WO2025166199A1 - Mirtracker for predicting and treating addiction - Google Patents

Abstract

The present disclosure provides methods of predicting, quantifying, and/or treating high-risk opioid addiction.

Description

MIRTRACKER FOR PREDICTING AND TREATING ADDICTION

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to, and the benefit of, U.S. Provisional Patent Application No. 63/549,100, filed February 2, 2024, and U.S. Provisional Patent Application No. 63/556,962, filed February 23, 2024, which are incorporated by reference herein in their entirety.

REFERENCE TO SEQUENCE LISTING

The sequence listing submitted on January 31, 2025, as an .XML file entitled “11708- 003W01-ST26” created on January 24, 2025, and having a file size of 96,481 bytes is hereby incorporated by reference pursuant to 37 C.F.R. § 1.52(e)(5).

FIELD

The present disclosure relates to methods of predicting, quantifying, and/or treating high-risk opioid addiction.

BACKGROUND

More than 11.5 million Americans aged 12 years or older reported misusing prescription opioids in 2016. From 1999-2018, approximately 450 thousand people died from a drug overdose, with nearly 70% of overdose deaths in 2018 involving an opioid. The financial impact of the opioid epidemic is also substantial, costing an estimated $15,000 per person in healthcare expenditures and $50 billion annually in societal costs.

Prescription drug overdose was announced as one of the top five health threats by the Center for Disease Control and Prevention (CDC) in 2014. Prescription opioids have been used for acute pain management for decades. However, exposure to opioids following trauma and/or surgery is associated with increased risk of opioid misuse. For example, patients who recently underwent either a major (n = 7,109; 19.7%) or minor surgical procedure (n = 29,068; 80.3%) exhibited similar rates of persistent opioid use (i.e., opioid prescription fulfillment between 90 to 180 days postop) between the two surgical groups (5.9% to 6.5%, respectively), compared to only 0.4% in a non-operative control group. A number of self-report and clinician administered tools are used to assess opioid misuse risk, but they all have limitations. For example, the Opioid Risk Tool (ORT) is the most commonly used screener for assessing opioid misuse risk. However, the ORT has a sensitivity and specificity ranging from 0.25 to 0.83 and 0.43 to 0.88, respectively, with likelihood ratios showing low predictive accuracy. These results underscore the need for developing and implementing both improved and adjunctive assessments for measuring risk of opioid misuse. The methods disclosed herein address these needs and more.

SUMMARY

The present disclosure provides methods of predicting, quantifying, and/or treating high-risk opioid addiction.

In some aspects, disclosed herein is a method of predicting and quantifying high-risk opioid addiction in a subject, the method comprising collecting a sample from the subject, detecting and quantifying a panel of microRNAs (miRNAs) in the sample, wherein the panel comprises hsa-miR-25p-3p, hsa-miR-130b-3p, hsa-miR-185-5p, hsa-miR-483-3p, hsa- miR550a-5p, hsa-miR-942-5p, hsa-5010-5p, and hsa-miR-4732-5p, generating a profile of the subject for at least 6 months, wherein the profile comprises an expression level of the panel of miRNAs, diagnosing a subject with high-risk opioid addiction, wherein the subject with high- risk opioid addiction expresses the panel of miRNAs for at least 6 months, and treating the subject with high-risk opioid addiction with an opioid at a lower dose relative to a control subject or a subject with low-risk opioid addiction, or treating the subject with high-risk opioid addiction with an opioid administered less frequently relative to a control subject or a subject with low-risk opioid addiction.

In some aspects, disclosed herein is a method of treating a subject with high-risk opioid addiction, the method comprising collecting a sample from the subject, detecting and quantifying a panel of microRNAs (miRNAs) in the sample, wherein the panel comprises hsa- miR-25p-3p, hsa-miR-130b-3p, hsa-miR-185-5p, hsa-miR-483-3p, hsa-miR550a-5p, hsa- miR-942-5p, hsa-5010-5p, and hsa-miR-4732-5p, generating a profile of the subject for at least 6 months, wherein the profile comprises an expression level of the panel of miRNAs, and treating the subject with an opioid when the profile comprises the panel of miRNAs being expressed for at least 6 months, wherein the opioid is administered at a lower dose relative to a control subject or a subject with low-risk opioid addiction, or the opioid is administered less frequently relative to a control subject or a subject with low-risk opioid addiction.

In some embodiments, the panel of miRNAs further comprises hsa-miR-let-i-5p, hsa- miR-15a-5p, hsa-miR-15b-3p, hsa-miR-16-5p, hsa-miR-103a-3p, or hsa-miR-140-3p. In some embodiments, the profile is generated for 6 months, 9 months, 12 months, or more. In some embodiments, the opioid comprises oxycodone, hydrocodone, morphine, methadone, fentanyl, codeine, tramadol, or a combination thereof. In some embodiments, the subject with high-risk opioid addiction is further treated with a non-opioid treatment. In some embodiments, the non-opioid treatment comprises physical therapy, exercise, acetaminophen, ibuprofen, naproxen, or a combination thereof.

In some embodiments, the subject is a post-operative subject. In some embodiments, the subject has a traumatic injury, a chronic disease, or a combination thereof. In some embodiments, the traumatic injury comprises a blunt force injury, a penetrating injury, a fracture, a bum, or combinations thereof. In some embodiments, the chronic disease comprises a microbial infection, an autoimmune disease, an inflammatory disease, a cancer, or combinations thereof. In some embodiments, the sample comprises a blood sample.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, which are incorporated in and constitute a part of this specification, illustrate several aspects described below.

FIG. 1 shows the overall study design.

FIGS. 2A and 2B show Suerat cluster analysis of miRNA- sequencing data. Figure 2A shows cluster analysis based on known miRNA function (miRbase v.22) identified three unique clusters. Figure 2B shows the distribution of PRS by Suerat clusters, wherein cluster 2 is significantly associated with PRS for OUD compared to clusters 1 and 2.

FIG. 3 shows the machine learning analysis showing minimum miRNA features necessary for building random forest models. Genes indicated in the middle panel are targets of the essential miRNAs, including opioid, dopamine, and serotonin receptors and potassium channels.

FIG. 4 shows the minimum features necessary for building random forest model for each classification.

FIG. 5 shows the top 20 up and down features of ML model.

FIGS. 6A, 6B, 6C, 6D, 6E, 6F, and 6G show the miRNA-mRNA target prediction outputs used for extracting the miRNAs linked to addiction.

FIG. 7 shows the visualization of clusters (Resolution 0.4/0.6/0.8 (2 clusters)).

