METHODS AND SYSTEMS FOR PROVIDING A PERSONALIZED TREATMENT REGIMEN USING CANNABINOID OR PSYCHEDELIC COMPOUNDS

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Dec. 20, 2018, is named GDNA-1-1000 SL.txt and is 17,411 bytes in size.

FIELD

The present invention is directed to the area of methods and systems for determining and providing treatment parameters for use of cannabinoids or psychedelic compounds. The present invention is also directed to methods and systems for utilizing patient DNA information to provide personalized treatment regimen using cannabinoid or psychedelic compounds.

BACKGROUND

Over 100 chemically and biosynthetically related cannabinoids, and well over 100 terpenes, have been identified in cannabis to date. Many of the compounds have been shown to have therapeutic or health-related benefits.

There are two major cannabinoids, cannabidiol (CBD) and Δ⁹-tetrahydrocannabinol (THC), along with several other less potent cannabinoids, such as cannabichromene (CBC), cannabichromevarin (CBCV), Δ⁹-tetrahydrocannabivarin (THCV), cannabigerol (CBG), cannabigerovarin (CBGV), cannabidivarin (CBDV), and cannabinol (CBN).

THC shows wide clinical benefit for symptoms of diseases such as energy metabolism, pain and inflammation, neuroprotection, Alzheimer's disease, Huntington's disease, anxiety and fear, sleep disorders, emesis, gastrointestinal disorders, cardiovascular disorders, cancer, and so on. A synthetic analog of THC, nabilone, was approved for the suppression of the nausea and vomiting caused by chemotherapy.

CBD is anxiolytic, antidepressant, antipsychotic, anticonvulsant, antinausea, antioxidant, anti-inflammatory, antiarthritic, and antineoplastic. Within the central nervous system (CNS) it is effective in animal models of epilepsy, anxiety, psychosis, and diseases of the basal ganglia, such as Parkinson's and Huntington's diseases, and CBD also shows beneficial effects in treatments of psychosis, epilepsy, anxiety, sleep, neuroprotection and neurodegenerative diseases, such as, Alzheimer's disease, Parkinson's disease, and Huntington's disease, pain, inflammation, autoimmunity, and retinal diseases, emesis, cancer, and so on.

Of the less potent cannabinoids there are many investigations which demonstrate that at least some of the therapeutic benefits of THC and CBD are also available from a handful of other cannabinoids, such as, CBC, CBG, CBDV, THCV, Δ⁹-tetrahydrocannabinolic acid (THCA), and cannabidiolic acid (CBDA). For example, the US National Academy of Sciences, Engineering and Medicine (NASEM) reported clinical evidence of an effect on chronic pain and good evidence of an effect on anxiety and sleep disturbance (i.e. insomnia).

Psychedelic compounds (“psychedelics”) of plant extractions such as mescaline (peyote cactus) and psilocybin (“magic mushrooms”), very similar to cannabis, have been used in different cultures around the world thousands of years.

BRIEF SUMMARY

One embodiment is a method of providing a personalized cannabinoid treatment regimen to a patient. The method includes obtaining two or more base values, wherein each of the base values is a different one of the following: a) a base dosage for a first cannabinoid; b) a base dosage for a second cannabinoid; c) a base dosage for a combination of the first and second cannabinoids; or d) a base ratio of the first and second cannabinoids; for each of a plurality of single nucleotide polymorphisms (SNPs) in a selected set of SNPs, obtaining, from a genetic test of the patient, a genotype for the SNP; for each of the SNPs in the selected set of SNPs, obtaining, for the obtained genotype of the SNP, at least one weighting value which reflects, for the obtained genotype of the SNP, one or more responses selected from the following: i) a response to the first and second cannabinoids; ii) a response to the first cannabinoid only; iii) a response to the second cannabinoid only; or iv) cannabinoid dependency; modifying the two or more base values based on the obtained weighting values to produce two or more regimen values, wherein each of the regimen values is a different one of the following: a) a regimen dosage for the first cannabinoid; b) a regimen dosage for the second cannabinoid; c) a regimen dosage for a combination of the first and second cannabinoids; or d) a regimen ratio of the first and second cannabinoids; and treating the patient using the first and second cannabinoids according to the two or more regimen values.

In at least some embodiments, the first cannabinoid is cannabidiol (CBD) and the second cannabinoid is Δ⁹-tetrahydrocannabinol (THC). In at least some embodiments, the method further includes obtaining a condition for treatment, wherein the selected set of SNPs includes a plurality of SNPs associated with the condition. In at least some embodiments, a value of at least one of the base values is dependent on the condition. In at least some embodiments, the condition is selected from pain, depression, anxiety, fear, sleep disorder, insomnia, energy metabolism disorder, inflammation, neuroprotection, Alzheimer's disease, Huntington's disease, Parkinson's disease, emesis, gastrointestinal disorder, cardiovascular disorder, cancer, nausea, vomiting, epilepsy, psychosis, diseases of the basal ganglia, neurodegenerative diseases, autoimmune disorder, retinal diseases, arthritis, convulsions, neoplastic diseases, or any combination thereof.

In at least some embodiments, modifying the two or more base values includes modifying at least one of the base values by multiplying the at least one of the base values by a product of at least one of the weighting values for each of a plurality of the SNPs.

In at least some embodiments, obtaining at least one weighting value includes obtaining the weighting values for each of the following responses individually: i) the response to the first and second cannabinoids, ii) the response to the first cannabinoid only; iii) the response to the second cannabinoid only, or iv) the cannabinoid dependency. In at least some embodiments, modifying the two or more base values includes modifying at least one first value, selected from the two or more base values, using the weighting values for a first one of the responses to produce at least one first intermediate value; modifying at least one second value, selected from the two or more base values and the at least one first intermediate value, using the weighting values for a second one of the responses to produce at least one second intermediate value; modifying at least one third value, selected from the two or more base values, the at least one first intermediate value, and the at least one second intermediate value, using the weighting values for a third one of the responses to produce at least one third intermediate value; and modifying at least one fourth value, selected from the two or more base values, the at least one first intermediate value, the at least one second intermediate value, and the at least one third intermediate value, using the weighting values for a fourth one of the responses to produce at least one of the regimen values.

In at least some embodiments, obtaining the two or base values includes determining the two or more base values using at least one factor selected from patient weight, condition for treatment, patient age, patient gender, patient body type, other medications taken by patient, or results of a patient blood test.

Another embodiment is a system for providing an individualized cannabinoid treatment regimen. The system includes a processor configured to perform actions to produce the individualized cannabinoid treatment regimen, the actions including: obtaining two or more base values, wherein each of the base values is a different one of the following: a) a base dosage for a first cannabinoid; b) a base dosage for a second cannabinoid; c) a base dosage for a combination of the first and second cannabinoids; or d) a base ratio of the first and second cannabinoids; for each of a plurality of single nucleotide polymorphisms (SNPs) in a selected set of SNPs, obtaining, from a genetic test of the patient, a genotype for the SNP; for each of the SNPs in the selected set of SNPs, obtaining, for the obtained genotype of the SNP, at least one weighting value which reflects, for the obtained genotype of the SNP, one or more responses selected from the following: i) a response to the first and second cannabinoids; ii) a response to the first cannabinoid only; iii) a response to the second cannabinoid only; or iv) cannabinoid dependency; and modifying the two or more base values based on the obtained weighting values to produce two or more regimen values, wherein each of the regimen values is a different one of the following: a) a regimen dosage for the first cannabinoid; b) a regimen dosage for the second cannabinoid; c) a regimen dosage for a combination of the first and second cannabinoids; or d) a regimen ratio of the first and second cannabinoids.

In at least some embodiments, the first cannabinoid is cannabidiol (CBD) and the second cannabinoid is Δ⁹-tetrahydrocannabinol (THC). In at least some embodiments, the actions further include obtaining a condition for treatment, wherein the selected set of SNPs includes a plurality of SNPs associated with the condition. In at least some embodiments, modifying the two or more base values includes modifying at least one of the base values by multiplying the at least one of the base values by a product of at least one of the weighting values for each of a plurality of the SNPs.

Another embodiment is a non-transitory processor readable storage media that includes instructions for producing an individualized cannabinoid treatment regimen, wherein execution of the instructions by one or more processors cause the one or more processors to perform actions, including: obtaining two or more base values, wherein each of the base values is a different one of the following: a) a base dosage for a first cannabinoid; b) a base dosage for a second cannabinoid; c) a base dosage for a combination of the first and second cannabinoids; or d) a base ratio of the first and second cannabinoids; for each of a plurality of single nucleotide polymorphisms (SNPs) in a selected set of SNPs, obtaining, from a genetic test of the patient, a genotype for the SNP; for each of the SNPs in the selected set of SNPs, obtaining, for the obtained genotype of the SNP, at least one weighting value which reflects, for the obtained genotype of the SNP, one or more responses selected from the following: i) a response to the first and second cannabinoids; ii) a response to the first cannabinoid only; iii) a response to the second cannabinoid only; or iv) cannabinoid dependency; and modifying the two or more base values based on the obtained weighting values to produce two or more regimen values, wherein each of the regimen values is a different one of the following: a) a regimen dosage for the first cannabinoid; b) a regimen dosage for the second cannabinoid; c) a regimen dosage for a combination of the first and second cannabinoids; or d) a regimen ratio of the first and second cannabinoids.

A further embodiment is a method of providing a personalized psychedelic compound treatment regimen to a patient. The method includes obtaining a base dosage for a psychedelic compound; for each of a plurality of selected single nucleotide polymorphisms (SNPs), obtaining, from a genetic test of the patient, a genotype for the selected SNP; for each of the selected SNPs, obtaining, for the obtained genotype of the selected SNP, at least one weighting value which reflects, for the obtained genotype of the selected SNP, one or more responses selected from the following: i) a response to the psychedelic compound or ii) a response by one or more receptors or genes in the metabolic pathway of the psychedelic compound; modifying the base dosage based on the obtained weighting values to produce a regimen dosage for the psychedelic compound; and treating the patient using the psychedelic compound according to the regimen dosage.

In at least some embodiments, modifying the base dosage includes modifying the base dosage using the weighting values for a first set of the selected SNPs to produce a first intermediate value; and modifying the first intermediate value using the weighting values for a second set of the selected SNPs to produce the regimen dosage. In at least some embodiments, the first set of the selected SNPs are SNPs from receptors or genes in the metabolic pathway of a plurality of psychedelic compounds. In at least some embodiments, the first set of the selected SNPs are SNPs of HT2A receptors or signaling genes in the metabolic pathway of the plurality of psychedelic compounds. In at least some embodiments, the second set of the selected SNPs are SNPs that provide a response to the psychedelic compound. In at least some embodiments, the second set of the selected SNPs are liver monoamine oxidase SNPs.

In at least some embodiments, modifying the base dosage includes modifying the base dosage using the weighting values for a first set of the selected SNPs to produce a first intermediate value; modifying the first intermediate value using the weighting values for a second set of the selected SNPs to produce a second intermediate value; and modifying the second intermediate value using the weighting values for a third set of the selected SNPs to produce the regimen dosage. In at least some embodiments, the first set of the selected SNPs are SNPs from receptors or genes in the metabolic pathway of a plurality of psychedelic compounds. In at least some embodiments, the first set of the selected SNPs are SNPs of HT2A receptors or signaling genes in the metabolic pathway of the plurality of psychedelic compounds. In at least some embodiments, the second set of the selected SNPs are liver monoamine oxidase SNPs. In at least some embodiments, the third set of the selected SNPs are SNPs that provide a response to the psychedelic compound.

In at least some embodiments, obtaining the base dosage includes determining the base dosage using at least one factor selected from patient weight, condition for treatment, patient age, patient gender, patient body type, other medications taken by patient, or results of a patient blood test.

Yet another embodiment is a system for providing an individualized psychedelic compound treatment regimen. The system includes a processor configured to perform actions to produce the individualized psychedelic compound treatment regimen. The actions include obtaining a base dosage for a psychedelic compound; for each of a plurality of selected single nucleotide polymorphisms (SNPs), obtaining, from a genetic test of the patient, a genotype for the selected SNP; for each of the selected SNPs, obtaining, for the obtained genotype of the selected SNP, at least one weighting value which reflects, for the obtained genotype of the selected SNP, one or more responses selected from the following: i) a response to the psychedelic compound or ii) a response by one or more receptors or genes in the metabolic pathway of the psychedelic compound; and modifying the base dosage based on the obtained weighting values to produce a regimen dosage for the psychedelic compound.

In at least some embodiments, the psychedelic compound includes at least one of psilocybin, N,N-dimethyltryptamine (DMT), mescaline, semisynthetic ergoline lysergic acid diethylamide (LSD), 3,4-methylenedioxymethamphetamine (MDMA), or ketamine. In at least some embodiments, modifying the base dosage includes modifying the base dosage using the weighting values for a first set of the selected SNPs to produce a first intermediate value; and modifying the first intermediate value using the weighting values for a second set of the selected SNPs to produce the regimen dosage. In at least some embodiments, modifying the base dosage includes modifying the base dosage using the weighting values for a first set of the selected SNPs to produce a first intermediate value; modifying the first intermediate value using the weighting values for a second set of the selected SNPs to produce a second intermediate value; and modifying the second intermediate value using the weighting values for a third set of the selected SNPs to produce the regimen dosage.

Another embodiment is a non-transitory processor readable storage media that includes instructions for producing an individualized psychedelic compound treatment regimen, wherein execution of the instructions by one or more processors cause the one or more processors to perform actions. The actions include obtaining a base dosage for a psychedelic compound; for each of a plurality of selected single nucleotide polymorphisms (SNPs), obtaining, from a genetic test of the patient, a genotype for the selected SNP; for each of the selected SNPs, obtaining, for the obtained genotype of the selected SNP, at least one weighting value which reflects, for the obtained genotype of the selected SNP, one or more responses selected from the following: i) a response to the psychedelic compound or ii) a response by one or more receptors or genes in the metabolic pathway of the psychedelic compound; and modifying the base dosage based on the obtained weighting values to produce a regimen dosage for the psychedelic compound.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the present invention are described with reference to the following drawings. In the drawings, like reference numerals refer to like parts throughout the various figures unless otherwise specified.

