System and methods for determining propensity to opioid addiction

Abstract

Embodiments include methods of determining a patient's susceptibility (i.e., risk) of becoming dependent and / or addicted to an addictive substance. The addictive substance can be an opioid receptor agonist. In aspects, the methods include use of a lateral flow assay (LFA) test with up converting phosphors (UCPs). If a patient is deemed susceptible to addiction, a monitoring / dispensing system can be used to administer the addictive substance (e.g., opioid receptor agonist).

Description

SYSTEM AND METHODS FOR DETERMINING PROPENSITY TO OPIOID ADDICTIONRELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 532,418 filed August 13, 2023, the contents of which are incorporated by reference.FIELD OF THE INVENTION

[0002] The invention relates generally to therapeutic diagnostics, more particularly, to systems and methods of determining a subject’s susceptibility to addiction.BACKGROUND

[0003] The term “opioid” refers to a substance that acts on opioid receptors to produce morphine-like effects. Opioids include the illegal drug heroin, synthetic opioids such as fentanyl, and pain relievers available legally by prescription, such as oxycodone (OxyContin®), hydrocodone (Vicodin®), codeine, morphine and others.

[0004] Opioids can be classified according to their effect on opioid receptors. In this manner opioids can be considered as agonists, partial agonists and antagonists. In addition, opioids can be classified according to the type of opioid receptor at which they produce their effects. Classically, there are considered to be three opioid receptors. These receptors are all G-protein-coupled receptors, and were originally named mu (after morphine), delta (after vas deferens, the tissue within which it was first isolated) and kappa (after the first ligand to act at this receptor, ketocyclazocine). In 1996 the International Union of Pharmacology (IUPHAR) renamed the receptors OP1 (the delta receptor), OP2 (the kappa receptor) and OP3 (the mu receptor). In 2000, this nomenclature was again changed to DOP, KOP and MOP. Now, however, owing to the extensive literature previously using the Greek nomenclature for opioid receptors (5, K and p) IUPHAR recommends using this classification and the DOP, KOP and MOP classification of 2000. In 1994, a fourth G-protein-coupled endogenous opioid like receptor was found, and was subsequently named the nociceptin (NOP) receptor.

[0005] The classical opioid receptors are distributed widely within the central nervous system and, to a lesser extent, throughout the periphery, occupying sites within the vas deferens, knee joint, gastrointestinal tract, heart and immune system, amongst others. In clinical practice the stimulation of the differing opioid receptors produces a range of effects, which are often dependent upon the location of the receptor, along with analgesia. Agonists binding to MOP receptors may cause analgesia, but also sedation, respiratory depression, bradycardia, nausea and vomiting and a reduction in gastric motility. Activation of DOP receptors can cause spinal and supraspinal analgesia and reduce gastric motility, while KOP receptor stimulation may produce spinal analgesia, diuresis and dysphoria.

[0006] Opioid agonist analgesics are used to treat moderate to severe, chronic cancer pain, often in combination with nonsteroidal anti-inflammatory drugs (NSAIDs), as well as acute pain (e.g. , during recovery from surgery and breakthrough pain). Further, their use is increasing in the management of chronic, non-malignant pain.

[0007] A perennial problem with potent opioid agonists however is one of abuse by drug addicts. Drug addiction is a worldwide problem of which opioid dependence is a major component. The World Health Organization (WHO) estimates that there are approximately 4.3 million opioid addicts globally, with approximately 0.7 million in Europe and 0.3 million in the US and Canada.

[0008] Well-known complications of opioids include the phenomenon of both physical and psychological dependence and addiction, which can in turn lead to opioid misuse, abuse and diversion. More than 70% of the illegal users obtain opioids by stealing them during pharmacy robberies, purchasing them illegally on the black market, or receiving them from family or friends. These individuals seek to achieve a “high” from prescription medications by taking an excess number of pills orally or by crushing the pills, followed by snorting, smoking, or injecting the new altered formulation. The misuse or abuse of prescription opioid medications is a growing problem, with abuse rates having quadrupled in the decade from 1990 to2000. The deaths associated with abuse and misuse of prescription pain drugs have also quadrupled between 1999 and 2011. Frequently death associated with the overdose of opioids occurs within an hour of the administration of the opioid due to respiratory suppression.

[0009] Because the availability of opioids has increased, there are now more deaths and overdoses from prescription opioids than deaths from heroin overdoses. The increased availability of opioids was partially brought about by The Joint Commission On Accreditation of Healthcare Organizations (JCAHO) standards from 2000 that demands pain be addressed by healthcare providers as the fifth vital sign. The evaluation of pain was thus made equivalent to the evaluation of a patient's vital signs including the heartbeat, blood pressure, respiratory rate, and temperature. A 2012 study showed that physicians played an important role in prescription drug overdoses as they attempted to comply with Federal mandates, avoid malpractice claims, avoid decreased patient satisfaction scores (as patients began demanding more prescriptions for opioid pain medications), and avoid decreased reimbursement. The same study found that of 3,733 fatalities associated with prescription opioid drugs, the drugs that caused or contributed to nearly half of the deaths were prescribed to patients by physicians. This public health issue confounds the clinical utility of opioids. The extent of their efficacy in the treatment of pain when utilized on a chronic basis has not been definitively proven and has made long-term treatment of non-cancer pain with opioids controversial. Coupled with the abuse and addiction potential, clinical safety concerns and side effect profile, proper physician prescribing of opioids may be prevented, and result in inadequate pain management.

[0010] Though the bulk of the current research is focused on abuse deterrence, addiction avoidance and patient safety, prescription drug are still abused, overdoses occur and death rates continue to rise. It is apparent that this approach is not successful in preventing patient deaths. The abuse deterrent developments have either been too difficult to manufacture or failed in clinical use. The CDC determined that abuse of prescription opioid drugs is an epidemic because it has led to a quadrupling of death rates in recent years.

[0011] The symptoms of withdrawal are a major reason for relapse and further prescription drug abuse. Treatments for addiction vary but generally include efforts to discontinue exposure to the drug while alleviating symptoms of withdrawal. Opioid replacement therapy (ORT) involves replacing an illegal opioid with a longer acting but less euphoric opioid; methadone or buprenorphine are typically used and the drug is taken under medical supervision. The driving principle behind ORT is the program's capacity to facilitate a resumption of stability in the user's life, while the patient experiences reduced symptoms of drug withdrawal and less intense drug cravings; a strong euphoric effect is not experienced as a result of the treatment drug. Some patients maintain complete abstinence from opioids while receiving opioid replacement therapy and others are able to reduce their use. Despite efforts, methadone programs often have poor compliance and polydrug abuse is often found in this subset of patients. Moreover, suboxone is expensive, few medical providers are licensed to prescribe the drug and compliance rates remain low while recidivism rates remain high.

[0012] Opioid abuse continues to grow which contributes untold emotional, social and economic damage. Yet opioids remain the standard of care for surgical and chronic pain relief. Many patients rely on effective medication to reduce pain from ailments and medical procedures. Improved methods of preventing addiction are needed to allow patients to receive proper treatment while preventing addiction. Aspects of the present invention fulfill this need and provide further related advantages as described in the following summary.SUMMARY OF THE INVENTION

[0013] Aspects of the present disclosure teach certain benefits in construction and use which give rise to the exemplary advantages described below.

[0014] The present disclosure solves the problems described above by providing health care providers the ability to determine one’s susceptibility of becoming dependent or addicted to a substance (e.g., an opioid medication).

[0015] Accordingly, embodiments include the use of biomarkers to determine a subject’s susceptibility to addiction. The biomarkers can include one or more of TMX2-CTNND1 , NICN1 , FOXP2, PDE4B, SLC39A8, NCAM1 , RABEPK, RP11- 254A17.1 , BARHL2, PLCL2, TMEM120A, TMPRSS5, GMEB1 , CTB-129P6.11 , FURIN, SEMA6D, HS6ST3 and FTO. In aspects, upregulation of one or more of the biomarkers indicates that the subject is susceptible to addiction.

[0016] Embodiments also include a method of treating a patient for pain that includes steps of (a) determining whether to administer an opioid receptor agonist for pain, (b) selecting an opioid medicament and determining a dosage regime, (c) deciding duration of initial opioid prescription and (d) assessing risk and addressing potential harms of the opioid medicament. In aspects, the method also includes a step of determining a patient’s susceptibility (i.e., risk) of becoming dependent and / or addicted to opioids. Precautions can be taken before / during administration to a patient who is deemed susceptible to addiction (e.g., use of a monitoring / dispensing system).

[0017] In aspects, the opioid receptor agonist is a pain medicament selected from morphine, codeine, thebaine, diacetyl morphine (heroin), hydromorphone, hydrocodone, oxymorphone, oxycodone, methadone and fentanyl.

[0018] Embodiments also include a method of assessing susceptibility to addiction to an opioid receptor agonist. The method can include steps of (a) obtaining a fluid sample from a subject, (b) applying the sample to a lateral flow immunoassay (LFA) strip, (c) analyzing the strip to identify causal biomarkers and calculate ratios and correlations between biomarker levels in the sample, (d) assessing the subject for susceptibility to addiction to the opioid receptor agonist, (e) determining that the subject is susceptible to addiction to the opioid receptor agonist, and (f) administering the opioid receptor agonist to the patient with a monitoring / dispensing system. In aspects, the monitoring / dispensing system administers the opioid receptor agonist as an inhalant.

[0019] In aspects, the methods described herein use up converting phosphors (UCPs) are used in lateral flow immunoassays. In aspects, machine learning is usedto identify causal biomarkers and calculating ratios and correlations between marker levels in a patient sample.

[0020] In aspects, a threshold value indicates that the subject is susceptible to addiction to the opioid receptor agonist. In aspects, causal biomarkers include one or more of proteins and mRNA, miRNA, siRNA, acetylation, proteomics, genomics, RANA, proteinomics, protein:protein interactions and protein:molecule interactions.

