Pharmacogenomic decision support for modulators of nmda, glycine and ampa receptors

Abstract

To provide a method and a computing device that allow for more effective determination regarding which patients will experience drug efficacy, and which patients will experience adverse drug events, and that provide personalized recommendations for patients regarding dose, the frequency of drug administration, and drug choice.SOLUTION: A method for identifying patients diagnosed with treatment resistant or refractory depression, pain or other clinical indications, who are eligible to receive N-methyl-D-aspartate receptor antagonist, glycine receptor beta modulator, or α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor-based therapies.SELECTED DRAWING: None

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims priority to and the benefit of the filing date of (1) U.S. Provisional Application No. 62 / 795,705, filed on January 23, 2019, entitled "Methods and Systems for Reconstructing a Drug Space Network from Pharmacogenomic Regulatory Interactions, and Their Use", and (2) U.S. Provisional Application No. 62 / 795,710, filed on January 23, 2019, entitled "Companion Diagnostic Assay for N - Methyl - D - Aspartate Receptor Modulators", the entire disclosure of each of which is hereby expressly incorporated by reference herein.

[0002] The technology described herein relates to pharmacogenomic clinical decision - support assays useful for the selection of therapies for N - methyl - D - aspartate (NMDA) receptor, glycine receptor, and α - amino - 3 - hydroxy - 5 - methyl - 4 - isoxazolepropionic acid (AMPA) receptor - based depression (particularly treatment - resistant or refractory depression), as well as other clinical indications including anesthesia and analgesia / hypoalgesia disorders, neuropsychiatric disorders, and neurological disorders, using ketamine and its enantiomers as examples. More specifically, these technologies relate to specific biomarkers including gene markers, clinical value, and disease phenotypes derived from patients for optimizing the selection of these receptor networks and drugs that affect dosing in individual patients.

Background Art

[0003] Existing antidepressants are not effective for many patients. A new class of antidepressants targeting glutamate receptors in the human forebrain is being developed. Ketamine (RS-2-chlorophenyl-2-methylamino-cyclohexanone), a glutamate N-methyl-D-aspartate receptor (NMDAR) non-competitive antagonist approved as an anesthetic by the US Food and Drug Administration (FDA), has been shown to be promising as an antidepressant for patients with treatment-resistant depression (TRD). The racemic formulation may have potent and undesirable psychotomimetic side effects and other side effects depending on several variables, but chemical analogs of ketamine show a reduction in adverse events. Intravenous and oral formulations have demonstrated efficacy and tolerability in controlled and open-label trials in patient populations well known to have little or no response from conventional antidepressants that target the serotonin transporter (also known as SLC6A4, 5HTT or SERT1), including serotonin-norepinephrine reuptake inhibitors (SNRI). Evidence suggests that ketamine, its enantiomers, and ketamine analogs exert their mechanism of action primarily through the regulation of NMDA receptors (NMDAR) and downstream receptors within this network in the human brain.

[0004] The pharmacodynamic (PD) targets of ketamine-like drugs are NMDARs consisting of GRIN1 and GRIN2 subunits, which bind to the binding sites for glutamate and N-methyl-D-aspartate, glycine and D-serine encoded by GLRB, as well as sites that bind polyamines, histamine and cations. Antagonists, partial antagonists, and receptor modulators (such as ketamine, and its NMDAR and other NMDARs), as well as glycine modulators (including feniciclidine, amantadine, dextromethorphan, tiletamine, riluzole, methoxetamine, methoxphenidine, and memantine) are inward Ca +2Block the influx and prevent postsynaptic depolarization. Neuroimaging studies have demonstrated that intravenous injection of ketamine causes a transient rapid increase in glutamate levels observed in the prefrontal cortex, in concert with its rapid antidepressant effect. After NMDAR blockade, glutamate has been shown to preferentially bind to the GRIA1, GRIA2, and GRIA4 subunits of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor. Several NMDAR antagonists and partial antagonists, GLRB modulators, and AMPAR agonists are under development for the treatment of treatment-resistant depression but exhibit dissociative effects in patients. Furthermore, NMDAR antagonists, GLRB antagonists and AMPAR modulators, partial antagonists and receptor modulators that act on the same network as drugs in this class that failed clinical trials based on safety concerns may have a higher likelihood of success in clinical trials by being diverted using the methods of the present disclosure.

[0005] The three-dimensional (3D) architecture of the human regulatory epigenome plays a major role in determining the human phenotype. Most of the single nucleotide polymorphisms (SNPs) associated with disease risk, drug response, and other human traits found by genome-wide association studies (GWAS) are now recognized to be within enhancers, promoters, and other non-coding regulatory elements. Combined with recent insights into the composition of the functional 4D nucleome, a wealth of biomedical "big data" has become accessible in open-source and proprietary resources, enabling the reconstruction of drug regulatory pathways in the human genome. New methods are being applied to the mining of regulatory variants from the results of genome-wide association studies (GWAS), phenome-wide association studies (PheWAS), and the mining of electronic health record data and clinical trial data. It is now common knowledge in the art that the association of single nucleotide polymorphism (SNP) characteristics from these data sources is important because it is re-evaluated from the perspective of pathway analysis and resolved into the same or related biological networks. Combining a wealth of biomedical data with innovative methods for mining biological networks provides a basis for detecting genetic variants that may affect the variability of drug response, including adverse drug events. This approach promises great advances in specialized fields such as psychiatry, where the lack of drug efficacy and a large number of drug adverse events have proven to be particularly problematic in patient care.

[0006] More than half of all Americans will experience symptoms of a mental disorder over the course of their lifetime. The most common lifetime mental disorders are stress and anxiety disorders, mood disorders (including major depressive disorder and bipolar disorder), impulse control disorders, substance use disorders, and schizophrenia spectrum disorders. The lifetime prevalence of any mental disorder in the United States is 53%, 28% have two or more lifetime disorders, and 18% have three or more lifetime disorders, indicating that comorbidity of mental disorders is a significant medical problem. Some mental disorders, such as bipolar I disorder, are inherited in families with a penetrance of approximately 80%, while other mental disorders do not show an obvious inheritance rate. In the case of major depressive disorder (MDD), genetic factors play an important role in the etiology of the disease, as shown by family, twin, and adoption studies. Twin studies suggest a heritability of 50%, and family studies show that the lifetime risk of developing MDD is two to three times higher among first-degree relatives. Several sociodemographic variables are significantly associated with the lifetime risk of mental disorders in cohort-controlled studies. For example, women (biological sex) have a significantly higher risk than men of anxiety disorders and major depressive disorder, and men (biological sex) have a significantly higher risk than women of impulse control disorders and substance use disorders. Non-Hispanic blacks and Hispanic whites have a significantly lower risk than non-Hispanic whites of anxiety, mood, and substance use disorders, and a lower educational level is associated with a higher risk of substance use disorders. The data indicate that many factors, from environmental and sociological factors, biological sex, ethnicity, and familial genetics, all contribute to the etiology of mental illness. Furthermore, the complexity of phenotypes within individual patients or patient cohorts, including comorbidity with other mental disorders and stress-related diseases, requires a series of different algorithmic classification solutions, including machine learning, and multiple statistical analyses, including linear regression, to accurately identify precision therapies beyond what is currently available.

[0007] Mental illnesses have 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, such as cancer, heart disease, stroke, chronic obstructive pulmonary disease, and HIV / AIDS, yet remains one of the most undiagnosed, misdiagnosed, untreated, or undertreated diseases known to humanity. In 2013, the National Institutes of Health (NIH) in the United States provided 13 times more funding ($5.3 billion compared to approximately $415 million) for oncology research than for depression research. In the United States, from 2009 to 2011, adverse events associated with prescribed antidepressants reached more than 25,000 emergency room visits per year and accounted for 30% of all prescription drug-related hospitalizations annually. Patients with major depressive disorder and comorbid medical conditions experience more severe depressive symptoms, as well as lower response and remission rates to antidepressant treatment, compared to patients without comorbid conditions. Treatment-resistant depression (TRD) accounts for 30 - 40% of all patients diagnosed with MDD and is defined as "failure to achieve remission after two established antidepressant courses known to be of evidence-based acceptable dosage and duration."

[0008] Current antidepressants are not effective in many patients, and in those who respond or remit, several weeks to months of drug therapy are required before symptom relief is achieved. Therefore, newer and more effective antidepressants are being developed. For example, both the racemic mixture of ketamine and the S-enantiomer of ketamine are examples of N-methyl-D-aspartate receptor (NMDAR) partial antagonists approved by the U.S. Food and Drug Administration (FDA) for the treatment of TRD. Ketamine induces a rapid antidepressant response and an accompanying increase in glutamate levels in the cortex in approximately 50% of TRD patients, as measured by the total score on the Montgomery-Åsberg Depression Rating Scale (MADRS). R,S-ketamine has been used for clinical indications such as chronic pain, perioperative analgesia, and sedation since 1970. However, drug adverse events (AEs) after ketamine treatment are common, and diversion is restricted by limiting its use to inpatient and outpatient treatment settings. For example, in the Phase III clinical trial of esketamine for treatment-resistant depression (TRD) before submission to the FDA, almost one-fourth of TRD patients experienced severe dissociative effects, two deaths were reported, and an additional 6.9% of TRD patients in the treatment group experienced severe psychiatric manifestations such as delirium, delusions, and suicidal thoughts and attempts.

[0009] One of the challenges in psychiatry is the accurate matching of pharmacotherapy to the complex symptoms of individual patients. Psychiatric patients present with a wide range of comorbidities, and there are few objective biomarkers that can be used as diagnostic criteria to accurately match antidepressants, antipsychotics, and mood stabilizers to patients. Diagnostic assessment scales such as the Hamilton Depression Rating Scale (HAM-D) have shown high inter-rater reliability, but mental disorders such as depression exist as a variety of different phenotypes. Non-pharmacological therapies may show improved efficacy in patients with TRD or recurrent depression. For example, repetitive transcranial magnetic stimulation (rTMS) is promising as an alternative non-pharmacological therapy for patients suffering from TRD. However, the best results in TRD are obtained when rTMS is used as an adjunct to conventional antidepressant pharmacotherapy, as is the case with drugs in the antidepressant class that include NMDAR antagonists or partial antagonists, GLRB modulators, or AMPAR agonists, which can only be provided in the clinical setting to patients already taking another antidepressant.

[0010] rTMS therapy requires dozens of clinical visits, remission varies widely among TRD patients, and rTMS is effective in only about 20 - 40% of cases where remission from depression lasts for 1 - 2 years. Recent results of rTMS combined with neuroimaging have shown a specific clustering of depressed patients into four different phenotypes along the anhedonia and anxiety axes, based on differences in the placement of the rTMS array. These results provide substantial evidence that psychotic patients can be stratified by phenotypes based on different brain connectivity pathways and activation of networks that show considerable interindividual variability among patients, greatly improving the chances of an exact match of the optimal therapy for individual patients.

[0011] Although rTMS is promising for TRD patients, independent research studies have shown that its mechanism of action remains elusive until it is demonstrated that TMS first acts on the prefrontal cortex and that glutamate levels increase significantly along with biomarkers of N-methyl-d-aspartate receptor (NMDAR) modulation.

[0012] These are notable findings as they demonstrate that the mechanism of rTMS brain activation is not substantially distinguishable from the mechanism of action of ketamine drug therapy. Thus, rTMS and ketamine show similar mechanisms of action for reducing TRD, but approximately half of all TRD patients do not achieve remission after treatment with either treatment option. Additionally, both rTMS and ketamine show transient but serious AEs, including dissociation (a putative basis for the analgesic effect of ketamine), emergence of psychopathology, and neurocognitive impairment. This suggests that it is very important to match individual patients to one of these therapies or other antidepressants when it is possible to select which patients will benefit from these treatments and which patients will unnecessarily suffer serious AEs without an appropriate antidepressant effect.

[0013] Recent studies combining transcranial magnetic stimulation (TMS) for alleviating depression with subsequent neuroimaging have demonstrated that TRD patients can be clearly stratified into four subtypes based on their response to the placement of the TMS device, by four different endogenous neuroanatomical pathways that are activated in association with distinct symptom clusters. Currently, these four subtypes are determined independently using a combination of clinical and molecular data, as demonstrated in the present disclosure, thereby providing improved pharmacophenomics decision support to provide a model for other mental and stress-related disorders that offer better treatment options to patients. Similarly, NMDAR antagonist therapy can be used as an adjunct to age-related degenerative conditions. SUMMARY OF THE INVENTION

[0014] Using various methods, the exact treatment requirements of an individual patient's phenotype or a cohort of phenotypes can be accurately determined. The present disclosure describes a method for constructing a companion diagnosis of psychotropic drugs that optimizes the suitability of a pharmacophenomics assay for clinical decision support or a therapeutic intervention for an individual patient or cohort of patients diagnosed with a psychiatric or related disorder (such as treatment-resistant depression, chronic pain, migraine, fibromyalgia, inflammatory diseases, and other conditions where ketamine or one of its analogs constitutes an effective treatment). In the context of the present disclosure, the phenotypes of a patient's drug response and adverse events are composed of a set of multiple variables described herein, ranging from the patient's unique constitution of the pharmacogenomic network of the drug, including its variant profile, to the behavioral phenotypes that may be obtained from clinical data.

[0015] The methods used for patient stratification in the present disclosure use different data sources, some of which may be incomplete, may require data cleansing and / or curation, or may not exist. Different methods as described herein range from methods that can accommodate different combinations of limited data, to methods that can accommodate broader computational solutions, or methods that can bridge missing data elements using probabilistic methods.

[0016] The present disclosure includes a series of different connected methods for providing accurate pharmacophenomics decision support to patients diagnosed with mental disorders. The output provides a quantitative score for ranking treatment interventions, including recommendations such as drug selection and dosage, transcranial magnetic stimulation, electroconvulsive therapy, and behavioral interventions. The present disclosure includes pharmacophenomics methods for classifying patients diagnosed with mental disorders into subtypes for optimization of treatment interventions. Using these methods, a diagnosis can be constructed that recommends an optimal therapeutic match for an individual patient. In another embodiment, these methods can be used to enhance patient selection based on pharmacophenomics stratification prior to a clinical trial. In another embodiment, these methods can be used to construct companion diagnostics for psychotropic drugs to ensure patient safety during pharmaceutical development, marketing, and post - commercialization.

[0017] In another embodiment, clinical values are obtained from an EHR or similar source, and SNPs of PD and PK genes are obtained from a patient's genotype, and these are input as quantitative values into a regression equation (nomogram) for determining the dosage of a drug such as ketamine for that individual patient. In this embodiment, the optimal treatment dosage is developed with a step - wise regression model equation that includes genetic and clinical values, and the regression model is retested and validated using a patient population to ensure the accuracy of the output of the regression equation, and may be judged as the area under the curve (AUC) by a receiver operating (ROC) curve.