For a better understanding of the present invention, reference will be made to the following Detailed Description, which is to be read in association with the accompanying drawings, wherein:

FIG. 1 is a block diagram of one embodiment of a computing system for practicing the invention;

FIG. 2 is a flow chart of one embodiment of a method of producing an individualized cannabinoid treatment regimen, according to the invention;

FIG. 3 is a flow chart of one embodiment of a method of modifying base values using weighting values to obtain regimen values, according to the invention;

FIG. 4 is a flow chart of another embodiment of a method of modifying base values using weighting values to obtain regimen values, according to the invention;

FIG. 5 is graph of different health conditions for participants in a study;

FIG. 6 is a graph of cannabis dosage versus body weight for the participants in the study based on conventional dosage determinations;

FIG. 7 is a graph of cannabis dosage versus body weight for the participants utilizing patient DNA information to provide a personalized cannabinoid treatment regimen, according to the invention;

FIG. 8 is a flow chart of a third embodiment of a method of modifying base values using weighting values to obtain regimen values, according to the invention; and

FIG. 9 is a flow chart of a fourth embodiment of a method of modifying base values using weighting values to obtain regimen values, according to the invention.

DETAILED DESCRIPTION

The present invention is directed to the area of methods and systems for determining and providing treatment parameters for use of cannabinoids. The present invention is also directed to methods and systems for utilizing patient DNA information to provide personalized cannabinoid treatment regimen.

In at least some embodiments, the systems and methods described herein can utilize a computer system for determining recommended regimen values for treatment using two or more cannabinoids. FIG. 1 is a block diagram of components of one embodiment of such a computer system 100. The computer system 100 can include a computing device 120 or any other similar device that includes a processor 122 and a memory 124, a display 126, and an input device 128.

The computing device 120 can be a computer, tablet, mobile device, field programmable gate array (FPGA), or any other suitable device for processing information. The computing device 120 can be local to the user (such as a clinician or patient) or can include components that are non-local to the user including one or both of the processor 122 or memory 124 (or portions thereof). For example, in at least some embodiments, the user may operate a terminal that is connected to a non-local computing device. In other embodiments, the memory 124 can be non-local to the user.

The computing device 120 can utilize any suitable processor 122 including one or more hardware processors that may be local to the user or non-local to the user or other components of the computing device. The processor 122 is configured to execute instructions provided to the processor 122.

Any suitable memory 124 can be used for the computing device 120. The memory 124 illustrates a type of computer-readable media, namely computer-readable storage media. Computer-readable storage media may include, but is not limited to, nonvolatile, non-transitory, removable, and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (“DVD”) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computing device. The memory 124 can be local or non-local (for example, cloud-based storage.)

Communication methods provide another type of computer readable media; namely communication media. Communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, data signal, or other transport mechanism and include any information delivery media. The terms “modulated data signal,” and “carrier-wave signal” includes a signal that has one or more of its characteristics set or changed in such a manner as to encode information, instructions, data, and the like, in the signal. By way of example, communication media includes wired media such as twisted pair, coaxial cable, fiber optics, wave guides, and other wired media and wireless media such as acoustic, RF, infrared, and other wireless media.

The display 126 can be any suitable display device, such as a monitor, screen, or the like, and can include a printer. In some embodiments, the display is optional. In some embodiments, the display 126 may be integrated into a single unit with the computing device 120, such as a tablet, smart phone, or smart watch. In at least some embodiments, the display is not local to the user. The input device 128 can be, for example, a keyboard, mouse, touch screen, track ball, joystick, voice recognition system, or any combination thereof, or the like. In at least some embodiments, the input device is not local to the user.

In at least some embodiments, the systems and methods described herein can provide personalized information, such as personalized treatment regimen values including personalized dosages, that can facilitate, or even accelerate, an individual's treatment or path to wellness using cannabinoids, the medicinal compounds produced from cannabis and hemp. In at least some embodiments, the systems and methods utilize personal genetic information to estimate how an individual's endocannabinoid system may be predisposed to function in response to cannabinoids. This information can facilitate a better understanding of the potential efficacy of cannabinoid dose regimes for the relief of conditions including, but not limited to, pain, depression, anxiety, fear, sleep disorder, insomnia, energy metabolism disorder, inflammation, neuroprotection, Alzheimer's disease, Huntington's disease, Parkinson's disease, emesis, gastrointestinal disorder, cardiovascular disorder, cancer, nausea, vomiting, epilepsy, psychosis, diseases of the basal ganglia, neurodegenerative diseases, autoimmune disorder, retinal diseases, arthritis, convulsions, neoplastic diseases, or the like.

The human endocannabinoid system includes receptors, enzymes, and proteins that process cannabinoids as well as other compounds that can regulate or otherwise affect aspects of human health and wellbeing. DNA encodes the genetic information to produce these receptors, enzymes, and metabolic proteins and there is substantial variance between individuals with respect to the DNA sequences for these genes. This natural genetic variation can affect how the endocannabinoid system functions in each person. The DNA variation can be determined by DNA sequence analysis to provide an overview of the genetic composition of the genes involved in the perception and response to cannabinoids.

Knowledge of individual endocannabinoid system, based on personal genetic information, can be used to provide insights as to the potential response to particular dose regimes of cannabinoids to treat, for example, conditions such as pain, depression, anxiety, fear, sleep disorder, insomnia, energy metabolism disorder, inflammation, neuroprotection, Alzheimer's disease, Huntington's disease, Parkinson's disease, emesis, gastrointestinal disorder, cardiovascular disorder, cancer, nausea, vomiting, epilepsy, psychosis, diseases of the basal ganglia, neurodegenerative diseases, autoimmune disorder, retinal diseases, arthritis, convulsions, neoplastic diseases, or the like. According to a 2016 WebMD survey, 48% of medical cannabis patients take between 3 to 6 months or longer, and spend up to $3,000, to find the appropriate cannabinoid combination to address their condition. The systems and methods described herein can be used to facilitate efficiently identifying a dosage, and ratio, of CBD and THC or other cannabinoids to treat a desired condition or conditions based on patient genetic information.

Studies of THC lead to the discovery of a cannabinoid receptor, CB1, and the human endocannabinoid system (ECS). In at least some embodiments, the ECS is defined as the ensemble of: a) two 7-transmembrane-domain and G protein-coupled receptors (GPCRs) for THC—cannabinoid receptor type 1 (CB1) and cannabinoid receptor type 2 (CB2); b) two endogenous ligands, the “endocannabinoids” N-arachidonoylethanolamine (anandamide) and 2-arachidonoylglycerol (2-AG); and c) the enzymes responsible for a) endocannabinoid biosynthesis (including N-acyl-phosphatidyl-ethanolamine-selective phospholipase D (NAPE-PLD) and diacylglycerol lipases (DAGL) α and β, for anandamide and 2-AG, respectively) and b) hydrolytic inactivation (including fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL), for anandamide and 2-AG, respectively).

Endocannabinoids and the ECS can regulate synaptic plasticity in the central nervous system to modulate brain functions such as memory, mood and emotions, and pain perceptions. The ECS may promote both non-rapid-eye movement and rapid-eye-movement sleep by interacting with melanin-concentrating hormone neurons in the lateral hypothalamus.

THC and THCV bind with high affinity to CB1 and CB2 (with agonist and antagonist activity for THC and THCV, respectively). CBD, on the other hand, may indirectly affect CB1/CB2 by weakly inhibiting AEA enzymatic hydrolysis (for example, inhibiting FAAH) to regulate the ECS and effect the pain, anxiety, and insomnia conditions. Cannabinoids also exhibit moderate activity on a wide array of molecular targets (for example, orphan GPCRs) including several channels belonging to the transient receptor potential (TRP) family, such as rat and human transient receptor potential vanilloid subtype 1 channel (TRPV1), 5-hydroxytryptamine receptors (5-HT) (for example, HT1A or serotonin receptors) to modulate brain functions (for example, pain perceptions).

The therapeutic efficacy of cannabinoids may be impacted by genetic variations of the receptor genes (CB1, CB2, TRPV1, and HT1A), the transport genes (ATP-Binding Cassette Subfamily B member 1 (ABCB1), Solute Carrier Family 6 member 4 (serotonin transporter) (SLC6A4)); the metabolism genes (Cytochrome P450, CYP2C9 and CYP3A4, and Catechol-O-Methyltransferase (COMT)), as well as interactions of the genetic variations between these genes. Pharmacogenomic and pharmacogenetic test-guided target therapy, as described herein, can provide a cost-effective approach to personalized treatments, and is particularly attractive for complex diseases or disorders for which it is often difficult to tailor treatments (for example, pain, depression, anxiety, fear, sleep disorder, insomnia, energy metabolism disorder, inflammation, neuroprotection, Alzheimer's disease, Huntington's disease, Parkinson's disease, emesis, gastrointestinal disorder, cardiovascular disorder, cancer, nausea, vomiting, epilepsy, psychosis, diseases of the basal ganglia, neurodegenerative diseases, autoimmune disorder, retinal diseases, arthritis, convulsions, neoplastic diseases, or the like). Chronic pain, anxiety, depression, and sleep disorders are used herein as examples.

Chronic pain is one example of a malady which may be treated by medical cannabis. There is substantial clinical evidence that cannabis is an effective treatment for chronic pain, often with fewer side effects compared to opioids. It is believed that endocannabinoids localize throughout the brain and activate CB1 and TRPV1. It is believed that stimulation of CB1 can exert anti-inflammatory and analgesic effects, whereas TRPV1 activation may increase inflammation, pain and fever through the enhancement of neurotransmitter release and the secretion of pro-inflammatory cytokines.

Genetic variations of cannabinoid receptors (CB1 and CB2), the principle cannabinoid catabolic enzyme (FAAH), the transport gene (ABCB1), and the metabolism genes (COMT and Cytochrome P450, CYP2C9 and CYP3A4) may result in different gene expression levels or activity in response to cannabinoids, as well as different levels of association to multiple drug dependence and adverse drug reactions (ADRs). For example, variations in TRPV1 have been associated with higher pain tolerance or higher risk of interferon-induced side effects in patients with multiple sclerosis. Genetic variations of the transport gene (ABCB1) and the metabolism genes (COMT and Cytochrome P450, CYP2C9 and CYP3A4) have been associated with drug efficacy and ADRs in pharmacogenomic studies. Identification of these genetic variations in an individual can be used to make recommendations to the individual with respect to the safety and efficacy of personalized cannabis use in pain management or other treatments.

Excessive fear and anxiety are symptoms of a number of neuropsychiatric disorders including generalized anxiety disorder (GAD), panic disorder (PD), and social anxiety disorder (SAD). The endocannabinoid system (ECS) can modulate synaptic plasticity that affect learning and response to emotional salient and aversive events. It is believed that activation of CB1 can produce anxiolytic effects and produce negative feedback to the neuroendocrine stress response. It is believed that chronic stress impairs ECS signaling in the hippocampus and amygdala and can lead to anxiety. It is believed that genetic variants of CB1 and FAAH in ECS are linked to high anxiety, particularly when interacting with gene variations in other systems, such as the serotonin transporter gene (SLC6A4), or with early life stress.

Cannabis use demonstrates a level of efficacy for anxiety reduction in studies. Anxiety may also be partially regulated by serotonin levels for which a number of currently available pharmacological treatments were developed, such as selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors, benzodiazepines, monoamine oxidase inhibitors, tricyclic antidepressant (TCA) drugs, and partial 5-HT_1Areceptor agonists. in particular. Genetic variations in the following genes have been shown to affect therapeutic efficacy and antidepressant (AD) response: SLC6A4, Serotonin Receptor 1A and 2A (HTR1A and HTR2A), Brain Derived Neurotrophic Factor (BDNF), and COMT. By genetic testing of these AD response gene variants along with the generic variants of CB1 and FAAH genes of the ECS and Cytochrome P450 genes that catabolize cannabis and antidepressants, a personalized anxiety/depression treatment recommendation for CBD and THC use can be rendered, as described herein.

Insomnia is a common sleep disorder and while its cause is often unknown it may often be a consequence of a chronic disease associated with stress, pain, or depression. It is believed that administration of cannabinoids can be an effective treatment as THC has been found to promote sleep in both humans and animals. Further, CB1 activation may lead to induction of sleep in a manner blocked by a selective CB1 antagonist. Genetic variants of FAAH were found to be associated with poor sleep quality.

Genetic variants of the β3 subunit of the GABAA receptor and the serotonin transporter are associated with insomnia. Currently, drug treatments of insomnia include classes of antagonists of histamine H1 receptors such as diphenhydramine; low-dose doxepin (a TCA with high affinity for the H1 receptor); Mirtazapine (an antidepressant with 5-HT and His antagonistic properties); benzodiazepines (BZD) and non-benzodiazepine agonistic allosteric modulators of GABAA receptors; and exogenous melatonin. Genetic variants affecting exposure and sensitivity to drugs that improve sleep include the isoenzymes of Cytochrome P450s such as CYP2D6, CYP1A2, CYP2C9, and CYP2C19; the HTR1B and HTR2A genes, and the melatonin receptor genes (MTNR1A). Genome-wide association analysis of insomnia complaints identified one high risk locus—MEIS 1. Personalized insomnia therapy based on CBD and THC use can be recommended by testing these gene variants, as described herein.

Genetic testing can be utilized to investigate single nucleotide polymorphisms (SNPs) of interest in genes associated with the ECS. Tables 1 to 4 provide examples of SNPs of interest relating to cannabis response (Table 1), pain treatment (Table 2), anxiety/depression (Table 3), and sleep disorders/insomnia (Table 4). As an example of the methods and systems, after analyzing the SNPs of interest in genes associated with the ECS, 38 SNPs of high potency, as determined by published studies, were selected and are presented in Tables 5A and 5B. PCR amplification and Next Generation Sequencing (NGS) sequencing primers were designed to investigate these SNPs.

It will be understood, however, that other selections of SNPs can be used. Moreover, SNPs may be selected based on factors such as, the condition being treated, whether cannabinoid dependency is to be investigated, the potency of SNP variation, and the like.

In one example, PCR primers were designed using the Primer3plus platform (available at https://primer3plus.com/), although any other suitable method of primer design can be used. Examples of primers are presented in Table 5 below. The PCR primers were obtained from Integrated DNA Technologies, Inc. (Skokie, Ill., United States) after adding proper sequence adaptors for NGS sequencing. In this example, using one control human DNA sample as the template, PCR amplification showed all amplified unique products. In this example, nine PCR products were larger than the expected size, which is not unexpected due to continuous updating of human genome sequencing and SNP annotations.