[0021] In aspects, the biomarkers include one or more of TMX2-CTNND1 , NICN1 , FOXP2, PDE4B, SLC39A8, NCAM1 , RABEPK, RP11-254A17.1 , BARHL2, PLCL2, TMEM120A, TMPRSS5, GMEB1 , CTB-129P6.11 , FURIN, SEMA6D, HS6ST3 and FTO.

[0022] In embodiments, machine learning is used in assessing a subject for susceptibility to addiction (e.g., by identifying causal biomarkers in a sample from the subject).

[0023] In aspects, the above methods include a step of calculating ratios and correlations between biomarker levels.

[0024] In aspects, the above methods include a step of administering the pain medication to the patient with a monitoring / dispensing system as an inhalant.

[0025] Other features and advantages of aspects of the present invention will become apparent from the following more detailed description, taken in conjunction with the accompanying drawings, which illustrate, by way of example, the principles of aspects of the invention.BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings illustrate aspects of the present invention. In such drawings:

[0027] FIG. 1 is a flow chart that depicts the steps in a method of determining a patient’s susceptibility (i.e., risk) of becoming dependent and / or addicted to opioids.

[0028] FIG. 2 lists genes and corresponding genetic risk loci to effector proteomic activity according to embodiments.Definitions

[0029] Reference in this specification to "one embodiment / aspect" or "an embodiment / aspect" means that a particular feature, structure, or characteristic described in connection with the embodiment / aspect is included in at least one embodiment / aspect of the disclosure. The use of the phrase "in one embodiment / aspect" or "in another embodiment / aspect" in various places in the specification are not necessarily all referring to the same embodiment / aspect, nor are separate or alternative embodiments / aspects mutually exclusive of other embodiments / aspects. Moreover, various features are described which may be exhibited by some embodiments / aspects and not by others. Similarly, various requirements are described which may be requirements for some embodiments / aspects but not other embodiments / aspects. Embodiment and aspect can in certain instances be used interchangeably.

[0030] The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Certain terms that are used to describe the disclosure are discussed below, or elsewhere in the specification, to provide additional guidance to the practitioner regarding the description of the disclosure. It will be appreciated that the same thing can be said in more than one way.

[0031] Consequently, alternative language and synonyms may be used for any one or more of the terms discussed herein. Nor is any special significance to be placed upon whether or not a term is elaborated or discussed herein. Synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only, and is not intended tofurther limit the scope and meaning of the disclosure or of any exemplified term. Likewise, the disclosure is not limited to various embodiments given in this specification.

[0032] Without intent to further limit the scope of the disclosure, examples of instruments, apparatus, methods and their related results according to the embodiments of the present disclosure are given below. Note that titles or subtitles may be used in the examples for convenience of a reader, which in no way should limit the scope of the disclosure. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the case of conflict, the present document, including definitions, will control.

[0033] The term “personalized health care” or “precision medicine” refers to an emerging practice of medicine that uses an individual's genetic profile to guide decisions made in regard to the prevention, diagnosis and treatment of disease. The approach can also take into account other characteristics of an individual (e.g., biomarker levels, protein and molecular interactions, etc.). For the patient / consumer, personalized health care means being recognized as a unique individual based on their health, genetic makeup, chemistry, history and circumstances, and receiving treatment tailored to one’s needs. This leads to better health outcomes in preventing, diagnosing, treating and managing health and disease.

[0034] The term “algorithm” refers to a procedure for solving a mathematical problem in a finite number of steps that frequently involves repetition of an operation.

[0035] The term “artificial intelligence” or “Al” refers to intelligence exhibited by machines, rather than humans. The term, as applied herein, refers to when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving."

[0036] The term “big data” refers to large data sets that can be analyzed computationally to reveal patterns, trends, and associations, especially relating tohuman physiological responses, human behavior and interactions. This can include, for example, a databases of electronic health records from a hospital or clinic.

[0037] The term “classifier” refers to the mathematical function, implemented by a classification algorithm that maps input data to a category. An algorithm that implements classification, especially in a concrete implementation, is known as a classifier.

[0038] The term “computer learning” or “machine learning refers to an application of artificial intelligence (Al) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

[0039] The term “transfer learning” or “deep transfer learning” is a type of machine learning that can leverage existing, generalizable knowledge from other related tasks to enable learning of a separate task with a small set of data. The data sets available to train models for in silico drug discovery efforts are often small. Indeed, the sparse availability of labeled data is a major barrier to artificial-intelligence- assisted drug discovery. One solution to this problem is to develop algorithms that can cope with relatively heterogeneous and scarce data. Deep transfer learning is the most commonly used type of transfer learning in the field of drug discovery.

[0040] The term “Markov model” refers to a stochastic model used to model pseudo-randomly changing systems. It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes the Markov property). Generally, this assumption enables reasoning and computation with the model that would otherwise be intractable. For this reason, in the fields of predictive modelling and probabilistic forecasting, it is desirable for a given model to exhibit the Markov property.

[0041] The term “omics” refers to utilizing multiple molecular disciplines that involve the characterization of global sets of biological molecules such as DNAs, RNAs,proteins, and metabolites. For example, genomics investigates thousands of DNA sequences, transcriptomics investigates all or many gene transcripts, proteomics investigates large numbers of proteins, and metabolomics investigates large sets of metabolites. Omic data can include genomic data, transcriptomics, proteomics, epigenomics and metabolomics.

[0042] The term “historical data” refers to data (e.g. , physiological measurements and actions of individuals) recorded and / or stored in a database that can be accessed for analysis and / or comparison. The data can be raw (e.g., sensor data) or processed (e.g. fused data). Historical data can include (a) data compiled from groups / populations of individuals and (b) data compiled from an individual person. For example, data can be recorded from healthy people and people with known ailments. An analysis of the data can indicate variations in physiological measurements that can be correlated with ailments. Likewise, data from an individual person can be recorded and stored. This can allow the system to identify patterns, variations and / or aberrations in activity for that particular person.

[0043] As used herein, "additional biomedical information" refers to one or more evaluations of an individual, other than using any of the biomarkers described herein, that are associated with health and / or susceptibility to addiction. Accordingly, "additional biomedical information" includes any of the following: physical descriptors of an individual, the height, weight and / or BMI of an individual, the gender of an individual, the ethnicity of an individual, family history, smoking history, occupational history, etc. Additional biomedical information can be obtained from an individual using routine techniques known in the art, such as from the individual themselves by use of a routine patient questionnaire or health history questionnaire, etc., or from a medical practitioner, etc. Testing of biomarker levels in combination with an evaluation of any additional biomedical information may, for example, improve sensitivity, specificity, and / or AUC for predicting vulnerability to addiction (or other related uses) as compared to biomarker testing alone or evaluating any particular item of additional biomedical information alone.

[0044] The term “comorbid disorders” refers to additional diseases or conditions that a subject has at the same time as a primary health concern. Conditions described as comorbidities are often chronic or long-term conditions.

[0045] The term “environmental factor” refers to exposures to substances (e.g., pesticides, industrial waste, etc.) where a subject lives or works. Common environmental factors include (a) chemicals (e.g., mold, pesticides, etc.), (b) air pollution, (c) climate change and natural disasters, (d) diseases caused by microbes, (e) lack of access to health care, (f) infrastructure issues, (g) poor water quality and (h) global environmental issues.

[0046] The term “physical dependence” refers to the physiological adaptation of the body to the presence of a substance, in this case opioid medication. It is defined by the development of withdrawal symptoms when the substance is discontinued, when the dose is reduced abruptly or, specifically in the case of opioids, when an antagonist (e.g., naloxone) or an agonist-antagonist (e.g., pentazocine) is administered. Physical dependence is a normal and expected aspect of certain medications and does not necessarily imply that the patient is addicted.

[0047] The term “drug addiction” refers to a complex set of behaviors typically associated with misuse of certain drugs, developing over time and with higher drug dosages. Addiction includes psychological compulsion, to the extent that the affected person persists in actions leading to dangerous or unhealthy outcomes. Opioid addiction includes insufflation or injection, rather than taking opioids orally as prescribed for medical reasons.

[0048] The term “opioid use disorder” or “OUD” refers to a chronic disorder, with serious potential consequences including disability, relapses and death. The Diagnostic and Statistical Manual of Mental Disorders describes opioid use disorder as a problematic pattern of opioid use leading to problems or distress, with at least two of the following occurring within a 12-month period: (a) Taking larger amounts or taking drugs over a longer period than intended; (b) Persistent desire or unsuccessful efforts to cut down or control opioid use; (c) Spending a great deal oftime obtaining or using the opioid or recovering from its effects; (d) Craving, or a strong desire or urge to use opioids; (e) Problems fulfilling obligations at work, school or home; (f) Continued opioid use despite having recurring social or interpersonal problems; (g) Giving up or reducing activities because of opioid use; (h) Using opioids in physically hazardous situations such as driving while under the influence of opiates; (i) Continued opioid use despite ongoing physical or psychological problem likely to have been caused or worsened by opioids; 0) Tolerance (i.e., need for increased amounts or diminished effect with continued use of the same amount); and (k) Experiencing withdrawal (opioid withdrawal syndrome) or taking opioids (or a closely related substance) to relieve or avoid withdrawal symptoms.

[0049] The term “formulation” as used herein refers to the antibodies disclosed herein and excipients combined together which can be administered and has the ability to bind to the corresponding receptors and initiate a signal transduction pathway resulting in the desired activity. The formulation can optionally comprise other agents.

[0050] The term "administration" refers to the introduction of an amount of a predetermined substance into a patient by a certain suitable method. The composition disclosed herein may be administered via any of the common routes, as long as it is able to reach a desired tissue, for example, but is not limited to, intraperitoneal, intravenous, intramuscular, subcutaneous, intradermal, oral, topical, intranasal, intrapulmonary, or intrarectal administration. However, since peptides are digested upon oral administration, active ingredients of a composition for oral administration should be coated or formulated for protection against degradation in the stomach.