FIG. 8 shows the visualization of clusters (Resolution 1.0 (3 clusters)).

FIG. 9 shows the distribution of PRS by Seurat Clusters at resolution 0.4 (Age Sex regressed out).

FIG. 10 shows differentially expressed miRs at fdr < 0.05. Regg_Res0.4. ClustO_over_Clustl. miRs upregulated: 169. miRs downregulated: 187.

FIG. 11 shows differentially expressed miRs at fdr < 0.05. Regg_Resl. Cluster2_over_Clusterl. miRs upregulated: 44. miRs downregulated: 160. FIG. 12 shows the machine learning for the clusters at different resolutions. The overall best performer model is using random forest.

FIG. 13 shows the top 20 up features ML model (Resolution 0.4. Cluster 0 over Cluster 1).

FIG. 14 shows the top down features ML model (Resolution 0.4. Cluster 0 over Cluster 1).

FIG. 15 shows the genes linked to addiction that are predicted targets of one or more down miRs in the high-risk group.

FIG. 16 shows the genes linked to addiction that are predicted targets of one or more up miRs in the high-risk group.

DETAILED DESCRIPTION

The following description of the disclosure is provided as an enabling teaching of the disclosure in its best, currently known embodiment(s). To this end, those skilled in the relevant art will recognize and appreciate that many changes can be made to the various embodiments of the invention described herein, while still obtaining the beneficial results of the present disclosure. It will also be apparent that some of the desired benefits of the present disclosure can be obtained by selecting some of the features of the present disclosure without utilizing other features. Accordingly, those who work in the art will recognize that many modifications and adaptations to the present disclosure are possible and can even be desirable in certain circumstances and are a part of the present disclosure. Thus, the following description is provided as illustrative of the principles of the present disclosure and not in limitation thereof.

Reference will now be made in detail to the embodiments of the invention, examples of which are illustrated in the drawings and the examples. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein.

Terminology

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this disclosure belongs. The term “comprising” and variations thereof as used herein is used synonymously with the term “including” and variations thereof and are open, non-limiting terms. Although the terms “comprising” and “including” have been used herein to describe various embodiments, the terms “consisting essentially of’ and “consisting of’ can be used in place of “comprising” and “including” to provide for more specific embodiments and are also disclosed. As used in this disclosure and in the appended claims, the singular forms “a”, “an”, “the”, include plural referents unless the context clearly dictates otherwise.

The following definitions are provided for the full understanding of terms used in this specification.

The terms "about" and "approximately" are defined as being “close to” as understood by one of ordinary skill in the art. In one non-limiting embodiment the terms are defined to be within 10%. In another non-limiting embodiment, the terms are defined to be within 5%. In still another non-limiting embodiment, the terms are defined to be within 1%.

As used herein, the terms "may," "optionally," and "may optionally" are used interchangeably and are meant to include cases in which the condition occurs as well as cases in which the condition does not occur. Thus, for example, the statement that a formulation "may include an excipient" is meant to include cases in which the formulation includes an excipient as well as cases in which the formulation does not include an excipient.

An "increase" can refer to any change that results in a greater amount of a symptom, disease, composition, condition, or activity. An increase can be any individual, median, or average increase in a condition, symptom, activity, composition in a statistically significant amount. Thus, the increase can be a 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100% or more increase so long as the increase is statistically significant.

A "decrease" can refer to any change that results in a smaller amount of a symptom, disease, composition, condition, or activity. A substance is also understood to decrease the genetic output of a gene when the genetic output of the gene product with the substance is less relative to the output of the gene product without the substance. Also, for example, a decrease can be a change in the symptoms of a disorder such that the symptoms are less than previously observed. A decrease can be any individual, median, or average decrease in a condition, symptom, activity, composition in a statistically significant amount. Thus, the decrease can be a 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% decrease so long as the decrease is statistically significant.

“Composition” refers to any agent that has a beneficial biological effect. Beneficial biological effects include both therapeutic effects, e.g., treatment of a disorder or other undesirable physiological condition, and prophylactic effects, e.g., prevention of a disorder or other undesirable physiological condition. The terms also encompass pharmaceutically acceptable, pharmacologically active derivatives of beneficial agents specifically mentioned herein, including, but not limited to, a vector, polynucleotide, cells, salts, esters, amides, proagents, active metabolites, isomers, fragments, analogs, and the like. When the term “composition” is used, then, or when a particular composition is specifically identified, it is to be understood that the term includes the composition per se as well as pharmaceutically acceptable, pharmacologically active vector, polynucleotide, salts, esters, amides, proagents, conjugates, active metabolites, isomers, fragments, analogs, etc.

By “prevent” or other forms of the word, such as “preventing” or “prevention,” is meant to stop a particular event or characteristic, to stabilize or delay the development or progression of a particular event or characteristic, or to minimize the chances that a particular event or characteristic will occur. Prevent does not require comparison to a control as it is typically more absolute than, for example, reduce. As used herein, something could be reduced but not prevented, but something that is reduced could also be prevented. Likewise, something could be prevented but not reduced, but something that is prevented could also be reduced. It is understood that where reduce or prevent are used, unless specifically indicated otherwise, the use of the other word is also expressly disclosed.

The term “subject” refers to any individual who is the target of administration or treatment. The subject can be a vertebrate, for example, a mammal. In one aspect, the subject can be human, non-human primate, bovine, equine, porcine, canine, or feline. The subject can also be a guinea pig, rat, hamster, rabbit, mouse, or mole. Thus, the subject can be a human or veterinary patient. The term “patient” refers to a subject under the treatment of a clinician, e.g., physician.

The term “therapeutically effective amount” refers to the amount of the composition used is of sufficient quantity to ameliorate one or more causes or symptoms of a disease or disorder. Such amelioration only requires a reduction or alteration, not necessarily elimination.

The term “treatment” refers to the medical management of a patient with the intent to cure, ameliorate, stabilize, or prevent a disease, pathological condition, or disorder. This term includes active treatment, that is, treatment directed specifically toward the improvement of a disease, pathological condition, or disorder, and also includes causal treatment, that is, treatment directed toward removal of the cause of the associated disease, pathological condition, or disorder. In addition, this term includes palliative treatment, that is, treatment designed for the relief of symptoms rather than the curing of the disease, pathological condition, or disorder; preventative treatment, that is, treatment directed to minimizing or partially or completely inhibiting the development of the associated disease, pathological condition, or disorder; and supportive treatment, that is, treatment employed to supplement another specific therapy directed toward the improvement of the associated disease, pathological condition, or disorder. "Comprising" is intended to mean that the compositions, methods, etc. include the recited elements, but do not exclude others. "Consisting essentially of' when used to define compositions and methods, shall mean including the recited elements, but excluding other elements of any essential significance to the combination. Thus, a composition consisting essentially of the elements as defined herein would not exclude trace contaminants from the isolation and purification method and pharmaceutically acceptable carriers, such as phosphate buffered saline, preservatives, and the like. "Consisting of' shall mean excluding more than trace elements of other ingredients and substantial method steps for administering the compositions provided and/or claimed in this disclosure. Embodiments defined by each of these transition terms are within the scope of this disclosure.