In this example, the PCR products were sequenced under MiSeq System (Illumina, San Diego, Calif., United States) and analyzed. High quality genome sequence coverages (the number of sequence reads per SNP) were produced, and 34 of the SNPs were successfully read through the SNP genome locations with NGS sequence read coverages from 348 to 11,263 as shown in Table 6A. Minor mutation alleles were identified from 18 SNPs as shown in the “Mutation Call: Relative to CDS” column in Table 6B.

Over the 100 chemically and biosynthetically related cannabinoids that have been identified in cannabis to date, the two major components, cannabidiol (CBD) and Δ⁹-tetrahydrocannabinol (THC), are widely adopted in the treatment and clinical studies with various dosages and ratios for different conditions. There are many different factors that can play a part in the effectiveness and user experiences of cannabis treatments. These include, but are not limited to, a) the symptoms or conditions to be treated, b) the intensity or progressiveness of the system or condition, c) individual biology and metabolism, d) the individual's endocannabinoid system and how it reacts to CBD and THC, e) body weight, f) individual sensitivity to cannabis compounds, g) other medications being taken, and h) daily food intake patterns including the quantity and quality of the food.

A common conventional practice to determine the dosage and ratio of CBD and THC begins with the lowest dosage and increases the dosage every two to four days based on the effects on the user. This process may take months and cost thousands of dollars before finding an appropriate dosage and ratio for a user's condition, for example, pain, anxiety/depression, insomnia, or the like, as well as the THC dependence of the user.

The methods and systems described herein utilize a pharmacogenomics approach and facilitate estimation of dosage and ratio of CBD and THC for treatment of a condition and, at least in some instances, also account for THC dependence. The systems and methods use genetic variations in the endocannabinoid systems to account for the impact in the responses to CBD and THC or other cannabinoids.

The systems and methods described herein can utilize any combination of the genes and SNPs described above or any other genes and SNPs. The systems and methods utilize a selected set of SNPs that contains multiple SNPs. In some embodiments, the systems and methods may utilize a selected set of SNPs regardless of the condition to be treated. In other embodiments, some or all of the SNPs in the selected set of SNPs may be selected based on the condition to be treated. In at least some embodiments, the number or identity of the SNPs in the selected set of SNPs may be modified by factors such as, for example, the condition to be treated, the results of a genetic test (for example, if the genotype of a SNP is not sufficiently determined), or the like or any combination thereof. As an example, from the 19 genes, 38 SNPs, 108 genotypes of the 38 SNPs, as well as five haplotype SNPs from CNR1, GABRA2, and MAPK14 genes, as presented in Tables 1 to 4, a selection of 38 SNPs is presented in Tables 5A, 5B, and 6. Table 7 also presents the different alleles for each SNP.

FIG. 2 is a flow chart for one method of determining regimen values for treating a patient. The methods and systems described herein will describe treatment using two cannabinoids as an example and, in particular, will describe treatment using CBD and THC as an example. It will be understood, however, that the systems and methods described herein can be utilized for determining regimen values, such as dosage or ratio of CBD to THC, and treatments using one, two, three, four, or more cannabinoids and using cannabinoids other than CBD or THC.

In steps 202, two or more base values are obtained. Examples of base values include the following: a) a base dosage for a first cannabinoid, such as CDB, b) a base dosage for a second cannabinoid such as THC, c) a base dosage for a combination of the first and second cannabinoids (for example, CDB and THC), or d) a base ratio of the first and second cannabinoids (for example, CBD/THC). In one embodiments, the method or system uses a starting CBD dosage, a starting THC dosage, and a starting CBD/THC ratio (or any two of these base values).

The base values can be selected using any suitable method including, but not limited to, published recommendations, clinician experience, public research studies, other data, or the like. The base values may take into account one or more factors, such as, but not limited to, condition to be treated, age, body weight, gender, body type, other medications, results of blood tests or other tests, or the like or any combination thereof. As an example, in one embodiment, for starting CBD and THC dosage and CBD/THC ratio, published recommendations in Leinow and Birnbaum. CBD, A Patient's Guide to Medical Cannabis (North Atlantic Books, Berkeley, Calif., 2017—incorporated herein by reference in its entirety) were used as a middle point base dosage (D1-Table 9) and ratio (R1-Table 9) after factoring the medical conditions, age, and body weight of the patient.

In step 204, a genotype for each SNP in a selected set of SNPs is obtained from a genetic test of the patient. As indicated above, the set of SNPs may be any suitable set of SNPs or may include SNPs selected specifically for the condition to be treated. Any suitable method can be used for determining the genotype including, but not limited to, PCR amplification and sequence determination. Table 8 presents one example of a set of SNPs and a corresponding allele, determined from a genetic test, for each of the SNPs.

In step 206, one or more weighting values are obtained based on the genotypes of the SNPs. Each of the weighting values reflects, for the obtained genotype of the SNP associated with the weighting value, one or more responses selected from the following: i) a response to the first and second cannabinoids (for example, CBD and THC); ii) a response to the first cannabinoid only (for example, CBD only); iii) a response to the second cannabinoid only (for example, THC only); or iv) cannabinoid dependency (i.e., a likelihood for developing dependency on a drug such as, for example, THC). Table 8 presents one example different weighting values for the determined allele for each of the SNPs (see columns labeled “Cannabis Dosage”, CBD Dosage”, “THC Dosage” and “Drug Dependence (THC)”). Table 7 presents one example of weighting values for each of the alleles for each SNP (see columns labeled “Cannabis Dosage”, CBD Dosage”, “THC Dosage” and “Drug Dependence (THC)”). In this illustrated embodiment, differences in weighting values were made in 0.25 increments, but it will be understood that other arrangements of weighting values can be determined with different in increments of 0.01, 0.05, 0.10, or the like or any other suitable increment.

In at least some embodiments, the weighting value is in the range of 0 to 5 or more or the range of 0 to 2 or more. In these embodiments, the weighting values may multiple the base value (or an intermediate value) to modify the base value (or intermediate value) as illustrated in the examples below. Thus, a weighting value of 1 indicates that the particular genotype associated with that weighting value is not expected to have an effect on the base value. In contrast, a weighting value of less than 1 for a base value related to dosage may indicate that, for the patient's genotype, the cannabinoid may have larger than average effect, thereby suggesting that a lower dosage is recommended. Similarly, a weighting value of more than 1 for a base value related to dosage may indicate that, for the patient's genotype, the cannabinoid may have smaller than average effect, thereby suggesting that a higher dosage is recommended.

The weighting values also reflect, in part, the use of a product function, as described below. It will be understood that other functions, such as a summation function or an exponential function, may be used which would then incorporate a different range for the weighting values. In some embodiments, the weighting values may also be presented as a percentile or fraction.

The weighting values can be selected based on literature studies, practitioner experience, public research studies, or other data, or the like or any combination thereof. Moreover, the weighting values may also take into account one or more factors, such as, for example, patient weight, patient gender, or the like or any combination thereof.

As an example, in at least some embodiments, the individual weighting values for each of the SNPS are determined using one or both of the following: 1) direct evidence of increasing or decreasing gene activity or treatment response to multiple drugs (for example, in one embodiment, the SNP variants from COMT, CYP2C9, CYP2C19, ABCB1, or HTR2A genes were evaluated based on this evidence) or 2) indirect evidence of increasing or decreasing of gene expressions, which typically leads to increased or reduced activity or responsiveness under cannabinoid treatments (for example, in one embodiment, the SNP variants CNR1, CNR2, HTR1A, HTR2A, AKT1, NRG1, or FAAH genes were evaluated based on this evidence).

In step 208, the weighting values are used to modify the base values in order to generate two or more regimen values. The regimen values can be, for example, a) a regimen dosage for the first cannabinoid (for example, CBD), b) a regimen dosage for the second cannabinoid (for example, THC), c) a regimen dosage for a combination of the first and second cannabinoids (for example, CBD or THC), or d) a regimen ratio of the first and second cannabinoids (for example, CBD/THC). In at least some embodiments, a report is provided to the patient or a clinician with the regimen values. The modification of the base values using the weighting values may include generating intermediate values and may include two or more substeps (examples provided below in the description of the flowcharts of FIGS. 3 and 4).

The modification of the base values, based on the weighting values, personalizes the treatment for the patient based on the patient's genetic information. The weighting values are used to personalize the treatment by accounting for the patient's genotypes in the selected set of SNPs. As an example, as indicated above, in some embodiments, the weighting values range from 0 to 2 or more and are used as a multiplier for the base value (or other intermediate value) to generate the regimen values. A specific example of one embodiment of this modification method is provided below. It will be understood, however, that other calculational methods for modification can be used including, but not limited to, summation of weighting values, averaging of weighting values, or the like. In such cases, the weighting values are likely to be given a different range of possible values.

In step 210, the patient can be treated using the regimen values. As indicated above, the regimen values personalize the treatment. It will be understood, however, that these regimen values may simply be a starting point for the treatment and further modifications may be made over time based, for example, on patient experience with the treatment, worsening or improvement of the condition, changes in medical situation (which may impact overall health), age, weight, or the like or any combination thereof.

One or more weighting values can be associated with the genotype of each SNP. For example, the genotype of each SNP may have a single weighting value associated with that genotype to represent the general response of a patient with that genotype to cannabinoids.

Alternatively, multiple (for example, two, three, four, or more) weighting values can be associated with at least some (or even all) of the SNPs and their genotypes. Such an arrangement can be used to account for different types of impact. For example, different weighting values may be provided for each of the following four different responses (or any subset of these four responses): i) a response to the first and second cannabinoids (for example, CBD and THC); ii) a response to the first cannabinoid only (for example, CBD only); iii) a response to the second cannabinoid only (for example, THC only); or iv) cannabinoid dependency (i.e., a likelihood for developing dependency on a drug such as, for example, THC). In at least some embodiments, a weighting value for each of these responses is provided for each genotype of each SNP. Alternatively, only a subset of the SNPs may be considered for each type or response and, therefore, weighting values for that type or response are provided for only that subset of SNPs.

As an example, in at least some embodiments, different types of impact of these variant SNP genotypes to the cannabis (CBD+THC) dosage and CBD/THC ratio can be considered. For example, Type I SNP genotypes respond differently to both THC and CBD. In one embodiment, 16 Type I SNP genotypes were identified, as illustrated in Table 7. It will be recognized, however, that other embodiments may include more or fewer Type I SNP genotypes.

As another example, Type II SNP genotypes respond differently to CBD only. In one embodiment, 5 Type II SNP genotypes were identified, as illustrated in Table 7.

It will be recognized, however, that other embodiments may include more or fewer Type II SNP genotypes.

As a further example, Type III SNP genotypes respond differently to THC only. In one embodiment, 10 Type III SNP genotypes were identified, as illustrated in Table 7. It will be recognized, however, that other embodiments may include more or fewer Type III SNP genotypes.

Type I, Type II, and Type III SNP genotypes, alone or in combination, may lead to reduced or increased overall dosage of cannabis (CBD+THC) and the ratio of CBD and THC in the treatments of conditions. The rate of dosage change from some genotypes provides a direct impact, whereas others may produce an indirect impact to gene expression and enzymatic activity.

As yet another example, Type IV SNP genotypes are associated with THC dependence only. In one embodiment, 13 Type IV SNP genotypes were identified, as illustrated in Table 7. It will be recognized, however, that other embodiments may include more or fewer Type IV SNP genotypes. These SNP genotypes may lead to reduced or increased THC dosage. In at least some embodiments, analysis of Type IV SNP genotypes may result in increase or reduction of the ratio of CBD to THC but not the overall cannabis (CBD+THC) dosage in the treatments (see, for example, Table 7).

As described above, the base values are then modified by taking into account one or more of the four types of SNP genotypes to estimate unique individual genetic impacts of CBD and THC (or other cannabinoids) to arrive at suggested regimen CBD and THC dosages and a regimen CBD/THC ratio based on patient DNA tests.

FIG. 3 illustrates one embodiment of a method for modifying base values using weighting values (for example, step 208 in FIG. 2) using the four types of SNP genotypes. In step 302, at least one of the base values is modified using the weighting values for a first type of SNP genotype (for example, the Type I SNP genotypes described above) to produce at least one first intermediate value. In step 304, at least one base value or first intermediate value is modified using the weighting values for a second type of SNP genotype (for example, the Type II SNP genotypes described above) to produce at least one second intermediate value. In step 306, at least one base value or first or second intermediate value is modified using the weighting values for a third type of SNP genotype (for example, the Type III SNP genotypes described above) to produce at least one third intermediate value. In step 308, at least one base value or first, second, or third intermediate value is modified using the weighting values for a fourth type of SNP genotype (for example, the Type IV SNP genotypes described above) to produce at least one regimen value.

The flowchart in FIG. 3 illustrates a process for four types of SNP genotypes, but it will be understood that the process can be readily contract for two or three types of SNP genotypes by removing one or two steps or expanded for four or more types of SNP genotypes by adding steps similar to steps 304 or 306.

FIG. 4 illustrates one embodiment of a process that implements the steps of FIG. 3 (steps 404 to 416) using the four types of SNP genotypes described above and provides an example of specific equations that can be used in this embodiment. It will be understood that these equations are examples and that other methods of modifying the base values to obtain the regimen values can be used. Table 8, below, provides an example of SNP genotypes and weighting values. Table 9, below, provides one specific case of determined SNP genotypes with the corresponding weighting values.

In step 402, specific base values (a base CBD+THC dosage and a base CBD/TCH ratio) are obtained. In the equations below, D1 is the base CBD+THC dosage and R1 is the base CBD to THC Ratio, which are obtained in step 402 (see, also step 202 described above).

In step 404, the base CBD+TCH dosage is modified based on the Type I SNP genotypes to obtain a first intermediate CBD+TCH dosage. In at least some embodiments, D2 is the first intermediate CBD+THC dosage after factoring the individual impact of the obtained Type I SNP genotypes from the genetic test of the patient's DNA. D2 can be determined according to the following equation:

$D 2 = D 1 \prod_{i = 1}^{n} a_{i}$

where

n=the number of Type 1 SNP genotypes tested and considered,

i=individual Type 1 SNP genotype, and

a_i=weighting value of the Type I SNP genotype i.

Alternatively, instead of limiting the calculation of D2 to Type I SNP genotypes, weighting values of all of the SNP genotypes can be used. It is likely, however, the weighting values of SNP genotypes other than the Type I SNP genotypes will have a value of 1 or a value near 1. Similarly, other steps described below include calculations using one of the types of SNP genotypes, but these steps can also be modified to include the weighting values for all of the SNP genotypes. In addition, as indicated above, in other embodiments, a summation function or exponential function can be used instead of the product function presented herein. This is also applicable to other equations presented below.