[0051] The term “medicament,” “active agent” or “active ingredient” refers to a substance, compound, or molecule, which is biologically active or otherwise, induces a biological or physiological effect on a subject to which it is administered to. In other words, “active agent” or “active ingredient” refers to a component or components of a composition to which the whole or part of the effect of the composition is attributed. An active agent can be a primary active agent, or in otherwords, the component(s) of a composition to which the whole or part of the effect of the composition is attributed. An active agent can be a secondary agent, or in other words, the component(s) of a composition to which an additional part and / or other effect of the composition is attributed.

[0052] The term “pharmaceutical composition” is intended to include the combination of an active agent with a carrier, inert or active, in a sterile composition suitable for diagnostic or therapeutic use in vitro, in vivo or ex vivo. In one aspect, the pharmaceutical composition is substantially free of endotoxins or is non-toxic to recipients at the dosage or concentration employed.

[0053] In an embodiment, a “subject” of diagnosis or treatment is, without limitation, a prokaryotic or a eukaryotic cell, a tissue culture, a tissue or an animal, e.g. a mammal, including a human. Non-human animals subject to diagnosis or treatment include, for example, without limitation, a simian, a murine, a canine, a leporid, such as a rabbit, livestock, sport animals, and pets.

[0054] The terms “treating,” “treatment” and the like are used herein, without limitation, to mean obtaining a desired pharmacologic and / or physiologic effect. The effect may be prophylactic in terms of completely or partially preventing a disorder or sign or symptom thereof, and / or may be therapeutic in terms of amelioration of the symptoms of the disease or infection, or a partial or complete cure for a disorder and / or adverse effect attributable to the disorder.

[0055] The term “prognosis” refers to the likely outcome or course of a disease and / or the chance of recovery or recurrence. This is in contrast to a “diagnosis” which refers to identifying an ailment or disease, usually from examining a subject.

[0056] The term “health evaluation” or “health assessment” refers to a plan of care that identifies the specific needs of a person and how those needs will be addressed by the healthcare system or healthcare provider. Conventionally, a health assessment follows an evaluation of a subject’s health status by performing a physical exam after taking a health history.

[0057] The term "biomarker" refers generally to a DNA, RNA, protein, carbohydrate, or glycolipid-based molecular marker, the expression or presence of which in a sample can be detected by standard methods (or methods disclosed herein) and is predictive or prognostic of the effective responsiveness or sensitivity of a mammalians subject with an ailment. Biomarkers may be present in a test sample but absent in a control sample, absent in a test sample but present in a control sample, or the amount or of biomarker can differ between a test sample and a control sample. For example, protein biomarkers can be present in such a sample, but not in a control sample, or certain biomarkers are seropositive in the sample, but seronegative in a control sample. Also, expression of such a biomarker may be determined to be higher than that observed from a control sample. The terms "marker" and "biomarker" are used herein interchangeably.

[0058] The amount of the biomarker can be measured in a test sample and compared to the “normal control level,” utilizing techniques such as reference limits, discrimination limits, or risk defining thresholds to define cutoff points and abnormal values for an ailment. The normal control level means the level of one or more biomarkers or combined biomarker indices typically found in a subject not suffering from the ailment. Such normal control level and cutoff points can vary based on whether a biomarker is used alone or in a formula combining with other biomarkers into an index. Alternatively, the normal control level can be a database of biomarker patterns from previously tested subjects who did not experience the ailment over a clinically relevant time.

[0059] The term "level" or "levels" with reference to a biomarker or a plurality of biomarkers is meant to encompass a score, quantitative measurement, a qualitative assessment, or other acceptable observation obtained when a biomarker or observation correlated to a biomarker is assessed.

[0060] The term “altered expression” or "differential level" of a biomarker may include either an increased or decreased level. For example, an altered expression level can refer to a biomarker level that is decreased by at least 5%, at least 10%, atleast 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% (i.e., indicative of the absence of the biomarker molecule). In another embodiment, an altered expression level refers to a level that is increased by at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, at least 200% or more.

[0061] An mRNA that is “upregulated” generally refers to an increase in the level of expression of the mRNA in response to a given treatment or condition. An mRNA that is “downregulated” generally refers to a decrease in the level of expression of the mRNA in response to a given treatment or condition. In some situations, the mRNA level can remain unchanged upon a given treatment or condition.

[0062] The term “nucleic acid probe” or “oligonucleotide probe” refers to a nucleic acid capable of binding to a target nucleic acid of complementary sequence, such as the mRNA biomarkers provided herein, through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation. As used herein, a probe may include natural (e.g., A, G, C, or T) or modified bases (7-deazaguanosine, inosine, etc.). In addition, the bases in a probe may be joined by a linkage other than a phosphodiester bond, so long as it does not interfere with hybridization. It will be understood by one of skill in the art that probes may bind target sequences lacking complete complementarity with the probe sequence depending upon the stringency of the hybridization conditions. The probes are preferably directly labeled with isotopes, for example, chromophores, lumiphores, chromogens, or indirectly labeled with biotin to which a streptavidin complex may later bind. By assaying for the presence or absence of the probe, one can detect the presence or absence of a target mRNA biomarker of interest.

[0063] After selection of a set of biomarkers, well-known techniques such as crosscorrelation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), related decision tree classification techniques, Shrunken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting,Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, Linear Regression or classification algorithms, Nonlinear Regression or classification algorithms, analysis of variants (ANOVA), hierarchical analysis or clustering algorithms; hierarchical algorithms using decision trees; kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel Fisher's discriminate analysis algorithms, or kernel principal components analysis algorithms, or other mathematical and statistical methods can be used to develop a formula for calculation of a risk score. A selected population of individuals is used, where historical information is available regarding the values of biomarkers in the population and their clinical outcomes. To calculate a risk score for a given individual, biomarker values are obtained from one or more samples collected from the individual and used as input data.

[0064] Tests to measure biomarkers and biomarker panels can be implemented on a variety of diagnostic test systems. Diagnostic test systems are apparatuses that typically include means for obtaining test results from biological samples. Examples of such means include modules that automate the testing (e.g., biochemical, immunological, nucleic acid detection assays). Some diagnostic test systems are designed to handle multiple biological samples and can be programmed to run the same or different tests on each sample. Diagnostic test systems typically include means for collecting, storing and / or tracking test results for each sample, usually in a data structure or database. Examples include well-known physical and electronic data storage devices (e.g., hard drives, flash memory, magnetic tape, paper printouts). It is also typical for diagnostic test systems to include means for reporting test results. Examples of reporting means include visible display, a link to a data structure or database, or a printer. The reporting means can be a data link to send test results to an external device, such as a data structure, data base, visual display, or printer.

[0065] The term "detecting" or "determining" with respect to a biomarker value includes the use of both the instrument required to observe and record a signal corresponding to a biomarker value and the material / s required to generate thatsignal. In various embodiments, the biomarker value is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.

[0066] The term “physiological event” refers to a response or reaction of the body to a stimulus. Most are automatic / instinctive physiological responses. The healthy state of the body depends upon the integrity of various organ systems. The organ systems in the body function in a particular manner constantly. The mechanisms, by which the organ systems of the body function, can be referred to as “physiological mechanisms.” Physiological mechanisms explain any health-related events or outcomes. Physiological mechanisms can be altered voluntarily. For example, exercise causes alteration in the cardiac physiology of resting state.

[0067] The term “prognosis” refers to a likely course (i.e. , a forecast) of a disease or ailment such as addiction. As used herein, it refers to the probable outcome of addiction, including whether addiction will respond to treatment or mitigation efforts and / or the likelihood that it will progress.

[0068] The term “immunoassay” refers to a biochemical test that measures the presence or concentration of a substance in a sample, such as a biological sample. It is common to use the reaction of an antibody to its cognate antigen, for example the specific binding of an antibody to a protein. Both the presence of antigen and the amount of antigen present can be measured. The presence and amount (i.e., abundance) of the protein can be determined or measured. Measuring the quantity of antigen (such as a biomarker) can be achieved by a variety of methods. A common method is to label either the antigen or antibody with a detectable label (e.g., a fluorescent tag, enzymatic linkage or radioactive isotope).

[0069] The term “lateral flow assay,” “lateral flow immunoassay” or “LFA” refers to a diagnostic device used to confirm the presence or absence of a target analyte. LFA-based tests often use a paper-based platform for the detection and quantification of analytes, where the sample is placed on a test device and the results are displayed within 5 - 30 minutes. LFA-based tests are widely used in hospitals and clinical laboratories for the qualitative and quantitative detection of specific antigens and antibodies, as well as products of gene amplification. The principle behind the LFA is relatively simple. A liquid sample (or its extract) containing the analyte of interest moves without the assistance of external forces (capillary action) through various zones of polymeric strips, on which molecules that can interact with the analyte are attached. A typical lateral flow test strip has overlapping membranes that are mounted on a backing card.

[0070] The term “target analyte” or “analyte” refers to a molecule, compound or particle to be detected. Target analytes bind to binding ligands (both capture and soluble binding ligands), as is more fully described below. In some embodiments, the target analyte is a protein such as a biomarker as described herein.

[0071] The term “substrate” or “solid support” refers to a material that can be modified to contain discrete individual sites appropriate of the attachment or association of capture ligands. Suitable substrates include metal surfaces such as gold, electrodes as defined below, glass and modified or functionalized glass, fiberglass, resins, silica or silica-based materials, carbon, metals, inorganic glasses and other polymers.