[0018] In another embodiment, clinical values are obtained from an EHR or similar source, SNPs of PD and PK genes are combined with disease - risk SNPs obtained from a genome - wide association study (GWAS), and by differentially annotating a sub - network specific to adverse events and efficacy of a pharmacogenomics network of a drug such as ketamine, it is predicted whether a patient will benefit from the drug, and if so, an appropriate dosage for the patient is determined.

[0019] In another embodiment, clinical values are obtained from an EHR or similar source, SNPs of PD and PK genes are combined with disease risk SNPs obtained from GWAS and PheWAS, and differentially annotated to a subnetwork specific to adverse events and efficacy of a pharmacogenomics network of a drug such as ketamine, to predict whether a patient will benefit from the drug and, if so, determine an appropriate dosage for the patient. In this embodiment, pharmacometabolomics-based therapeutic drug monitoring is used to collect more accurate data on existing prescription and over-the-counter drugs and their metabolites used by the patient through analysis of biological samples (blood, buccal swabs, urine or other body fluids) obtained from the patient or a patient cohort.

[0020] In yet another embodiment, clinical values are obtained from an EHR or similar source, SNPs of PD and PK genes are combined with disease risk SNPs obtained from GWAS and PheWAS, differentially annotated to a subnetwork specific to adverse events and efficacy of the pharmacogenomics of a drug such as ketamine, and these data are matched to one of four phenotypes determined using the scoring of the Hamilton Depression Rating Scale (HAMD) from the perspective of antidepressants such as ketamine.

[0021] In another embodiment, pharmacophenomics decision support is determined using inputs from a model that includes: (1) molecular profiling of a drug-induced subnetwork in a patient or patient cohort, (2) clinical variables obtained from electronic health records or equivalent measurements made by a clinician, (3) patient subtyping based on clinical variables and neuroimaging studies, and (4) SNPs of PD and PK that stratify patients by drug response. Further, drug-drug and drug-gene interactions objectively measured using drug metabolism methods can be used to minimize adverse drug events in individual patients or patient cohorts.

[0022] Another embodiment of this system is a companion diagnostic configuration that can be used for patient selection for clinical trials between the commercial and post - commercial stages of drugs such as antidepressants that act as NMDAR antagonists, partial antagonists, GLRB modulators, and AMPAR agonists.

[0023] Another embodiment of this system is to determine and select the addition of a therapeutic agent to an NMDAR modulator to improve the outcome. Another embodiment of the methods and systems described herein can be used for re - evaluation of drugs and drug repurposing for clinical trials.

Brief Description of the Drawings

[0024]

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MODE FOR CARRYING OUT THE INVENTION

[0025] The following text describes detailed explanations of numerous different embodiments, but it should be understood that the legal scope of this specification is defined by the language of the claims described at the end of this disclosure. The detailed explanations should be construed as merely illustrative, and it is not possible, nor realistic, to describe all possible embodiments, so it does not describe all possible embodiments. Many alternative embodiments may be implemented using either current technology or technology developed after the filing date of this patent, but these will still fall within the scope of the claims.

[0026] Unless a term is clearly defined within the patent using a statement such as "As used herein, the term '______' is defined to mean... in this specification" or a similar statement, there is no intent to limit the meaning of the term beyond its plain or ordinary meaning, either explicitly or implicitly, and it should also be understood that such a term should not be construed as limited within the scope of any description made in any section of this patent (other than the language of the claims). If any term described within the last claims of this patent is referred to within the patent in a manner that does not conflict with a single meaning, it is merely for clarification so as not to confuse the reader, and it is not intended that such terms of the claims be limited to that single meaning by implication or otherwise. Finally, unless the elements of the claims are defined by listing the word "means" and the function without any detailed description of the structure, the scope of any element of the claims is not intended to be construed based on the application of 35 U.S.C. § 112, ¶ 6.

[0027] The present disclosure includes systems and methods for stratifying patients or cohorts of patients diagnosed with a mental disorder or otherwise in need of these drugs for accurate drug selection and dosing of NMDAR antagonists. Although ketamine is used as an exemplar, these methods can be used for NMDAR antagonists or α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor modulators in clinical trials for the indication of treatment-resistant depression. Ketamine and its enantiomers are novel antidepressants that show higher efficacy and reduced side effects compared to other antidepressants for patients diagnosed with TRD or a specific subset of patients. A pharmacogenomics decision support system can determine which patients diagnosed with TRD should take ketamine or one of its enantiomers as an antidepressant and, if they should take it, what the appropriate dosage should be to maximize efficacy, minimize adverse drug events and drug-drug interactions, and reduce the harmful effects of drug-gene and drug-drug interactions. The adverse psychotropic side effects, including the psychotomimetic and neurocognitive effects of glutamate receptor-targeted drugs for the remission of depression, in combination with 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.

[0028] This system includes several methods that can be used in the construction of clinical decision support diagnostics regarding the selection and dosing of ketamine or one of its enantiomers as an antidepressant for treatment-resistant depression, and this system can be generalized to other psychiatric drugs. One embodiment includes an integrated multi-scale measurement system that includes the components shown in FIG. 1D, where a patient's biological sample can be analyzed extensively as shown in FIG. 2, and other relevant clinical data can also be obtained.

[0029] In some embodiments, the system is configured to analyze a minimal amount of results from a biological sample necessary to match a stored cohort subnet reference set. In this case, the patient data is compared with a reference set of drug subnetworks spanning the full range of the human drug response cohort using a pre-trained learning machine for the cohort response range of a specific drug, and the match with the reference set facilitates the recommendation of clinical decisions. For example, the reference set of drug subnetworks can include a set of the pharmacodynamic efficacy subnetwork of a specific drug, the pharmacodynamic adverse event subnetwork of a reference drug, the reference chromatin remodeling subnetwork, as well as the reference drug metabolism enzyme and hormone subnetworks.

[0030] An electronic encryption broker is first used to protect health information through anonymization and prevent patient identification. Biological sample(s) (e.g., blood, buccal swab, saliva, urine, or other body fluids) are obtained from a patient or a cohort of patients with clinical data from medical records such as electronic health records (EHRs) or other sources. An initial pharmacometabolic analysis of a small blood sample or its plasma components collected from a patient or a cohort of patients is performed to determine potential drug-drug and drug-gene interactions that may alter subsequent pharmacogenomic decision support. These objective measurements reinforce self-reported data, data reported by clinicians, or other data included in the EHR or another patient record.

[0031] Generally speaking, 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 dosage of the drug to administer to the patient can be implemented in a system including one or several client devices, one or several network servers, or a combination of these devices. However, for clarity, the following examples mainly focus on embodiments in which a medical professional obtains a biological sample from a patient and provides the biological sample to an assay laboratory for analysis.

[0032] Biological samples may include a subject's skin, blood, urine, sweat, lymph, bone marrow, buccal cells, saliva, cell lines, tissues, and the like. Next, cells are extracted from the biological sample and reprogrammed into stem cells such as induced pluripotent stem cells (iPSCs). Next, the iPSCs are differentiated into various tissues such as neurons and cardiomyocytes and assayed to obtain the patient's genomic data, chromosomal data, metabolomics data, and the like. In some embodiments, the iPSCs can be assayed for loci associated with or causally related to the phenotypic response to a drug of interest. The iPSCs constitute part of a reference set used to derive variables for assessing individual patients.

[0033] The drug and dosage determination server depends on the drug and dosage determination support engine. As shown in FIG. 2, this engine receives a numerical score representing the overlap between the input patient sample and the relevant drug-specific reference pharmacogenomic network stored in the database of such a reference 154. This system uses a trained learning machine for the entire range of the human drug response cohort, which consists of a reference set of pharmacogenomic networks for a particular drug. This encompasses the range of human drug response variability for a particular drug, including a reference set that matches the input patient sample.

[0034] The drug and dosage determination server analyzes the experimental results to determine the patient's subnetwork representation of a drug gene set of an NMDAR antagonist or partial antagonist, such as ketamine, a GLRB modulator, or an AMPAR agonist. Further, the drug and dosage determination server searches for a reference pharmacogenomic network and a reference constituent subnetwork of an NMDAR antagonist or partial antagonist, a GLRB modulator, or an AMPAR agonist, for example, from a pharmacogenomic network database of a reference drug. Next, the drug and dosage determination server compares the patient's drug subnetwork representation with this drug's reference pharmacogenomic network and reference constituent subnetwork to determine whether to administer this drug to the patient. For example, the drug and dosage determination server may compare the patient's efficacy drug-specific (e.g., ketamine) subnetwork with the reference efficacy drug-specific (e.g., ketamine) subnetwork, and may compare the patient's adverse event drug-specific (e.g., ketamine) subnetwork with the reference adverse event drug-specific (e.g., ketamine) subnetwork. Next, if the similarity between the patient's efficacy drug-specific subnetwork and the reference efficacy drug-specific subnetwork is greater than a threshold (indicating a high likelihood that the drug is effective for the patient), the drug and dosage determination server may determine that the drug should be administered to the patient. The drug and dosage determination server may also determine that the 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 the threshold (indicating a low likelihood that the patient will experience an adverse event), or based on a certain combination of the two.

[0035] Accordingly, the drug and dosage determination server provides a recommendation indicating that the patient should take the drug to the client device of the medical professional, thereby causing the medical professional to administer the drug to the patient. As a result, the medical professional can administer the drug to the patient. In some embodiments, the drug and dosage determination server may determine the dosage of the drug to be administered to the patient according to a dosing algorithm. The dosing algorithm can be determined using machine learning techniques such as linear regression and can be based on patient demographic data, patient clinical data, patient biological data, and the like.

[0036] The drug and dosage determination server includes regression algorithms (e.g., least squares regression, linear regression, logistic regression, stepwise regression, multivariate adaptive regression splines, local estimated scatterplot smoothing, etc.), instance-based algorithms (e.g., k-nearest neighbor method, learning vector quantization, self-organizing map, locally weighted learning, etc.), regularization algorithms (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, least angle regression, etc.), decision tree algorithms (e.g., classification and regression trees, ID3 (iterative dichotomizer), C4.5. C5, chi-square automatic interaction detection, decision trees, M5, conditional decision trees, etc., clustering algorithms (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, spectral clustering, mean shift, density-based pharmacogenomic clustering of applications with noise, OPTICS (ordering points to identify the clustering structure), etc.), association rule learning algorithms (e.g., Apriori algorithm, Eclat algorithm, etc.), Bayesian algorithms (e.g., naive Bayes, Gaussian naive Bayes, polynomial naive Bayes, AODE (averaged one-dependence estimators), 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, adversarial generative network, etc.), dimensionality reduction algorithms (e.g., principal component analysis, principal component regression, partial least squares regression, Sammon mapping, multidimensional scaling, projection pursuit, linear discriminant analysis, mixture discriminant analysis, quadratic discriminant analysis, flexible discriminant analysis, factor analysis, independent component analysis, non-negative matrix factorization, t-distributed stochastic neighbor embedding, etc.), ensemble algorithms (e.g., boosting, bootstrap aggregating, AdaBoost, stacking generalization, gradient boosting machine, gradient boosting regression tree, random decision forest, etc.), reinforcement learning (e.g., temporal difference learning, Q-learning, learning automata, SARSA (State-Action-Reward-State-Action), etc.), support vector machines, mixture models, evolutionary algorithms, probabilistic graphical models, etc., and various machine learning techniques including but not limited to these can be used to determine the dosage of a drug to be administered to a patient and perform other methods described herein.

[0037] Referring to FIG. 1A, an exemplary pharmacogenomics decision support system 100 determines whether to administer a psychotropic drug to a patient suffering from depression, such as a patient with TRD, and the appropriate dosage of the psychotropic drug. The pharmacogenomics decision support system 100 includes a drug and dosage determination 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 determination server 102 and the client devices 106-116 communicate via a wireless signal 120 via any suitable local or wide area network(s) 130 that can include a WiFi network, a Bluetooth network, a cellular network such as 3G, 4G, Long Term Evolution (LTE), 5G, the Internet, and the like. In some examples, the client devices 106-116 can communicate with the communication network 130 via an intermediate wireless or wired device 118 that can be a wireless router, a wireless repeater, a base transceiver station of a mobile phone communication provider, and the like. As an example, the client devices 106-116 can include a tablet computer 106, a smartwatch 107, a network-enabled mobile phone 108, a wearable computing device such as Google Glass (trademark) or Fitbit (registered trademark) 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 phablet, any device configured for wired or wireless RF (radio frequency) communication, and the like. Additionally, any other suitable client device for recording a patient's clinical data may also communicate with the drug and dosage determination server 102.

[0038] Each of client devices 106-116 can interact with a drug and dosage determination server 102 to receive recommendations regarding whether to administer a psychotropic drug to a patient and the dosage of the psychotropic drug. The client devices 106-116 can present the recommendations via a user interface for display to a medical professional.

[0039] In an exemplary implementation, the drug and dosage determination server 102 can be a cloud-based server, an application server, a web server, etc., and includes a memory 150, one or more processors (CPUs) 142 such as a microprocessor coupled to the memory 150, a network interface unit 144, and an I / O module 148 that can be, for example, a keyboard or a touch screen.

[0040] The drug and dosage determination server 102 can also be communicatively connected to a database 154 of constituent subnetworks such as a pharmacogenomics network of reference drugs and a subnetwork of drug efficacy and adverse events.

[0041] The memory 150 may be a tangible non-volatile memory and may include any suitable type of memory module including random access memory (RAM), read-only memory (ROM), flash memory, other types of persistent memory, etc. For example, the memory 150 can store instructions executable by the processor 142 for an operating system (OS) 152 that can be any suitable type of operating system such as, for example, a latest smartphone operating system. The memory 150 can also store instructions executable on the processor 142 for, for example, a drug and dosage determination support engine 146. The drug and dosage determination server 102 will be described in more detail below with reference to FIG. 1B. In some embodiments, the drug and dosage determination support engine 146 can be part of one or more of the client devices 106-116, the drug and dosage determination server 102, or a combination of the drug and dosage determination server 102 and the client devices 106-116.

[0042] In any case, the drug and dosage determination support engine 146 can obtain experimental results from a patient's biological sample only if necessary to match one of a set of pharmacogenomic networks that define human drug response variability for a particular drug and their constituent subnetworks. These include genomic variations defined by SNPs in PD and PK genes, pharmacogenomic interactions between regulatory elements, genes within the patient's genome that can be defined using chromosome conformation data such as Hi-C, and / or measurements specific to direct topological associated domains (TADs) (including differential gene expression determined using RNA sequencing (RNA-Seq) or expression microarray profiling), and patient-specific TAD contactome measurements in related or proxy cell types using chromosome conformation capture (e.g., 3C, 4C, 5C, Hi-C, ChIA-PET, and GAM). The molecular data can assay loci that are related to or causally associated with the phenotypic response to the drug of interest. Additionally, the drug and dosage determination support engine 146 can obtain a reference pharmacogenomic network and reference constituent subnetworks for a target psychotropic drug (e.g., ketamine) from the reference pharmacogenomic network database 154.