In Step 406, C2, the first intermediate CBD dosage after factoring the individual impact of Type I SNP genotypes, is determined according to the following equation:

$C 2 = D 2 (\frac{R 1}{R 1 + 1})$

Also, T2, the first intermediate THC dosage after factoring the individual impact of Type I SNP genotypes is determined according to the following equation:

$T 2 = \frac{D 2}{R 1 + 1}$

In step 408, the impact of the Type II SNP genotypes is introduced. C3, the second intermediate CBD dosage after factoring the individual impact of Type II SNP genotypes is given by the following equation:

$C 3 = C 2 \prod_{i = 1}^{n} b_{i}$

where

n=the number of Type II SNP genotypes tested and considered,

i=individual Type II SNP genotype, and

b_i=individual impact of the Type II SNP genotype i.

In step 410, T3, the second intermediate THC dosage after factoring the individual impact of Type III SNP genotypes, is given by the following equation:

$T 3 = T 2 \prod_{i = 1}^{n} c_{i}$

n=the number of Type III SNP genotypes tested and considered,

i=individual Type III SNP genotype, and

c_i=individual impact of Type III SNP genotype i.

In step 412, D3, the second intermediate CBD+THC dosage after factoring the individual impact of Types I-III SNP genotypes, is given by D3=C3+T3.

In step 414, the impact of the Type IV SNP genotypes is considered. T4, the regimen THC dosage after factoring the individual impact of Type IV SNP genotypes, is given by the following equation:

$T 4 = T 3 \prod_{i = 1}^{n} d_{i}$

n=the number of Type IV SNP genotypes tested and considered,

i=individual Type IV SNP genotype, and

d_i=individual impact of the Type IV SNP genotype i.

These calculations then lead to the following dosages and ratios:

C4 is the regimen CBD dosage after factoring the individual impact of Type IV SNP genotypes and is given by: C4=D3−T4

R_fis the regimen CBD to THC ratio after factoring the impact of SNP genotypes and is given by: R_f=C4/T4

D_fis the regimen CBD+TCH dosage after factoring the impact of SNP genotypes and is given by: D_f=C4+T4.

Table 9 illustrates the base, intermediate, and regimen values for three examples of different treatments.

The final recommendations of the CBD+THC dosage and the CBD to THC ratio can be provided to a clinician or patient in, for example, a report or recommendation card. In at least some embodiments, details of the SNP genotypes (e.g., genetic variants) and their impacts on the dosage and ratio may also be delivered to a clinician or patient in the same or different report.

Example

Selected variants and the algorithm were used to predict the CBD/THC dosage in treating different conditions or combinations of different health conditions including pain, anxiety, and insomnia. Samples were obtained from participants who were exploring cannabis solutions to resolve either the individual conditions of pain (P), anxiety (A), or insomnia (I), or combinations of these individual conditions: pain/anxiety (P/A), pain/insomnia (P/I), anxiety/insomnia (A/I), or all three conditions (P/A/I). FIG. 5 shows the number of donors showing interest in each one of these health conditions.

Saliva samples were processed for DNA preparation, PCR and sequencing, and for the subsequent identification and analysis of the gene variants. The variant Linkage Disequilibrium and the genotype association to the health conditions was analyzed as illustrated in FIG. 6. Linkage Disequilibrium (LD) tests (see Reference 53) verified several groups of associated variants from common genes (e.g. variant rs 2229579, rs35761398, and rs2501432 from the CNR2 gene; rs279871, rs279856, and rs279826 from the GABRA2 gene; and rs806368, rs12720071, and rs1049353 from the CNR1 gene) as well as associated variants from different genes (e.g. rs12199654 from MAPK14 and rs12720071 and other SNP variants from CNR1) indicating high quality variant genotype data were generated in this study. A number of variants were associated with statistical significance with pain and with other health conditions suggesting highly quality genetic variants were selected in this study (see, Table 10 below).

In addition to the variants demonstrating association to different health conditions, a number of different variants that may impact the reception, signaling, as well as metabolism of cannabinoids, and thus lead to different dosage requirement for individuals were also identified from every saliva sample (see Table 11 that presents examples of variant alleles identified from two saliva samples 1002 and 1013).

To determine the dosage of cannabis, it is a common practice to start by weighting in body weight and different health conditions of concern. Conventionally, larger body weight leads to an increase in dosage, whereas different health conditions also result in variation of the dosage for a given body weight. As an example, conventionally, a micro-dose is considered effective for insomnia, but a standard to macro-dose may be recommended for pain and anxiety conditions. For the 19 participant samples analyzed in this Example, standard dose recommendations for their different body weight and different health concern are presented in FIG. 6.

In contrast, a genotyping procedure, as described herein, identified a unique set of 5 to 12 variants, see Table 12, likely impacting the cannabis dosage for each participants. The genetic impact of the variants on the dosage of CBD and THC were calculated using the algorithm described above. Results, presented in FIG. 7, showed highly differentiated and personalized CBD/THC dosage comparing to the standard dose recommendations, suggesting that this selected group of variants and the dosage calculating algorithm is a useful approach to predicting the CBD/THC dosage for the health conditions described here. After delivering the genetic report and dosage recommendation to the saliva donors, follow-up interviews all returned positive feedbacks from these donors.

As classic hallucinogens, psychedelics are part of a group of psychoactive compounds including, but not limited to, natural phenethylamine mescaline (“mescaline”), natural tryptamines such as N,N-dimethyltryptamine (DMT) and psilocybin (4-phosphoryloxy-N,N-DMT), semi synthetic ergoline lysergic acid diethylamide (LSD), as well as other compounds such as, for example, 3,4-methylenedioxymethamphetamine (MDMA) and ketamine (Reference 57).

A large number of preclinical studies demonstrated anxiolytic, antidepressive, and antiaddictive therapeutic effects of psychedelics without adverse effects (References 54 and 57), and a number of multicenter and multi-country clinical trials are entering in their late stage studies as well, such as psylocybin treatments in patients who have failed two prior antidepressant treatments in their current episode and MDMA treatment of post-traumatic stress disorder (PTSD) (References 60 and 61). Studies also show that psilocybin can give positive life experiences, such as insightfulness, and produce a sense of well-being that lasts for many years in healthy individuals (Reference 58).

Pharmacological studies show that each psychedelic is metabolized in a unique pathway of enzymes in human body (Table 13). For example, psilocybin is dephosphorylated under the acidic environment of the stomach or by alkaline phosphatase (and other nonspecific esterases) in the intestine, kidney and perhaps in the blood to generate psilocin. This is followed by demethylation and oxidative deamination catalyzed by liver monoamine oxidase (MAO) or aldehyde dehydrogenase and extensive glucuronidation by UDP-glucuronosyltransferases (UGT)1A10 in the small intestine, UGT1A9 is likely the main contributor to its glucuronidation once it has been absorbed into the circulation. On the other hand, LSD is likely metabolized by Cytochrome P450 (CYP) enzymes in the liver.

Psychedelic compounds bind and activate mostly to a cortical serotonin 5-HT2A receptor. Activation of the 5-HT2A receptor produces glutamate release and activation of AMPA glutamatergic receptors, thus increasing cortical electrical activity and information processing. These compounds increase neuroplasticity by stimulating c-fos expression in the medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC) and by increasing Brain-Derived Neurotrophic Factor (BDNF) expression in the PFC, which were mediated through agonism of cortical 5-HT2A receptors and activation of BDNF's high-affinity receptor (tyrosine kinase B receptor, TrkB) and of the mammalian target of rapamycin (mTOR). The enhanced neuroplasticity may be a mechanism involved in the antidepressive and anxiolytic effects of the psychedelics (Reference 57).

As described above, the therapeutic efficacy of cannabinoids may be impacted by the genetic variations of various receptor genes, and many other genes involving the metabolism and signaling transduction. The therapeutic impact of these gene variants can be weighted individually and factored into a cannabis (THC/CBD) dosage recommendation to specific health conditions as described above. Similarly, there may be large interindividual variations with regard to psilocin plasma concentrations after oral administration of psilocybin (Reference 59). Considerable physiological variability between individuals can influence dose-response and toxicological profile (Reference 55). These variations may be associated with the genetic variations and their relevant activity of metabolic enzymes. For example, the genetic variations of Monoamine Oxidase A (MAOA), a major metabolic enzyme of several psychedelics (Table 13) have been studied and a variant (s6323) provides increased MAOA activity which may lead to dopamine deficiency and Attention Deficit Hyperactivity Disorder (ADHD). In another example, a low activity MAOA variant (A VNTR) may influence antidepressant treatment response with major depression (Reference 56). Similar impacts were also reported for the genetic variations of psychedelic receptor and signaling genes (Table 14), suggesting that a similar pharmacogenomics approach to the one described above for cannabinoids can be used to determined recommended dosages of psychedelic compounds.

Table 15 is a comprehensive list of 41 SNPs showing changes of perception and activity from genes involving metabolism and signaling responses of psychedelic compounds. Given many shared metabolic, receptor and signaling pathways and some unique metabolic pathways of psychedelic compounds, in at least some embodiments, these 41 SNPs can be divided into two generally applicable groups of SNPs and into groups of SNPs for individual psychedelic compounds. The Group 1 of generally applicable SNPs include fifteen (15) SNPs of HT2A receptors and signaling genes shared by the psychedelics. The Group 2 of generally applicable groups of SNPs are three (3) MAO SNPs that are shared in the metabolism of psilocybin, DMT, and mescaline. The individual SNPS are twenty-three (23) SNPs unique to the metabolism of specific psychedelics (see Table 16).

The flowchart in FIG. 3 illustrates a process for four types of SNP genotypes. In at least some embodiments, the process for the psychedelic compounds can be reduced to two or three types of SNP genotypes by eliminating one or two steps, as described below. Other embodiments of the process for the psychedelic compounds, however, might use four or more types of SNP genotypes where the process in FIG. 3 can be expanded for five or more types of SNP genotypes by adding steps similar to steps 304 or 306.

FIG. 8 illustrates one embodiment of a process that implements the steps of FIG. 3 (steps 804 to 816) using two generally applicable types of SNP genotypes and, optionally, additional identified SNP genotypes specific to the particular psychedelic compound, as described above, and provides an example of specific equations that can be used in this embodiment. Psilocybin, DMT, and mescaline are examples of psychedelic compounds that may use the process illustrated in FIG. 8, although it will be understood that the process could be used for any psychedelic compound. FIG. 9 illustrates one embodiment of a process that implements the steps of FIG. 3 (steps 804 to 816) using the two types of SNP genotypes as described above and provides an example of specific equations that can be used in this embodiment. LSD, MDMA, ketamine, and 5-Meo-DMT are examples of psychedelic compounds that may use the process illustrated in FIG. 8, although it will be understood that the process could be used for any psychedelic compound. It will be understood that these equations are examples and that other methods of modifying the base values to obtain the regimen values can be used.

In step 802, a base psychedelic compound dosage is obtained. In the equations below, Ps1 is the base psychedelic compound dosage.

In step 804, the base psychedelic compound dosage is modified based on the Type I SNP genotypes to obtain a first intermediate psychedelic compound dosage. In at least some embodiments, Ps2 is the first intermediate psychedelic compound dosage after factoring the individual impact of the obtained Type I SNP genotypes from the genetic test of the patient's DNA. Ps2 can be determined according to the following equation:

$Ps 2 = Ps 1 \prod_{i = 1}^{n} a_{i}$

where

n=the number of Type 1 SNP genotypes tested and considered,

i=individual Type 1 SNP genotype, and

a_i=weighting value of the Type I SNP genotype i.

Alternatively, instead of limiting the calculation of Ps2 to Type I SNP genotypes, weighting values of all of the SNP genotypes can be used. It is likely, however, the weighting values of SNP genotypes other than the Type I SNP genotypes will have a value of 1 or a value near 1. Similarly, other steps described below include calculations using one of the types of SNP genotypes, but these steps can also be modified to include the weighting values for all of the SNP genotypes. In addition, as indicated above, in other embodiments, a summation function or exponential function can be used instead of the product function presented herein. This is also applicable to other equations presented below.

In step 806, the impact of the Type II SNP genotypes is introduced. In at least some embodiments, the impact of the Type II SNP genotypes is introduced for determination of dosages of psilocybin, DMT, or mescaline, although the dosages of other psychedelic compounds may also implement this step 806. Ps3, the second intermediate psychedelic compound dosage after factoring the individual impact of Type II SNP genotypes is given by the following equation:

$Ps 3 = Ps 2 \prod_{i = 1}^{n} b_{i}$

where

n=the number of Type II SNP genotypes tested and considered,

i=individual Type II SNP genotype, and

b_i=individual impact of the Type II SNP genotype i.

In at least some embodiments, the process stops at step 806 if there are no specifically identified SNP genotypes for the psychedelic compound, in which case Ps3 becomes the regimen psychedelic compound dosage. For example, in at least some embodiments, Ps3 is the regimen dosage for DMT or mescaline.

In optional step 808, the impact of identified SNP genotypes on the specific psychedelic compound, j, is considered (see, for example, Table 16). Ps4, the regimen psychedelic compound dosage after factoring the individual impact of the identified SNP genotypes for the specific psychedelic compound, is given by the following equation:

$Ps 4 = Ps 3 \prod_{i = 1}^{n} c_{i, i}$

n=the number of identified SNP genotypes for the psychedelic compound, j, tested and considered,

i=individual identified SNP genotype for the psychedelic compound, and

c_i,j=individual impact of the identified SNP genotype, i, for the psychedelic compound, j.

In at least some embodiments, Ps4 is the regimen dosage for psilocybin.

Turning to FIG. 9, in step 902, a base psychedelic compound dosage is obtained. In the equations below, Ps1 is the base psychedelic compound dosage.

In step 904, the base psychedelic compound dosage is modified based on the Type I SNP genotypes to obtain a first intermediate psychedelic compound dosage. In at least some embodiments, Ps2 is the first intermediate psychedelic compound dosage after factoring the individual impact of the obtained Type I SNP genotypes from the genetic test of the patient's DNA. Ps2 can be determined according to the following equation:

$Ps 2 = Ps 1 \prod_{i = 1}^{n} a_{i}$

where

n=the number of Type 1 SNP genotypes tested and considered,

i=individual Type 1 SNP genotype, and

a_i=weighting value of the Type I SNP genotype i.