[0072] The term “up-converting nano-phosphors” or “upconverting materials” refers to compounds that emit light at a wavelength that is shorter than the wavelength of light they have been photoexcited with which give them applications in biomedical imaging. The so-called anti-Stokes shift in these materials limits the autofluorescence of nearby molecules within a sample. Compared with gold nanoparticles, a lateral flow test using upconverting phosphor nanoparticles (UCNPs) is more sensitive (approximately tenfold) and robust, due to the unique feature of using the lower energy 980 nm infrared light (excitation light) to generate higher energy visual light (emission light). This light process is called “up- conversion,” which does not occur in nature. Thus, UCNPs as a reporter label donot generate background fluorescence (autofluorescence) compared with conventional fluorescent labels, such as fluorescently labeled nanoparticles and quantum dots. Moreover, UCNPs do not fade, allowing the lateral flow strips based on UCNPs to be stored in the long term. More importantly, the lateral flow test based on UCNPs has no interference from red blood cell hemolysis that is a problem sometimes encountered in lateral flow test based on colloidal gold-labeled using blood.

[0073] The term “stimulant” refers to a substance that increases activity of the central nervous system and / or the body. Stimulants are widely used as prescription medicines as well as without a prescription (either legally or illicitly) as performanceenhancing or recreational drugs. Most stimulants exert their activating effects by enhancing catecholamine neurotransmission. Common stimulants include, amphetamine, caffeine, ephedrine, MDMA, MDPV, mephedrone, methamphetamine, methylphenidate, cocaine, phenylpropanolamine, propylhexedrine, pseudoephedrine, Catha edulis (Khat) and modafinil.

[0074] The term “omics” refers to comprehensive approaches for analysis of complete genetic or molecular profiles of humans and other organisms. Since the process of mapping and sequencing the human genome began, new technologies have made it possible to obtain a huge number of molecular measurements within a tissue or cell. These technologies can be applied to a biological system of interest to obtain a snapshot of the underlying biology at a resolution that has never before been possible. Broadly speaking, the scientific fields associated with measuring such biological molecules in a high-throughput way are called “omics.”

[0075] The term “treatment regime refers” to a treatment plan that specifies the dosage, the schedule, and the duration of treatment for a medicament (e.g., an opioid agonist). A “standard regime” refers to one that would be proposed by a competent medical professional (e.g., a licensed physician) pursuant to current guidelines, laws, regulations and safety protocols for a particular substance.

[0076] The term “risk mitigation strategies” refers to efforts that can be employed to help prevent dependence / addiction to a substance such as an opioid. These caninclude (a) urine drug screenings on patients, (b) pill count callbacks, (c) regular follow-up visits with patients to screen for opioid use disorder (OUD), (d) consultations with pain specialists, (e) administration using a “smart” monitoring / dispensing system, etc.

[0077] As applicable, the terms "about" or "generally", as used herein in the specification and appended claims, and unless otherwise indicated, means a margin of + / - 20%. Also, as applicable, the term "substantially" as used herein in the specification and appended claims, unless otherwise indicated, means a margin of + / - 10%. It is to be appreciated that not all uses of the above terms are quantifiable such that the referenced ranges can be applied.

[0078] Many known and useful compounds and the like can be found in Remington’s Pharmaceutical Sciences (13thEd), Mack Publishing Company, Easton, PA— a standard reference for various types of administration. As used herein, the term “formulation(s)” means a combination of at least one active ingredient with one or more other ingredient, also commonly referred to as excipients, which may be independently active or inactive. The term “formulation” may or may not refer to a pharmaceutically acceptable composition for administration to humans or animals and may include compositions that are useful intermediates for storage or research purposes.

[0079] As the patients and subjects of the invention method are, in addition to humans, veterinary subjects, formulations suitable for these subjects are also appropriate. Such subjects include livestock and pets as well as sports animals such as horses, greyhounds, and the like.DETAILED DESCRIPTION

[0080] Embodiments of the invention relate to the fields of addiction and substance abuse. Particular embodiments include systems and methods for determining the susceptibility of a subject to addiction to a substance. In aspects, the substance is an opioid receptor agonist.

[0081] Opioids remain the standard of care for surgical and chronic pain.Guidelines suggest that the risk of opioids is likely greater than their benefits when used for most non-cancer chronic conditions including headaches, back pain, and fibromyalgia. Further, recent studies suggest that 25% of people who take prescription opioids misuse them. Thus, they should be used cautiously in chronic non-cancer pain. If used the benefits and harms should be periodically reassessed (e.g., at least every three months).

[0082] Aspects of the invention allow a healthcare provider to determine one’s susceptibility to opioid addiction. This allows a provider to confidently prescribe opioids to patients at low risk of addiction. Patients at higher risk of addiction can be administered prescription opioid medication in a clinical setting (i.e., under supervision). Alternatively, “at risk” patients can also be administered medication utilizing a monitoring / dispensing device as described below.

[0083] FIG. 1 depicts a method according to aspects of the invention. In the first step, a subject or patient who seeks pain medication 105 from a healthcare professional. For example, a patient may be anticipating pain from an upcoming surgery or medical procedure. Alternatively, a patient may be experiencing chronic pain without effective non-opioid pain medications.

[0084] The patient provides a blood sample 110 or a saliva sample 115. In aspects, other fluid samples 120 are included (e.g., urine, bile, mucus, etc.). The samples are applied to a lateral flow immunoassay (LFA) 125. In aspects, the LFA uses Up Converting Phosphors (UCPs). UCPs are more sensitive and stable than conventional fluorescent and colorimetric reporters as described in detail below.

[0085] The LFA can be analyzed by a reader to detect and / or quantify levels of hormones, proteins, DNA, RNA and metabolites 130. Other omic data can also be analyzed (e.g., genomics and epigenomics). Data from the reader can be transferred to the cloud 135 and analyzed. Predictive machine learning engines can use the input data and Al tools 140 to identify causal biomarkers and calculate ratios and correlations between marker levels in serum, saliva and / or urine resulting in agradient scale. In aspects, other factors can be considered such as (a) environmental factors, (b) comorbid disorders, (c) historical data and (d) additional biomedical information. Although an LFA is depicted here, it is contemplated that other assays can be used to measure levels of hormones, proteins, DNA, RNA and / or metabolites.

[0086] The result can provide an accurate prediction of an individual’s susceptibility to becoming addicted 145. Based on the results, the patient can be classified as low susceptibility 150 or high susceptibility 155 to addiction. In aspects, the result is an integer. An integer below / above a threshold value can indicate that a subject is susceptible to addiction.

[0087] A healthcare provider can confidently prescribe opioids to a patient with a low susceptibility to addiction. Patients at higher risk of addiction can be administered prescription opioid medication in a clinical setting (i.e., under supervision) and periodically evaluated. Alternatively, “at risk” patients can also be administered medication utilizing a monitoring / dispensing device 160 as described below (Example 1).

[0088] In aspects, one or more biomarkers from Table 1 are measured using the LFA (step 125). Levels of protein, DNA, RNA and / or metabolites can be used in the method. FIG. 2 provides mechanistic details.Table 1 - List of Biomarkers

[0089] In aspects, a subject is deemed “at risk” or susceptible to addiction if they are statistically more likely to develop a dependency than not (i.e., a 51 % chance of becoming opioid dependent) after finishing a standard regime (i.e., as prescribed under recognized guidelines). In aspects, a subject is deemed susceptible if they are about 25% likely to become opioid dependent. In aspects, a subject is deemed susceptible if they are about 10%, are about 20%, are about 30%, about 40%, about 45%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85% or more likely to become opioid dependent after a standard regime.

[0090] In aspects, a subject is deemed “at risk” or susceptible to opioid use disorder (OUD) if they are statistically more likely to develop OUD than not (i.e., a 51 % chance of becoming opioid dependent) after finishing a standard regime (i.e., as prescribed under recognized guidelines). In aspects, a subject is deemed susceptible if they are about 25% likely to develop OUD. In aspects, a subject is deemed susceptible if they are about 10%, are about 20%, are about 30%, about 40%, about 45%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85% or more likely to develop OUD after a standard regime.Up Converting Phosphors (UCPs)

[0091] Conventional laboratory tests are not suited for quantifying most biomarkers. For example, colorimetric reporters (e.g., for covid or pregnancy) are used with in- home tests are qualitative; they can provide a positive or negative result. The signal is binary (i.e., not quantitative) and must also be read within one hour due to signal bleaching. The signal is unreliable after a certain amount of time (usually an hour). A quantitative test is required to determine variations in levels of a protein or biomarker.

[0092] It is possible to use an ELISA assay to measure levels of biomarkers. Common fluorescent reporter ELISA tests in laboratory settings are quantitative.However, they also quench and must be read within a short period of time using expensive laboratory equipment. Moreover, the ELISA reports used today react to produce a background signal with body fluid component. Because they autofluoresce, they reduce sensitivity and complicate sample processing.

[0093] Embodiments include a lateral flow assay system that includes multiplexed panels of quantifiable protein biomarkers which incorporate a unique reporter system (UCP-reporters) with output signals that can be collectively analyzed. The test can be performed with finger-prick blood samples, saliva, urine, etc. which can collected at a clinical or home setting.

[0094] The reporter system includes uniform nanocrystals created from rare-earth elements, known as up-converting phosphors (UCPs). Unlike fluorescent and colorimetric reporters, UPCs do not quench and emit a stable lifetime signal which can be read and accurately analyzed even after three years. In comparison, the fluorescent reporter counterparts must be analyzed within about one hour. The UCP reporters do not auto-fluoresce, and thus produce zero background signal for biological samples. These characteristics improve sensitivity from 10 to 10,000-fold over existing methods because there is no interference when using whole blood. By comparison, fractionated serum must be used in the current ELISA tests.