The term “administer,” “administering”, or derivatives thereof refer to delivering a composition, substance, inhibitor, or medication to a subject or object by one or more the following routes: oral, topical, intravenous, subcutaneous, transcutaneous, transdermal, intramuscular, intra-joint, parenteral, intra-arteriole, intradermal, intraventricular, intracranial, intraperitoneal, intralesional, intranasal, rectal, vaginal, by inhalation or via an implanted reservoir. The term “parenteral” includes subcutaneous, intravenous, intramuscular, intraarticular, intra- synovial, intrastemal, intrathecal, intrahepatic, intralesional, and intracranial injections or infusion techniques.

“Quantify”, “quantifying”, “quantification”, and any other grammatical variations thereof refer to the process of acquiring numerical values to determine, express, or measure an amount of a substance or signal.

As used herein, a “therapeutic regimen” refers to a structured treatment plan or strategy designed to improve and maintain health. Generally, a therapeutic regimen will be designed, prescribed, and/or administered by a licensed medical practitioner. The therapeutic regimen generally specifies the treatment dosage, the treatment scheduling, and the duration of the treatment. In some embodiments, the therapeutic regimen comprises one or more therapeutic compositions. In some embodiments, the therapeutic regimen comprises one or more therapeutic agents. In some embodiments, the therapeutic regimen comprises any combination of therapeutic compositions and therapeutic agents, such as for example the combination of an inhibitor and an antibody. In some embodiments, a therapeutic regimen comprises modifying, continuing, and/or initiating at least one therapeutic agent and/or therapeutic composition. In some embodiments, a therapeutic regimen comprises treating and/or preventing a disease, disorder, and/or condition.

Methods The present disclosure provides methods of predicting, quantifying, and/or treating high-risk opioid addiction.

In some aspects, disclosed herein is a method of predicting and quantifying high-risk opioid addiction in a subject, the method comprising collecting a sample from the subject, detecting and quantifying a panel of microRNAs (miRNAs) in the sample, wherein the panel comprises hsa-miR-25p-3p, hsa-miR-130b-3p, hsa-miR-185-5p, hsa-miR-483-3p, hsa- miR550a-5p, hsa-miR-942-5p, hsa-5010-5p, and/or hsa-miR-4732-5p, generating a profile of the subject for at least 6 months, wherein the profile comprises an expression level of the panel of miRNAs, diagnosing a subject with high-risk opioid addiction, wherein the subject with high-risk opioid addiction expresses the panel of miRNAs for at least 1 month, and treating the subject with high-risk opioid addiction with an opioid at a lower dose relative to a control subject or a subject with low-risk opioid addiction, or treating the subject with high-risk opioid addiction with an opioid administered less frequently relative to a control subject or a subject with low-risk opioid addiction.

In some aspects, disclosed herein is a method of predicting and quantifying high-risk opioid addiction in a subject, the method comprising collecting a sample from the subject, detecting and quantifying a panel of microRNAs (miRNAs) in the sample, wherein the panel comprises hsa-miR-25p-3p, hsa-miR-130b-3p, hsa-miR-185-5p, hsa-miR-483-3p, hsa- miR550a-5p, hsa-miR-942-5p, hsa-5010-5p, and/or hsa-miR-4732-5p, generating a profile of the subject for at least 6 months, wherein the profile comprises an expression level of the panel of miRNAs, diagnosing a subject with high-risk opioid addiction, wherein the subject with high-risk opioid addiction expresses the panel of miRNAs for at least 1 month.

In some aspects, disclosed herein is a method of treating a subject with high-risk opioid addiction, the method comprising collecting a sample from the subject, detecting and quantifying a panel of microRNAs (miRNAs) in the sample, wherein the panel comprises hsa- miR-25p-3p, hsa-miR-130b-3p, hsa-miR-185-5p, hsa-miR-483-3p, hsa-miR550a-5p, hsa- miR-942-5p, hsa-5010-5p, and/or hsa-miR-4732-5p, generating a profile of the subject for at least 1 month, wherein the profile comprises an expression level of the panel of miRNAs, and treating the subject with an opioid when the profile comprises the panel of miRNAs being expressed for at least 1 month, wherein the opioid is administered at a lower dose relative to a control subject or a subject with low-risk opioid addiction, or the opioid is administered less frequently relative to a control subject or a subject with low-risk opioid addiction.

MicroRNAs (miRNAs) are a class of non-coding RNAs that play important roles in regulating gene expression. miRNAs are reported to interact with 3’ untranslated regions (3’- UTRs), 5’-UTRs, and/or gene promoters to induce gene expression, mRNA degradation, and/or translational repression. miRNAs are also reported to have extracellular functions including, but not limited to chemical messengers to mediate cell-cell communication. Thus, the present disclosure detects extracellular miRNAs as biological signatures to further detect, predict, assess, prevent, quantify, and/or treat high-risk opioid addiction.

In some embodiments, the panel of miRNAs further comprises hsa-miR-let-i-5p, hsa- miR-15a-5p, hsa-miR-15b-3p, hsa-miR-16-5p, hsa-miR-103a-3p, or hsa-miR-140-3p. In some embodiments, the profile is generated for at least one month. In some embodiments, the profile is generated for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or more months. In some embodiments, the opioid comprises oxycodone, hydrocodone, morphine, methadone, fentanyl, codeine, tramadol, or a combination thereof.

In some embodiments, the opioid is administered at a lower dose or less frequently to the subject relative to a control subject or a subject with low-risk opioid addiction. Non-limiting examples of opioid administration include, but are not limited to morphine being administered at less than 20 mg to the subject, oxycodone being administered at less than 15mg, and codeine being administered at less than 60mg. In some embodiments, morphine is administered at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 milligrams, or at a dose less than Img. In some embodiments, oxycodone is administered at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 milligrams, or at a dose less than 1 mg. In some embodiments, codeine is administered at 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60mg. In general, opioids are administered about every four hours to a control subject or a subject with low-risk opioid addiction. In some embodiments, the opioid is administered to the subject more than once every 4 hours, such as for example opioid administration occurs once every 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, 10.5, 11, 11.5, 12 or more hours.