In step 906, the impact of identified SNP genotypes on the specific psychedelic compound, j, is considered (see, for example, Table 16). Ps5, the regimen psychedelic compound dosage after factoring the individual impact of the identified SNP genotypes for the specific psychedelic compound, is given by the following equation:

$Ps 5 = Ps 2 \prod_{i = 1}^{n} d_{i, i}$

n=the number of identified SNP genotypes for the psychedelic compound, j, tested and considered,

i=individual identified SNP genotype for the psychedelic compound, and

d_i,j=individual impact of the identified SNP genotype, i, for the psychedelic compound, j.

In at least some embodiments, Ps5 is the regimen dosage for LSD, MDMA, ketamine, or 5-Meo-DMT. It will be understood that c_iand d_imay be different for different psychedelic compounds.

It will be understood that each block of the flowchart illustrations, and combinations of blocks in the flowchart illustrations and methods disclosed herein, can be implemented by computer program instructions. These program instructions may be provided to a processor to produce a machine, such that the instructions, which execute on the processor, create means for implementing the actions specified in the flowchart block or blocks disclosed herein. The computer program instructions may be executed by a processor to cause a series of operational steps to be performed by the processor to produce a computer implemented process. The computer program instructions may also cause at least some of the operational steps to be performed in parallel. Moreover, some of the steps may also be performed across more than one processor, such as might arise in a multi-processor computer system. In addition, one or more processes may also be performed concurrently with other processes, or even in a different sequence than illustrated without departing from the scope or spirit of the invention.

The computer program instructions can be stored on any suitable computer-readable medium including, but not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (“DVD”) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computing device. The memory can be local or non-local (for example, cloud-based storage.)

The above specification provides a description of the manufacture and use of the invention. Since many embodiments of the invention can be made without departing from the spirit and scope of the invention, the invention also resides in the claims hereinafter appended.

References (Cited in the Text and Tables and Incorporated Herein by Reference in their Entireties)

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TABLE 1

SNPs from genes of endocannabinoid systems and response to cannabinoids

Target

Nucleotide

Conditions
Function
Category
Gene
SNP number
Change
Reference(s)

Cannabis Response
Receptor
Receptor
CNR1
rs806380
c.-63-9597 T > C
Ref. #24

Cannabis Response
Receptor
Receptor
CNR1
rs806368
c.*3475 A > G
Ref. #24

Cannabis Response
Receptor
Receptor
CNR1
rs1049353
c.1359 A > G
Ref. #24

Cannabis Response
Receptor
Receptor
CNR1
rs2180619

Refs. #46 and #23

Cannabis Response
Receptor
Receptor
CNR1
rs2023239

Refs. #47 and #27

Cannabis Response
Receptor
Receptor
CNR2
rs2501432/rs35761398

Refs. #24, #38,

(Same locus)

#42, and #45

Cannabis Response
Receptor
Receptor
CNR2
rs2229579
His316Tyr
Ref. #24

Cannabis Response
Transport
Transporters
ABCB1
rs1045642
3435C > T
Ref. #24

Cannabis Response
Biotransformation
Enzyme
FAAH
rs34420
385C > A
Ref. #24

Cannabis Response
Biotransformation
Enzyme
COMT
rs4680
472A > G
Ref. #24

Cannabis Response
Others
Receptor
GABRA2
rs279858
231A > G
Ref. #1

Cannabis Response
Others
Receptor
GABRA2
rs279871

Ref. #1

Cannabis Response
Others
Receptor
GABRA2
rs279826

Ref. #1

Cannabis Response
Others
Signaling
NRG1
rs17664708
122-16329C > T
Ref. #19

Cannabis Response
Enzymes
Enzyme
CYP1A2
rs762551

Ref. #21

Cannabis Response
Enzymes
Enzyme
CYP2C9
rs1057910

Ref. #24

Cannabis Response
Enzymes
Enzyme
CYP2C19
rs4244285

Ref. #24

Cannabis Response
Enzymes
Enzyme
CYP3A4
rs67666821

Ref. #24

Cannabis Response
Enzymes
Enzyme
CYP3A4
rs4646438

Ref. #24

Cannabis Response
Signaling
Signaling
MAPK14
rs12199654

Ref. #24

Cannabis Response
Signaling
Signaling
NRG1
rs17664708

Ref. #24

TABLE 2

SNPs associated responses of pain treatment

Target

Nucleotide

Conditions
Functions
Category
Gene
SNP number
Change
Reference(s)

Pain medicine
Receptors
Receptor
TRPV1
rs222747

Ref. #7

Pain medicine
Receptors
Receptor
TRPV1
rs8065080

Ref. #14

Pain medicine
Receptors
Transporters
FABP1
rs2241883

Ref. #39

Pain medicine
Receptors
Receptor
OPRM1
rs1799971
A118G
Ref. #37

Pain medicine
Transport
Transporters
ABCB1
rs1045642
3435C > T
Ref. #29

Pain medicine
Biotransformation
Enzyme
COMT
rs4680
472A > G
Ref. #37

Pain medicine
Metabolism
Enzyme
CYP2D6
rs16947
CYP2D6*1/*2
Ref. #29

Pain medicine
Metabolism
Enzyme
CYP2D6
rs1135840
CYP2D6*1/*2
Ref. #29

Pain medicine
Metabolism
Enzyme
CYP2D6
rs35742686
CYP2D6*3/*3
Ref. #29

Pain medicine
Metabolism
Enzyme
CYP2B6
rs35303484
CYP2B6*11; c136A > G;
Ref. #48

M46V

Pain medicine
Metabolism
Enzyme
CYP2C9
rs1057910
CYP2C9*3/*3
Ref. #29

Pain medicine
Immune Hypersensitivity
Signaling
HLA
rs3909184
HLA-B*1502
Ref. #29

Pain medicine
Immune Hypersensitivity
Signaling
HLA
rs2844682
HLA-B*1502
Ref. #29

Pain medicine
Immune Hypersensitivity
Signaling
HLA
rs1061235
HLA-A*3101
Ref. #29

Pain medicine
Immune Hypersensitivity
Signaling
HLA
rs2734331
HLA-B*3801
Ref. #29

Pain medicine
Immune Hypersensitivity
Signaling
HLA
(Q126H)
HLA-DBQ1 (126Q)
Ref. #29

Pain medicine
Immune Hypersensitivity
Signaling
HLA
A158T
HLA-B(158T)
Ref. #29

TABLE 3

SNPs associated with anxiety/depression and responses of treatment

Target Conditions
Functions
Category
Gene
SNP number
Reference(s)

Depression/anxiety
Endocannabinoids
Receptor
CNR1
rs2180619
Refs. #46, #23, and

#30

Depression/anxiety
Endocannabinoids
Receptor
CNR1
rs1049353
Ref. #45

Depression/anxiety
Endocannabinoids
Receptor
CNR1
rs806368
Ref. #45

Depression/anxiety
Endocannabinoids
Receptor
CNR1
rs806371
Ref. #45

Depression/anxiety
Endocannabinoids
Receptor
CNR1
rs2023239
Ref. #45

Depression/anxiety
Endocannabinoids
Receptor
CNR1
rs806379
Ref. #45

Depression/anxiety
Endocannabinoids
Receptor
CNR1
rsl535255
Ref. #45

Depression/anxiety
Endocannabinoids
Receptor
CNR1
rs806369
Ref. #45

Depression/anxiety
Endocannabinoids
Receptor
CNR1
rs4707436
Ref. #45

Depression/anxiety
Endocannabinoids
Receptor
CNR1
rs12720071
Ref. #45

Depression/anxiety
Endocannabinoids
Receptor
CNR1
rs806366
Ref. #45

Depression/anxiety
Endocannabinoids
Receptor
CNR1
rs7766029
Ref. #45

Depression/anxiety
Endocannabinoids
Receptor
CNR2
rs2501431
Ref. #45

Depression/anxiety
Endocannabinoids
Enzyme
FAAH
rs2295633
Refs. #49 and #45

Depression/anxiety
Endocannabinoids
Enzyme
FAAH
rs324420
Refs. #33 and #45

Depression/anxiety
Endocannabinoids
Signaling
AKT1
rs1130233
Ref. #5

Depression/anxiety
Autoimmune
Signaling
IL-1β
rs16944
Ref. #45

Depression/anxiety
Autoimmune
Signaling
IL-1β
rs1143627
Ref. #45

Depression/anxiety
Autoimmune
Signaling
IL-1β
rs1143633
Ref. #45

Depression/anxiety
Autoimmune
Signaling
IL-1β
rs1143643
Ref. #45

Depression/anxiety
Autoimmune
Enzyme
COX-2
rs4648308
Ref. #45

Depression/anxiety
Autoimmune
Enzyme
COX-2
rs20417
Ref. #45

Depression/anxiety
HPA Axis
Receptor
NR3C1
rs6189
Ref. #45

Depression/anxiety
HPA Axis
Receptor
NR3C1
rs6190
Ref. #45

Depression/anxiety
HPA Axis
Receptor
NR3C1
rs41423247
Ref. #45

Depression/anxiety
HPA Axis
Receptor
NR3C1
rsl876828
Ref. #13

Depression/anxiety
HPA Axis
Receptor
NR3C1
rs242939
Ref. #13

Depression/anxiety
HPA Axis
Receptor
NR3C1
rs242941
Ref. #13

Depression/anxiety
HPA Axis
Receptor
NR3C1
rs6198
Ref. #45

Depression/anxiety
HPA Axis
Enzyme
FKBP5
rs4713916
Ref. #45

Depression/anxiety
HPA Axis
Enzyme
FKBP5
rs1360780
Ref. #45

Depression/anxiety
Glutamatergic System
Signaling
GRIK4
rs12800734
Ref. #50

Depression/anxiety
Glutamatergic System
Signaling
GRIK4
rs1954787
Ref. #50

Depression/anxiety
Serotoninergic System
Receptor
HTR2A
rs17288723
Ref. #50

Depression/anxiety
Serotoninergic System
Transporters
SLC6A4
5HTTLPR
Refs. #13 and #51

Depression/anxiety
Serotoninergic System
Transporters
SLC6A4
STin2 VNTR
Ref. #13

Depression/anxiety
Serotoninergic System
Receptor
HTR1A
rs6295
Refs. #13 and #3

Depression/anxiety
Serotoninergic System
Receptor
HTR1B
rs62898
Ref. #13

Depression/anxiety
Serotoninergic System
Receptor
HTR2A
rs6311
Ref. #13

Depression/anxiety
Serotoninergic System
Receptor
HTR2A
rs7997012
Ref. #13

Depression/anxiety
Serotoninergic System
Receptor
HTR2A
rs1928040
Ref. #13

Depression/anxiety
Serotoninergic System
Enzyme
TPH1
rs1800532
Ref. #13

Depression/anxiety
Serotoninergic System
Enzyme
TPH2
rs120074175
Ref. #13

Depression/anxiety
Noradrenergic System
Enzyme
COMT
rs4680
Ref. #13

Depression/anxiety
Noradrenergic System
Enzyme
MAOA
VNTR 1.2 kb
Ref. #13

upstream coding

sequence

Depression/anxiety
Noradrenergic System
Transporters
SLC6A2
rs5569
Ref. #13

Depression/anxiety
Dopaminergic System
Transporters
SLC6A3
3′UTR 40-bp VNTR
Ref. #13

Depression/anxiety
Signaling and Growth
Signaling
BDNF
rs6265
Ref. #13

Factors

Depression/anxiety
Signaling and Growth
Signaling
GNB3
rs5443
Ref. #13

Factors

Depression/anxiety
Enzymes
Enzyme
ACE
Insertion or deletion
Ref. #13

Depression/anxiety
Enzymes
Enzyme
GSK3B
rs334558
Ref. #13

Depression/anxiety
Pharmacokinetics
Transporters
ABCB1
rs2032582
Refs. #13 and #43

Depression/anxiety
Pharmacokinetics
Transporters
ABCB1
rs1045642
Ref. #26

TABLE 4

SNPs associated insomnia and responses of treatment

Target

Conditions
Functions
Category
Gene
SNP number
Reference(s)