[0095] Compared with gold nanoparticles, a lateral flow test using upconverting phosphor nanoparticles (UCNPs) is more sensitive (approximately tenfold) and robust, due to the unique feature of using the lower energy 980 nm infrared light (excitation light) to generate higher energy visual light (emission light). This light process is called “up-conversion,” which does not occur in nature. Thus, UCNPs as a reporter label do not generate background fluorescence (autofluorescence) compared with conventional fluorescent labels, such as fluorescently labeled nanoparticles and quantum dots. Moreover, UCNPs do not fade, allowing the lateral flow strips based on UCNPs to be stored in the long term. More importantly, the lateral flow test based on UCNPs has no interference from red blood cell hemolysis that is a problem sometimes encountered in lateral flow test based on colloidal gold- labeled using blood.

[0096] Thus, the test can be run directly from finger-pricked, heel-pricked, dried blood samples (DBS), saliva, urine, etc. The test can be completed at home setting or at a clinic. The completed strips can be shipped to a reference lab for accurate analysis up to weeks after the test was run. Alternatively, they can be analyzed with an at-home optical reader that uploads the scanned strip, data uploaded into the cloud. The UCPs have been shown to accurately measure biomarkers on dried strips, five years after the test was run. The test strips can multiplex measurements for accurate assessment of complex disease states.

[0097] There are several benefits of the UCP-reporter lateral flow assay described herein, including:• UCP-LFAs provide stable, optical signals with 10 - 1000 times higher sensitivity than in existing lateral flow technology.• UCP-LFAs have zero background signal, neither from bodily fluids nor LFA cartridge substrate materials.• Signal generation is confined to UCP particles only at the capture strip.• There is no quenching, fading or bleaching of the signal which is stable even if stored for years.• There is unlimited signal integration time with simultaneous excitation and emission.• Optical reader-assisted algorithms can prevent operator error in diagnosis of disease states.• There is a low limit of detection (i.e. , 1 pg for protein analytes and 1 fg for DNA).

[0098] The LFA tests can be used to detect a range and / or quantify levels of hormones, proteins, DNA, RNA and metabolites. Other omic data can also be analyzed (e.g. , genomics and epigenomics). Data from the reader can be transferred to the cloud and analyzed. Predictive machine learning engines can use the input data and Al tools to identify causal biomarkers and calculate ratios and correlations between biomarker levels.Method of Testing Biomarkers

[0099] Relevant biomarkers can be identified and levels determined using antibodybased methods, such as, an enzyme-linked immunosorbent assay (ELISA), a radioimmunoassay (RIA) or a lateral flow immunoassay (LFA). In one embodiment, the method uses a lateral flow assay that can detect protein biomarkers at low levels. The levels of protein can also be determined by analyzing the amount of an indicator of fluorescence.

[0100] LFA-based tests are well known in the art. A typical LFA can include the following components: sample pad, conjugate release pad, membrane with immobilized antibodies and adsorbent pad. The components of the strip are usually fixed to an inert backing material. A biological sample is applied to a portion of the strip (i.e. , sample pad) of a lateral flow assay, resulting in the accumulation of a labelled binding reagent, in an analyte concentration-dependent manner (proportional or inversely proportional) at a detection zone on the test strip. The test strip can then be analyzed to detect / quantify the amount of target analytes (i.e., biomarkers) by assay reading components mounted on the PCBA. A microprocessor, ASIC or the like can analyze and interpret the readings and display the assay results to a user and / or health care provider.

[0101] In one embodiment, the method includes a multi-line lateral flow test strip that uses a sandwich-antibody capture technique to quantify one or more biomarkers. The method can use an up-converting phosphor (UCP) detection system that provides high sensitivity, zero biological background florescent which results in ten-fold dynamic range increase over other antibody detection systems. Further, the UCP is quantitative and lasts for over twenty years.

[0102] The amount of an analyte present in a sample can be determined in absolute terms (e.g., in terms of a numerical value per unit volume) or in relative terms (e.g., by reference to a predetermined threshold). In particular, the “relative amount of one or more biomarkers” is not intended to mean that the concentrations of different biomarkers in a sample are compared with one another, but rather thatthe concentration of one or more such analytes may be compared to a predetermined threshold.

[0103] The measurement of the assay and / or interpretation of the assay result can include one or more data processing steps, in which assay data are subjected to one or more computations or other type of processing. Such processing can be performed by a digital electronic device such as a microprocessor or the like, which will typically form part of an extrinsic, or an integral, assay result reader. For example, the data processing can include the calculation of a ratio.

[0104] In other embodiments, levels of one or more mRNA biomarkers are measured using methods known in the art. An mRNA from a patient sample can be “upregulated,” i.e. , the level of mRNA can be increased, for example, by about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 100%, about 200%, about 300%, about 500%, about 1 ,000%, about 5,000% or more of the comparative control mRNA level or a reference level.

[0105] An mRNA from a patient sample can be “upregulated,” i.e., the level of mRNA can be increased, for example, by not more than 5%, not more than 10%, not more than 20%, not more than 30%, not more than 40%, not more than 50%, not more than 60%, not more than 70%, not more than 80%, not more than 90%, not more than 100%, not more than 200%, not more than 300%, not more than 500%, not more than 1 ,000%, not more than 5,000% or more of the comparative control mRNA level or a reference level.

[0106] An mRNA from a patient sample can be “upregulated,” i.e., the level of mRNA can be increased, for example, by at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 100%, at least 200%, at least 300%, at least 500%, at least 1 ,000%, at least 5,000% or more of the comparative control mRNA level or a reference level.

[0107] Alternatively, an mRNA can be “downregulated,” i.e. , the level of mRNA level can be decreased, for example, by about 99%, about 95%, about 90%, about 80%, about 70%, about 60%, about 50%, about 40%, about 30%, about 20%, about 10%, about 5%, about 2%, about 1 % or less of the comparative control mRNA level or a reference level.

[0108] An mRNA can be “downregulated,” i.e., the level of mRNA level can be decreased, for example, by no more than 99%, no more than 95%, no more than 90%, no more than 80%, no more than 70%, no more than 60%, no more than 50%, no more than 40%, no more than 30%, no more than 20%, no more than 10%, no more than 5%, no more than 2%, no more than 1 % or less of the comparative control mRNA level or a reference level.

[0109] An mRNA can be “downregulated,” i.e., the level of mRNA level can be decreased, for example, by at least 99%, at least 95%, at least 90%, at least 80%, at least 70%, at least 60%, at least 50%, at least 40%, at least 30%, at least 20%, at least 10%, at least 5%, at least 2%, at least 1 % or less of the comparative control mRNA level or a reference level.

[0110] Similarly, the level of a polypeptide, protein, or peptide from a patient sample can be increased as compared to a control or a reference level. This increase can be about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 100%, about 200%, about 300%, about 500%, about 1 ,000%, about 5,000% or more of the comparative control protein level or a reference level. Alternatively, the level of a protein biomarker can be decreased. This decrease can be, for example, present at a level of about 99%, about 95%, about 90%, about 80%, about 70%, about 60%, about 50%, about 40%, about 30%, about 20%, about 10%, about 5%, about 2%, about 1 % or less of the comparative control protein level or a reference level.

[0111] Similarly, the level of a polypeptide, protein, or peptide from a patient sample can be increased as compared to a control or a reference level. This increase can be no more than 5%, no more than 10%, no more than 20%, no morethan 30%, no more than 40%, no more than 50%, no more than 60%, no more than 70%, no more than 80%, no more than 90%, no more than 100%, no more than 200%, no more than 300%, no more than 500%, no more than 1 ,000%, no more than 5,000% or more of the comparative control protein level or a reference level. Alternatively, the level of a protein biomarker can be decreased. This decrease can be, for example, present at a level of no more than 99%, no more than 95%, no more than 90%, no more than 80%, no more than 70%, no more than 60%, no more than 50%, no more than 40%, no more than 30%, no more than 20%, no more than 10%, no more than 5%, no more than 2%, no more than 1 % or less of the comparative control protein level or a reference level.

[0112] Similarly, the level of a polypeptide, protein, or peptide from a patient sample can be increased as compared to a control or a reference level. This increase can be at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 100%, at least 200%, at least 300%, at least 500%, at least 1 ,000%, at least 5,000% or more of the comparative control protein level or a reference level. Alternatively, the level of a protein biomarker can be decreased. This decrease can be, for example, present at a level of at least 99%, at least 95%, at least 90%, at least 80%, at least 70%, at least 60%, at least 50%, at least 40%, at least 30%, at least 20%, at least 10%, at least 5%, at least 2%, at least 1 % or less of the comparative control protein level or a reference level.

[0113] One or more of the biomarkers can be used in a method of predicting the susceptibility that an addiction (e.g. , opioids). In this manner, one biomarker or a combination of biomarkers can be used in the method. In this manner, at least one biomarker or a combination of biomarkers can be used in the method.

[0114] In a first step, the expression levels of one or more proteins are measured in plasma samples from subjects who suffered from addiction. In an embodiment, the expression levels can be used to generate a footprint or signature for subsequent diagnosis of patients. In an embodiment, the expression levels can be used to generate a footprint or signature for subsequent diagnosis of patients.

[0115] Next, expression levels of the same proteins are measured in plasma, blood or tissue samples from healthy subjects (i.e., patient who is not prone to addiction). This is used as a control. Thereafter, samples from healthy patients can be compared to identifying proteins that have altered levels of expression in the plasma samples from the subjects with an ailment. A biomarker fingerprint or signature can be created from the proteins with altered levels of expression. This can be used for diagnosing or determining the prognosis of an ailment in the test subject by comparing of levels of proteins from plasma of the test subject. Conventional statistical analysis can be used to determine, for example, confidence levels.

[0116] In embodiments, a patient is prescribed opioid agonists only if they are not susceptible to addiction (e.g., below a threshold level). Thus, a healthcare provider can confidently prescribe opioids to a patient with a low susceptibility to addiction. Patients at higher risk of addiction can be administered prescription opioid medication in a clinical setting (i.e., under supervision) and periodically evaluated. Alternatively, “at risk” patients can also be administered medication utilizing a monitoring / dispensing device and / or be treated for addiction. Treatment can include counselling, detoxification, rehabilitation and administration of opioid antagonists.Diagnostic Kit for Determining Susceptibiiity to Addiction

[0117] The following working example is based on configurations described above. Embodiments of the invention can be compiled into a diagnostic kit for diagnosing an ailment or determining that a subject is vulnerable to addiction.