[0043] Next, the drug and dosage determination support engine 146 can analyze the patient's experimental results to determine a subnet representation of the target psychotropic drug, such as an efficacy subnet and an adverse event subnet. The drug and dosage determination support engine 146 can compare the patient's efficacy subnet and adverse event subnet with the reference efficacy and adverse event subnets to determine whether to administer the target psychotropic drug to the patient. If the similarity between the patient's drug-specific efficacy subnet and the reference drug-specific efficacy subnet is greater than a threshold, and / or the similarity between the patient's drug-specific adverse event subnet and the reference drug-specific adverse event subnet is below the threshold, the drug and dosage determination support engine 146 can determine that the patient should be administered the target psychotropic drug. Next, the drug and dosage determination support engine 146 can provide a recommendation indicating that the patient should take the target psychotropic drug to the client devices 106-116 of medical professionals. Otherwise, the drug and dosage determination support engine 146 can provide a recommendation for another drug to be administered to the patient to treat depression. Further, the drug and dosage determination support engine 146 can determine the dosage of the target psychotropic drug to be administered to the patient according to a dosing algorithm. The drug and dosage determination support engine 146 can also provide the recommended dosage of the target psychotropic drug to the client devices 106-116 of medical professionals.

[0044] The drug and dosage determination server 102 can communicate with the client devices 106-116 via the network 130. The digital network 130 may be a private network, a secure public Internet, a virtual private network, and / or some other types of networks such as, for example, dedicated access lines, ordinary telephone lines, satellite links, combinations thereof. If the digital network 130 includes the Internet, data communication can be performed via the digital network 130 by an Internet communication protocol.

[0045] Referring now to FIG. 1B, the drug and dosage determination 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) circuitry 234, all of which may be interconnected via an address / data bus 232. Also, in some embodiments, the controller 224 may include or be communicatively coupled to a database 239 or other data storage mechanism (e.g., one or more hard disk 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, as well as other data necessary to interact with a user via the network 130. The database 239 may include data similar to the database 154 described above with reference to FIG. 1A.

[0046] Although FIG. 1B depicts only one microprocessor 228, it will be understood that 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. FIG. 1B depicts the I / O circuitry 234 as a single block, but the I / O circuitry 234 may include a number of different types of I / O circuitry. The controller 224 may implement the RAM(s) 230 and / or the program memory 226 as semiconductor memory, magnetically readable memory, and / or optically readable memory.

[0047] As shown in FIG. 1B, the program memory 226 and / or the RAM 230 may store various applications for execution by the microprocessor 228. For example, the user interface application 236 may provide a user interface to the drug and dosage determination server 102, whereby the user interface may enable, for example, a system administrator to configure, troubleshoot, or test various aspects of the server's operation. The server application 238 may receive patient molecular data, analyze the molecular data to determine a patient's subnet related to a particular drug of interest, compare the patient's subnet with a reference subnet of the particular drug of interest, determine to administer the particular drug of interest to the patient based on the comparison, and operate to send a recommendation to administer the particular drug of interest to the patient to the client devices 106-116. The server application 238 may be a single module 238, such as the drug and dosage determination support engine 146, or multiple modules 238A, 238B.

[0048] The server application 238 is depicted in FIG. 1B as including two modules 238A and 238B, but the server application 238 may include any number of modules to accomplish tasks related to the implementation of the drug and dosage determination server 102. Furthermore, although only one drug and dosage determination server 102 is depicted in FIG. 1B, it will be understood that multiple drug and dosage determination servers 102 may be provided for purposes such as distributing server load, providing services to different web pages, etc. Examples of these dosage determination servers 102 may include web servers, enterprise-specific servers (e.g., Apple® servers, etc.), servers disposed in a retail network or a private network.

[0049] Referring now to FIG. 1C, the laptop computer 114 (or any client device 106-116) may include a display 240, a communication unit 258, a user input device (not shown), and a controller 242 such as the drug and dosage determination server 102. Similar to the controller 224, the controller 242 may include a program memory 246, a microcontroller or microprocessor (MP) 248 (random access memory (RAM) 250), and / or input / output (I / O) circuitry 254, all of which may be interconnected via an address / data bus 252. The program memory 246 may include an operating system 260, data storage 262, a plurality of software applications 264, and / or a plurality of software routines 268. The operating system 260 may include, for example, Microsoft Windows®, OS X®, Linux®, Unix®, etc. The data storage 262 may include data such as application data 264 for a plurality of applications, routine data 268 for a plurality of routines, and / or other data necessary to interact with the drug and dosage determination server 102 through the digital network 130. Also, in some embodiments, the controller 242 may include or be communicatively connected to other data storage mechanisms (e.g., one or more hard disk drives, optical storage drives, solid state storage devices, etc.) present within the laptop computer 114.

[0050] The communication unit 258 can communicate with the drug and dosage determination server 102 through any suitable wireless communication protocol network such as a radio telephone network (e.g., GSM, CDMA, LTE, etc.), a Wi-Fi network (802.11 standard), a WiMAX network, a Bluetooth network, etc. Examples of user input devices (not shown) 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 described with respect to the controller 224, although FIG. 1C depicts only one microprocessor 248, it should be understood that the controller 242 can include multiple microprocessors 248. Similarly, the memory of the controller 242 can include multiple RAMs 250 and / or multiple program memories 246. Although FIG. 1C depicts the I / O circuit 254 as a single block, the I / O circuit 254 can include many different types of I / O circuits. The controller 242 can implement the RAM(s) 250 and / or the program memory 246 as, for example, semiconductor memory, magnetically readable memory, and / or optically readable memory.

[0051] One or more processors 248 can be adapted and configured to execute not only any one or more of the plurality of software applications 264 and / or any one or more of the plurality of software routines 268 present in the program memory 246 but also other software applications. One of the plurality of applications 264 can be a client application 266 that can be implemented as a series of machine-readable instructions for performing various tasks related to receiving information on the laptop computer 114, displaying information on the laptop computer 114, and / or transmitting information from the laptop computer 114.

[0052] One of the plurality of applications 264 can be a native application and / or a web browser 270, such as Apple's Safari (registered trademark), Google Chrome (trademark), Microsoft Internet Explorer (registered trademark), and Mozilla Firefox (registered trademark), which is implemented as a series of machine-readable instructions for receiving, interpreting, and / or displaying web page information from the drug and dosage determination server 102, and also for receiving input from a user, such as a medical professional or researcher. Another application of the plurality of applications can include an embedded web browser 276 that can be implemented as a series of machine-readable instructions for receiving, interpreting, and / or displaying web page information from the drug and dosage determination server 102.

[0053] One of the plurality of routines can include a drug recommendation display routine 272 that presents on the display 240 a recommendation as to whether or not to administer a target antidepressant to a patient and / or a recommended dosage.

[0054] 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 determination server 102 to implement the companion diagnostic system 100. Further, 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 one of the plurality of software applications 264) to access the drug and dosage determination server 102 to embody the companion diagnostic system 100.

[0055] FIG. 1D shows a flowchart representing an exemplary method 160 for determining a drug and dosage to administer to a patient suffering from depression based on comparing data from the patient's biological sample to a reference pharmacogenomics network and constituent subnetworks of the target drug. The method 160 can be executed by the drug and dosage determination server 102.

[0056] In some embodiments, a patient's biological sample is analyzed as shown in FIG. 1D for personalized therapy to quantify the relative activation of different pathways that mediate the mechanism of action of ketamine in the human CNS as determined using the methods described herein. A patient's or patient cohort's biological sample can be analyzed using a pharmacometabolomics assay to determine drugs or metabolites in the sample that can cause undesirable drug-drug interactions and affect drug efficacy, adverse events, and dosing. Biological sample measurements include the following: (1) genotyping of pharmacokinetic SNPs shown to determine metabolic status (poor, below normal, normal, or ultrarapid subtype) in the context of a model of ketamine consisting of CYP2B6 gene mutations, (2) targeting of pharmacodynamic SNPs as input into pharmacogenomic networks and subnetworks profiling to determine both efficacy and adverse events analyzed using a pharmacogenomics classifier and a pharmacodynamic subnetwork profiling system, (3) direct topological associated domain (TAD)-specific measurements (including differential gene expression determined using RNA sequencing (RNA-Seq) or expression microarray profiling), and patient-specific TAD contactome measurements in relevant or proxy cell types using chromosome conformation capture (e.g., 3C, 4C, 5C, Hi-C, ChIA-PET, and GAM), and (4) pharmacometabolomics analysis.

[0057] The data processing pipeline shown in FIG. 1D consists of parallel routes for analyzing a biological sample using multiple methods and the analysis of available clinical data of the same patient that may be collected from an electronic health record (EHR). This example shows an analysis used to determine whether a particular patient should receive an N-methyl-D-aspartate receptor (NMDAR) modulator. Adverse events associated with NMDAR modulators such as ketamine can be severe, such as severe dissociation, hallucinations, and nightmares. Thus, the first decision made from these parallelized analyses is to prevent the patient from taking an NMDAR modulator such as ketamine if the system predicts that the individual will experience moderate to severe adverse events.

[0058] The data processing pipeline shown in FIG. 1D may make a "withhold" decision regarding the administration of an NMDAR modulator such as ketamine based on the molecular network representation in the patient, specific disease risk SNPs that the patient may have as shown in FIG. 23, the phenotype of treatment-resistant depression in the patient as shown in FIGS. 20 and 22, and dose adjustment based on the patient's drug metabolism profile. If the system determines that the patient may take an NMDAR modulator such as ketamine, a dose determination algorithm is initiated.

[0059] Method 160 is used as a clinical decision support diagnosis to determine whether a patient should be prescribed an NMDAR modulator as an antidepressant (block 162) and to determine the optimal dosage for the patient. In some embodiments, a patient's biological sample is analyzed for personalized therapy to quantify the relative activation of different pathways that mediate the mechanism of action of ketamine in the human CNS determined using the methods described herein (block 164a). A biological sample of a patient or patient cohort can be analyzed using a drug metabolism assay to determine drugs or metabolites in the sample that can cause undesirable drug-drug interactions and affect the efficacy, adverse events, and administration of the drug. The biological sample measurements at block 166 include: (1) genotyping of pharmacokinetic SNPs shown to determine the metabolic state (poor, below normal, normal, or ultrarapid subtype) in the paradigm of ketamine consisting of mutations in the CYP2B6 gene, (2) targeting of pharmacodynamic SNPs as input to pharmacogenomic networks and subnet profiling to determine both efficacy and adverse events analyzed using a pharmacogenomic classifier and a pharmacodynamic subnet profiling system, (3) direct topological associated domain (TAD)-specific measurements (including differential gene expression determined using RNA sequencing (RNA-Seq) or expression microarray profiling) and patient-specific TAD contactome measurements in related or proxy cell types using chromosome conformation capture (e.g., 3C, 4C, 5C, Hi-C, ChIA-PET, and GAM), and (4) pharmacometabolomics analysis (block 164c).

[0060] As described above, examples of biological sample measurements include targeting pharmacodynamic SNPs as input into pharmacogenomic network and subnet profiling (blocks 168, 170b). This is analyzed using a pharmacogenomic classifier and a pharmacodynamic subnet profiling system to determine both efficacy and adverse events. At block 170a, a reference pharmacogenomic network and subnet for the drug of interest are retrieved from a reference database. Next, a patient subnet for the specific drug of interest, including efficacy and adverse event subnets, is compared to the reference pharmacogenomic network and subnet for the drug of interest (block 172). To determine similarity to the reference set, two different pairs of reference patient metrics include an exact measure of similarity and an output similarity score for each of the efficacy and adverse event subnets. At block 172, the similarity scores of the efficacy and adverse event subnets for the drug of interest can be used to determine whether to administer the drug of interest to a patient. For example, if the similarity score of the efficacy subnet exceeds a threshold similarity score, method 160 can determine that the drug of interest should be administered to the patient (block 176). Otherwise, method 160 determines to select a different agent (block 174).

[0061] In addition to comparing the patient subnetwork of the drug of interest to the reference subnetwork of the drug of interest, at block 164b, the patient's clinical data is collected and analyzed to determine whether to administer the drug to the patient and / or what dosage of the drug to administer. More specifically, the patient's HAMD score and / or the patient's symptoms can be analyzed to classify the patient into one of four TRD patient subtypes (block 180). The subtypes of TRD patients are described in detail below with reference to FIGS. 15-17. If the patient is classified as TRD subtype 3 (block 182), method 160 determines to select a different agent (block 184). Other clinical data of the patient can also be analyzed, such as the patient's drug-drug interactions, age, weight, biological sex, body mass index, ethnicity, family history, patient history of drug abuse, diagnostic codes, hospitalization history, drug-gene interactions, psychiatric history, whether the patient smokes or uses nicotine, etc. (block 186).

[0062] Next, the dosage of the drug of interest to administer to the patient is determined (block 178). The dosage can be determined based on a dosing algorithm having a predetermined constant applied to each of several patient characteristics such as biological characteristics, demographic characteristics, clinical characteristics, etc. In other embodiments, the dosing algorithm can be generated using machine learning techniques. The patient characteristics utilized in the dosing algorithm may include biological data such as SNPs that have been reported to stratify response to human ketamine. The patient characteristics may also include the patient's demographic data such as the patient's sex, height and weight, age, and ethnicity. Further, the patient characteristics may include clinical data such as family history, drug-drug interactions, history of mental illness, whether the patient smokes or uses nicotine, and Hamilton Depression Scale (HAM-D) score.

[0063] Figure 2 shows various measures that can be taken from a patient's biological sample. In one embodiment, samples of blood and buccal swabs are obtained and processed. For ketamine and other drugs that undergo first-pass metabolism by the protein encoded by the superinducible and ultra-variable CYP2B6 gene, genotyping of the target SNPs is performed using a 4-SNP panel, but most importantly, the splicing variant SNP rs3745274 is examined. This is because the splicing variant SNP rs3745274 is relatively common among the human population (>10% frequency), and carriers of this SNP include ultrametabolizers of any drug that is mainly metabolized by this enzyme, and are very likely to experience adverse drug events from NMDAR modulators such as ketamine.

[0064] In the method shown in Figure 1D and the measurements collected in Figure 2, it is recommended to perform a drug metabolism analysis of the patient's blood to rule out the possibility of negative drug-drug or drug-gene interactions.

[0065] Figure 2 shows the analysis that can be performed on a patient's biological sample to obtain the minimum information required to match the drug-specific patterns of a reference set of comprehensive subnetworks, including the activation of topologically associated domains (TADs) contained in database 154, to a machine or deep learning algorithm. These methods 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 targeting genotyping after or in anticipation of inducing pharmacogenomic genomic geometric changes by a drug, through the analysis of buccal swabs obtained from the patient before deciding whether to administer the drug.