Sleep Disorder
Endocannabinoids
Receptor
CNR1
rs78783387
Ref. #26

Sleep Disorder
Endocannabinoids
Enzyme
FAAH
rs324420
Refs. #33, #45

Sleep Disorder
Enzymes
Enzyme
CYP2D6
Gene Copy Nos.
Ref. #21

Sleep Disorder
Enzymes
Enzyme
CYP2C19
Various Alleles
Ref. #21

Sleep Disorder
Enzymes
Enzyme
CYP3A4
Various Alleles
Ref. #21

Sleep Disorder
Serotoninergic System
Receptor
HTR2A
rs6311
Ref. #21

Sleep Disorder
Enzymes
Enzyme
CYP1A2
rs762551
Ref. #21

Sleep Disorder
Melatoninergic System
Receptor
MTNR1d
rs2119882
Ref. #21

Sleep Disorder
Organic Cation
Transporters
SLC22A4
rs195152
Ref. #21

Sleep Disorder
Serotoninergic System
Receptor
HTR1B
rs130060
Ref. #21

Sleep Disorder
Serotoninergic System
Receptor
HTR2A
rs6313
Ref. #21

GWAS/Insomnia
NA

SCFD2
rs574753165
Ref. #18

GWAS/Insomnia
NA

WDR27
rs13192566
Ref. #18

GWAS/Insomnia
NA

MEIS1
rs113851554
Ref. #18

GWAS/Insomnia
NA

WDR27
rs71554396
Ref. #18

GWAS/Insomnia
NA

CEP152
rs2725544
Ref. #18

TABLE 5

Primers designed for selected SNPs for testing

Chromo-

some

Genes
SNPs
Notes
Position
Forward Primer
Reverse Primer

OPRM1
rs1799971
NC_000006.12:g.154039662
154360797
AAAAGTCTCGGTGCTCC
CTGGCGCTTTCCTTAC

A > G

TGG-SEQ ID NO: 1
CTGA-SEQ ID NO: 2

TRPV1
rs222747
NC_000017.11:g.3589906
3493200
CTAAGGGGAGGTTTGGG
AGCCCTACAGGCTGGT

C > A

CAG-SEQ ID NO: 3
ATGA-SEQ ID NO: 4

TRPV1
rs8065080
NC_000017.11:g.3577153
3480447
GTCATGTGAGATGGGGC
CAGTGTGTCCTCTGTC

T > C

CAA-SEQ ID NO: 5
CACC-SEQ ID NO: 6

HTR2A
rs6311
NC_000013.11:g.46897343
47471478
AGGTACAGACTTGGCCA
GGCCTTTTGTGCAGAT

C > T

CAA-SEQ ID NO: 7
TCCC-SEQ ID NO: 8

HTR2A
rs6313
NC_000013.11:g.46895805
47469940
GCATGTACACCAGCCTC
GTGGCATGCACATGCT

G > A

AGT-SEQ ID NO: 9
CTTT-SEQ ID NO: 10

ABCB1
rs1045642
NC_000007.14:g.87509329
87138645
TGAATGTTCAGTGGCTC
ACAGGAAGTGTGGCC

A > G

CGA-SEQ ID NO: 11
AGATG-SEQ ID NO: 12

ABCB1
rs2032582
NC_000007.14:g.87531302
87160618
GCAGGCTATAGGTTCCA
AGTCCAAGAACTGGCT

A > C

GGC-SEQ ID NO: 13
TTGCT-SEQ ID NO: 14

CNR1
rs1049353
NC_000006.12:g.88143916
88853635
CCGGAGCATGTTTCCCT
GTAGCCAAAGGTTTCC

C > T

CTT-SEQ ID NO: 15
CTCCT-SEQ ID NO: 16

CNR1
rs2180619
NC_000006.12:g.88168233
88877952
ACCAGGGTGTGTCAGTG
TGGGGAAGGCTCTACT

G > A

TTG-SEQ ID NO: 17
CACA-SEQ ID NO: 18

CNR1
rs806368
NC_000006.12:g.88140381
88850100
GCCCAACCACCAGATGA
TGCAACGATGTTACCA

T > C

GAA-SEQ ID NO: 19
GCTCA-SEQ ID NO: 20

CNR1
rs806380
NC_000006.12:g.88154934
88864653
TCACTGTTGCTATGGAC
GTGCCTTGGCACTTTT

A > G

TCCT-SEQ ID NO: 21
CTTGA-SEQ ID NO: 22

CNR2
rs2229579
NC_000001.11:g.23874672
24201162
GGCTGTGCTCCTCATCT
GGGTCCGTGTCTAGGT

G > A

GTT-SEQ ID NO: 23
G-SEQ ID NO: 24

CNR2
rs35761398
NC_000001.11:g.23875429
24201919
AGGTGAGGTCATTCTTG
AGTCACGCTGCCAATC

23875430delTTinsCC

TGCT-SEQ ID NO: 25
TTCA-SEQ ID NO: 26

COMT
rs4680
NC_000022.11:g.19963748
19951271
CTGCTCTTTGGGAGAGG
CCACCTTGGCAGTTTA

G > A

TGG-SEQ ID NO: 27
CCCA-SEQ ID NO: 28

CYP2C19
rs4244285
NC_000010.11:g.94781859
96541616
TGTGCAAACTCTTTTAA
CACAAATACGCAAGC

G > A

CCTATGCT-SEQ ID NO:
AGTCACA-SEQ ID NO:

29
30

CYP2C9
rs1057910
NC_000010.11:g.94981296
96741053
ACCCCTGAATTGCTACA
ACCCGGTGATGGTAG

A > C

ACA-SEQ ID NO: 31
AGGTT-SEQ ID NO: 32

CYP2C9
rs1799853
NC_000010.11:g.94942290
96702047
GCAGTGAAGGAAGCCC
CCCTTGGCTCTCAGCT

C > T

TGAT-SEQ ID NO: 33
TCAA-SEQ ID NO: 34

CYP3A4
rs55785340
NC_000007.14:g.99768360
99365983
GTCTTTGGGGCCTACAG
AAGTGGATGAATTAC

A > G

CAT-SEQ ID NO: 35
ATGGTGA-SEQ ID NO:

36

CYP3A4
rs67784355
NC_000007.14:g.99762206
99359829
GGATTTCAGTCCCTGGG
GGGCCTTGTACCTTTC

G > A

GTG-SEQ ID NO: 37
AGGG-SEQ ID NO: 38

CYP3A4
rs12721629
NC_000007.14:g.99762177
99359800
GGATTTCAGTCCCTGGG
GGGCCTTGTACCTTTC

G > A

GTG-SEQ ID NO: 39
AGGG-SEQ ID NO: 40

CYP3A4
rs4987161
NC_000007.14:g.99768458
99366081
GAAGAGGAATCGGCTCT
TGAGAGAAAGAATGG

A > G

GGG-SEQ ID NO: 41
ATCCAAAA-SEQ ID

NO: 42

FAAH
rs324420
NC_000001.11:g.46405089
46870761
TCCCTAGTGAGGCAGAT
TGACCCAAGATGCAG

C > A

GCT-SEQ ID NO: 43
AGCAG-SEQ ID NO: 44

FAAH
rs2295633
NC_000001.11:g.46408711
46874383
ACTGCAGGGTCCTGGAA
AACCCTGCCCACAAG

A > G

GTA-SEQ ID NO: 45
ATAGC-SEQ ID NO: 46

MGLL
rs604300
NC_000003.12:g.127724009
127442852
GAAGGAAAGGGGAGTT
CTAACCCCCAGGATCT

A > G

GGGG-SEQ ID NO: 47
CGGA-SEQ ID NO: 48

GABRA2
rs279826
NC_000004.12:g.46332192
46334209
CACATAATGGGGAGTG
ACCAGTTCCATAGAAT

A > G

GGGG-SEQ ID NO: 49
CCAAGAGT-SEQ ID

NO: 50

GABRA2
rs279858
NC_000004.12:g.46312576
46314593
TGGAGCAGTTTGACTGA
ACAGCTAGATTGGCTG

T > C

GACC-SEQ ID NO: 51
GTTGT-SEQ ID NO: 52

GABRA2
rs279871
NC_000004.12:g.46303716
46305733
CAATATCATGGGACGTG
AAAACAATACTCCCCG

T > C

AGCTG-SEQ ID NO: 53
CCC-SEQ ID NO: 54

MAPK14
rs12199654
NC_000006.12:g.36041718
36009495
ACTTCCGTTGGAATGGG
ACTGGGTTCACCCTAC

A > G

ATTCA-SEQ ID NO: 55
CTGA-SEQ ID NO: 56

NRG1
rs17664708
NC_000008.11:g.32579499
32437017
CAGCACTGGGAGGTGAT
TGTCATGTTGTTGGCT

C > T

CTG-SEQ ID NO: 57
TGGA-SEQ ID NO: 58

AKT1
rs1130233
NC_000014.9:g.104773557
105239894
GGGTGACTTGTTCCTGC
GCACAGAGAGGACAC

C > T

TGA-SEQ ID NO: 59
AGCAT-SEQ ID NO: 60

CNR2
rs2501431
NC_000001.11:g.23875153
24201643
TCTGATCCTGTCCTCCC
TCTTGGCCAACCTCAC

G > A

ACC-SEQ ID NO: 61
ATCC-SEQ ID NO: 62

HTR1A
rs6295
NC_000005.10:g.63962738
63258565
GAGGTTTGCAGGCTCTG
GTGTCAGCATCCCAGA

C > G

GTA-SEQ ID NO: 63
GTGG-SEQ ID NO: 64

HTR2A
rs7997012
NC_000013.11:g.46837850
47411985
CTTGGAGGCACAGCTCA
ACTGCCTCACTCTTGC

A > G

TCA-SEQ ID NO: 65
CATC-SEQ ID NO: 66

CNR1
rs806371
NC_000006.12:g.88146644
88856363
GATTGTCTCTCCCCCAA
AGCAGGTTGGTGACA

T > G

CCC-SEQ ID NO: 67
CAAGT-SEQ ID NO: 68

CNR1
rs12720071
NC_000006.12:g.88141462
88851181
TTGCCAGTCTTTTGTCCT
AATGCATGGTCAGGG

T > C

GC-SEQ ID NO: 69
CAAGT-SEQ ID NO: 70

CNR1
rs1406977
NC_000006.11:g.88884821
88884821
GCACACTTGTGTCACCA
ATGTGGGGAGAGATG

C > T

ACC-SEQ ID NO: 71
CTCCT-SEQ ID NO: 72

PTGS2
rs20417
NC_000001.11:g.186681189
186650321
CCTGCAAATTCTGGCCA
CACTTGGCTTCCTCTC

C > G

TCG-SEQ ID NO: 73
CAGG-SEQ ID NO: 74

SLC6A4
5-HTTLPR
AC104984
26096
ATGCCAGCACCTAACCC
GGACCGCAAGGTGGG

CTAATGT SEQ ID NO:
CGGGA-SEQ ID NO: 76

75

TABLE 6A

NGS data report of SNPs

SNP

Index
db_xref
Gene
Chrom
Position
Coverage
Trans Accession

1
rs2229579
CNR2
1
24201162
3004
NM_001841.2

2
rs2501431
CNR2
1
24201643
0
NM_001841.2

3
rs35761398
CNR2
1
24201919
3305
NM_001841.2

4
rs2501432
CNR2
1
24201920
3275
NM_001841.2

5
rs324420
FAAH
1
46870761
4096
NM_001441.2

6
rs2295633
FAAH
1
46874383
2303
NM_001441.2

7
rs20417
PTGS2
1
186650321
2102

8
rs604300
MGLL
3
127442852
8670
NM_007283.5

9
rs279871
GABRA2
4
46305733
3473
NM_000807.2

10
rs279858
GABRA2
4
46314593
470
NM_000807.2

11
rs279826
GABRA2
4
46334209
1021
NM_000807.2

12
rs6295
HTR1A
5
63258565
998

13
rs12199654
MAPK14
6
36009495
1203
NM_001315.2

14
rs806368
CNR1
6
88850100
348
NM_001160226.1

15
rs12720071
CNR1
6
88851181
1334
NM_001160226.1

16
rs1049353
CNR1
6
88853635
4142
NM_001160226.1

17
rs806371
CNR1
6
88856363
9379
NM_001160226.1

18
rs806380
CNR1
6
88864653
1116
NM_001160226.1

19
rs2180619
CNR1
6
88877952
2302

20
rs1406977
CNR1
6
88884821
0

21
rs1799971
OPRM1
6
154360797
4218
NM_001145279.1

22
rs1045642
ABCB1
7
87138645
11263
NM_000927.4

23
rs2032582
ABCB1
7
87160618
10755
NM_000927.4

24
rs12721629
CYP3A4
7
99359800
4954
NM_017460.5

25
rs67784355
CYP3A4
7
99359829
4756
NM_017460.5

26
rs55785340
CYP3A4
7
99365983
6977
NM_017460.5

27
rs4987161
CYP3A4
7
99366081
6667
NM_017460.5

28
rs17664708
NRG1
8
32437017
1277
NM_013956.3

29
rs4244285
CYP2C19
10
96541616
737
NM_000769.1

30
rs28371674
CYP2C9
10
96702047
1002
NM_000771.3

31
rs1057910
CYP2C9
10
96741053
3383
NM_000771.3

32
rs7997012
HTR2A
13
47411985
5884
NM_000621.3

33
rs6313
HTR2A
13
47469940
1982
NM_000621.3

34
rs6311
HTR2A
13
47471478
1193

35
rs1130233
AKT1
14
105239894
2057
NM_001014431.1

36
rs8065080
TRPV1
17
3480447
1624
NM_018727.5

37
rs222747
TRPV1
17
3493200
1
NM_018727.5

38
rs4680
COMT
22
19951271
1537
NM_000754.3

TABLE 6B

NGS data report of SNPs

Mutation Call:

Index
Reference
Alternative
A %
C %
G %
T %
Relative To CDS
CDS

1
G
A
0.23
0
99.73
0.03

1

2
G
—
0
0
0
0

1

3
T
C
0.15
53.92
0.33
45.6
c.189A > AG
1

4
T
C
0.12
53.25
0.06
46.56
c.188A > AG
1

5
C
A
48.19
51.27
0.27
0.27
c.385C > AC
3

6
A
G
1.22
0
98.74
0
c.1077 + 127A > G

7
C
T
0.05
99.67
0
0.29

8
A
G
0.14
0
99.86
0
c.263-1443T > C

9
T
C
0.03
99.91
0
0.06
c.704-104A > G

10
T
C
0
100
0
0
c.396A > G
4

11
A
G
0.1
0
99.8
0.1
c.255 + 423T > C

12
C
G
0.1
48.5
51.3
0.1
c.-1019C > CG

13
A
G
99.42
0.17
0.42
0

14
T
C
0
47.99
0
52.01
c.*3475A > AG

15
T
C
0.22
52.4
0.22
47.15
c.*2394A > AG

16
C
T
0.05
99.59
0
0.36

1

17
T
C
0.04
0.33
0.03
99.59

18
A
G
0
0
100
0
c.-206-7128T > C

19
G
A
99
0.35
0.52
0.13
c.-452-2185G > A

20
C
—
0
0
0
0

21
A
G
99.36
0.05
0.55
0.05

2

22
A
G
99.15
0.18
0.56
0.1

25

23
A
G
99.38
0.07
0.45
0.1

20

24
G
T
0.16
0
99.64
0.2

11

25
G
A
0.17
0.02
99.81
0

11

26
A
G
99.64
0.04
0.3
0.01

7

27
A
G
99.46
0.04
0.31
0.18

7

28
C
A
0.08
99.84
0
0.08

29
G
A
0.14
0
99.73
0.14

5

30
C
T
0
99.6
0.1
0.3

3

31
A
G
99.29
0.03
0.62
0.06

7

32
A
G
0.05
0
99.9
0.05
c.614-2211T > C

33
G
A
48.69
0
51.16
0.15
c.102C > CT
1

34
C
T
0
49.12
0
50.88
c.-689-309C > CT

35
C
T
0.05
48.71
0.05
51.14
c.726G > AG
8

36
T
C
0
1.05
0.12
98.83

11

37
C
—
0
100
0
0

5

38
G
A
48.8
0
51.2
0
c.472G > AG
2

Zygosity:

Heterozygous: Index #s 1-5, 7, 12-17, 20-31, and 33-38

Homozygous: Index #s 6, 8-11, 18, 19, and 32

TABLE 7

Weighting Values for Dosage Impacts of SNP Genotypes

Drug

Cannabis
CBD
THC
Dependence

Gene
Gene Group
SNP
Allele
Dosage
Dosage
Dosage
THC)