In some embodiments, the subject with high-risk opioid addiction is further treated with a non-opioid treatment. In some embodiments, the non-opioid treatment comprises physical therapy, exercise, or a combination thereof. In some embodiments, the non-opioid treatment comprises an anti-inflammatory compound, an antibiotic, a sedative, an anesthetic, or a combination thereof. In some embodiments, the non-opioid treatment includes, but is not limited to penicillins (including, but not limited to amoxicillin, clavulanate and amoxicillin, ampicillin, dicloxacillin, oxacillin, and penicillin V potassium), tetracyclins (including, but not limited to demeclocycline, doxycycline, eravacycline, minocycline, omadacycline, sarecycline, and tetracycline), cephalosporins (cefaclor, cefadroxil, cefdinir, cephalexin, cefprozil, cefepime, cefiderocol, cefotaxime, cefotetan, ceftaroline, cefazidme, ceftriaxone, and cefuroxime), quinolones (also referred to as fluoroquinolones include, but are not limited to ciprofloxacin, delafloxacin, levofloxacin, moxifloxacin, and gemifloxacin), lincomycins (including clindamycin and lincomycin), macrolides (including, but not limited to azithromycin, clarithromycin, erythromycin, and fidaxomicin (ketolide)), sulfonamides (including sulfamethoxazole and trimethoprim, and sulfasalazine), glycopeptides (including, but not limited to dalbavancin, oritavancin, telavancin, and vancomycin), aminoglycosides (including, but not limited to gentamicin, tobramycin, and amikacin), carbapenems (including, but not limited to imipenem and cilastatin, meropenem, and ertapenem), and topical antibiotics (including, but not limited to neomycin, bacitracin, polymyxin B, and praxomine), aspirin, ibuprofen, ketoprofen, naproxen, steroids, glucocorticoids (including, but not limited to betamethasone, budesonide, dexamethasone, hydrocortisone, hydrocortisone acetate, methylprednisolone, prednisolone, prednisone, and triamcinolone), methotrexate, sulfasalazine, lefunomide, anti-Tumor Necrosis Factor (TNF) medications, cyclophosphamide, and my cophenolate. In some embodiments, the anesthetic includes, but is not limited to chloroprocaine, procaine, tetracaine, lidocaine, bupivacaine, ropivacaine, mepivacaine, and levobupivacaine. In some embodiments, the sedative can include, but is not limited to barbiturates, benzodiazepines, nonbenzodiazepines hypnotics, antihistamines, muscle relaxants, opioids, methaqualone, or any combination thereof.

In some embodiments, the subject is a post-operative subject, such as for example a subject who has undergone surgery within the past year or more. The post-operative period is the time after surgery when a patient is recovering and/or requires close monitoring by medial professions. During the post-operative period patient may require monitoring of complications, including but not limited to pain, infection, and/or bleeding; administration of medication, such as opioids; admitted to physical therapy to move around and help prevent blood clots and/or strengthen muscles; and/or encouraged to perform breathing exercises to prevent respiratory complications. It should be noted that the post-operative period depends on the type of surgery and the subject’ s overall health before and/or after said surgery. Thus, the post-operative period can take from 1 hour to several days. In some embodiments, the post-operative subject has been in the post-operative period for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 hours, or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,

23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,

48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,

73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,

98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173,

174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192,

193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211,

212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230,

231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249,

250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268,

269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287,

288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306,

307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,

326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344,

345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,

364, 365, or more days.

In some embodiments, the subject has a traumatic injury, a chronic disease, or a combination thereof. In some embodiments, the traumatic injury comprises a blunt force injury (including, but not limited to contusions, concussions, abrasions, lacerations, and internal or external hemorrhages), a penetrating injury (including, but not limited to fragments of broken bones, stab wounds, and gunshot wounds), a bone fracture (including, but not limited to stress fractures, impact fractures, buckled fractures, compound fractures, transverse fractures, and comminuted fractures), a bum (including, but not limited to first, second, and third degree bums), or combinations thereof.