[0066] For the biological sample measurement in FIG. 2, it includes genotyping of target pharmacokinetics and pharmacodynamic SNPs (block 292), direct TAD-specific measurements such as differential gene expression and pharmacogenomic contacts (block 294), and pharmacometabolomics (block 296). Genotyping of target pharmacokinetics and pharmacodynamic SNPs includes identifying mutations in the CYP2B6 gene shown to determine the metabolic state (block 282), and classifying the strength of the efficacy and adverse event subnetworks according to the profiling of the pharmacogenomic network and subnetworks (block 284). Direct chromatin contact-specific measurements include differential gene expression determined using external RNA sequencing (RNA-Seq) (block 286), and chromatin conformation capture analysis for identifying pharmacogenomic contacts (block 288). Furthermore, pharmacometabolomics analysis identifies existing drugs and metabolites in a patient's biological sample and is used, for example, to evaluate potential drug-drug interactions (block 290) and can be used to regularly monitor a patient's drug-drug interactions and medication compliance.

[0067] FIG. 3 shows a simple example of how an SNP within an enhancer in a network can disrupt the contact between the enhancer and one of its target gene promoters within a TAD, leading to harmful drug events in patients within the drug response cohort. FIG. 3A shows how measurements can be obtained in three dimensions from a chromatin pharmacogenomic interactome using various experimental methods and the data analyzed as a two-dimensional plot of enhancer-gene promoter interactions. FIG. 3B shows how an SNP can disrupt the chromatin loop between the enhancer and one of two gene promoters regulated within the TAD. This disruption results in the loss of the pharmacogenomic connection between the enhancer and gene promoter 1, leading to dysregulation of gene 1 and harmful events in this patient and their cohort in response to the administration of the specific drug of interest.

[0068] Figure 4 shows a first of several embodiments for matching reference data to patient input for clinical decision support. In this example, a reference set of a pharmacogenomic drug subnetwork for a particular drug is combined with sparse result biological sample input from a patient, and a combined co-training method can be used to derive a drug effectiveness score for a decision.

[0069] For example, as shown in FIG. 5, the drug and dosage determination server 102 compares the effectiveness and adverse event subnetworks of a patient for a target psychotropic drug with the reference effectiveness and adverse event subnetworks of the target psychotropic drug. 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 effectiveness subnetwork. The subnetwork 510 of patient X is different from the adverse event subnetworks (reference subnetworks 1 and 2 (reference numbers 502, 504)) and is similar to the effectiveness subnetwork (reference subnetwork 3 (reference number 506)). Therefore, the drug and dosage determination server 102 determines that the target psychotropic drug should be administered to patient 520. The subnetwork 512 of patient Y is different from one of the adverse event subnetworks (reference subnetwork 1 (reference number 502)) and is similar to the other adverse event subnetworks and the effectiveness subnetwork (reference subnetworks 2 and 3 (reference numbers 504, 506)). Due to the adverse event in subnetwork 2 (reference number 504), the drug and dosage determination server 102 determines that the target psychotropic drug should be administered to the patient, but at a reduced dosage 522. The subnetwork 514 of patient Z is similar to both of the adverse event subnetworks (reference subnetworks 1 and 2 (reference numbers 502, 504)) and is different from the effectiveness subnetwork (reference subnetwork 3 (reference number 506)), so the drug and dosage determination server 102 determines not to administer the target psychotropic drug to patient 524.

[0070] Figure 5 shows an example of a method of collating a reference set of drugs for the signature of efficacy versus adverse events of antidepressants obtained from a gene set optimizer with that of a biological sample of an input patient using bioinformatics analysis. In Figure 5, based on the match of the efficacy of the antidepressant from sub-network 2 of the reference pharmacogenomics network and the decrease in the match to the adverse event sub-network 3 for the efficacy of the antidepressant from the pharmacogenomics network that an individual would experience, ketamine can be administered to patient X. However, in patient Z, since this individual has a part of the disease risk SNPs from GWAS found in the sub-network of the pharmacogenomics network, it is necessary to adjust the ketamine dosage for patient Z.

[0071] Figure 6 shows how two different methods are derived based on post hoc bioinformatics analysis or annotation of disease risk SNPs from GWAS, and a method of determining that a patient should not take ketamine based on the presence of disease risk SNPs associated with the adverse event sub-network and the efficacy sub-network of ketamine in the pharmacogenomics drug network of ketamine. These simple and preliminary methods can be used to first screen patients regarding potential negative outcomes of ketamine administration.

[0072] Biological samples 2002 and 2004 are collected from patient A and patient B. The biological sample 2002 of patient A is analyzed to perform targeting of pharmacodynamic SNPs as an input to profiling of the pharmacogenomics network and sub-networks, and to determine the efficacy and adverse event sub-networks of patient A (block 2006). The biological sample 2004 of patient B is analyzed to identify pharmacokinetic SNPs related to ketamine response (block 2008). Next, the efficacy and adverse event sub-networks of patient A are compared with the reference pharmacogenomics network of ketamine and the reference efficacy and adverse event sub-networks (block 2010). The SNPs of patient B are compared with the SNPs included in the reference pharmacogenomics network and the reference efficacy and adverse event sub-networks of ketamine (block 2012). In FIG. 6, before administering ketamine, based on the coincidence of the ketamine sub-network mediating adverse events and the efficacy of the antidepressant obtained from the pharmacogenomics network that an individual would experience, and the decrease in the coincidence of the antidepressant efficacy sub-network of the reference pharmacogenomics network, it is necessary to adjust the dose of patient A. (block 2014). Also, in patient B, since this individual has many disease risk SNPs from GWAS found in the sub-networks of the pharmacogenomics network, it is necessary to adjust the dose of ketamine administered to patient B (block 2016).

[0073] FIG. 7 shows an inclusive strategy for making the first "go or no-go" decision on whether to administer ketamine to a particular patient and how other clinical data from a particular patient can be finely tuned to make an information-based clinical decision using a library spanning human drug response cohort phenotypes stored in a reference database.

[0074] Figure 8 shows a flowchart that uses a well-known pattern matching algorithm from deep learning in computer vision to match the effectiveness and adverse events of a patient's drug with those of a reference drug's pharmacogenomics network using a similarity score. Figure 8 also shows a method of discovering new similar drugs using this strategy.

[0075] Figure 9A shows an overview of the integrated multi-scale data analysis used in the system. Figure 9B shows a flowchart representing an exemplary method for generating a reconstructed drug pharmacogenomics network and a corresponding sub-network of the drug of interest (such as a human pharmacogenomics SNP input filter, a drug pharmacogenomics network reconstruction engine, and an iterative gene set optimization engine).

[0076] As described above, examples of biological sample measurements include the targeting of pharmacodynamic SNPs as input to pharmacogenomics network and sub-network profiling. This determines both effectiveness and adverse events when analyzed using a pharmacogenomics genomic classifier and a pharmacodynamic sub-network profiling system. Next, the patient's sub-network of the specific drug of interest is compared with the reference pharmacogenomics network of the drug of interest and its constituent sub-networks. Figure 9B shows a method 300 for identifying a reference pharmacogenomics network and constituent sub-networks of a specific drug of interest, such as ketamine. In some embodiments, the drug and dosage determination server 102 executes method 300 to identify the pharmacogenomics network and constituent sub-networks of the specific drug of interest and stores the pharmacogenomics network and constituent sub-networks in the reference pharmacogenomics network database 154. In other embodiments, method 300 is executed by another computing device, and the output of this method is provided to the drug and dosage determination server 102 and stored in the reference pharmacogenomics network database 154.

[0077] SNP Selection In any case, in block 302, the SNPs are obtained from human clinical studies that have shown a significant association with response to the drug of interest and adverse events. Since the location of the SNPs associated with the trait under study is most often inaccurately assigned to the nearest gene or nearest candidate gene in the published literature and the linear sequence of the reference human genome assembly for each GWAS, accurate localization and annotation techniques using substitution are used to determine the actual location of the reported SNPs.

[0078] The new research has several important implications for the identification of the pharmacogenomics network. First, the target mechanism of a new drug can be identified by collecting pharmacogenomics network outputs in the training set using computer vision-based TAD matching using deep learning (machine learning) and validation using correspondence to a known drug-induced genomic-wide TAD matrix. Second, clustering of the target mechanisms of new drugs in previously defined but incompletely informed biological pathways will increase the likelihood of success. Third, the insights obtained using 3D genome architecture to determine drug targets from pharmacogenomics GWAS lead to next-generation drug candidates and significantly improve the accuracy of pharmacogenomics clinical decision support diagnostics.

[0079] In block 304, pharmacodynamics, pharmacokinetics, and other SNPs are evaluated using a pharmacogenomics informatics pipeline. The pharmacogenomics informatics pipeline uses lead SNPs reported from GWAS and candidate gene studies to find genetically linked candidate SNPs using TAD boundaries instead of measures of linkage disequilibrium. These SNPs are evaluated in two different workflows: an enhancer regulatory SNP workflow and a coding SNP workflow. The enhancer regulatory SNP workflow evaluates candidate SNPs in disease-related tissues for transcription factor binding site disruption using DNA methylation, transcription factor binding, histone marks, DNase I hypersensitivity, chromatin state, quantitative trait loci (QTL), and tissue-specific omics datasets. The coding SNP workflow finds common non-synonymous coding SNPs within the pool of candidate SNPs and examines histone modifications to exclude enhancer SNPs that contain exons. Both sets of SNPs are mapped to their TADs and host genes and screened for expression in relevant tissues. Next, the final output SNPs are evaluated using an open-source machine learning algorithm to determine whether the SNPs are causal (block 306), and the causal variants are retained for further analysis in the workflow (block 308). Exon SNPs are also evaluated as splice donors or splice acceptors using the Altrans algorithm. If exon SNPs are found to be involved in alternative splicing, they are stored as such.

[0080] Casual use of enhancer SNPs for questions In block 310, enhancer SNPs are used as probes to determine target genes within the same TAD where the enhancer is located, and Hi-C chromosome conformation capture and ChIA-PET datasets generated from the cell types and tissues on which the drug of interest acts are used to determine pharmacogenomic interactions with other TADs (block 314). Genes containing other functional elements such as long non-coding RNAs herein are located within the same TAD that is the target of an enhancer that significantly alters drug response in the human population, and if the TAD has strong boundaries as predicted by a significant association between the amount of bound CTCF and the super enhancer, it is selected for the drug pharmacogenomic network (block 312). Next, the TADs that constitute the top three statistically significant pharmacogenomic contacts of the first set of pharmacogenomic TADs within the same cells and / or tissue types on which the drug of interest acts are evaluated, and if they are controlled by the same cell- and / or tissue-specific enhancer on which the drug of interest acts, the genes within these "trans TADs" are selected (block 316).

[0081] In block 318, a combined set of genes is evaluated for connectivity, with the combined set of genes selected from the first set of TADs having pharmacogenomic SNPs and genes selected from "trans TADs" that contain genes that are co-regulated with the first set of TAD genes. For example, third-party software such as Ingenuity Pathway Analysis™ can be utilized to examine the connectivity of the combined set of genes. Using Fisher's right-sided exact probability test, if there are significant connections within the combined set of genes based on published literature, the genes are placed within a preliminary set of genes that constitute the pharmacogenomic network of the drug of interest. Genes that do not form a connected network are discarded as non-candidate genes for the pharmacogenomic network (block 320).

[0082] Revisions to the knowledge base of a preliminary pharmacogenomics network of drug-specific interconnected genes Next, in block 322, manual, semi-automated or automated curation, or combinations thereof, are performed for each gene within this gene set that includes the preliminary drug pharmacogenomics network, and if the function is determined to be particularly affected by the drug of interest in the acting cell and / or tissue type, the function should remove genes not related to the drug of interest in the acting cell and / or tissue type or add other genes that are not part of this preliminary set of the drug pharmacogenomics network that should be added to the set. The questioning step includes the definition of the function of individual genes, the phenotypic consequences of disorders due to gene mutations, and the human cells and tissues in which the gene is expressed, and checks whether it can be a candidate for membership in the pharmacogenomics network of the specific drug of interest.

[0083] In one embodiment, these determinations can be made using manual, semi-automated, or automated strategies by combining the curation of each gene, its mutation profile, and the localization of expression within human tissues. These are realized by various web-based search tools such as gene definitions, genome browser annotations, GWAS catalogs, and other bioinformatics resources. For example, an application programming interface (API) may include executable files written in R, Python, PERL, or other programming languages to facilitate data access, data cleansing, and data analysis. This embodiment is an enhanced model of manual curation, but it may be time-limited when there are many genes within a gene set of a pharmacogenomics network or a gene subset of a subnetwork, especially when the functional genomic elements may include functional RNAs such as regulatory RNAs or long non-coding RNAs, or when the function of the gene is not well understood. Since these databases are the most comprehensive, the enumeration and analysis of the mutation status of a given gene (±10Kb upstream and downstream) are the easiest among the three question steps to perform. There are other resources for analyzing the tissue distribution of gene expression patterns. When comparing these patterns with the sites where a particular drug of interest acts, the results of imaging modalities can be analyzed, including radiological studies, optical microscopy analysis in pathology, and more advanced methods. In some embodiments, this analysis is performed using machine learning techniques such as neural networks.

[0084] In another embodiment, a Bayesian probability classifier can be used based on machine learning or using Bayesian probability calculations. An automated method can be used to reduce the complexity of data analyzed from heterogeneous data sources, where gene functional knowledge profiles, their mutation status, and their tissue expression mapping in these heterogeneous data sources are inputs to a learning machine that is trained in many such instances and individually tested in a separate set of instances to determine accuracy. The predictive functions selected by the trained neural network can be implemented with a support vector machine classifier to construct a gene function and mutation prediction model. The validity of the statistical fit to the drug pharmacogenomics network is determined by subsequent machine states.

[0085] In some scenarios, machine learning is affected by overfitting and outputs false positives or false negatives. In another embodiment, semi-automated naive Bayes classification is performed using machine learning in parallel, which can increase the accuracy of the final output.

[0086] Knowledge-based curation can be performed in the following steps. First, gene definitions are examined from multiple databases to understand whether the drug of interest is specifically affected, rather than comprehensively, by the drug. Further, published literature containing text word strings including functions related to the drug of interest, in addition to gene names or precursor gene names or equivalent protein names, is evaluated according to a thorough Internet search using, for example, Google Scholar (trademark) and / or PubMed. These may include binding affinity studies that reproducibly discovered molecules that bind with an affinity within 10-fold of the affinity of the drug of interest for binding to the same pharmacodynamic target. Second, the drug and dosing server 102 examines each gene for all mutations including SNPs, variable tandem repeats, duplications, and all other known mutational changes, and extends linearly ±10 kb from the transcription start site(s) and stop codon(s) of the gene examined in a genome browser such as the UCSC Genome Browser or the Ensembl Genome Browser. If any of these mutations are found in any of the sources such as published literature or unpublished clinical trial data and they are involved in the action of the drug of interest such as efficacy, adverse events, first-pass metabolism, etc., they are added to the preliminary set of genes that make up the pharmacogenomics network (block 324). Third, particularly in the case of complex tissues such as the brain, skin, cardiovascular system, etc., the drug and dosing server 102 qualitatively performs a concordance mapping to compare the expression of all genes within this final set with the location (if known) where the drug of interest exerts its action. Genes whose expression does not match the pharmacodynamic substrate of the drug of interest are discarded (block 324). Finally, a third-party software such as Ingenuity Pathway Analysis (trademark) is used to examine the connections of this gene set (block 326). Using Fisher's right-sided exact probability test, if the drug and dosing server 102 determines that there are significant interconnectivities based on published literature, they are placed in the preliminary set of genes that make up the pharmacogenomics network of the drug of interest.Genes that do not form a connected network are discarded as non-candidate genes for the drug pharmacogenomics network (block 328).

