (DRD2) gene, eukaryotic translation elongation factor 2 kinase (EEF2K) gene, fragile X mental retardation 1 (FMR1) gene, ganglioside-induced differentiation-associated protein 1-like 1 (GDAP1L1) gene, glutamate metabotropic receptor 5 (GRM5) gene, Homer scaffold protein (Homer scaffold protein)1 (HOMER1) gene, serotonin receptor 1B (HTR1B) gene, serotonin receptor 2A (HTR2A) gene, Kruppel-like factor 6 (KLF6) gene, Lin-7 homolog C, lint cell polarity complex component (LIN7C) long noncoding RNA, LOC105379109 long noncoding RNA, myocyte enhancer factor 2D (MEF2D) gene, myosin VI (MYO6) gene, myelin transcription factor 1-like (MYT1L) gene, neuronal differentiation 1 (NEUROD1) gene, neuronal differentiation 2 (NEUROD2) gene, nonsense helix-loop-helix 2 (NHLH2) gene, neuromedin B (NMB) gene, NMDA receptor synaptonuclearsignaling and neuronal migration factor (NMDAreceptor synaptonuclearsignaling and neuronal migration factor factor, NSMF) gene, neurotrophic receptor tyrosine kinase 2 (NTRK2) gene, phosphatase and tensin homolog (PTEN) gene, prostaglandin-endoperoxide synthase 2 (PTGS2) gene, Rac family small GTPase 1 (RAC1) gene, Ras protein-specific guanine nucleotide release factor 2 (RASGRF2) gene, Ras homolog family member A (RHOA) gene, Roundabout guidance receptor 2 (ROBO2) gene, RP11_360A181 long noncoding RNA, Semaphorin 3A (Semaphorin 3A,SEMA3A) gene, SH3 and multiple ankyrin repeat domains 1 (SHANK1) gene, SH3 and multiple ankyrin repeat domains 2 (SHANK2) gene, SH3 and multiple ankyrin repeat domains 3 (SHANK3) gene, solute carrier family 22 member 15 (SLC22A15) gene, solute carrier family 6 member 2 (SLC6A2) gene, slit guiding ligand 1 (SLIT1) gene, slit guiding ligand 2 (SLIT2) gene, synaptosome associated protein 25 (SNAP25) gene, synapsin I (SYN1) gene, synapsin II (SYN2) gene, synapsin III (SYN3) gene, T box, brain 1 (TBR1) gene, transcription factor 4 (TCF4) gene, acetylcholinesterase (ACHE) gene, activating transcription factor 7 interacting protein (ATF7IP) gene, Activating transcription factor 7 interacting protein 2 (ATF7IP2) gene, ATPase Na+ / K+ transporter subunit α1 (ATP1A1) gene, BLOC-1 related complex unit 7 (BORCS7) gene, bromodomain-containing 4 (BRD4) gene, calcium voltage-gated channel subunit α1C (CACNA1C) gene, calcium voltage-gated channel auxiliary subunit β1 (CACNB1) gene, calcium voltage-gated channel auxiliary subunit β2 (CACNB2) gene, calcium voltage-gated channel auxiliary subunit γ2 (CACNG2) gene, cholinergic receptor muscarinic 2 (CHRM2) gene, cholinergic receptor nicotinic α3 subunit (CHRNA3) gene, cholinergic receptor nicotinic α5 subunit (CHRNA5) gene, cholinergic receptor nicotinic α7 subunit (CHRNA7) gene, cannabinoid receptor 1 (CNR1) gene, Disks large homolog 3 (Disks large homolog 3,DLG3) gene, disc large homolog 4 (DLG4) gene, DNA methyltransferase 1 (DNMT1) gene, euchromatic histone lysine methyltransferase 1 (EHMT1) gene, gamma-aminobutyric acid type A receptor alpha 2 subunit (GABRA2) gene, gamma-aminobutyric acid type A receptor alpha 5 subunit (GABRA5) gene, glutamate decarboxylase 1 (GAD1) ​​gene, glycine receptor alpha 1 (GLRA1) gene, glycine receptor alpha 2 (GLRA2) gene, glycine receptor beta (GLRB) gene, glutamate ionotropic receptor AMPA type subunit 1 ( GRIA1) gene, glutamate ionotropic receptor AMPA subunit 2 (GRIA2) gene, glutamate ionotropic receptor AMPA subunit 2 (GRIA4) gene, glutamate ionotropic receptor NMDA subunit 1 (GRIN1) gene, glutamate ionotropic receptor NMDA subunit 2A (GRIN2A) gene, glutamate ionotropic receptor NMDA subunit 2B (GRIN2B) gene, glutamate ionotropic receptor NMDA subunit 2C (GRIN2C) gene, glutamate ionotropic receptor NMDA subunit 2D (GRIN2D ) gene, glutamate ionotropic receptor NMDA-type subunit 3A (GRIN3A) gene, glutamate ionotropic receptor NMDA-type subunit 3B (GRIN3B) gene, hyperpolarization-activated cyclic nucleotide-gated potassium channel 1 (HCN1) gene, histone deacetylase 5 (HDAC5) gene, methyl-CpG binding domain protein 1 (MBD1) gene, M-phase phosphoprotein 8 (MPHOSPH8) gene, neural cell adhesion molecule 1 (NCAM1) gene, nitrate synthase 1 (NOS1) gene, nitrate synthase 2 (NOS2) gene, nitrate synthase 3 (NOS 3) gene, NAD(P)H quinone dehydrogenase 1 (NQO1) gene, opioid receptor kappa 1 (OPRK1) gene, opioid receptor μ1 (OPRM1) gene, ring-shaped guidance receptor 2 (ROBO2) gene, SET domain bifurcation 1 (SETDB1) gene, SH3 and multiple ankyrin repeat domains 2 (SHANK2) gene, sigma non-opioid intracellular receptor 1 (SIGMAR1) gene, solute carrier family 6 member 9 (SLC6A9) gene, transcriptional activation inhibitor (TASOR) gene, axonemal microtubules TOG array regulator 2 (TOGarray regulator of axonemal microtubules 2),TOGORAM2) gene, triple motif-containing 28 (TRIM28) gene, zinc finger protein 274 (ZNF274) gene, anaphase promoting complex subunit 2 (ANAPC2) gene, cytochrome P450 family 2 subfamily A member 6 (CYP2A6) gene, cytochrome P450 family 2 subfamily B member 6 (CYP2B6) gene, cytochrome P450 family 3 subfamily A member 4 (CYP3A4) gene, disc large homolog 4 (DLG4), eukaryotic elongation factor 2 kinase (EEF2K) gene, estrogen receptor 1 ( ESR1) gene, glutamate ionotropic receptor AMPA subunit 1 (GRIA1) gene, glutamate ionotropic receptor AMPA subunit 2 (GRIA4) gene, glutamate ionotropic receptor NMDA subunit 1 (GRIN1) gene, glutamate ionotropic receptor NMDA subunit 2B (GRIN2B) gene, myosin VI (MYO6) gene, ring-shaped guidance receptor 2 (ROBO2) gene, SH3 and multiple ankyrin repeat domains 2 (SHANK2) gene or transcription elongation regulator 1 (TCERG1) gene.

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