[0118] For example, immunoassay kits can be used as described herein. Such kits can include, (a) antibodies having binding specificity for the polypeptides identified as biomarkers; and (b) anti-antibody immunoglobulins. This immunoassay kit can be utilized for the practice of the various methods provided herein. The antibodies and the anti-antibody immunoglobulins can be provided in an amount of about 0.001 mg to 100 grams, and more preferably about 0.01 mg to 1 gram. The anti-antibody immunoglobulin may be a polyclonal immunoglobulin, protein A or protein G or functional fragments thereof, which may be labeled prior to use by methods known inthe art. In embodiments, the immunoassay kit includes antibodies that specifically bind to butyrylcholinesterase. The kit can also include reagents that can be used to identify variations in expression levels of one or more proteins in a sample from a test subject.

[0119] An indicator compound can be used to determine the amount of each biomarker. The indicator compound can be any compound, chemical, or biological component which may interact with a target or byproduct of a target. For example, an indicator compound may comprise an antibody, a reactive chemical compound, a labeled molecule, or any combination thereof. Antibodies used in an embodiment of the present disclosure may be monoclonal or polyclonal and derived from any species (e.g., human, rat, mouse, rabbit, pig). Further, indicator molecules may be aptamers, proteins, peptides, small organic molecules, natural compounds, nonpeptide polymers, MHC multimers (including MHC-dextramers, MHC-tetramers, MHC-pentamers and other MHC-multimers), or any other molecules that specifically and efficiently bind to other molecules are also marker molecules.

[0120] Labeled molecules, for use as indicator compounds, may be any molecule that absorbs, excites, or modifies radiation, such as the absorption of light (e.g., dyes and chromophores) and the emission of light after excitation (fluorescence from fluorochromes). Additionally, labeled molecules may have an enzymatic activity, by which it catalyzes a reaction between chemicals in the near environment of the labeling molecules, producing a signal which include production of light (chemiluminescence) or precipitation of chromophores, dyes, or a precipitate that can be detected by an additional layer of detection molecules.

[0121] The indictor signal in at least one embodiment of the present disclosure may comprise any detectable signal, including but not limited to, color change, fluorescence, and chemical / structural change of the target (and / or indicator compound) so as to be amenable to reacting with a secondary detection marker. Further, the detection of a signal may involve a secondary reactive molecule.

[0122] The target of an exemplary indicator compound of the present disclosure may be any diagnostic marker or diagnostic condition for a disease state (or predisease state) of an individual. For example, the disease state may be diabetes, prediabetes or metabolic syndrome.

[0123] A kit can include one biomarker or a combination of biomarkers relevant to addiction. The skilled artisan will appreciate that the number of biomarkers may be varied without departing from the nature of the disclosure, and thus other combinations of biomarkers are also encompassed by the disclosure. The skilled artisan will know which one biomarker or a combination of biomarkers to use. It is also contemplated that additional medical information can be used in determining the susceptibility and / or vulnerability of a patient to addiction.

[0124] In a specific embodiment, a kit includes a single biomarker or a combination of biomarkers disclosed herein. The kit can further optionally include instructions for use. The kit can further optionally include tubes, applicators, vials or other storage container with the above-mentioned biomarker and / or vials containing one or more of the biomarkers. In an embodiment, each biomarker is in its own tube, applicator, vial or storage container.

[0125] The kits, regardless of type, will generally include one or more containers into which the one biomarker or a combination of biomarkers are placed and, preferably, suitably aliquoted. The components of the kits may be packaged either in aqueous media or in lyophilized form.Opioid Agonist Administration Device

[0126] In aspects, an administration device can be used to dispense a pain medicament to a patient deemed susceptible to addiction. An administration device can prevent over administration of a medicament and / or improper use of a medicament. Such devices are known in the art. For example, US Patent Publication No. US20220131699A1 describes a method and system for monitoring and controlling high risk substances. A device includes a smart inhaler coupled with an onboard or external biometric sensor. A physician or pharmacist can connect an opioid medication cartridge into the chassis of the device and register the patient'sfingerprint and a prescription to the device. Patients can self-administer the medication by placing their finger on the fingerprint scanner. A controller that is coupled to the scanner and the network can release the medication pursuant to a pre-programmed prescription regimen. The regimen can be adjusted remotely by the physician or automatically in the event of the detection of a system anomaly. The system allows delivery of drugs slowly pursuant to a regime that prevents abuse, diversion and overdose.Operating Environment:

[0127] The system is typically comprised of a central server that is connected by a data network to a user's (e.g., healthcare provider’s) computer. The central server can be comprised of one or more computers connected to one or more mass storage devices. The precise architecture of the central server does not limit the claimed invention. Further, the user's (i.e. healthcare practitioner’s) computer can be a laptop or desktop type of personal computer. It can also be a cell phone, smart phone or other handheld device, including a tablet. The precise form factor of the user's computer does not limit the claimed invention. Examples of well-known computing systems, environments, and / or configurations that may be suitable for use with the invention include, but are not limited to, personal computers, server computers, hand held, laptop or mobile computer or communications devices such as cell phones and PDA's, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The precise form factor of the user's computer does not limit the claimed invention. In one embodiment, the user's computer is omitted, and instead a separate computing functionality provided that works with the central server. In this case, a user would log into the server from another computer and access the system through a user environment.

[0128] The user environment can be housed in the central server or operatively connected to it. Further, the user can receive from and transmit data to the central server by means of the Internet, whereby the user accesses an account using an Internet web-browser and browser displays an interactive web page operativelyconnected to the central server. The central server transmits and receives data in response to data and commands transmitted from the browser in response to the customer's actuation of the browser user interface. Some steps of the invention may be performed on the user's computer and interim results transmitted to a server. These interim results may be processed at the server and final results passed back to the user.

[0129] The methods described herein can be executed on a computer system, generally comprised of a central processing unit (CPU) that is operatively connected to a memory device, data input and output circuitry (I / O) and computer data network communication circuitry. Computer code executed by the CPU can take data received by the data communication circuitry and store it in the memory device. In addition, the CPU can take data from the I / O circuitry and store it in the memory device. Further, the CPU can take data from a memory device and output it through the I / O circuitry or the data communication circuitry. The data stored in memory may be further recalled from the memory device, further processed or modified by the CPU in the manner described herein and restored in the same memory device or a different memory device operatively connected to the CPU including by means of the data network circuitry. The memory device can be any kind of data storage circuit or magnetic storage or optical device, including a hard disk, optical disk or solid state memory. The I / O devices can include a display screen, loudspeakers, microphone and a movable mouse that indicate to the computer the relative location of a cursor position on the display and one or more buttons that can be actuated to indicate a command.

[0130] The computer can display on the display screen operatively connected to the I / O circuitry the appearance of a user interface. Various shapes, text and other graphical forms are displayed on the screen as a result of the computer generating data that causes the pixels comprising the display screen customer’s actuation of the browser user interface. Some steps of the invention can be performed on the user's computer and interim results transmitted to a server. These interim results can be processed at the server and final results passed back to the user.

[0131] The invention may also be entirely executed on one or more servers. A server may be a computer comprised of a central processing unit with a mass storage device and a network connection. In addition, a server can include multiple of such computers connected together with a data network or other data transfer connection, or, multiple computers on a network with network accessed storage, in a manner that provides such functionality as a group. Practitioners of ordinary skill will recognize that functions that are accomplished on one server may be partitioned and accomplished on multiple servers that are operatively connected by a computer network by means of appropriate inter process communication. In addition, the access of the website can be by means of an Internet browser accessing a secure or public page or by means of a client program running on a local computer that is connected over a computer network to the server. A data message and data upload or download can be delivered over the Internet using typical protocols, including TCP / IP, HTTP, TCP, UDP, SMTP, RPC, FTP or other kinds of data communication protocols that permit processes running on two remote computers to exchange information by means of digital network communication. As a result a data message can be a data packet transmitted from or received by a computer containing a destination network address, a destination process or application identifier, and data values that can be parsed at the destination computer located at the destination network address by the destination application in order that the relevant data values are extracted and used by the destination application. The precise architecture of the central server does not limit the claimed invention. In addition, the data network may operate with several levels, such that the user's computer is connected through a fire wall to one server, which routes communications to another server that executes the disclosed methods.

[0132] Computer program logic implementing all or part of the functionality previously described herein may be embodied in various forms, including, but in no way limited to, a source code form, a computer executable form, and various intermediate forms (e.g., forms generated by an assembler, compiler, linker, or locator.) Source code may include a series of computer program instructions implemented in any of various programming languages (e.g., an object code, an assembly language, or a high-level language such as C, C-HF, C#, Action Script,PHP, EcmaScript, JavaScript, JAVA, or 5 HTML) for use with various operating systems or operating environments. The source code may define and use various data structures and communication messages. The source code may be in a computer executable form (e.g., via an interpreter), or the source code may be converted (e.g., via a translator, assembler, or compiler) into a computer executable form.

[0133] The invention may be described in the general context of computerexecutable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The computer program and data may be fixed in any form (e.g., source code form, computer executable form, or an intermediate form) either permanently or transitorily in a tangible storage medium, such as a semiconductor memory device (e.g., a RAM, ROM, PROM, EEPROM, or Flash-Programmable RAM), a magnetic memory device (e.g., a diskette or fixed hard disk), an optical memory device (e.g., a CD-ROM or DVD), a PC card (e.g., PCMCIA card), or other memory device. The computer program and data may be fixed in any form in a signal that is transmittable to a computer using any of various communication technologies, including, but in no way limited to, analog technologies, digital technologies, optical technologies, wireless technologies, networking technologies, and internetworking technologies. The computer program and data may be distributed in any form as a removable storage medium with accompanying printed or electronic documentation (e.g., shrink wrapped software or a magnetic tape), preloaded with a computer system (e.g., on system ROM or fixed disk), or distributed from a server or electronic bulletin board over the communication system (e.g., the Internet or World Wide Web.) It is appreciated that any of the software components of the present invention may, if desired, be implemented in ROM (read-only memory) form. The software components may, generally, be implemented in hardware, if desired, using conventional techniques.