[0087] Optimization of the Iterative Gene Set As shown in block 330 and more particularly in FIG. 10, the optimization of the iterative gene set is performed on the set of candidate genes identified in the pharmacogenomic network of a particular drug of interest. An exemplary method 400 for iterative gene set optimization for decomposing a drug pharmacogenomic network into subnetworks is shown in the flowchart of FIG. 10. The optimization of the iterative gene set can be performed to identify subnetworks of a drug pharmacogenomic network. More specifically, the optimization of the iterative gene set includes converting all input molecular terms, using an API, for example, from Human Gene Nomenclature Committee (HGNC) names to gene or long non-coding RNA names (block 402). The optimization of the iterative gene set differs from gene set enrichment methods in that it not only combines various statistical techniques but also does not hierarchically rank genes like threshold-dependent methods. The optimization of the iterative gene set does not rely on the comparison of experimental results such as whole distribution tests. Instead, the optimization of the iterative gene set uses the Jaccard distance to group genes or long non-coding RNAs from a pharmacogenomics pharmacogenomic network (block 404) and first measures the similarity between two genes or long non-coding RNAs based on the dissimilarity of terms selected by the user. Here, the Jaccard distance is expressed as the ratio of the size of the symmetric difference gene A Δ gene B = A ∩ B - A ∪ B to the union (block 406). This can be extended to clusters of different related gene names. Next, the drug and dosage determination server 102 automatically sorts these sets or a user-defined number of clusters into subsets of functionally related gene clustering subsets using a minimum entropy sort algorithm such as the COOLCAT algorithm (block 408). Following the optimization of the gene subset using entropy minimization, manual curation can be used to assign efficacy, adverse events, or functional mechanism subnetworks based on known attributes of the mechanism of action of the drug under consideration (blocks 410, 412).

[0088] Post-verification using third-party bioinformatics tools For the scientific validation of the decomposition of a pharmacogenomics network into mechanical subnetworks based on the optimization of a functional gene subset, the subnetworks of each pharmacogenomics network are post-evaluated (block 332) for top Gene Ontology terms (molecular function and biological process) when determined using, for example, other proprietary or open-source pathway analysis software, for top canonical pathways, for disease risk gene variant analysis when determined using, for example, other proprietary or open-source pathway analysis software, and for the determination of upstream heterologous regulators using different bioinformatics resources. Further, the GWAS catalogs of the European Bioinformatics Institute, National Human Genome Research Institute, and National Institutes of Health can be searched to find the relevance of significant SNP characteristics of each gene in the gene set of each subnetwork. By providing examples of SNPs from statistically significant GWASs, additional evidence may be provided that the mutation disorders of the genes included in each subnetwork provide insights into the normal and disorder-free function of the subnetwork.

[0089] In some embodiments, after post-verification is performed as shown in FIG. 11, the resulting pharmacogenomics network and constituent subnetworks of a particular drug of interest are stored in database 154, as shown, for example, in FIG. 1A.

[0090] For example, Hi-C chromosome conformation capture data was used in samples obtained from the A735 astrocyte cell line, H1 neuron cell line, SK-N-SH cell line, and postmortem human brains to map SNPs that cause the discretization of the response to ketamine in the human population, their target genes within TADs, and the pharmacogenomic contacts of these TADs.

[0091] Figure 12 shows a comparative example of the results of eight different algorithms testing the predicted causal relationship of GWAS SNP rs12967143-G, an intragenic enhancer in the TCF4 gene, which is a member of the pharmacogenomic efficacy subnetwork of ketamine, with respect to other GWAS SNPs as described using numerical output from the machine learning algorithms used in the analysis (*p≦0.05; **p≦0.01; ANOVA).

[0092] Overlapping trans-TADs are mapped between different data sources using H-GREEN, a user-adjustable binning software package. The top three genome-wide trans-TAD pharmacogenomic contacts can be selected for each original causal SNP TAD locus. Using the prior knowledge of ketamine as an anesthetic and analgesic, and in recent research and clinical trials of ketamine as an antidepressant and other glutamate receptor modulators, the methods described herein can be used to score the top trans-TAD contacts, and for each causal SNP, the top three can be selected for inclusion in the pharmacogenomic network. The recent availability of databases of validated enhancers and their targets can be used for both the original and target TADs of this workflow to reconstruct the pharmacogenomic network of ketamine.

[0093] Intra-TAD and trans-TAD gene sets may function as seeds to initiate pathway analysis. Filters and thresholds can be applied to exclude genes expressed in cell types, neurons, and astrocytes, as well as in brain regions where ketamine exerts its mechanism of action. These include not only PD genes but also PK genes. PK genes have recently been shown to be highly expressed in relevant human brain regions where ketamine acts. In the case of the CYP2B6 gene, this psychotropic drug induces much higher levels of expression in the brain than in the liver, gastrointestinal tract, or kidney.

[0094] Following the output of the automated pathway analysis, the validity of the drug pharmacogenomics network gene set is evaluated, and genes not selected by the pathway analysis program may be added to the pathway. From previous studies of binding affinity using methods of molecular pharmacology, genes whose products show 10-fold affinity for NMDAR of racemic R,S-ketamine or enantiomers and indicate molecular interconnectivity are added. Other expression studies and research on the metabolism of ketamine may provide additional genes to be added to the pharmacogenomics network of ketamine.

[0095] The ketamine pharmacogenomics network was analyzed into three subnetworks by optimizing gene sets, two of which are significantly different subsets of genes and regulatory RNAs using iterative analysis. The three subnetworks include (1) antidepressant efficacy and neuroplasticity, (2) glutamate receptor signaling, chromatin remodeling, and adverse events, (3) drug-related pharmacokinetics and hormonal regulation. The (2) glutamate receptor signaling, chromatin remodeling, and adverse events of the second subnetwork may include two different subnetworks, a chromatin remodeling subnetwork and a pharmacodynamic adverse event subnetwork of the drug. Four additional analyses are performed to understand and validate the pharmacogenomics network and its mechanical subnetworks. First, questions are asked about the presence of enhancer SNPs related to the associated traits in GWAS for the genes of the pharmacogenomics network and each subnetwork. Second, pathway enrichment, including biological processes and molecular functions, is performed using Gene Ontology standards to determine the most important top pathways of these gene sets. Third, disease gene risk variant analysis is performed. This analyzes the significance of the contribution of mutations in these sets in humans for each subset and superset of genes to appropriately assign both to the parent pathway and its constituent subnetworks, and assigns top diseases to the superset and subnetwork sets (the most important being Fisher's exact test). Fourth, the top (the most important being Fisher's exact test) xenobiotic drugs that regulate the superset of genes that make up the pharmacogenomics network are determined. In the last case, the pharmacogenomics network set of genes needs to be regulated by drugs that mediate the mechanism of the pharmacogenomics network, but in some subnetworks, depending on the mechanistic attributes of that network, drugs that are relevant to the subnetwork of the pharmacogenomics network mechanism may be most significantly related.

[0096] Figure 13 shows the top Gene Ontology terms of two significantly different ketamine pharmacogenomic subnetworks in the human brain. More specifically, Figure 13A shows the subnetwork that mediates efficacy and neuroplasticity. Figure 13B shows the ketamine subnetwork that mediates glutamate receptor signaling and adverse events in the human CNS.

[0097] Figure 14A shows a graphical representation of the pharmacogenomics subnetworks of ketamine in the human brain that mediate efficacy and neuroplasticity. Figure 14B shows a graphical representation of the pharmacogenomics subnetworks of ketamine in the human brain that mediate glutamate receptor signaling and adverse events. More specifically, as shown in Figures 14A and 15, the genes and regulatory RNAs located within the efficacy and neuroplasticity subnetworks of ketamine include one or more of the following: Activity-regulated cytoskeleton-associated protein (ARC) gene, Achaete-Scute family bHLH transcription factor 1 (ASCL1) gene, Brain-derived neurotrophic factor (BDNF) gene, BDNF antisense RNA (BDNF-AS) gene, Calcium / calmodulin-dependent protein kinase II alpha (CAMK2A) gene, Cyclin-dependent kinase inhibitor 1A (CDKN1A) gene, cAMP response element modulator (CREM) gene, Cut-like homeobox 2 (CUX2) gene, DCC netrin 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, 5-Hydroxytryptamine receptor 1B (HTR1B) gene, 5-Hydroxytryptamine receptor 2A (HTR2A) gene, Kruppel-like factor 6 (KLF6) gene, Lin-7 homolog C, long non-coding RNA of the crumbs cell polarity complex component (LIN7C), long non-coding RNA of LOC105379109, Myocyte enhancer factor 2D (MEF2D) gene, Myosin VI (MYO6) gene, Myelin transcription factor 1-like (MYT1L) gene, Neurogenic differentiation 1 (NEUROD1) gene, Neurogenic differentiation 2 (NEUROD2) gene, Nescient helix-loop-helix 2 (NHLH2) gene, Neuromedin B (NMB) gene, NMDA receptor synaptic nuclear signaling and neuron 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-releasing factor 2 (RASGRF2) gene, Ras homolog family member A (RHOA) gene, roundabout guidance receptor 2 (ROBO2) gene, long non-coding RNA of RP11_360A181, 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 guidance ligand 1 (SLIT1) gene, slit guidance 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.,

[0098] Also, as shown in FIGS. 14B and 16, genes and regulatory RNAs located within the ketamine-glytamate receptor signaling and adverse event subnetworks 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+ transport subunit alpha 1 (ATP1A1) gene, BLOC-1 related complex subunit 7 (BORCS7) gene, bromodomain containing 4 (BRD4) gene, calcium voltage-gated channel subunit alpha 1C (CACNA1C) gene, calcium voltage-gated channel auxiliary subunit beta 1 (CACNB1) gene, calcium voltage-gated channel auxiliary subunit beta 2 (CACNB2) gene, calcium voltage-gated channel auxiliary subunit gamma 2 (CACNG2) gene, cholinergic receptor muscarinic 2 (CHRM2) gene, cholinergic receptor nicotinic alpha 3 subunit (CHRNA3) gene, cholinergic receptor nicotinic alpha 5 subunit (CHRNA5) gene, cholinergic receptor nicotinic alpha 7 subunit (CHRNA7) gene, cannabinoid receptor 1 (CNR1) gene, discs large homolog 3 (DLG3) gene, discs 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, glutamic acid decarboxylase 1 (GAD1) gene, glycine receptor alpha 1 (GLRA1) gene, glycine receptor alpha 2 (GLRA2) gene, glycine receptor beta (GLRB) gene, glutamate ion channel-containing receptor AMPA type subunit 1 (GRIA1) gene, glutamate ion channel-containing receptor AMPA type subunit 2 (GRIA2) gene, glutamate ion channel-containing receptor AMPA type subunit 2 (GRIA4) gene,Glutamate ion channel - associated NMDA receptor subunit 1 (GRIN1) gene, glutamate ion channel - associated NMDA receptor subunit 2A (GRIN2A) gene, glutamate ion channel - associated NMDA receptor subunit 2B (GRIN2B) gene, glutamate ion channel - associated NMDA receptor subunit 2C (GRIN2C) gene, glutamate ion channel - associated NMDA receptor subunit 2D (GRIN2D) gene, glutamate ion channel - associated NMDA receptor subunit 3A (GRIN3A) gene, glutamate ion channel - associated NMDA receptor 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, nitric oxide synthase 1 (NOS1) gene, nitric oxide synthase 2 (NOS2) gene, nitric oxide synthase 3 (NOS3) gene, NAD(P)H quinone dehydrogenase 1 (NQO1) gene, opioid receptor kappa 1 (OPRK1) gene, opioid receptor mu 1 (OPRM1) gene, roundabout guidance receptor 2 (ROBO2) gene, SET domain bifurcated 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 activator suppressor (TASOR) gene, TOG array regulator of axonemal microtubules 2 (TOGORAM2) gene, tripartite motif - containing 28 (TRIM28) gene, or zinc finger protein 274 (ZNF274) gene.,

[0099] As shown in Figure 17, the genes and regulatory RNAs in the pharmacokinetic enzyme and hormone subnetworks 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, discs large homolog 4 (DLG4), eukaryotic elongation factor 2 kinase (EEF2K) gene, estrogen receptor 1 (ESR1) gene, glutamate ion channel - associated receptor AMPA type subunit 1 (GRIA1) gene, glutamate ion channel - associated receptor AMPA type subunit 4 (GRIA4) gene, glutamate ion channel - associated receptor NMDA type subunit 1 (GRIN1) gene, glutamate ion channel - associated receptor NMDA type subunit 2B (GRIN2B) gene, myosin VI (MYO6) gene, roundabout guidance receptor 2 (ROBO2) gene, SH3 and multiple ankyrin repeat domains 2 (SHANK2) gene, or transcriptional elongation regulator 1 (TCERG1) gene.

[0100] Automated iterative gene set optimization for a subnetwork of the pharmacogenomics network of psychotropic drugs may limit the user in order to investigate other functions of the pharmacogenomics network. As previously described with reference to Figure 10, the ketamine pharmacogenomics network is repeatedly decomposed until certain specific genes are no longer significantly associated with the subnetwork or are associated with some of the subnetworks decomposed from the drug pharmacogenomics network. The gene ESR1 encodes the nuclear hormone receptor for estrogen and regulates the expression of some genes in the ketamine pharmacogenomics network. It is well known that estrogen greatly induces the CYP2B6 gene in both the human brain and other locations.

[0101] The learning architecture for training the pattern matching subnetwork includes pre-training of a reference set (reference number 710). More specifically, in block 704, the drug and dosage determination server 102 develops a patient's pattern matching subnetwork derived from the patient input biological sample, and co-develops different pre-trained pattern metrics (block 712) including the characteristics of the efficacy and adverse event subnetworks, and the co-functional representation metric. To determine the similarity with the reference set (blocks 706, 708), two different pairs of reference patient metrics include an accurate measure of similarity, as well as output similarity scores for each of the efficacy and adverse event subnetworks (blocks 714, 716). In block 702, the biological sample obtained from the patient (buccal swab, saliva, blood, or urine sample) undergoes genotyping of target enhancer SNPs and a combination of chromosome conformation capture and RNA-seq. Next, in block 704, the drug and dosage determination server 102 performs the analysis necessary to construct an input patient-specific map of the efficacy and adverse event subnetworks for a particular 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 a new patient is input, the reference set of pre-trained drug-specific efficacy and adverse event subnetworks for pattern matching is re-optimized for subsequent patients, generating a more accurate measure of pharmacogenomic variation between humans with enhanced clinical utility. This matching task assumes that the patches pass through the same functional encoding before calculating and outputting the similarity score, significantly improving efficiency while reducing computational requirements.