In some embodiments, the chronic disease comprises a microbial infection (including, but not limited to common cold, influenza (including, but not limited to human, bovine, avian, porcine, and simian strains of influenza), measles, acquired immune deficiency syndrome/human immunodeficiency vims (AIDS/HIV), anthrax, botulism, cholera, Campylobacter infections, chickenpox, chlamydia infections, cryptosporidosis, dengue fever, diphtheria, hemorrhagic fevers, Escherichia coli (E. coli) infections, ehrlichiosis, gonorrhea, hand-foot-mouth disease, hepatitis A, hepatitis B, hepatitis C, legionellosis, leprosy, leptospirosis, listeriosis, malaria, meningitis, meningococcal disease, mumps, pertussis, polio, pneumococcal disease, paralytic shellfish poisoning, rabies, rocky mountain spotted fever, rubella, salmonella, shigellosis, small pox, syphilis, tetanus, trichinosis (trichinellosis), tuberculosis (TB), typhoid fever, typhus, west nile vims, yellow fever, yersiniosis, and zika), an autoimmune disease, an inflammatory disease, a cancer (including, but is not limited to acoustic neuroma, adenocarcinoma, adrenal gland cancer, anal cancer, angiosarcoma (e.g., lymphangiosarcoma, lymphangioendotheliosarcoma, hemangiosarcoma), appendix cancer, benign monoclonal gammopathy, biliary cancer (e.g., cholangiocarcinoma), bladder cancer, breast cancer (e.g., adenocarcinoma of the breast, papillary carcinoma of the breast, mammary cancer, medullary carcinoma of the breast), brain cancer (e.g., meningioma; glioma, e.g., astrocytoma, oligodendroglioma; medulloblastoma), bronchus cancer, carcinoid tumor, cervical cancer (e.g., cervical adenocarcinoma), choriocarcinoma, chordoma, craniopharyngioma, colorectal cancer (e.g., colon cancer, rectal cancer, colorectal adenocarcinoma), epithelial carcinoma, ependymoma, endotheliosarcoma (e.g., Kaposi's sarcoma, multiple idiopathic hemorrhagic sarcoma), endometrial cancer (e.g., uterine cancer, uterine sarcoma), esophageal cancer (e.g., adenocarcinoma of the esophagus, Barrett's adenocarinoma), Ewing's sarcoma, eye cancer (e.g., intraocular melanoma, retinoblastoma), familiar hypereosinophilia, gall bladder cancer, gastric cancer (e.g., stomach adenocarcinoma), gastrointestinal stromal tumor (GIST), head and neck cancer (e.g., head and neck squamous cell carcinoma, oral cancer (e.g., oral squamous cell carcinoma (OSCC), throat cancer (e.g., laryngeal cancer, pharyngeal cancer, nasopharyngeal cancer, oropharyngeal cancer)), hematopoietic cancers (e.g., leukemia such as acute lymphocytic leukemia (ALL) (e.g., B-cell ALL, T-cell ALL), acute myelocytic leukemia (AML) (e.g., B-cell AML, T-cell AML), chronic myelocytic leukemia (CML) (e.g., B-cell CML, T-cell CML), and chronic lymphocytic leukemia (CLL) (e.g., B-cell CLL, T-cell CLL); lymphoma such as Hodgkin lymphoma (HL) (e.g., B-cell HL, T-cell HL) and non-Hodgkin lymphoma (NHL) (e.g., B-cell NHL such as diffuse large cell lymphoma (DLCL) (e.g., diffuse large B-cell lymphoma (DLBCL)), follicular lymphoma, chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL), mantle cell lymphoma (MCL), marginal zone B-cell lymphomas (e.g., mucosa- associated lymphoid tissue (MALT) lymphomas, nodal marginal zone B-cell lymphoma, splenic marginal zone B- cell lymphoma), primary mediastinal B-cell lymphoma, Burkitt lymphoma, lymphoplasmacytic lymphoma (i.e., “Waldenstrom's macroglobulinemia”), hairy cell leukemia (HCL), immunoblastic large cell lymphoma, precursor B-lymphoblastic lymphoma and primary central nervous system (CNS) lymphoma; and T-cell NHL such as precursor T- lymphoblastic lymphoma/leukemia, peripheral T-cell lymphoma (PTCL) (e.g., cutaneous T- cell lymphoma (CTCL) (e.g., mycosis fungiodes, Sezary syndrome), angioimmunoblastic T- cell lymphoma, extranodal natural killer T-cell lymphoma, enteropathy type T-cell lymphoma, subcutaneous panniculitis-like T-cell lymphoma, anaplastic large cell lymphoma); a mixture of one or more leukemia/lymphoma as described above; and multiple myeloma (MM)), heavy chain disease (e.g., alpha chain disease, gamma chain disease, mu chain disease), hemangioblastoma, inflammatory myofibroblastic tumors, immunocytic amyloidosis, kidney cancer (e.g., nephroblastoma a.k.a. Wilms' tumor, renal cell carcinoma), liver cancer (e.g., hepatocellular cancer (HCC), malignant hepatoma), lung cancer (e.g., bronchogenic carcinoma, small cell lung cancer (SCLC), non-small cell lung cancer (NSCLC), adenocarcinoma of the lung), leiomyosarcoma (LMS), mastocytosis (e.g., systemic mastocytosis), myelodysplastic syndrome (MDS), mesothelioma, myeloproliferative disorder (MPD) (e.g., polycythemia Vera (PV), essential thrombocytosis (ET), agnogenic myeloid metaplasia (AMM) a.k.a. myelofibrosis (MF), chronic idiopathic myelofibrosis, chronic myelocytic leukemia (CML), chronic neutrophilic leukemia (CNL), hypereosinophilic syndrome (HES)), neuroblastoma, neurofibroma (e.g., neurofibromatosis (NF) type 1 or type 2, schwannomatosis), neuroendocrine cancer (e.g., gastroenteropancreatic neuroendoctrine tumor (GEP-NET), carcinoid tumor), osteosarcoma, ovarian cancer (e.g., cystadenocarcinoma, ovarian embryonal carcinoma, ovarian adenocarcinoma), papillary adenocarcinoma, pancreatic cancer (e.g., pancreatic adenocarcinoma, intraductal papillary mucinous neoplasm (IPMN), Islet cell tumors), penile cancer (e.g., Paget's disease of the penis and scrotum), pinealoma, primitive neuroectodermal tumor (PNT), prostate cancer (e.g., prostate adenocarcinoma), rectal cancer, rhabdomyosarcoma, salivary gland cancer, skin cancer (e.g., squamous cell carcinoma (SCC), keratoacanthoma (KA), melanoma, basal cell carcinoma (BCC)), small bowel cancer (e.g., appendix cancer), soft tissue sarcoma (e.g., malignant fibrous histiocytoma (MFH), liposarcoma, malignant peripheral nerve sheath tumor (MPNST), chondrosarcoma, fibrosarcoma, myxosarcoma), sebaceous gland carcinoma, sweat gland carcinoma, synovioma, testicular cancer (e.g., seminoma, testicular embryonal carcinoma), thyroid cancer (e.g., papillary carcinoma of the thyroid, papillary thyroid carcinoma (PTC), medullary thyroid cancer), urethral cancer, vaginal cancer and vulvar cancer (e.g., Paget's disease of the vulva), or combinations thereof.

In some embodiments, the sample comprises blood, cerebrospinal fluid (CSF), saliva, serum, urine, stool, or a tissue biopsy.

Further embodiments disclosed herein include:

1. A method of treating a subject with high-risk opioid addiction, the method comprising: collecting a sample from the subject; detecting and quantifying a panel of microRNAs (miRNAs) in the sample, wherein the panel comprises hsa-miR-25p-3p, hsa-miR-130b-3p, hsa-miR-185-5p, hsa-miR-483-3p, hsa- miR550a-5p, hsa-miR-942-5p, hsa-5010-5p, and hsa-miR-4732-5p; generating a profile of the subject for at least 6 months, wherein the profile comprises an expression level of the panel of miRNAs; and treating the subject with an opioid when the profile comprises the panel of miRNAs being expressed for at least 6 months, wherein the opioid is administered at a lower dose relative to a control subject or a subject with low-risk opioid addiction, or the opioid is administered less frequently relative to a control subject or a subject with low-risk opioid addiction.

2. The method of claim 1, wherein the panel of miRNAs further comprises hsa-miR-let-i- 5p, hsa-miR-15a-5p, hsa-miR-15b-3p, hsa-miR-16-5p, hsa-miR-103a-3p, or hsa-miR-140-3p.

3. The method of claim 1, wherein the profile is generated for 6 months, 9 months, 12 months, or more.

4. The method of claim 1, wherein the opioid comprises oxycodone, hydrocodone, morphine, methadone, fentanyl, codeine, tramadol, or a combination thereof.