[0134] The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through acommunications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices. Practitioners of ordinary skill will recognize that the invention may be executed on one or more computer processors that are linked using a data network, including, for example, the Internet. In another embodiment, different steps of the process can be executed by one or more computers and storage devices geographically separated but connected by a data network in a manner so that they operate together to execute the process steps. In one embodiment, a user's computer can run an application that causes the user's computer to transmit a stream of one or more data packets across a data network to a second computer, referred to here as a server. The server, in turn, may be connected to one or more mass data storage devices where the database is stored. The server can execute a program that receives the transmitted packet and interpret the transmitted data packets in order to extract database query information. The server can then execute the remaining steps of the invention by means of accessing the mass storage devices to derive the desired result of the query. Alternatively, the server can transmit the query information to another computer that is connected to the mass storage devices, and that computer can execute the invention to derive the desired result. The result can then be transmitted back to the user's computer by means of another stream of one or more data packets appropriately addressed to the user's computer. In one embodiment, the relational database may be housed in one or more operatively connected servers operatively connected to computer memory, for example, disk drives. In yet another embodiment, the initialization of the relational database may be prepared on the set of servers and the interaction with the user's computer occur at a different place in the overall process.

[0135] It should be noted that the flow diagrams are used herein to demonstrate various aspects of the invention and should not be construed to limit the invention to any particular logic flow or logic implementation. The described logic may be partitioned into different logic blocks (e.g., programs, modules, functions, or subroutines) without changing the overall results or otherwise departing from the true scope of the invention. Oftentimes, logic elements may be added, modified, omitted, performed in a different order, or implemented using different logic constructs (e.g.,logic gates, looping primitives, conditional logic, and other logic constructs) without changing the overall results or otherwise departing from the true scope of the invention.EXAMPLES

[0136] The following non-limiting examples are provided for illustrative purposes only in order to facilitate a more complete understanding of representative embodiments now contemplated. These examples are intended to be a mere subset of all possible contexts in which the components of the formulation may be combined. Thus, these examples should not be construed to limit any of the embodiments described in the present specification, including those pertaining to the type and amounts of components of the formulation and / or methods and uses thereof.Example 1Prediction of Opioid Addiction and use of Pulmonary Inhalation Device

[0137] The latest guidelines recommend that when opioids are needed for acute pain, they should be prescribed at the lowest effective dose and for no longer than the expected duration of pain severe enough to warrant opioids. However, additional measures are warranted to prevent opioid dependency / addiction.

[0138] In this example, a patient seeks opioid pain medication from her physician. The patient indicates that she has tried a host of non-opioid treatments to help with the intense daily pain caused by a collapsed spinal disc and facet joint arthritis. But she indicates that nothing has worked as well as methadone, an opioid she has used for the past three years.

[0139] The physician is concerned because of her long-term use of methadone. Blood and saliva samples are analyzed using the methods described herein. An analysis of biomarkers indicates that the patient is susceptible to addiction. Specifically, the patient has a 75% chance of becoming addicted based on levels of biomarkers and additional biomedical information.

[0140] The physician prescribes the opioid for administration using a “smart” medication dispensing system. The device includes a smart inhaler coupled with an onboard or external biometric sensor. The physician connects an opioid medication cartridge into the chassis of the device and registers the patient's fingerprint and a prescription to the device. The patient can self-administer the medication by placing her finger on a fingerprint scanner. A controller can release the medication pursuant to a pre-programmed prescription regimen. The regimen can be adjusted remotely by the physician or automatically in the event of the detection of a system anomaly.

[0141] In this example, the patient maintains her use of opioid pain reliever until recovery from back surgery. Three months after surgery, the patient replaces the opioid medication with acetaminophen.* * *

[0142] Embodiments provide a system for predicting addiction. In an embodiment, addiction can be identified in an individual using a system that includes: an apparatus configured to determine expression levels of nucleic acids, proteins, peptides or other molecule from a biological sample taken from the individual; and hardware logic designed or configured to perform operations including: (a) receiving expression levels of a collection of signature genes / proteins / peptides from a biological sample taken from said individual, wherein the collection of signature genes / proteins / peptides includes at least one mRNA and / or at least one protein from those listed in Table 2.

[0143] Information relevant to the patient's diagnosis can include additional biomedical information such as, for example, age, ethnicity, medical history, family history, physical exam findings, neurological exam findings, imaging and lab tests, etc. These clinical variables may be included in the predictive model in various embodiments.

[0144] Once a biomarker or biomarker panel is selected, the biomarker or biomarker panel can be used in a method for predicting addiction in an individual. In an embodiment, a biomarker or biomarker panel is selected, a method fordiagnosing an individual that may be susceptible to addiction and can include one or more of the following steps: 1 ) collect or otherwise obtain a biological sample; 2) perform an analytical method to detect and measure the biomarker or biomarkers in the panel in the biological sample; 3) perform any data normalization or standardization required for the method used to collect biomarker values; 4) calculate a biomarker score; 5) combine the biomarker scores to obtain a total diagnostic score; and 6) report the individual's diagnostic score. This method of diagnosis can be conducted using a computer and software programs for analysis of data collected from nucleic acid, protein, peptide or other biological molecules. In this approach, the diagnostic score may be a single number determined from the sum of all the marker calculations that is compared to a preset threshold value that is an indication of the presence or absence of disease. Or the diagnostic score may be a series of bars that each represent a biomarker value and the pattern of the responses may be compared to a pre-set pattern for determination of the presence or absence of disease.

[0145] For both DNA and RNA, the nucleic acid can be isolated from plasma, blood sample or cell biopsy (e.g., brain tissue). The DNA or RNA can be extracellular or extracted from a cell in the plasma or blood sample. For a protein or peptide or other biological molecule, such can be isolated from a saliva, plasma, blood sample or cell biopsy. The protein or peptide or other biological molecule can be extracellular or extracted from a cell in the saliva, plasma or a blood sample.

[0146] It is also noted that many of the structures, materials, and acts recited herein can be recited as means for performing a function or step for performing a function. Therefore, it should be understood that such language is entitled to cover all such structures, materials, or acts disclosed within this specification and their equivalents, including the matter incorporated by reference.

[0147] The biomarker analysis system can provide functions and operations to complete data analysis, such as data gathering, processing, analysis, reporting and / or diagnosis. For example, in one embodiment, the computer system can execute the computer program that may receive, store, search, analyze, and reportinformation relating to the biomarkers. The computer program may comprise multiple modules performing various functions or operations, such as a processing module for processing raw data and generating supplemental data and an analysis module for analyzing raw data and supplemental data to determine one’s susceptible to addiction. This step can include generating or collecting any other information, including additional biomedical information, regarding the condition of the individual relative to the disease, identifying whether further tests may be desirable, or otherwise evaluating the health status of the individual.

[0148] The biomarker analysis system can provide functions and operations to complete data analysis, such as data gathering, processing, analysis, reporting and / or diagnosis. For example, in one embodiment, the computer system can execute the computer program that may receive, store, search, analyze, and report information relating to the biomarkers. The computer program can include multiple modules performing various functions or operations, such as a processing module for processing raw data and generating supplemental data and an analysis module for analyzing raw data and supplemental data. Determining one’s susceptibility to addiction can include generating or collecting any other information, including additional biomedical information, regarding the condition of the individual relative to the disease, identifying whether further tests may be desirable, or otherwise evaluating the health status of the individual.

[0149] Certain embodiments of the present invention are described herein, including the best mode known to the inventors for carrying out the invention. Of course, variations on these described embodiments will become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventor expects skilled artisans to employ such variations as appropriate, and the inventors intend for the present invention to be practiced otherwise than specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described embodiments in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context.

[0150] Groupings of alternative embodiments, elements, or steps of the present invention are not to be construed as limitations. Each group member may be referred to and claimed individually or in any combination with other group members disclosed herein. It is anticipated that one or more members of a group may be included in, or deleted from, a group for reasons of convenience and / or patentability. When any such inclusion or deletion occurs, the specification is deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

[0151] Unless otherwise indicated, all numbers expressing a characteristic, item, quantity, parameter, property, term, and so forth used in the present specification and claims are to be understood as being modified in all instances by the term “about.” As used herein, the term “about” means that the characteristic, item, quantity, parameter, property, or term so qualified encompasses a range of plus or minus ten percent above and below the value of the stated characteristic, item, quantity, parameter, property, or term. Accordingly, unless indicated to the contrary, the numerical parameters set forth in the specification and attached claims are approximations that may vary. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical indication should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and values setting forth the broad scope of the invention are approximations, the numerical ranges and values set forth in the specific examples are reported as precisely as possible. Any numerical range or value, however, inherently contains certain errors necessarily resulting from the standard deviation found in their respective testing measurements. Recitation of numerical ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate numerical value falling within the range. Unless otherwise indicated herein, each individual value of a numerical range is incorporated into the present specification as if it were individually recited herein.

[0152] Specific embodiments disclosed herein may be further limited in the claims using consisting of or consisting essentially of language. When used in the claims,whether as filed or added per amendment, the transition term “consisting of” excludes any element, step, or ingredient not specified in the claims. The transition term “consisting essentially of” limits the scope of a claim to the specified materials or steps and those that do not materially affect the basic and novel characteristic(s). Embodiments of the present invention so claimed are inherently or expressly described and enabled herein.