[0102] Thus, each set of inputs (the reference set (reference number 710) and the patient set (reference number 720)) is constructed in different ways by extracting feature sets and inferring sparse data using probabilistic computing based on Bayesian distributions, thereby improving the accuracy of the reference map and the patient map. The trained functional network is based on a "sham" network approach, but there is a constraint that the two sets need to share the same parameters. Once completed, the patient's drug-induced trained pattern network is combined with the network obtained from the reference database, pairing the effective feature set pairs and the adverse event feature set pairs. These provide the basis for the development of trained effectiveness metrics and trained adverse event metrics, attempting to match all features from the patient with the reference set of the drug of interest. These pairwise matching scores result in separate effectiveness and adverse event similarity scores between the reference and the patient.

[0103] In a further embodiment, a reference pattern matching set can be developed for each patient that can be used to create a patient-specific database of such reference maps, and additional biological samples can be obtained longitudinally from the patient and updated periodically when obtained over time at the clinical site or at an outpatient pharmacy.

[0104] In any case, the drug and dosage determination server 102 can then use the similarity scores of the effectiveness and adverse event subnetworks of the target antipsychotic drug generated via method 700 to determine whether to administer the target antipsychotic drug to the patient. For example, the similarity score of the effectiveness subnetwork can be compared to a threshold similarity score. If the similarity score of the effectiveness subnetwork exceeds the threshold similarity score, the drug and dosage determination server 102 can determine that the target antipsychotic drug should be administered to the patient. The similarity score of the adverse event subnetwork can also be compared to the threshold similarity score. If the similarity score of the adverse event subnetwork is below the threshold similarity score, the drug and dosage determination server 102 can determine that the target antipsychotic drug should be administered to the patient. In another embodiment, the similarity scores of the efficacy subnetworks and the adverse event subnetworks can be combined or aggregated in any suitable way. For example, the similarity score of the adverse event subnetworks can be subtracted from the similarity score of the efficacy subnetworks. If the combined score exceeds a threshold similarity score, the drug and dosage determination server 102 can determine that the target antidepressant should be administered to the patient.

[0105] Otherwise, the drug and dosage determination server 102 can determine not to administer the target antidepressant and provide recommendations to the client devices 106 - 116 of the medical professionals to administer another drug to treat the patient's depression.

[0106] In some embodiments, the drug and dosage determination server 102 can determine whether to administer the target antidepressant to a patient by generating a machine learning model based on training data from the drug responses of patients previously prescribed the target antidepressant. The machine learning model can be generated based on some characteristics of previous patients. These characteristics include: the drug-induced subnetworks of previous patients, PD and PK SNPs that stratify patients by drug response, neuroimaging data, direct TAD-specific measurements including differential gene expression, and previous patient clinical variables such as age, weight, biological sex, body mass index, ethnicity, family history, patient history of substance abuse, diagnostic codes, hospitalization history, drug-drug interactions, psychiatric history, whether the patient smokes or uses nicotine, as well as the Hamilton Depression Scale (HAM-D) score. The drug and dosage determination server 102 can obtain the same characteristics regarding the current patient, including molecular and clinical data, apply the characteristics of the current patient to the generated machine learning model, and determine whether to administer the target antidepressant to the patient.

[0107] In addition to determining whether to administer a psychotropic drug to a patient, the Drug and Dosage Determination Server 102 determines a dosage for administration to the patient. FIGS. 18A and 18B show an analysis performed on an independent cohort data set to develop a regression model for ketamine dosage estimation. Published literature and other sources provide both pharmacokinetic SNP data and clinical values useful for determining dosage based on CYP2B6 SNPs and clinical data. As can be seen from the differential analysis, the greatest contribution to ketamine dosage is the presence or absence of the variant rs3745274 in poor metabolizers, exon skipping, and loss of high-inducibility CYP2B6 first-pass metabolism of ketamine. The range of quantitative intranasal ketamine dosages in this model-derived cohort was 0.4 - 0.8 mg / kg. The Drug and Dosage Determination Server 102 can generate a model based on the published clinical trial results of ketamine obtained from clinicaltrials.org. A general example of the total value based on regression variables that can be included in the ketamine administration algorithm is shown in the following equation. Dosage = exp[2.00×rs3745274 + 0.25×Female (biological sex) + 0.22×rs3786547 + 0.22×Clopidogrel + 0.19×rs11083595 + 0.11×BSA + 0.20×Smoking + 0.17xHistory of suicide attempt + 0.07×Age in 10-year units + 0.09×Non-Hispanic white ethnicity + 020×Ticlopidine + 0.15×Previous psychiatric hospitalization]

[0108] More specifically, the Drug and Dosage Determination Server 102 can generate a dosing algorithm based on published literature and have predetermined constants for each of several patient characteristics such as biological characteristics, demographic characteristics, and clinical characteristics, as in the above equation. In other embodiments, the Drug and Dosage Determination Server 102 can generate a dosing algorithm using machine learning techniques. For example, the Drug and Dosage Determination Server 102 can collect dosing information regarding patients previously prescribed ketamine as training data. The dosing information may include the dose prescribed for each patient, along with an indication of whether the patient's dose was adjusted during treatment and / or whether the patient experienced an adverse event. Next, the drug and dose determination server 102 can analyze the training data to generate a machine learning model (e.g., neural network, decision tree, hyperplane, regression model, etc.) to determine the dose for a new patient based on the new patient's biological, demographic, and clinical characteristics. The characteristics of the patient utilized in the dosing algorithm may include biological data such as SNPs that have been reported to stratify response to human ketamine. The characteristics of the patient may also include the patient's demographic data such as the patient's gender, height and weight, age, and ethnicity. Further, the characteristics of the patient may include clinical data such as family history, drug-drug interactions, history of mental illness, whether the patient smokes or uses nicotine, and the Hamilton Depression Scale (HAM-D) score.

[0109] In any case, the drug and dose determination server 102 applies the characteristics of the patient to the dosing algorithm to determine the dose of ketamine to administer to the patient. Next, the drug and dose determination server 102 provides the recommended dose to the client devices 106-116 of the medical professionals.

[0110] In the regression analysis of patient-specific dose optimization, almost half of the pharmacogenomic and clinical variables required for accuracy cannot be explained, but several published studies have reported variables to include in the algorithm determination of antidepressant selection and dose estimation. As shown in FIG. 13, clinical values obtained from medical records are also important for determining a reduction in the dose of ketamine. These include a body mass index (BMI) over 30, a family history of alcohol use disorder (once), a history of suicide attempts (multiple possible), previous psychiatric hospitalizations, female biological sex (premenopausal), non-Hispanic white ethnicity, and an individual who smokes tobacco. As shown, these values contribute to ketamine administration that may be substantial but do not exclude the use of this agent.

[0111] In addition to comparing the patient subnetwork of the target antidepressant with the target antidepressant reference subnetwork, the drug and dosage determination server 102 analyzes the patient's clinical data and neuroimaging data to determine whether to administer a drug to the patient. For example, the drug and dosage determination server 102 can analyze the patient's HAMD score and / or the patient's symptoms to classify the patient into one of four TRD patient subtypes.

[0112] FIG. 19 shows four TRD patient subtypes determined by transcranial magnetic stimulation (TMS) and neuroimaging of the resting connectivity network in the human brain. In patients with TRD, subtypes are identified and replicated using transcranial magnetic stimulation (TMS) and neuroimaging studies, and the subtypes are discretized by differential activation and suppression of the resting connectivity network in the human brain. TMS has four different anatomical arrangements on the outside of the human head and activates various neuroanatomical structures that are part of the limbic cortical circuit, including the resting connectivity network, the default mode network, the defensive response including the amygdala, the reward circuit located in the basal forebrain including the nucleus accumbens (NA) or the orbitofrontal cortex (OFC) of depression subtype 3, the thalamus, sensory stimulus gating to the cortex via the cortical regions S1 and the insula, and the enhancement of memory in the entire anterior cingulate cortex and the hippocampus, including part of the suppression of the impulsive prefrontal cortex involved in the suppression of the dorsolateral and dorsomedial prefrontal cortex of amygdala activation in the limbic cortex.

[0113] FIG. 19 also shows that the TRD subtypes can be identified using structured values or clinical data obtained from EHRs or other clinical records from the records using natural language processing.

[0114] Figure 20 shows the brain regions that are consistently activated during ketamine antidepressant response and how they are differently mapped to four TRD major depressive disorder subtypes. Brain regions are involved, and these are consistent with both different major depressive disorder subtypes defined by TMS and clinical values and the results of neuroimaging studies examining ketamine-induced activation and suppression.

[0115] Figure 21 shows recommendations regarding switching medications from ketamine for four different TRD subtypes, based on defining the HAMD-17 assessment (Hamilton Depression Scale), neuroimaging meta-analysis, relevant clinical values shown in Figure 19, and available information regarding efficacy, indications and recommendations of currently available antidepressants, and expert guidelines from the American Psychiatric Association.

[0116] Figure 21 shows examples of recommended medications and alternative medication options for each of the four different subtypes of TRD major depressive disorder patients.

[0117] To identify which subtypes of depression 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, patients with depression and treatment-resistant depression (TRD), and healthy controls. Patients with TRD subtype 3 consistently show hyperactive subgenual anterior cingulate cortex (sgACC), dorsolateral and dorsomedial prefrontal (executive) cortex (dlPFC, dmPFC), and hyperactive orbitofrontal cortex (OFC), so these patients are recommended not to receive ketamine drug therapy. This patient cohort does not respond or remit, but rather there is a potential for an exacerbation of psychotropic adverse drug events. Independent analysis of the neuroanatomical localization of all genes found in the ketamine pharmacogenomics network shows that they are all highly expressed in the anterior cingulate gyrus, prefrontal cortex, supplementary motor cortex, orbitofrontal cortex, temporal cortex, amygdala, hippocampus, anterior caudate nucleus and nucleus accumbens (but not in other cortical brain regions), hypothalamus or brainstem. This is the same pattern as that of the 24 functional neuroimaging studies examined that indicate where in the human brain ketamine first exerts its antidepressant effects (Table 1).

Table 1-1

Table 1-2

Table 1-3

Table 1-4

Table 1-5

[0118] In another embodiment, disease risks and pharmacogenomic SNPs that distinguish 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 shows enhancer and super-enhancer SNPs found in the ketamine efficacy subnetwork that can be used to determine the manifestation of mutations significantly associated with an effective response to ketamine. In contrast, Table 2B shows enhancer and super-enhancer SNPs found in the ketamine adverse event subnetwork that can be used to determine the manifestation of mutations significantly associated with adverse CNS events in response to ketamine.

Table 2-1

Table 2-2

Table 2-3

Table 2-4

Table 2-5

Table 2-6

Table 2-7

Table 2-8

Table 3-1

Table 3-2

Table 3-3

Table 3-4

Table 3-5

Table 3-6

Table 3-7

Table 3-8

Table 3-9

Table 3-10

Table 3-11

Table 3-12

Table 3-13

Table 3-14

Table 3-15

Table 3-16

Table 3-17

Table 3-18

Table 3-19

Table 3-20

Table 3-21

Table 3-22

Table 3-23

Table 3-24

Table 3-25

Table 3-26

Table 3-27

Table 3-28

Table 3-29

Table 3-30

Table 3-31

Table 3-32

Table 4-1

Table 4-2

Table 4-3

Table 4-4

Table 5-1

Table 5-2

Table 5-3

Table 5-4

Table 5-5

Table 5-6

Table 5-7

Table 5-8

Table 6-1

Table 6-2

Table 6-3

Table 6-4

Table 6-5

Table 6-6

Table 6-7

Table 6-8

Table 6-9

Table 6-10

Table 6-11

Table 6-12

Table 6-13

Table 6-14

Table 6-15

Table 6-16

Table 6-17

Table 6-18

Table 6-19

Table 6-20

Table 6-21

Table 6-22

Table 6-23

Table 6-24

Table 6-25

Table 6-26

Table 6-27

Table 7-1

Table 7-2

Table 7-3

Table 7-4

Table 7-5

[0119] In another embodiment of the method in the present disclosure, the generalization of this method can be used to identify combinations of FDA-approved drugs that can be used to enhance the treatment of neuropsychiatric disorders through corresponding network or subnetwork mechanisms in biology. FIG. 22 shows an example of how the pharmacogenomic network of valproic acid and the pharmacogenomic network of ketamine function complementarily to support neurogenesis. Valproic acid induces the conversion of neural progenitor cells to restricted neural progenitor cells via the npBAF complex (1, top), and ketamine may act on restricted neural progenitor cells via the human silencing complex (HUSH) to convert progenitor cells into differentiated neurons (1, bottom).

[0120] FIG. 23 shows how the process shown in FIG. 22 works through the progressive deacetylation of the histone 3 lysine 9 (H3K9) moiety induced by the pharmacogenomic network of valproic acid (FIG. 23A), and the acetylation of the H3K9 moiety following the activation of the pharmacogenomic network of ketamine (FIG. 23B).

[0121] FIG. 24 shows how the complementary pharmacogenomic networks of valproic acid and ketamine convert neural progenitor cells into mature differentiated neurons.

[0122] In another embodiment of the present disclosure, these methods can be used for other antidepressants targeting the NMDAR network. For example, other NMDAR partial antagonists such as AVP-786 and GLYX-13 (rapastinel) are undergoing clinical trials as antidepressants. Also, blockers of GLRB of NMDAR are under development as antidepressants such as AV101 and D-cycloserine (seromycin). Selective antagonists of GRIN2B of NMDAR are also under development as antidepressants including EVT103, CP101, and MK-0657. Downstream of this pathway is AMPAR, and some antidepressants are being developed as agonists of GRIA1 and GRIA2 such as ORG265576.

[0123] In another embodiment, these methods can be used to optimize the drug selection of other antidepressants and provide greater power than commercially available pharmacogenomic clinical decision support assays that rely exactly on coating SNPs for classifying patients with respect to drugs. The methods encompassed by the techniques disclosed herein utilize pharmacogenomic epigenomic knowledge, including the compilation of TAD and TAD-TAD pharmacogenomic connections, to enhance insights into CNS drug mechanisms. Furthermore, these methods enable objective monitoring of drug-drug interactions and dosages, and measurement of parent drugs and their metabolites from serum, similar to the case of S-ketamine and its active metabolite nor-ketamine, and additional insights into the subtypes of metabolizers can be obtained.

[0124] Throughout this specification, multiple instances can implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed simultaneously, and the operations need not be performed in the illustrated order. Structures and functions presented as separate components within an exemplary configuration may be implemented as a combined structure or component. Similarly, structures and functions presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements are included within the scope of the subject matter of this specification.