OPRM1
Transporter/Receptor
rs1799971 - Refs. 37 and 11
A/A
1
1
1
1

OPRM1
Transporter/Receptor
rs1799971 - Ref. 37
A/G
1
1
1
1

OPRM1
Transporter/Receptor
rs1799971 - Ref. 37
G/G
1
1
1
1

TRPV1
Transporter/Receptor
rs222747 - Refs. 6 and 7
C/C
1
1
1
1

TRPV1
Transporter/Receptor
rs222747 - Refs. 6 and 7
C/G
1
1
1
1

TRPV1
Transporter/Receptor
rs222747- Refs. 6 and 7
G/G
1
1
1
1

TRPV1
Transporter/Receptor
rs8065080 - Refs. 6 and 14
T/T
1
1
1
1

TRPV1
Transporter/Receptor
rs8065080 - Refs. 6 and 14
T/C
1
1
1
1

TRPV1
Transporter/Receptor
rs8065080 - Refs. 6 and 14
C/C
1
0.5
1
1

HTR2A
Transporter/Receptor
rs6311 - Refs. 13 and 21
C/C
1
1
1
1

HTR2A
Transporter/Receptor
rs6311 - Refs. 13 and 21
C/T
1
1
1.5
1

HTR2A
Transporter/Receptor
rs6311 - Refs. 13 and 21
T/T
1
1
1.5
1

HTR2A
Transporter/Receptor
rs6313 - Refs. 21 and 12
G/G
1
1
1
1

HTR2A
Transporter/Receptor
rs6313 - Refs. 21 and 12
G/A
1
1
1
1

HTR2A
Transporter/Receptor
rs6313 - Refs. 21 and 12
A/A
1
1
0.64
1

ABCB1
Transporter/Receptor
rs1045642 - Refs. 17, 26, and 4
A/A
1
1
1
1

ABCB1
Transporter/Receptor
rs1045642 - Refs. 17, 26, and 4
A/G
1
1
1
1

ABCB1
Transporter/Receptor
rs1045642 - Refs. 17, 26, and 4
G/G
1
1
1
0.5

ABCB1
Transporter/Receptor
rs2032582 - Refs. 13 and 43
A/A
1
1
1
1

ABCB1
Transporter/Receptor
rs2032582 - Refs. 13 and 43
A/C
1
1
1
1

ABCB1
Transporter/Receptor
rs2032582 - Refs. 13 and 43
C/C
1
1
1
1

CNR1
Transporter/Receptor
rs1049353 - Refs. 24, 45, and 35
C/C
1.25
1
1
1

CNR1
Transporter/Receptor
rs1049353 - Refs. 24, 45, and 35
C/T
1
1
1
1

CNR1
Transporter/Receptor
rs1049353 - Refs. 24, 45, and 35
T/T
0.75
1
1
1

CNR1
Transporter/Receptor
rs2180619 - Refs. 46, 30, and 23
G/G
1
1
1
0.5

CNR1
Transporter/Receptor
rs2180619 - Refs. 46, 30, and 23
G/A
1
1
1
1

CNR1
Transporter/Receptor
rs2180619 - Refs. 46, 30, and 23
A/A
1
1
1
1

CNR1
Transporter/Receptor
rs806368 - Ref. 35
T/T
1.5
1
1
1

CNR1
Transporter/Receptor
rs806368 - Ref. 35
T/C
1
1
1
1

CNR1
Transporter/Receptor
rs806368 - Ref. 35
C/C
1
1
1
1

CNR1
Transporter/Receptor
rs806371 - Refs. 45 and 35
T/T
1
1
1
1

CNR1
Transporter/Receptor
rs806371 - Refs. 45 and 35
T/G
1
1
1
1

CNR1
Transporter/Receptor
rs806371 - Refs. 45 and 35
G/G
1
1
1
1

CNR1
Transporter/Receptor
rs806368-rs806371 - Ref. 45
T/T/T/T
1.5
1
1
1

CNR1
Transporter/Receptor
rs806368-rs806371 - Ref. 45
Other
1
1
1
1

CNR1
Transporter/Receptor
rs806380 - Ref. 22
A/A
1
1
1
0.75

CNR1
Transporter/Receptor
rs806380 - Ref. 22
A/G
1
1
1
1

CNR1
Transporter/Receptor
rs806380 - Ref. 22
G/G
1
1
1
1

CNR1
Transporter/Receptor
rs12720071 - Ref. 20
T/T
1
1
1
1

CNR1
Transporter/Receptor
rs12720071 - Ref. 20
T/C
1
1
1
1

CNR1
Transporter/Receptor
rs12720071 - Ref. 20
C/C
1
1
1
1

CNR2
Transporter/Receptor
rs2229579 - Refs. 44 and 9
G/G
1
1
1
1

CNR2
Transporter/Receptor
rs2229579 - Refs. 44 and 9
G/A
1
1
1
1

CNR2
Transporter/Receptor
rs2229579 - Refs. 44 and 9
A/A
1
1
1
1

CNR2
Transporter/Receptor
rs35761398 - Refs. 25 and 9
T/T
1
1
1
1

CNR2
Transporter/Receptor
rs35761398 - Refs. 25 and 9
T/C
1
1
1
1

CNR2
Transporter/Receptor
rs35761398 - Refs. 25 and 9
C/C
1.5
1
1
1

CNR2
Transporter/Receptor
rs2501432 - Refs. 25 and 9
T/T
1
1
1
1

CNR2
Transporter/Receptor
rs2501432 - Refs. 25 and 9
T/C
1
1
1
1

CNR2
Transporter/Receptor
rs2501432 - Refs. 25 and 9
C/C
1.5
1
1
1

COMT
Metabolic Enzyme
rs4680 - Ref. 24
G/G
1
1
1
0.75

COMT
Metabolic Enzyme
rs4680 - Ref. 24
G/A
1
1
1
1

COMT
Metabolic Enzyme
rs4680 - Ref. 24
A/A
1
1
1
1

CYP2C19
Metabolic Enzyme
rs4244285 - Refs. 15 and 41
G/G
1
1
1
1

CYP2C19
Metabolic Enzyme
rs4244285 - Refs. 15 and 41
G/A
1
0.75
1
1

CYP2C19
Metabolic Enzyme
rs4244285 - Refs. 15 and 41
A/A
1
0.5
1
1

CYP2C9
Metabolic Enzyme
rs1057910 - Refs. 29 and 41
A/A
1
1
1
1

CYP2C9
Metabolic Enzyme
rs1057910 - Refs. 29 and 41
A/C
1
1
0.65
1

CYP2C9
Metabolic Enzyme
rs1057910 - Refs. 29 and 41
C/C
1
1
0.3
1

CYP2C9
Metabolic Enzyme
rs28371674 - Refs. 29 and 41
C/C
1
1
1
1

CYP2C9
Metabolic Enzyme
rs28371674 - Refs. 29 and 41
C/T
1
1
0.8
1

CYP2C9
Metabolic Enzyme
rs28371674 - Refs. 29 and 41
T/T
1
1
0.6
1

CYP3A4
Metabolic Enzyme
rs55785340 - Refs. 41
A/A
1
1
1
1

CYP3A4
Metabolic Enzyme
rs55785340 - Refs. 41
A/G
0.75
1
1
1

CYP3A4
Metabolic Enzyme
rs55785340 - Refs. 41
G/G
0.5
1
1
1

CYP3A4
Metabolic Enzyme
rs67784355 - Refs. 41
G/G
1
1
1
1

CYP3A4
Metabolic Enzyme
rs67784355 - Refs. 41
G/A
0.75
1
1
1

CYP3A4
Metabolic Enzyme
rs67784355 - Refs. 41
A/A
0.5
1
1
1

CYP3A4
Metabolic Enzyme
rs12721629 - Refs. 41
G/G
1
1
1
1

CYP3A4
Metabolic Enzyme
rs12721629 - Refs. 41
G/A
0.75
1
1
1

CYP3A4
Metabolic Enzyme
rs12721629 - Refs. 41
A/A
0.5
1
1
1

CYP3A4
Metabolic Enzyme
rs4987161 - Refs. 41
A/A
1
1
1
1

CYP3A4
Metabolic Enzyme
rs4987161 - Refs. 41
A/G
0.75
1
1
1

CYP3A4
Metabolic Enzyme
rs4987161 - Refs. 41
G/G
0.5
1
1
1

FAAH
Metabolic Enzyme
rs324420 - Refs. 33 and 40
C/C
1
1
1
1

FAAH
Metabolic Enzyme
rs324420 - Refs. 33 and 40
C/A
1
0.75
1
0.75

FAAH
Metabolic Enzyme
rs324420 - Refs. 33 and 40
A/A
1
0.5
1
0.5

FAAH
Metabolic Enzyme
rs2295633 - Refs. 28 and 32
A/A
1
1
1
1

FAAH
Metabolic Enzyme
rs2295633 - Refs. 28 and 32
A/G
1
1
1
1

FAAH
Metabolic Enzyme
rs2295633 - Refs. 28 and 32
G/G
1
1
1
1

MGLL
Metabolic Enzyme
rs604300 - Ref. 8
A/A
1
1
1
1

MGLL
Metabolic Enzyme
rs604300 - Ref. 8
A/G
1
1
1
1

MGLL
Metabolic Enzyme
rs604300 - Ref. 8
G/G
1
1
1
0.5

GABRA2
Transporter/Receptor
rs279826 - Ref. 1
A/A
1
1
1
1

GABRA2
Transporter/Receptor
rs279826 - Ref. 1
A/G
1
1
1
1

GABRA2
Transporter/Receptor
rs279826 - Ref. 1
G/G
1
1
1
1

GABRA2
Transporter/Receptor
rs279858 - Ref. 1
T/T
1
1
1
1

GABRA2
Transporter/Receptor
rs279858 - Ref. 1
T/C
1
1
1
1

GABRA2
Transporter/Receptor
rs279858 - Ref. 1
C/C
1
1
1
1

GABRA2
Transporter/Receptor
rs279871 - Ref. 1
T/T
1
1
1
1

GABRA2
Transporter/Receptor
rs279871 - Ref. 1
T/C
1
1
1
1

GABRA2
Transporter/Receptor
rs279871 - Ref. 1
C/C
1
1
1
1

GABRA2
Transporter/Receptor
rs279826-rs279858-rs279871 -
A/T/T
1
1
1
0.5

Ref. 1

GABRA2
Transporter/Receptor
rs279826-rs279858-rs279871 -
G/C/C
1
1
1
0.75

Ref. 1

GABRA2
Transporter/Receptor
rs279826-rs279858-rs279871 -
Other
1
1
1
1

Ref. 1

MAPK14
Signaling
rs12199654 - Ref. 36
A/A
1
1
1
1

MAPK14
Signaling
rs12199654 - Ref. 36
A/G
1
1
1
1

MAPK14
Signaling
rs12199654 - Ref. 36
G/G
1
1
1
1

MAPK14/
Signaling
rs12199654-rs12720071 -
A/A/T/C
1
1
1
0.5

CNR1

Ref. 36

MAPK14/
Signaling
rs12199654-rs12720071 -
A/A/C/C
1
1
1
0.5

CNR1

Ref. 36

NRG1
Signaling
rs17664708 - Ref. 19
C/C
1
1
1
1

NRG1
Signaling
rs17664708 - Ref. 19
C/T
1
1
1
0.75

NRG1
Signaling
rs17664708 - Ref. 19
T/T
1
1
1
0.5

AKT1
Signaling
rs1130233 - Ref. 5
C/C
1
1
1
1

AKT1
Signaling
rs1130233 - Ref. 5
C/T
1
1
0.5
1

AKT1
Signaling
rs1130233 - Ref. 5
T/T
1
1
0.5
1

CNR2
Transporter/Receptor
rs2501431 - Ref. 24

1
1
1
1

CNR2
Transporter/Receptor
rs2501431 - Ref. 24

1
1
1
1

CNR2
Transporter/Receptor
rs2501431 - Ref. 24

1
1
1
1

HTR1A
Transporter/Receptor
rs6295 - Refs. 2 and 3
C/C
1
1
1
1

HTR1A
Transporter/Receptor
rs6295 - Refs. 2 and 3
C/G
1.5
1
1
1

HTR1A
Transporter/Receptor
rs6295 - Refs. 2 and 3
G/G
1.5
1
1
1

HTR2A
Transporter/Receptor
rs7997012 - Ref. 34
A/A
1
1
1
1

HTR2A
Transporter/Receptor
rs7997012 - Ref. 34
A/G
1
1
1
1

HTR2A
Transporter/Receptor
rs7997012 - Ref. 34
G/G
1
1
1.22
1

CNR1
Transporter/Receptor
rs1406977 - Ref. 24

1
1
1
1

CNR1
Transporter/Receptor
rs1406977 - Ref. 24

1
1
1
1

CNR1
Transporter/Receptor
rs1406977 - Ref. 24

1
1
1
1

PTGS2
Metabolic Enzyme
rs20417 - Refs. 16, 10, and 24
C/C
1
1
1
1

PTGS2
Metabolic Enzyme
rs20417 - Refs. 16, 10, and 24
C/G
1
1
1
1

PTGS2
Metabolic Enzyme
rs20417 - Refs. 16, 10, and 24
G/G
1
1
1
1

SLC6A4
Transporter/Receptor
5-HTTLPR - Ref. 24

1
1
1
1

SLC6A4
Transporter/Receptor
5-HTTLPR - Ref. 24

1
1
1
1

SLC6A4
Transporter/Receptor
5-HTTLPR - Ref. 24

1
1
1
1

TABLE 8

Weighting Values for Genotypes of a Test Example

Drug

Cannabis
CBD
THC
Dependence

Dosage
Dosage
Dosage
(THC)

Gene
Gene Group
SNP
Allele
(a_i)
(b_i)
(c_i)
(d_i)