5. The method of claim 1, wherein the subject is further treated with a non-opioid treatment.

6. The method of claim 5, wherein the non-opioid treatment comprises physical therapy, exercise, acetaminophen, ibuprofen, naproxen, or a combination thereof.

7. The method of claim 1, wherein the subject has a traumatic injury, a chronic disease, or a combination thereof.

8. The method of claim 7, wherein the traumatic injury comprises a blunt force injury, a penetrating injury, a fracture, a bum, or combinations thereof.

9. The method of claim 7, wherein the chronic disease comprises a microbial infection, an autoimmune disease, an inflammatory disease, a cancer, or combinations thereof.

10. A method of predicting and quantifying high-risk opioid addiction in a subject, the method comprising: collecting a sample from the subject; detecting and quantifying a panel of microRNAs (miRNAs) in the sample, wherein the panel comprises hsa-miR-25p-3p, hsa-miR-130b-3p, hsa-miR-185-5p, hsa-miR-483-3p, hsa- miR550a-5p, hsa-miR-942-5p, hsa-5010-5p, and hsa-miR-4732-5p; generating a profile of the subject for at least 6 months, wherein the profile comprises an expression level of the panel of miRNAs; diagnosing a subject with high-risk opioid addiction, wherein the subject with high-risk opioid addiction expresses the panel of miRNAs for at least 6 months; and treating the subject with high-risk opioid addiction with an opioid at a lower dose relative to a control subject or a subject with low-risk opioid addiction, or treating the subject with high- risk opioid addiction with an opioid administered less frequently relative to a control subject or a subject with low-risk opioid addiction.

11. The method of claim 10, wherein the panel of miRNAs further comprises hsa-miR-let- i-5p, hsa-miR-15a-5p, hsa-miR-15b-3p, hsa-miR-16-5p, hsa-miR-103a-3p, or hsa-miR-140- 3p.

12. The method of claim 10, wherein the profile is generated for 6 months, 9 months, 12 months, or more.

13. The method of claim 10, wherein the opioid comprises oxycodone, hydrocodone, morphine, methadone, fentanyl, codeine, tramadol, or a combination thereof.

14. The method of claim 10, wherein the subject with high-risk opioid addiction is further treated with a non-opioid treatment.

15. The method of claim 14, wherein the non-opioid treatment comprises physical therapy, exercise, acetaminophen, ibuprofen, naproxen, or a combination thereof.

A number of embodiments of the disclosure have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. Accordingly, other embodiments are within the scope of the following claims.

By way of non-limiting illustration, examples of certain embodiments of the present disclosure are given below.

EXAMPLES

The following examples are set forth below to illustrate the compositions, devices, methods, and results according to the disclosed subject matter. These examples are not intended to be inclusive of all aspects of the subject matter disclosed herein, but rather to illustrate representative methods and results. These examples are not intended to exclude equivalents and variations of the present invention which are apparent to one skilled in the art.

Example 1: Identifying miRNA signatures of opioid misuse risk in trauma patients.

Opioid use disorder (OUD) is a public health crisis in the U.S. causing >80,000 overdose deaths annually. Both people living with chronic pain and those needing high doses of opioid medications due to surgery are at high risk of developing tolerance and opioid dependence due to repeated and prolonged opioid use. The mechanisms leading to opioid tolerance or dependence are not well understood and there are currently no biomarkers for predicting who is at risk for development of OUD.

Opioid exposure causes epigenetic modifications, including changes in microRNA (miRNA) expression. Several miRNAs involved in regulation of synaptic plasticity are contemplated to underlie drug addiction and miRNAs have been shown to regulate p-opioid receptor levels and modulate opioid tolerance. Building on the growing appreciation that miRNAs can cross the blood-brain barrier and thus serve as blood-based biomarkers that reflect brain function, miRNA profiling was performed in same-subject postmortem samples from brain and blood tissues of patients with OUD compared to controls. Differentially expressed miRNAs were identified in OUD, including Let7f that has been implicated in mechanisms of opioid tolerance, and the miRNA target genes and corresponding enriched pathways overlapped strongly in brain and blood tissues.

Based on the findings, a preliminary study was performed to assess the utility of the blood miRNA biomarkers for opioid misuse risk prediction in traumatically injured patients. Trauma patients are at greater than average risk for post-surgical opioid misuse and therefore represent a priority population for early screening and prevention interventions. miRNA sequencing was performed on blood from 89 trauma subjects, classified as high and low risk for opioid dependence based on the patients’ polygenic risk score (PRS), and compared the blood miRNA biosignatures to those identified in postmortem blood from OUD patients. Using machine learning analysis a miRNA signature, including members of the Let family, was identified that predicted OUD PRS-associated groups in trauma patients.

The present disclosure expands on critical prior work to support the feasibility of detecting a miRNA bio-behavioral signature of opioid misuse risk that demonstrates improved predictive precision compared to current tools such as PRS. Specifically, the present disclosure develops scalable measures assessing individual addiction susceptibility and quantifying addiction risk and progression during prescription drug use. Behavioral, genomics, and bioinformatics pipelines are utilized to characterize opioid-induced miRNA expression dynamics among trauma patients prescribed opioids at discharge. Based on results, a high- throughput screening assay is developed to predict risk for OUD. It is contemplated that miRNAs serve as powerful bio- signatures for predicting long-term clinical outcomes in patients treated with opioids for pain management following trauma injury.

A blood miRNA signature of opioid misuse risk. miRNA sequencing is performed on blood from 180 trauma surgery patients admitted to the UTHealth Red Duke Trauma Institute (RDTI) and discharged with an opioid prescription to identify miRNAs associated with opioid misuse. miRNAs associated with opioid tolerance and pain regulation is the focus. PRS, measures of pain intensity, opioid use, and miRNA levels is assessed at discharge and at 1, 3, 6, 9, 12 months post-surgery, and use of machine learning to identify a blood miRNA signature as a relevant tool for prediction of opioid misuse.

The present disclosure specifically addresses 2) Demonstration and validation of neurobiological, behavioral, and digital biomarkers; and 3) Longitudinal Studies of Prescription Opioid Use, Addiction Risk Trajectories, and Prevention Strategies.

Identifying biomarkers of opioid misuse risk.

The Opioid Risk Tool (ORT) is the most commonly used screener for assessing opioid misuse risk. The ORT has a sensitivity and specificity ranging from 0.25 to 0.83 and 0.43 to 0.88, respectively, with likelihood ratios showing low predictive accuracy. These results underscore the need for developing and implementing both improved and adjunctive assessments for measuring risk of opioid misuse.