[0125] Furthermore, specific embodiments are described herein as including logic or a number of routines, subroutines, applications, or instructions. These can constitute either software (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware. In hardware, routines and the like are tangible units capable of performing specific operations and can be configured or arranged in a specific manner. In an exemplary embodiment, one or more computer systems (e.g., a stand-alone, client, or server computer system), or one or more hardware modules of a computer system (e.g., a processor or group of processors), can be configured as a hardware module that operates to perform the specific operations described herein by software (e.g., an application or a part of an application).

[0126] In various embodiments, a hardware module can be implemented mechanically or electronically. For example, a hardware module can include dedicated circuitry or logic (e.g., a special-purpose processor such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC)) that is permanently configured to perform certain operations. A hardware module can also include programmable logic or circuitry (e.g., as implemented in a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that whether to implement a hardware module mechanically, with dedicated and permanently configured circuitry, or with temporarily configured circuitry (e.g., configured by software) can be determined considering cost and time.

[0127] Accordingly, it is to be understood that the term "hardware module" encompasses a tangible entity that is physically constructed, permanently configured (e.g., physically incorporated), or temporarily configured (e.g., programmed) to operate in a particular manner described herein or to perform a particular operation. When considering embodiments in which a hardware module is temporarily configured (e.g., programmed), each hardware module need not be configured or instantiated at all times. For example, if a hardware module includes a general-purpose processor configured using software, the general-purpose processor can be configured as different hardware modules at different times. Thus, software can configure a processor, for example, to constitute a particular hardware module at one time and a different hardware module at another time.

[0128] A hardware module can provide information to other hardware modules and receive information from other hardware modules. Thus, the hardware modules described herein can be understood as being communicatively coupled. When such hardware modules are present simultaneously, communication can be achieved via signal transmission (through appropriate circuits and buses) that connects the hardware modules. In embodiments where multiple hardware modules are configured or instantiated at different times, communication between such hardware modules can be achieved, for example, through storage and retrieval of information in a memory structure accessible by the multiple hardware modules. For example, a hardware module can execute an operation and store the output of that operation in a memory device to which the hardware module is communicatively coupled. Subsequently, a further hardware module can later access the memory device to retrieve and process the stored output. A hardware module can also initiate communication with an input or output device and operate on a resource (such as collection of information).

[0129] The various operations of the exemplary methods described herein can be performed, at least in part, by one or more processors that are temporarily (e.g., by software) configured to perform the relevant operations or permanently configured to perform the relevant operations. Whether temporarily configured or permanently configured, such processors can configure processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein can, in some exemplary embodiments, include processor-implemented modules.

[0130] Similarly, the methods or routines described herein can be at least partially processor-implemented. For example, at least some of the operations of a method can be performed by one or more processors or processor-implemented hardware modules. Certain performance of the operations can exist not only within a single machine, but can be distributed among one or more processors deployed across several machines. In some embodiments, one or more processors can be present in a single location (e.g., within a home environment, within a workplace environment, or as a server farm), but in other embodiments, the processors can be distributed across a number of locations.

[0131] Reliable performance of the operations can exist not only within a single machine, but can be distributed among one or more processors deployed across several machines. In some exemplary embodiments, one or more processors or processor-implemented modules can be present in a single location (e.g., within a home environment, within a workplace environment, or as a server farm). In other exemplary embodiments, one or more processors or processor-implemented modules can be distributed across a number of locations.

[0132] Unless otherwise indicated, the descriptions herein using words such as "processing," "computing," "calculating," "determining," "presenting," "displaying," etc., can refer to operations or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within 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.

[0133] 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 appearances of the phrase "in one embodiment" in various places in this specification are not necessarily all referring to the same embodiment.

[0134] Some embodiments may be described using the terms "coupled" and "connected" along with their conjugations. For example, some embodiments may be described using the term "coupled" to indicate that two or more elements are in direct physical contact or in electrical contact. However, the term "coupled" may also mean that two or more elements are not in direct contact with each other but still cooperate or interact with each other. The embodiments are not limited to this context.

[0135] As used herein, the terms "comprises," "comprising," "includes," "including," "has," "having," or any other variation 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 and not an exclusive or. For example, condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or absent), A is false (or absent) and B is true (or present), and both A and B are true (or present).

[0136] In addition, the use of "a" or "an" is employed to describe the elements and components of the embodiments of this specification. This is merely for convenience and to impart a general gist to this specification. This description should be read to include one or at least one, and the singular also includes the plural unless it is clear that the contrary is meant. The detailed description should be construed as merely providing examples, and is not intended to describe all possible embodiments, as it is not possible, if not unrealistic, to describe all possible embodiments. Using either the current technology or technologies developed after the filing date of this application, numerous other embodiments could be implemented.

Claims

1. 1. A computing device for determining one or more drugs to administer to a patient suffering from a neuropsychiatric disorder, the computing device comprising: A communication network; one or more processors; a non-transitory computer-readable memory coupled to the one or more processors and storing instructions, the instructions, when executed by the one or more processors, causing the computing device to: Obtaining data from a biological sample of the patient; Obtaining a reference pharmacological pharmacogenomic network representation of a glutamate N-methyl-d-aspartate receptor (NMDAR) antagonist or partial antagonist from a reference database; analyzing the data from the biological sample in view of the reference pharmacogenomic network representation; determining a patient's pharmacogenomic network representation of the NMDAR antagonist or partial antagonist based on said analysis; determining a similarity score according to a comparison of the pharmacogenomic network representation of the patient with the reference pharmacogenomic network representation of the NMDAR antagonist or partial antagonist; determining existing drugs, metabolites of the existing drugs, and dosages of the existing drugs in the patient by analyzing the data from the biological sample using pharmacokinetic pharmaco-metabolomic analysis; (i) determining to administer the NMDAR antagonist or partial antagonist to the patient based on the similarity score, and (ii) drug-gene or drug-drug interactions between the patient's existing medications and the NMDAR antagonist or partial antagonist; displaying instructions for administering the NMDAR antagonist or partial antagonist, thereby causing the NMDAR antagonist or partial antagonist to be administered to the patient; The computing device, wherein the NMDAR antagonist or partial antagonist is administered to the patient.

2. The instructions further cause the computing device to: obtaining from the reference database, for the NMDAR antagonist or partial antagonist, constituent subnetworks including at least a reference drug pharmacodynamic efficacy subnetwork and a reference drug pharmacodynamic adverse events subnetwork, wherein a first subset of variants of the reference drug pharmacodynamic adverse events subnetwork are causally related to adverse events of the NMDAR antagonist or partial antagonist, and a second subset of variants of the reference drug pharmacodynamic efficacy subnetwork are causally related to efficacy of the NMDAR antagonist or partial antagonist; The pharmacogenomic network representation of the patient includes a pharmacodynamic efficacy sub-network of the patient's drugs and a pharmacodynamic adverse event sub-network of the patient's drugs, and to determine the similarity score, the instructions further include: A comparison of the pharmacodynamic efficacy sub-network of the reference drug with the pharmacodynamic efficacy sub-network of the patient's drug; or a comparison of the pharmacodynamic adverse events sub-network of the reference drug with the pharmacodynamic adverse events sub-network of the patient's drug; The computing device of claim 1 , further comprising:

3. the patient's pharmacogenomic network representation of the NMDAR antagonist or partial antagonist includes a pharmacodynamic efficacy sub-network of the patient's drug, a pharmacodynamic adverse events sub-network of the patient's drug, a chromatin remodeling sub-network, and a reference pharmacokinetic enzyme and hormone sub-network, and the constituent sub-networks of the NMDAR antagonist or partial antagonist from the reference database include the pharmacodynamic efficacy sub-network of the reference drug, the pharmacodynamic adverse events sub-network of the reference drug, a reference chromatin remodeling sub-network, and a reference pharmacokinetic enzyme and hormone sub-network spanning human drug response variability; To determine the similarity score, the instructions cause the computing device to: assigning a first score to the patient's drug pharmacodynamic efficacy subnetwork based on an amount of similarity between the patient's drug pharmacodynamic efficacy subnetwork and the reference drug pharmacodynamic efficacy subnetwork; 3. The computing device of claim 2, further comprising: a computing device configured to: assign a second score to the patient's drug pharmacodynamic adverse events subnetwork based on an amount of similarity between the patient's drug pharmacodynamic adverse events subnetwork and the reference drug's pharmacodynamic adverse events subnetwork.

4. To determine administration of the NMDAR antagonist or partial antagonist to the patient based on the similarity score, the instructions cause the computing device to: The computing device of claim 3 , further comprising: determining to administer the NMDAR antagonist or partial antagonist to the patient if the first score exceeds a first threshold score or the second score is below a second threshold score.

5. To determine to administer the NMDAR antagonist or partial antagonist to the patient when the first score exceeds a first threshold score or the second score is below a second threshold score, the instructions cause the computing device to: combining the first and second scores to determine the similarity score; The computing device of claim 4 , further comprising: determining to administer the NMDAR antagonist or partial antagonist to the patient if the similarity score exceeds a third threshold score.

6. The instructions further cause the computing device to: determining a dosage of the NMDAR antagonist or partial antagonist to administer to the patient based on the similarity score; 10. The computing device of claim 1, which causes the patient to administer the determined dosage of the NMDAR antagonist or partial antagonist by displaying instructions for administering the NMDAR antagonist or partial antagonist at the determined dosage.

7. To determine a dosage of the NMDAR antagonist or partial antagonist to administer to the patient, the instructions cause the computing device to:

7. The computing device of claim 6, further comprising: a computing device configured to determine the dosage using a regression model based on a combination of two or more of the patient's gender, the patient's age, whether the patient smokes, the patient's ethnicity, the patient's height, the patient's weight, and the patient's psychiatric history.

8. 2. The computing device of claim 1, wherein the reference pharmacogenomic network representation of the NMDAR antagonist or partial antagonist obtained from the reference database is a ketamine pharmacogenomic network representation and includes one or more of the following: an activity-regulated cytoskeleton-associated protein (ARC) gene, an Achaete-Scute family bHLH transcription factor 1 (ASCL1) gene, a brain-derived neurotrophic factor (BDNF) gene, a BDNF antisense RNA (BDNF-AS) gene, , calcium / calmodulin-dependent protein kinase II alpha (CAMK2A) gene, cyclin-dependent kinase inhibitor 1A (CDKN1A) gene, cAMP response element modulator (CREM) gene, Cut-like homeobox 2 (CUX2) gene, DCC netrin 1 receptor (DCC) gene, dopamine receptor D2 (DRD2) gene, fragile X mental retardation 1 (FMR1) gene, ganglioside-induced differentiation-related protein 1-like 1 (GDP1L1) gene, glutamate metabotropic receptor 5 (GRM5) gene child, Homer scaffolding protein 1 (HOMER1) gene, 5-hydroxytryptamine receptor 1B (HTR1B) gene, 5-hydroxytryptamine receptor 2A (HTR2A) gene, Krüppel-like factor 6 (KLF6) gene, Lin-7 homolog C, crumbs cell polarity complex component (LIN7C) long non-coding RNA, LOC105379109 long non-coding 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, neshent helix loop helix 2 (NHLH2) gene, neuromedin B (NMB) gene, NMDA receptor synaptonuclear signaling and neuronal migration factor (NSMF) gene, neurotrophic receptor tyrosine kinase 2 (NTRK2) gene, phosphotase and tensin homolog (PTEN) gene, prostaglandin-endoperoxide synthase 2 (PTGS2) gene, Rac family small GTPase 1 (RAC1) gene, Ras protein-specific guanine nucleotide releasing factor 2 (RASGRF2) gene,Ras homolog family member A (RHOA) gene, roundabout guidance receptor 2 (ROBO2) gene, long non-coding RNA of RP11_360A181, 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 (SLC6A 2) genes, slit guidance ligand 1 (SLIT1) gene, slit guidance 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, transcription activator 7 interacting protein 2 (ATF7IP2) gene, ATPase Na+ / K+ transport 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 (CACNA1C) gene, calcium voltage-gated channel auxiliary subunit beta 1 (CACNB1) gene, calcium voltage-gated channel auxiliary subunit beta 2 (CACNB2) gene, calcium voltage-gated channel auxiliary subunit gamma 2 (CACNG2) gene child, cholinergic receptor muscarinic 2 (CHRM2) gene, cholinergic receptor nicotinic alpha 3 subunit (CHRNA3) gene, cholinergic receptor nicotinic alpha 5 subunit (CHRNA5) gene, cholinergic receptor nicotinic alpha 7 subunit (CHRNA7) gene, cannabinoid receptor 1 (CNR1) gene, disc 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, glutamic acid decarboxylase 1 (GAD1) ​​gene, glycine receptor alpha 1 (GLRA1) gene, glycine receptor alpha 2 (GLRA2) gene, glycine receptor beta (GLRB) gene, glutamate ion channel integrated receptor AMPA type subunit 1 (GRIA1) Gene, glutamate ion channel-integrated receptor AMPA type subunit 2 (GRIA2) gene, glutamate ion channel-integrated receptor AMPA type subunit 4 (GRIA4) gene, glutamate ion channel-integrated receptor NMDA type subunit 1 (GRIN1) gene, glutamate ion channel-integrated receptor NMDA type subunit 2A (GRIN2A) gene, glutamate ion channel-integrated receptor NMDA type subunit 2B (GRIN2B) gene, glutamate ion channel-integrated receptor NMDA type subunit GRIN2C gene, glutamate ion channel-integrated receptor NMDA type subunit 2D (GRIN2D) gene, glutamate ion channel-integrated receptor NMDA type subunit 3A (GRIN3A) gene, glutamate ion channel-integrated 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 phosphorylation protein 8 (MPHOSPH8) gene, neural cell adhesion molecule 1 (NCAM1) gene, nitrate synthase 1 (NOS1) gene, nitrate synthase 2 (NOS2) gene, nitrate synthase 3 (NOS3) gene, NAD(P)H quinone dehydrogenase 1 (NQO1) gene, opioid receptor kappa 1 (OPRK1) gene, opioid receptor mu 1 (OPRM1) gene, roundabout guidance receptor 2 (ROBO2) gene, SET domain divergence 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, suppressor of transcriptional activation (TASOR) gene, TOG array regulator of axonemal microtubules 2 (TO ... 2) (TOGORAM2) gene, tripartite 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, eukaryotic elongation factor 2 kinase (EEF2K) gene, estrogen receptor 1 (ESR1) gene, or transcription elongation regulator 1 (TCERG1) gene. ,

9. 1. A method for treating a patient suffering from a neuropsychiatric disorder, comprising: Obtaining a biological sample from the patient; obtaining a reference pharmacogenomic network representation of a glutamate N-methyl-d-aspartate receptor (NMDAR) antagonist or partial antagonist from a reference database; analyzing the biological sample in view of the reference pharmacogenomic network representation; determining a pharmacogenomic network representation of the patient of the NMDAR antagonist or partial antagonist based on the analysis; determining a similarity score according to a comparison of the pharmacogenomic network representation of the patient with the reference pharmacogenomic network representation of the NMDAR antagonist or partial antagonist; obtaining clinical data of said patient; determining existing drugs, metabolites of the existing drugs, and dosages of the existing drugs in the patient by analyzing data from the biological sample using pharmacokinetic pharmaco-metabolomic analysis; (i) determining a dose of the NMDAR antagonist or partial antagonist for administration to the patient based on the similarity score, and (ii) a drug-gene or drug-drug interaction between the patient's existing medications and the NMDAR antagonist or partial antagonist; administering said NMDAR antagonist or partial antagonist to said patient.