OPRM1
Transporter/Receptor
rs1799971
A/A
1
1
1
1

TRPV1
Transporter/Receptor
rs8065080
T/T
1
1
1
1

HTR2A
Transporter/Receptor
rs6311
C/T
1
1
1.5
1

HTR2A
Transporter/Receptor
rs6313
G/A
1
1
1
1

ABCB1
Transporter/Receptor
rs1045642
A/A
1
1
1
1

ABCB1
Transporter/Receptor
rs2032582
A/A
1
1
1
1

CNR1
Transporter/Receptor
rs1049353
C/C
1.25
1
1
1

CNR1
Transporter/Receptor
rs2180619
A/A
1
1
1
1

CNR1
Transporter/Receptor
rs806368
T/C
1
1
1
1

CNR1
Transporter/Receptor
rs806371
T/T
1
1
1
1

CNR1
Transporter/Receptor
rs806380
G/G
1
1
1
1

CNR1
Transporter/Receptor
rs12720071
T/C
1
1
1
1

CNR2
Transporter/Receptor
rs2229579
G/G
1
1
1
1

CNR2
Transporter/Receptor
rs35761398
T/C
1
1
1
1

CNR2
Transporter/Receptor
rs2501432
T/C
1
1
1
1

COMT
Metabolic Enzyme
rs4680
G/A
1
1
1
1

CYP2C19
Metabolic Enzyme
rs4244285
G/G
1
1
1
1

CYP2C9
Metabolic Enzyme
rs1057910
A/A
1
1
1
1

CYP2C9
Metabolic Enzyme
rs28371674
C/C
1
1
1
1

CYP3A4
Metabolic Enzyme
rs55785340
A/A
1
1
1
1

CYP3A4
Metabolic Enzyme
rs67784355
G/G
1
1
1
1

CYP3A4
Metabolic Enzyme
rs12721629
G/G
1
1
1
1

CYP3A4
Metabolic Enzyme
rs4987161
A/A
1
1
1
1

FAAH
Metabolic Enzyme
rs324420
C/A
1
0.75
1
0.75

FAAH
Metabolic Enzyme
rs2295633
G/G
1
1
1
1

MGLL
Metabolic Enzyme
rs604300
G/G
1
1
1
0.5

GABRA2
Transporter/Receptor
rs279826
G/G
1
1
1
1

GABRA2
Transporter/Receptor
rs279858
C/C
1
1
1
1

GABRA2
Transporter/Receptor
rs279871
C/C
1
1
1
1

GABRA2
Transporter/Receptor
rs279826-
G/C/C
1
1
1
0.75

rs279858-

rs279871

MAPK14
Signaling
rs12199654
A/A
1
1
1
1

MAPK14/
Signaling
rs12199654-
A/A/T/C
1
1
1
0.5

CNR1

rs12720071

NRG1
Signaling
rs17664708
C/C
1
1
1
1

AKT1
Signaling
rs1130233
C/T
1
1
0.5
1

HTR1A
Transporter/Receptor
rs6295
C/G
1.5
1
1
1

HTR2A
Transporter/Receptor
rs7997012
G/G
1
1
1.22
1

PTGS2
Metabolic Enzyme
rs20417
C/C
1
1
1
1

TABLE 9

Calculated dosage and ratio for examples (see FIG. 4 and associated text)

Body

Weight

Genetic
Genetic
Cannabis
Cannabis

Body
Adjust-
CBD/THC
Test
Test
Dependence
Dependence
Final
Final

Dosage
Weight
ment
Standard
Adjusted
Adjusted
Adjusted
Adjusted
Ratio
Dosage

Conditions
(mg)
(lb)
(D1)
Ratio (R1)
CBD (C3)
THC (T3)
CBD (C4)
THC (T4)
(R_f)
(D_f)

Insomnia
0.5-20
181-190
9.5
16:1
12.6
1.0
13.4
0.1
99
13.5

Anxiety/
10-100
181-190
57
20:1
76.3
4.7
80.3
0.7
123
81.0

Depression

Pain
10-100
181-190
57
4:1
64.1
19.6
80.9
2.8
29
83.7

TABLE 10

Variants showing statistically significant association with pain.

rs2501432(Genotype):
rs6311(Genotype):

Group
C/C(freq)
C/T(freq)
T/T(freq)
Group
C/C(freq)
C/T(freq)
T/T(freq)

Pain
4(0.333)
8(0.667)
0(0.000)
Pain
5(0.455)
6(0.545)
0(0.000)

No pain
1(0.333)
0(0.000)
2(0.667)
No pain
1(0.333)
0(0.000)
2(0.667)

Fisher's p value is 0.006772
Fisher's p value is 0.010876

Pearson's p value is 0.006738
Pearson's p value is 0.010832

TABLE 11

Genetic variants and their functions and impact on CBD/THC dosage identified from saliva samples 1002 and 1013.

Sample

ID
Genes
Gene Family
SNPs
Alleles
Brief Functional Description

1002
CNR1
Transporter and
rs806371
T/G
CNR1 Variant: Associated with a reduced response to drug-based

Receptor Genes

treatments for depression and less responsive to THC.

1002
GABRA2
Transporter and
rs279826-
G/C/C
GABRA2 Variant: Associated with increased risk of alcohol and THC

Receptor Genes
rs279858-

dependence.

rs279871

1002
COMT
Metabolic Enzyme
rs4680
G/G
COMT Variant: Associated with increased risk of exhibiting THC-induced

Genes

cognitive impairment that may result in sleep disorders and/or anxiety.

1002
CYP2C9
Metabolic Enzyme
rs28371674
T/T
CYP2C9 Variant: Associated with a decrease in metabolizing certain

Genes

drugs and THC, leading to an increase persistence of THC in the body.

1002
FAAH
Metabolic Enzyme
rs324420
C/A
FAAH Variant: Associated with increased risk for substance use

Genes

disorders.

1002
PTGS2
Metabolic Enzyme
rs20417
G/G
PTGS2 Variant: May lead to enhanced neuropsychiatric and cognitive

Genes

side effects of THC exposure

1013
HTR2A
Transporter and
rs6311
C/T
HTR2A Variant: Less responsive to anti-depressants and THC.

Receptor Genes

1013
CNR1
Transporter and
rs806368
T/T
CNR1 Variant: Associated with response to drug-based treatments for

Receptor Genes

depression,

1013
CNR1
Transporter and
rs806368-
T/T/T/T
CNR1 Variant: Associated with the risk of the reduced efficacy in

Receptor Genes
rs806371

antidepressant and cannabis treatment(s).

1013
CNR2
Transporter and
rs35761398
C/C
CNR2 Variant: Reduced receptor activity and may increase the risk of

Receptor Genes

depression and alcohol dependence.

1013
CNR2
Transporter and
rs2501432
C/C
CNR2 Variant: Reduced receptor activity and may increase the risk of

Receptor Genes

depression and alcohol dependence.

1013
NRG1
Signaling Genes
rs17664708
C/T
NRG1 Variant: Associated with certain levels of substance dependence.

1013
AKT1
Signaling Genes
rs1130233
C/T
AKT1 Variant: Associated with lower tolerances to THC.

1013
CYP2C9
Metabolic Enzyme
rs1057910
A/C
CYP2C9 Variant: Associated with a decrease in metabolizing certain

Genes

drugs and THC, leading to an increase persistence of THC in the body.

1013
MGLL
Metabolic Enzyme
rs604300
G/G
MGLL Variant: Associated with increased risk for substance use

Genes

disorders.

TABLE 12

Number of CBD/THC dosage relevant variants identified

from different participant samples.

Metabolic

Saliva Sample
Enzyme
Signaling
Transporter and
Grand

ID
Genes
Genes
Receptor Genes
Total

1002
4

2
6

1003
3

6
9

1004
3

3
6

1005
2
1
4
7

1006
3
1
6
10

1007
2
1
3
6

1008
4
1
6
11

1012
3

9
12

1013
2
2
5
9

1014
1

4
5

1015
3
1
3
7

1016
3
1
7
11

1017
2
1
6
9

1018
3
2
7
12

1019
3
1
11
15

1020
3

5
8

1021
2
1
4
7

1022
3
1
4
8

1023
2
2
5
9

TABLE 13

Enzymes involved in major and minor metabolisms of psychedelics

Psychedelics
Major Metabolism
Minor Metabolism

Psilocybin
MAO
UGT1A9, UGT1A10

DMT
MAO

LSD
CYP3A4
CYP2E1, CYP2C9, CYP2D6,

CYP1A2

Mescaline
MAO

MDMA
CYP2D6, CYP3A4, COMT

Ketamine
CYP3A4
CYP2B6, CYP2C9

5-Meo-DMT
CYP2D6

TABLE 14

Impacts of genetic variations of psychedelic

receptor and signaling genes

Genes
Variant
Impacts

5-HT2A
rs6311, rs6312,
Influences the clinical response

receptor
and rs7997012
to antidepressant treatment and

may modulate the likelihood of

adverse drug reactions with

certain SSRIs

AMPA
rs707176,
Glutamatergic dysfunction is one

glutamatergic
rs2963944, and
of the major hypotheses for the

receptor
rs10631988
pathogenesis of schizophrenia

Tyrosine kinase B
rs2289656 and
Associate depression as well

receptor (TrkB)
rs1187327
as PTSD

Mammalian target
rs2536, rs1883965,
Associated with the risk of

of rapamycin
rs1034528, and
pediatric epilepsy or correlated

(mTOR)
rs17036508
with increased cancer risk

TABLE 15

SNPs identified for psychedelic dosage analysis

Ref SNP

Functions
Gene
number
Genotype Association and Reference

Serotoninergic
HTR2A
rs17288723
Significant interaction effects between the protective genotypes of each

System

SNP: (1) GG of GRIK4 and TT of FKBP5 (p¼0.022), and (2) CC of HTR2A and

GG of GRIK4 (p¼0.039).

Serotoninergic
HTR2A
rs6311
Associated with positive response in SSRIs treatments.

System

Serotoninergic
HTR2A
rs7997012
Associated with positive response in SSRIs or other, mixed treatments.

System

Serotoninergic
HTR2A
rs1928040
Associated with positive response in SSRIs or other, mixed treatments.

System

Serotoninergic
HTR2A
rs6312

System

Serotoninergic
HTR2A
rs6313
Receptor binding with Ketanserin.

System

Noradrenergic
COMT
rs4680
Associated with positive response in SSRIs or other, mixed treatments. The

System

C472G > A SNP of COMT (rs4680, Val158Met) can causes a valine to

methionine substitution at codon 158 in the enzyme. The Met allele leads

to an enzyme up to four-times less active than the Val allele.

Glutamatergic
AMPA
rs707176
Significant association to the pathogenesis.

Receptor

Glutamatergic
AMPA
rs2963944
Significant association to the pathogenesis.

Receptor

Glutamatergic
AMPA
rs10631988
Significant association to the pathogenesis.

Receptor

Tyrosine Kinase
TrkB
rs2289656
Associated with depression as well as PTSD.

B Receptor

Tyrosine Kinase
TrkB
rs1187327
Associate with depression as well as PTSD.

B Receptor

Mammalian
mTOR
rs2536
Associated with the risk of pediatric epilepsy.

target of

rapamycin

Mammalian
mTOR
rs1883965
Associated with increased cancer risk.

target of

rapamycin

Mammalian
mTOR
rs1034528
Associated with increased cancer risk.

target of

rapamycin

Mammalian
mTOR
rs17036508
Associated with increased cancer risk.

target of

rapamycin

Metabolism
CYP2D6
Gene Copy
Multiple drug responses.

Numbers

Metabolism
CYP3A4
Various
Multiple drug responses.

Alleles

Metabolism
MAOA
vVNTR
Associated with ADHD.

Metabolism
MAOA
rs6323
Associated with ADHD.

Metabolism
MAOB
rs1799836
Associated with side effects of antipsychotic drugs.

Metabolism
UGT1A9
*22/*22
Increased activity in liver.

Metabolism
UGT1A10
139LYS
Decreased activity.

Metabolism
CYP2D6
rs16947
Ultra-rapid metabolizers (CYP2D6*1/*1 and *1/*2) should avoid usage of

Codeine due to potential for toxicity

Metabolism
CYP2D6
rs1135840
Ultra-rapid metabolizers (CYP2D6*1/*1 and *1/*2) should avoid usage of

Codeine due to potential for toxicity

Metabolism
CYP2D6
rs35742686
Poor metabolizers (CYP2D6*3/*3) should reduce dose by 60% of Doxepin

to avoid arrhythmia and myelosuppression

Metabolism
CYP2B6
rs35303484
The rs35303484 (*11; c136A > G; M46V) polymorphism was

overrepresented in the high (S)-methadone level group, suggesting an

association with decreased CYP2B6 activity.

Metabolism
CYP2C9
rs1057910
Consider starting treatment at half the lowest recommended dose in poor

metabolizers (CYP2C9*3/*3) to avoid adverse cardiovascular and

gastrointestinal events

Metabolism
CYP2C9
rs1057910
CYP2C9*3 homozygote; average 80% reduction in warfarin metabolism;

reduced metabolism of number of other drugs

Metabolism
CYP3A4
rs67666821
The normal/common form for this SNP is actually the null (ie deleted)

form; the very rare (<0.06% frequency in Caucasians) form encoding a

nonfunctional CYP3A4 protein has a T (in dbSNP orientation) at this

location. As of 2006, it was the only CYP3A4 SNP with a known functional

consequence.

Metabolism
CYP3A4
rs4646438
Known as 830_831insA, 17661_176622insA or 277Frameshift, is a SNP in

the CYP3A4 gene. The rs4646438(A) allele defines the CYP3A4*6 variant.

Frameshift; likely to be of lower activity

Metabolism
CYP1A2
rs762551
Multiple drug responses.

Metabolism
CYP1A2
rs762551
CYP1A2 slows caffeine metabolization. Melatoninis also degraded by

CYP1A2, caffeine and melatonin compete for the same metabolizing

enzyme.

Metabolism
CYP1A2
rs2069514
Decreased activity; also known as −3860G > A.

Metabolism
CYP1A2
rs762551
Increased activity; also known as −163C > A.

Metabolism
CYP1A2
rs12720461
Decreased activity.

Metabolism
CYP1A2
rs2069526
Decreased activity.

Metabolism
CYP1A2
rs56276455
Decreased activity; also known as D348N.

Metabolism
CYP1A2
rs72547516
Decreased activity; also known as I386F.

Metabolism
CYP1A2
rs28399424
Decreased activity; also known as R431W.

Metabolism
CYP1A2
rs72547513
Known as F186L, 5% vmax of wild allele.

TABLE 16

Genes with SNPs of individual impact

to be calculated for the dosage

MDMA
Unique SNPs

Psilocybin
UGT1A9, UGT1A10

LSD
CYP3A4, CYP2E1, CYP2C9, CYP2D6, CYP1A2

MDMA
CYP2D6, CYP3A4, COMT

Ketamine
CYP3A4, CYP2B6, CYP2C9

5-Meo-DMT
CYP2D6

METHODS AND SYSTEMS FOR PROVIDING A PERSONALIZED TREATMENT REGIMEN USING CANNABINOID OR PSYCHEDELIC COMPOUNDS

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CROSS-REFERENCE TO RELATED APPLICATIONS

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