The ability to recruit trauma surgery patients is evident from two recently published pilot studies. Across both studies, various pain- and opioid- related measures were remotely assessed at 4-weeks post discharge (N = 107) as well as in a subset of patients at 1-year post discharge (N = 34). Note that participants were not compensated in these studies. Most recently, another pilot study was completed assessing similar outcomes, but also including more standard measures of pain (Brief Pain Inventory, BPI) and problematic opioid use (Current Opioid Misuse Measure, COMM). Additionally, participants (N = 60) were provided compensation ($40 gift card) for completing REDCap-based follow-up assessments at 7-days, 1-month, and 3-months post discharge. Relevant to the present disclosure, the enrollment target of approximately 10 participants per month was achieved with nearly 70% compliance rates across follow-up timepoints. This preliminary work demonstrates the ability to collect longitudinal data from trauma patients and, in doing so, identify a high-risk subgroup of patients who report continuous pain, stress and opioid use over an extended follow up period. Establishing a genetic/epigenetic signature of opioid misuse risk. miRNA sequencing was performed on blood from a subset of 82 trauma patients (from the total of 107 above) for which ORT data was available, and classified these patients as high or low risk for opioid dependence based on the patients’ polygenic risk score (PRS), calculated using the summary statistics from the largest discovery GWAS for opioid dependence available. The blood miRNA biosignatures was compared to those identified in postmortem blood from OUD patients. Using machine learning analysis a miRNA signature, including the Let family, was identified that predicted OUD PRS groups in trauma patients.

Experimental Activities

The present disclosure builds upon and extends these promising findings by: (1) adding clinically relevant time points to coincide with standard opioid prescribing practices at discharge and CDC guidelines for defining persistent opioid use after surgery (> 90 days); (2) taking advantage of new remote data collection methods by using REDCap to assess pain and pill taking behavior in the daily life of the patient following hospitalization; (3) providing compensation to enhancing study retention, compliance, and representativeness of the sample; (4) including standardized outcome measures of opioid use/misuse, psychiatric symptoms, and social support to inform treatment needs; (5) performing repeated longitudinal assessments of miRNA levels in blood.

Study Overview. A prospective cohort observational design is utilized to assess associations between opioid demand and clinically/biologically relevant outcomes measures (i.e., pain, opioid use, miRNA levels) and to assess if miRNA signatures at discharge predict a patient’s likelihood to exhibit continued opioid use at 90-days or greater post-discharge. Table 1 provides an outline of study -related procedures and assessments.

Participants . Participants are recruited and provided consent following admission to the RDTI. Only patients who were discharged with an opioid prescription are recruited.

Measures.

In-Hospital Assessments. Total morphine milligrams equivalent (MME) per day per hospital stay is calculated based on best available evidence converting opioids to oral MME. The ORT assesses family history of substance use, age, history of pre-adolescent sexual abuse, and presence of psychiatric diseases. A revised, 9-item unweighted version is utilized. The NPRS measures participants’ self-reported pain on a 0 (“no pain”) to 10 (“most severe pain”) scale. Ambulatory (post-discharge) Assessments. At one virtual visit (Day 7 following discharge), REDCap mobile is used to administer surveys related to pain and pain medication consumption. Participants are presented with the following questions related to the past 24-hrs: “How intense was your pain”, Did you take your pain medication?” and “Did you get a refill of your medication?”. Opioid demand is assessed using a hypothetical purchasing task, assessing opioid purchase and consumption at different opioid price points. The timeline follow-back (TLFB) instrument assesses self-reported use of opioids and other substances. The COMM is used as a valid and reliable measure of opioid misuse among chronic pain patients. Pain-related assessment includes the NPRS and the BPI. Additional information related to psychological and psychiatric functioning is collected using standard measures, including the PROMIS questionnaire on instrumental and emotional support, HAM-A, PSS, PCL-5, BDI. The CSSR is used to assess the presence of suicidal ideation or behavior. Blood is drawn for miRNA sequencing and bioinformatics analysis at each in -person follow-up visit (1, 3, 6, 9, 12-month) focusing on candidate miRNAs previously identified.

Data Analysis. Generalized linear mixed modeling (GLMM) is used to evaluate relationships between predictors and outcomes with random effects for longitudinal data (e.g., level 2 intercept terms). Data is partitioned into sets for training and testing. Model performance is quantified by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Missing data is handled via robust methods via maximum likelihood, explicit modeling of missingness, and/or imputation. Multiple comparisons use false discovery rate to control for Type I error for any post hoc models. GLM evaluates relationships between baseline sample characteristics, predictors, and outcomes. Confounders are defined as any characteristics that demonstrate a relationship with both the predictor and outcome in a given model. Models are tested with and without confounders. Analyses also evaluate sample characteristics (e.g., sex) as moderators. Sensitivity analyses evaluate robustness to different prior distributions.

Machine learning. PRS is calculated based on the latest GWAS data available, clinical measures and miRNA signatures into a machine learning algorithm (linear and non-linear) to build a classification model for opioid misuse risk. Linear algorithms include penalized linear regression algorithms (i.e., least absolute shrinkage selection operator (LASSO) and Elastic Net) and a linear kernel learning algorithm (i.e., linear support vector machine). A non-linear support vector machine is also examined using a polynomial kernel function and a gaussian radial basis function (RBF). Penalized linear regression algorithms are optimal in mitigating the curse-of-dimensionality or small-n-large-p problem. A kernel-based non-linear support vector machine is also employed and evaluates both polynomial and RBF kernel functions. In both penalized regression and kernel-based learning approaches, optimal hyper-parameters are selected using a 10-fold cross-validation process while the overall model is validated using leave-one-out cross-validation process to establish the utility of the multi-marker biosignatures in predicting groups.

Power analyses. For gene expression analyses, performed either using bulk RNA-Seq or pseudobulk aggregates, assuming a standard deviation of 50% of the mean in the population, with at least N=20 per group can detect a fold change of 2x using a parametric test at the significance level of alpha=0.05 with a power of over 97%. For clinical correlation analyses, assuming N = 180 and r = 0.50 for correlated observations within person over time, a moderate average correlation r = 0.30 between baseline demand and a given outcome increasing linearly over time was found in 87.6% of simulations. A less conservative model assuming a static correlation r = 0.30 yielded a significant main effect of baseline demand in 96.3% of simulations, controlling for time.

The present disclosure identifies a miRNA signature associated with risk for opioid misuse in patients following exposure to opiate medication after trauma surgery. Success of this invention provides the go signal for a subsequent implementation study to optimize the use of this screening tool for identification of patients at risk for opioid misuse, and informs targeted interventions to prevent opioid misuse and OUD after traumatic injury.