10. obtaining from the reference database a constituent subnetwork for the NMDAR antagonist or partial antagonist, the constituent subnetwork including at least a pharmacodynamic efficacy subnetwork of a reference drug and a pharmacodynamic adverse event subnetwork of a reference drug; a first subset of variants of the pharmacodynamic adverse events subnetwork of the reference drug causally related to adverse events of the NMDAR antagonist or partial antagonist, and a second subset of variants of the pharmacodynamic efficacy subnetwork of the reference drug causally related to efficacy of the NMDAR antagonist or partial antagonist; the pharmacogenomics network representation of the patient includes a pharmacodynamic efficacy sub-network of the patient's drug and a pharmacodynamic adverse event sub-network of the patient's drug; Determining the similarity score includes: A comparison of the pharmacodynamic efficacy sub-network of the reference drug with the pharmacodynamic efficacy sub-network of the patient's drug; or a comparison of the pharmacodynamic adverse events sub-network of the reference drug with the pharmacodynamic adverse events sub-network of the patient's drug; determining the similarity score according to at least one of 10. The method of claim 9.

11. the patient's pharmacogenomic network representation of the NMDAR antagonist or partial antagonist includes a pharmacodynamic efficacy sub-network of the patient's drug, a pharmacodynamic adverse events sub-network of the patient's drug, a chromatin remodeling sub-network, and a pharmacokinetic enzyme and hormone sub-network; and the constituent sub-networks of the NMDAR antagonist or partial antagonist from the reference database include a reference drug pharmacodynamic efficacy sub-network, a pharmacodynamic adverse events sub-network of the reference drug, a reference chromatin remodeling sub-network, and a reference pharmacokinetic enzyme and hormone sub-network spanning human drug response variability; Determining the similarity score includes: assigning a first score to the patient's drug pharmacodynamic efficacy subnetwork based on an amount of similarity between the patient's drug pharmacodynamic efficacy subnetwork and the reference drug pharmacodynamic efficacy subnetwork; and assigning a second score to the patient's pharmacodynamic adverse events subnetwork based on an amount of similarity between the patient's pharmacodynamic adverse events subnetwork and the reference drug's pharmacodynamic adverse events subnetwork.

12. Determining administration of the NMDAR antagonist or partial antagonist to the patient based on the similarity score includes: The method of claim 11, further comprising determining to administer the NMDAR antagonist or partial antagonist to the patient if the first score exceeds a first threshold score or the second score is below a second threshold score.

13. Determining to administer the NMDAR antagonist or partial antagonist to the patient if the first score exceeds a first threshold score or the second score is below a second threshold score includes: combining the first and second scores to determine the similarity score; and and determining to administer the NMDAR antagonist or partial antagonist to the patient if the similarity score exceeds a third threshold score.

14. determining a dosage of the NMDAR antagonist or partial antagonist to administer to the patient based on the similarity score; 10. The method of claim 9, further comprising administering the determined dosage of the NMDAR antagonist or partial antagonist to the patient.

15. Determining the dosage of the NMDAR antagonist or partial antagonist to administer to the patient includes:

15. The method of claim 14, comprising determining the dosage using a regression model based on a combination of two or more of the patient's sex, the patient's age, whether the patient smokes, the patient's ethnicity, the patient's height, the patient's weight, and the patient's psychiatric history.

16. 11. The method of claim 10, wherein the reference pharmacogenomic network representation of the NMDAR antagonist or partial antagonist obtained from the reference database is a ketamine pharmacogenomic network representation and includes one or more of the following: an activity-regulated cytoskeleton-associated protein (ARC) gene, an Achaete-Scute family bHLH transcription factor 1 (ASCL1) gene, a brain-derived neurotrophic factor (BDNF) gene, a BDNF antisense RNA (BDNF-AS) gene, a calcium / calcium agonist (Ca2+) gene, a ketamine agonist (KAG ... Lumodulin-dependent protein kinase II alpha (CAMK2A) gene, cyclin-dependent kinase inhibitor 1A (CDKN1A) gene, cAMP response element modulator (CREM) gene, Cut-like homeobox 2 (CUX2) gene, DCC netrin 1 receptor (DCC) gene, dopamine receptor D2 (DRD2) gene, fragile X mental retardation 1 (FMR1) gene, ganglioside-induced differentiation-associated protein 1-like 1 (GDP1L1) gene, glutamate metabolic receptor 5 (GRM5) gene, homeobox 2 (HOMEOBOX2) gene, -Scaffolding protein 1 (HOMER1) gene, 5-hydroxytryptamine receptor 1B (HTR1B) gene, 5-hydroxytryptamine receptor 2A (HTR2A) gene, Krüppel-like factor 6 (KLF6) gene, Lin-7 homolog C, crumbs cell polarity complex component (LIN7C) long non-coding RNA, LOC105379109 long non-coding RNA, myocyte enhancer factor 2D (MEF2D) gene, myosin VI (MYO6) gene, myelin transcription factor 1-like (MYT1L) gene child, neuronal differentiation 1 (NEUROD1) gene, neuronal differentiation 2 (NEUROD2) gene, neshent helix loop helix 2 (NHLH2) gene, neuromedin B (NMB) gene, NMDA receptor synaptonuclear signaling and neuronal migration factor (NSMF) gene, neurotrophin receptor tyrosine kinase 2 (NTRK2) gene, phosphotase and tensin homolog (PTEN) gene, prostaglandin-endoperoxide synthase 2 (PTGS2) gene, Rac family small GTPase 1 (RAC1) gene, Ras protein-specific guanine nucleotide releasing factor 2 (RASGRF2) gene,Ras homolog family member A (RHOA) gene, roundabout guidance receptor 2 (ROBO2) gene, long non-coding RNA of RP11_360A181, 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 (SLC6A 2) genes, slit guidance ligand 1 (SLIT1) gene, slit guidance 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, transcription activator 7 interacting protein 2 (ATF7IP2) gene, ATPase Na+ / K+ transport 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 (CACNA1C) gene, calcium voltage-gated channel auxiliary subunit beta 1 (CACNB1) gene, calcium voltage-gated channel auxiliary subunit beta 2 (CACNB2) gene, calcium voltage-gated channel auxiliary subunit gamma 2 (CACNG2) gene child, cholinergic receptor muscarinic 2 (CHRM2) gene, cholinergic receptor nicotinic alpha 3 subunit (CHRNA3) gene, cholinergic receptor nicotinic alpha 5 subunit (CHRNA5) gene, cholinergic receptor nicotinic alpha 7 subunit (CHRNA7) gene, cannabinoid receptor 1 (CNR1) gene, disc 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, glutamic acid decarboxylase 1 (GAD1) ​​gene, glycine receptor alpha 1 (GLRA1) gene, glycine receptor alpha 2 (GLRA2) gene, glycine receptor beta (GLRB) gene, glutamate ion channel integrated receptor AMPA type subunit 1 (GRIA1) Gene, glutamate ion channel-integrated receptor AMPA type subunit 2 (GRIA2) gene, glutamate ion channel-integrated receptor AMPA type subunit 4 (GRIA4) gene, glutamate ion channel-integrated receptor NMDA type subunit 1 (GRIN1) gene, glutamate ion channel-integrated receptor NMDA type subunit 2A (GRIN2A) gene, glutamate ion channel-integrated receptor NMDA type subunit 2B (GRIN2B) gene, glutamate ion channel-integrated receptor NMDA type subunit GRIN2C gene, glutamate ion channel-integrated receptor NMDA type subunit 2D (GRIN2D) gene, glutamate ion channel-integrated receptor NMDA type subunit 3A (GRIN3A) gene, glutamate ion channel-integrated 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 phosphorylation protein 8 (MPHOSPH8) gene, neural cell adhesion molecule 1 (NCAM1) gene, nitrate synthase 1 (NOS1) gene, nitrate synthase 2 (NOS2) gene, nitrate synthase 3 (NOS3) gene, NAD(P)H quinone dehydrogenase 1 (NQO1) gene, opioid receptor kappa 1 (OPRK1) gene, opioid receptor mu 1 (OPRM1) gene, roundabout guidance receptor 2 (ROBO2) gene, SET domain divergence 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, suppressor of transcriptional activation (TASOR) gene, TOG array regulator of axonemal microtubules 2 (TO ... 2) (TOGORAM2) gene, tripartite 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, eukaryotic elongation factor 2 kinase (EEF2K) gene, estrogen receptor 1 (ESR1) gene, or transcription elongation regulator 1 (TCERG1) gene. ,

17. 17. The method of claim 16, wherein the pharmacodynamic efficacy sub-network of the reference drug comprises one or more of the following: activity-regulated cytoskeleton-associated protein (ARC) gene, Achaete-Scute family bHLH transcription factor 1 (ASCL1) gene, brain-derived neurotrophic factor (BDNF) gene, BDNF antisense RNA (BDNF-AS) gene, calcium / calmodulin-dependent protein kinase II alpha (CAMK2A) gene, cyclin-dependent kinase inhibitor 1A (CDK1A) gene, N1A) gene, cAMP response element modulator (CREM) gene, Cut-like homeobox 2 (CUX2) gene, DCC netrin 1 receptor (DCC) gene, dopamine receptor D2 (DRD2) gene, eukaryotic elongation factor 2 kinase (EEF2K) gene, fragile X mental retardation 1 (FMR1) gene, ganglioside-induced differentiation-associated protein 1-like 1 (GDP1L1) gene, glutamate metabolic receptor 5 (GRM5) gene, homer scaffolding protein 1 (HOMER1) gene, 5-hydroxytryptamine receptor 1B (HTR1B) gene, 5-hydroxytryptamine receptor 2A (HTR2A) gene, Krüppel-like factor 6 (KLF6) gene, Lin-7 homolog C, crumbs cell polarity complex component (LIN7C) long non-coding RNA, LOC105379109 long non-coding RNA, myocyte enhancer factor 2D (MEF2D) gene, myosin VI (MYO6) gene, myelin transcription factor 1-like (MYT1L) gene, neuronal differentiation 1 (NE UROD1) gene, neuronal differentiation 2 (NEUROD2) gene, neshent helix loop helix 2 (NHLH2) gene, neuromedin B (NMB) gene, NMDA receptor synaptonuclear signaling and neuronal migration factor (NSMF) gene, neurotrophin receptor tyrosine kinase 2 (NTRK2) gene, phosphotase and tensin homolog (PTEN) gene, prostaglandin-endoperoxide synthase 2 (PTGS2) gene, Rac family small GTPase1 (RAC1) gene, Ras protein-specific guanine nucleotide releasing factor 2 (RASGRF2) gene, Ras homolog family member A (RHOA) gene, roundabout guidance receptor 2 (ROBO2) gene, long non-coding RNA of RP11_360A181, 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, (SHANK3) gene, solute carrier family 22 member 15 (SLC22A15) gene, solute carrier family 6 member 2 (SLC6A2) gene, slit guidance ligand 1 (SLIT1) gene, slit guidance 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.

18. 17. The method of claim 16, wherein the pharmacodynamic adverse event subnetwork of the reference drug comprises one or more of the following: acetylcholinesterase (ACHE) gene, activating transcription factor 7 interacting protein (ATF7IP) gene, transcription activator 7 interacting protein 2 (ATF7IP2) gene, ATPase Na+ / K+ transport 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 (CACNA1C) gene, calcium voltage-gated channel auxiliary subunit beta 1 (CACNB1) gene, calcium voltage-gated channel auxiliary subunit beta 2 (CACNB2) gene, calcium voltage-gated channel auxiliary subunit gamma 2 (CACNG2) gene, cholinergic receptor muscarinic 2 (CHRM2) gene, cholinergic receptor nicotinic alpha 3 subunit (CHRNA3) gene, cholinergic receptor nicotinic alpha 5 subunit (CHRNA5) gene, cholinergic receptor nicotinic alpha 7 subunit (CHRNA7) gene, cannabinoid receptor 1 (CNR1) gene, disc large homolog 3 (DLG3) gene, disc large 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, glutamic acid decarboxylase 1 (GAD1) ​​gene, glycine receptor alpha 1 (GLRA1) gene, glycine Glycine receptor alpha 2 (GLRA2) gene, glycine receptor beta (GLRB) gene, glutamate ion channel-integrated receptor AMPA type subunit 1 (GRIA1) gene, glutamate ion channel-integrated receptor AMPA type subunit 2 (GRIA2) gene, glutamate ion channel-integrated receptor AMPA type subunit 4 (GRIA4) gene, glutamate ion channel-integrated receptor NMDA type subunit 1 (GRIN1) gene,Glutamate ion channel-integrated receptor NMDA type subunit 2A (GRIN2A) gene, glutamate ion channel-integrated receptor NMDA type subunit 2B (GRIN2B) gene, glutamate ion channel-integrated receptor NMDA type subunit 2C (GRIN2C) gene, glutamate ion channel-integrated receptor NMDA type subunit 2D (GRIN2D) gene, glutamate ion channel-integrated receptor NMDA type subunit 3A (GRIN3A) gene, glutamate ion channel-integrated 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 phosphate phospholipase 8 (MPHOSPH8) gene, neural cell adhesion molecule 1 (NCAM1) gene, nitrate synthase 1 (NOS1) gene, nitrate synthase 2 (NOS2) gene, nitrate synthase 3 (NOS3) gene, NAD(P)H quinone dehydrogenase 1 (NQO1) gene, opioid receptor kappa 1 (OPRK1) gene, opioid receptor mu 1 (OPRM1) gene, roundabout guidance receptor 2 (ROBO2) gene, SET domain branching 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, suppressor of transcription activation (TASOR) gene, TOG array regulator of axonal microtubules 2 (TOG array regulator of axonemal microtubules 2 (TOGORAM2) gene, triplicate motif-containing 28 (TRIM28) gene, or zinc finger protein 274 (ZNF274) gene.

19. 17. The method of claim 16, wherein the constituent sub-networks of the ketamine pharmacogenomic network representation include a reference pharmacokinetic enzyme and hormone sub-network, the reference pharmacokinetic enzyme and hormone sub-network including 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 (EEF2 kinase), or a combination thereof. 2K) gene, estrogen receptor 1 (ESR1) gene, glutamate ionotropic receptor AMPA type subunit 1 (GRIA1) gene, glutamate ionotropic receptor AMPA type subunit 4 (GRIA4) gene, glutamate ionotropic receptor NMDA type subunit 1 (GRIN1) gene, glutamate ionotropic receptor NMDA type subunit 2B (GRIN2B) gene, myosin VI (MYO6) gene, roundabout 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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