[0153] Groupings of alternative embodiments, elements, or steps of the present invention are not to be construed as limitations. Each group member may be referred to and claimed individually or in any combination with other group members disclosed herein. It is anticipated that one or more members of a group may be included in, or deleted from, a group for reasons of convenience and / or patentability. When any such inclusion or deletion occurs, the specification is deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

[0154] All patents, patent publications, and other publications referenced and identified in the present specification are individually and expressly incorporated herein by reference in their entirety for the purpose of describing and disclosing, for example, the compositions and methodologies described in such publications that might be used in connection with the present invention. These publications are provided solely for their disclosure prior to the filing date of the present application. Nothing in this regard should be construed as an admission that the inventors are not entitled to antedate such disclosure by virtue of prior invention or for any other reason. All statements as to the date or representation as to the contents of these documents is based on the information available to the applicants and does not constitute any admission as to the correctness of the dates or contents of these documents.

[0155] In closing, it is to be understood that although aspects of the present specification are highlighted by referring to specific embodiments, one skilled in the art will readily appreciate that these disclosed embodiments are only illustrative of the principles of the subject matter disclosed herein. Therefore, it should beunderstood that the disclosed subject matter is in no way limited to a particular methodology, protocol, and / or reagent, etc., described herein. As such, various modifications or changes to or alternative configurations of the disclosed subject matter can be made in accordance with the teachings herein without departing from the spirit of the present specification. Lastly, the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention, which is defined solely by the claims. Accordingly, the present invention is not limited to that precisely as shown and described.

Claims

CLAIMSWhat is claimed is:1 . A method of treating a patient for pain that includes steps of: a) determining whether to administer a pain medicament the patient, b) selecting a pain medicament and determining a dosage, d) deciding a duration of an initial prescription, e) administering a therapeutic amount of the pain medicament to the patient to alleviate one or more symptoms of pain.

2. The method of claim 1 , wherein the pain medicament is an opioid receptor agonist.

3. The method of claim 2, wherein the opioid receptor agonist is selected from morphine, codeine, thebaine, diacetyl morphine (heroin), hydromorphone, hydrocodone, oxymorphone, oxycodone, methadone and fentanyl.

4. The method of claim 1 , further comprising a step of assessing the subject for susceptibility to addiction to the pain medicament.

5. The method of claim 4, wherein the susceptibility is determined by levels of one or more biomarkers in a fluid sample from the subject.

6. The method of claim 5, wherein the biomarkers are selected from one or more of TMX2-CTNND1 , NICN1 , FOXP2, PDE4B, SLC39A8, NCAM1 , RABEPK, RP11- 254A17.1 , BARHL2, PLCL2, TMEM120A, TMPRSS5, GMEB1 , CTB-129P6.11 , FURIN, SEMA6D, HS6ST3 and FTO.

7. The method of claim 4, wherein one or more fluid samples from the subject are applied to a lateral flow immunoassay (LFA) in the step of assessing the subject for susceptibility to addiction.

8. The method of claim 7, wherein Up-Converting Phosphors (UCPs) are used in the lateral flow immunoassay (LFA) strip.

9. The method of claim 4, wherein the step of assessing the subject for susceptibility to addiction uses machine learning.

10. The method of claim 9, wherein the machine learning identifies causal biomarkers in the sample from the subject.

11. The method of claim 9, further comprising a step of calculating ratios and correlations between biomarker levels.

12. The method of claim 4, further comprising a step of administering the pain medication to the patient with a monitoring / dispensing system.

13. The method of claim 12, wherein the monitoring / dispensing system administers the pain medication as an inhalant.

14. A method of assessing susceptibility to addiction to a substance, the method comprised of: a) obtaining a fluid sample from a subject, b) applying the sample to a lateral flow immunoassay (LFA) strip, c) analyzing the strip to identify causal biomarkers and calculate ratios and correlations between biomarker levels in the sample, and d) assessing the subject for susceptibility to addiction to the substance.

15. The method of claim 14, wherein up converting phosphors (UCPs) are used in the lateral flow immunoassay (LFA) strip.

16. The method of claim 14, wherein the step of identifying causal biomarkers and calculating ratios and correlations between biomarker levels uses machine learning.

17. The method of claim 14, further comprising a step of administering the pain medication to the patient with a monitoring / dispensing system.

18. The method of claim 14, wherein the substance is an opioid receptor agonist.

19. The method of claim 18, wherein the opioid receptor agonist is selected from morphine, codeine, thebaine, diacetyl morphine (heroin), hydromorphone, hydrocodone, oxymorphone, oxycodone, methadone and fentanyl.

20. The method of claim 14, wherein the substance is alcohol, nicotine, caffeine, cannabis, a hallucinogen, an inhalant, a sedative, a hypnotic, an anxiolytic or a stimulant.

21. The method of claim 14, wherein the fluid sample is blood, serum, saliva or urine.

22. The method of claim 14, further comprising a step of administering the substance to the subject with a monitoring / dispensing system.

23. The method of claim 22, wherein the monitoring / dispensing system administers the substance as an inhalant.

24. The method of claim 14, wherein the step of assessing the subject for susceptibility to addiction to the substance comprises comparing a calculated sample value to a threshold value, wherein a threshold value indicates that the subject is susceptible to addiction.

25. The method of claim 14, further comprising a step of administering the substance to the patient with a monitoring / dispensing system.

26. The method of claim 25, wherein the monitoring / dispensing system administers the pain medication as an inhalant.

27. The method of claim 14, wherein the biomarkers are selected from one or more of TMX2-CTNND1 , NICN1, FOXP2, PDE4B, SLC39A8, NCAM1 , RABEPK, RP11-254A17.1 , BARHL2, PLCL2, TMEM120A, TMPRSS5, GMEB1 , CTB- 129P6.11 , FURIN, SEMA6D, HS6ST3 and FTO.

28. The method of claim 14, further comprising steps of (e) determining that the subject is susceptible to addiction to the substance and (f) administering the substance to the patient with a monitoring / dispensing system.

29. The method of claim 28, wherein the monitoring / dispensing system administers the pain medication as an inhalant.

30. A method of assessing susceptibility to addiction to an opioid receptor agonist, the method comprised of: a) obtaining a fluid sample from a subject, b) applying the sample to a lateral flow immunoassay (LFA) strip, c) analyzing the strip to identify causal biomarkers and calculate ratios and correlations between biomarker levels in the sample, d) assessing the subject for susceptibility to addiction to the opioid receptor agonist, e) determining that the subject is susceptible to addiction to the opioid receptor agonist, and f) administering the opioid receptor agonist to the patient with a monitoring / dispensing system.

31. The method of claim 30, wherein up converting phosphors (UCPs) are used in the lateral flow immunoassay (LFA) strip.

32. The method of claim 30, wherein the step of assessing the subject for susceptibility to addiction comprises machine learning.

33. The method of claim 30, wherein machine learning is used in the step of identifying causal biomarkers and calculating ratios and correlations between marker levels in the sample.

34. The method of claim 30, wherein the monitoring / dispensing system administers the opioid receptor agonist as an inhalant.

35. The method of claim 30, wherein the step of assessing the subject for susceptibility to addiction to the opioid receptor agonist comprises comparing a calculated sample value to a threshold value, wherein a threshold value indicates that the subject is susceptible to addiction to the opioid receptor agonist.

36. The method of claim 30, wherein the causal markers comprise one or more of proteins and mRNA, miRNA, siRNA, acetylation, proteomics, genomics, RANA, proteinomics, protein:protein interactions and protein:molecule interactions.

37. The method of claim 30, wherein deep learning and / or a neural network are used in the step of identifying causal biomarkers and calculating ratios and correlations between marker levels.

38. The method of claim 30, wherein deep learning and / or a neural network are used in the step of identifying causal biomarkers and calculating ratios and correlations between marker levels.

39. The method of claim 30, further comprising a step of treating the subject for dependance, addiction and / or withdrawal symptoms related the opioid use.

40. The method of claim 39, wherein the treatment comprises counselling, detoxification, rehabilitation and / or administration of opioid antagonists.

41. The method of claim 30, wherein the biomarkers are selected from one or more of TMX2-CTNND1 , NICN1, FOXP2, PDE4B, SLC39A8, NCAM1 , RABEPK,RP11-254A17.1 , BARHL2, PLCL2, TMEM120A, TMPRSS5, GMEB1 , CTB- 129P6.11 , FURIN, SEMA6D, HS6ST3 and FTO.

42. A method of determining susceptibility to addiction of an opioid receptor agonist in a subject, the method comprising a) obtaining a fluid sample from a subject, b) analyzing the fluid sample for one or more biomarker levels, c) assessing the subject for susceptibility to addiction to the opioid receptor agonist, and d) treating the subject to avoid addiction to the opioid receptor agonist if deemed susceptible to addiction.

43. The method of claim 42, wherein the one or more biomarkers are selected from mRNA levels of TMX2-CTNND1 , NICN1 , FOXP2, PDE4B, SLC39A8, NCAM1 , RABEPK, RP11-254A17.1 , BARHL2, PLCL2, TMEM120A, TMPRSS5, GMEB1 , CTB-129P6.11 , FURIN, SEMA6D, HS6ST3 and FTO.

43. The method of claim 42, wherein the one or more biomarkers are selected from protein levels of TMX2-CTNND1, NICN1 , FOXP2, PDE4B, SLC39A8, NCAM1 , RABEPK, RP11-254A17.1 , BARHL2, PLCL2, TMEM120A, TMPRSS5, GMEB1 , CTB-129P6.11 , FURIN, SEMA6D, HS6ST3 and FTO.

44. The method of claim 42, wherein upregulation of the one or more of the biomarkers indicates that the subject is susceptible to addiction.

45. The method of claim 42, wherein the step of treating the subject to avoid addiction comprises counselling, detoxification, rehabilitation and monitored administration of opioid antagonists.

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