1. Identify a miRNA biosignature that predicts patient risk classification at baseline. A unique miRNA signature is shown to predict patient classification based on PRS.

2. Demonstrate improved predictive precision with the miRNA signature compared to current tools (PRS, ORT).

3. Perform longitudinal measures of the miRNA biosignature, to confirm continued altered levels of miRNAs associated with progression to opioid misuse, tolerance, and dependence.

4. Utilize longitudinal monitoring and predictive modeling to inform the development of subsequent prevention interventions to improve outcomes.

Many variables are contemplated to confound group differences in epigenomic signatures, including exercise and eating history, previous use of medications, body mass index, smoking status, and race/ethnicity. These are included as covariates in the analyses (thus controlling them for their confounding effects) and also explore their specific effects by independent post-hoc stratified analyses, whenever applicable.

Non-response and attrition pose methodological challenges in longitudinal studies. The most recent study yielded nearly 70% follow-up rates at 1-week, 1-month, and 3-months postdischarge. Compensation reflects both increased participant response requirements (coming in to the clinic, providing biological samples, etc.) as well as goal of increasing compliance rates. Additional strategies include updating participant contact information frequently, offering virtual visits, and assisting with transportation.

Opioid tolerance

Repeated long-term exposure to opioids causes an enhanced sensitivity to painful stimuli, known as opioid- induced hyperalgesia, which in turn leads to higher and more frequent opioid doses needed to reduce pain, known as opioid tolerance. Opioid tolerance can lead to opioid misuse and dependence in susceptible individuals. Although the mechanisms underlying opioid tolerance are not completely understood, it is well known that chronic opioid treatment leads to a reduction in postsynaptic potassium conductance and voltage-gated calcium and potassium channels activity, as well as increased neuroinflammatory responses, especially those mediated by toll-like receptor-4 (TLR4) and the NLRPs inflammasome, in the brain and spinal cord. More recently, studies have shown that the mechanisms leading to opioid tolerance are regulated by miRNAs. Importantly, miRNAs regulate inflammation through activation of NF-kB and the NLR3P inflammasome, a group of cytosolic multi-protein signaling complexes that regulate maturation of the interleukin (IL)-l family of cytokine.

The role of genes and environment in opioid use disorder

The risk of developing an addiction, including opioid use disorder (OUD), has both a genetic and environmental component. In regards to genetics, efforts have been made to understand the individual variability in the response to opioid exposure, with the goal of identifying individuals who are at risk for developing opioid dependence and addiction, and a number of risk loci have been identified by genome wide association studies (GWAS), including opioid, dopamine and serotonin receptors. However, the polygenic nature of the disorder, where hundreds of genes of small effect are likely to be involved, has led to inconsistency across GWAS findings. The generation of polygenic risk scores (PRS), in which risk genes are weighted and added to quantify an individual’s risk for a particular disorder, attempts to overcome the challenges of polygenic disorders. Indeed, the FDA recently approved the first genetic test for OUD risk, AvertD, which detects polymorphisms in 15 genes to determine a risk score. However, being bom with a set of mutated genes is not a de facto sentence to having the disorder, but merely confers susceptibility to the illness. Environmental factors, via epigenetic modifications, play an important role in modulating the risk for opioid misuse and the development opioid dependence. Epigenetics is the study of the control of changes in gene expression that are not caused by alterations in DNA sequence. Epigenetic control of gene expression is highly regulated by the environment, including exposure to substances. Therefore, a comprehensive understanding of the epigenetic factors influencing opioid dependence leads to more precise tests for identification of individual at risk for developing OUD, and importantly, also leads to identification of specific treatments that alters disease course or reduce severity. miRNAs as small molecule probes in Opioid Overuse Disorder (OUD).

While 70 to 90% of the mammalian genome is transcribed into RNA, <2% of the genome represents protein-coding genes. MicroRNAs, acting via regulation of messenger RNAs (mRNA), are one of the key epigenetic modulators of gene expression and intercellular communication across the brain. Several miRNAs involved in regulation of synaptic plasticity are contemplated to underlie drug addiction and miRNAs have been shown to regulate p-opioid receptor levels and modulate opioid tolerance. These studies point towards miRNAs as critical short-term and long-term epigenetic modulators of opioid effects in the brain through regulation of gene expression. Importantly, miRNAs can cross the blood-brain barrier, via exosomes, and assessment of differential miRNA expression in blood has been used to identify surrogate blood-based biomarkers in brain diseases, including Alzheimer’s disease, depression, and cancer. Brain- specific miRNAs identified in peripheral blood have been proposed as markers for traumatic brain injury. Specifically for opioids, a recent study found differential expression of miRNAs in heroin- and methamphetamine-dependent patients that functionally predicted anxiety and depression symptoms and a specific set of blood miRNAs was found to predict analgesic efficacy of hydromorphone in cancer patients.

Based on the above studies, a new framework is developed to validate miRNAs as biomarkers of addiction risk in trauma patients who are prescribed opioids after surgery (Figure 1).

The present disclosure represents a new step forward in developing a biological marker with sufficient sensitivity and specificity to predict opioid misuse in a highly vulnerable patient population. The AvertD test is the only clinical test to date that has been approved by the U.S. FDA. The present disclosure provides a highly innovative alternative to the AvertD test. Specifically, blood miRNAs offer advantages over genetic tests alone as a risk assessment tool by reflecting the epigenetic mechanisms involved in an individual’s risk for OUD. The present disclosure is the first of its kind to validate a miRNA biomarker signature with a clear pathway to commercialization. The present disclosure also provides a new and positive impact in preventing and reducing elevated risk of OUD in patients with trauma and other populations exposed to opioid medications for pain.

Identification of miRNA-mRNA functional pairs in brain and blood tissues from OUD subjects.

To understand mechanisms and identify targets for intervention in the current crisis of OUD, postmortem brains represent an under-utilized resource. To refine previously reported gene signatures of neurobiological alterations in OUD from the dorsolateral prefrontal cortex, the role of miRNAs as regulators of opioid- induced gene alterations was explored. Building on the growing appreciation that miRNAs can cross the blood-brain barrier, miRNA profiling was performed in same-subject postmortem samples from OUD brain and blood tissues. miRNA- mRNA network analysis showed that miRNA target genes and corresponding enriched pathways overlapped strongly in brain and blood. Among the dominant enriched biological processes, MAPK signaling pathways were identified that are strongly associated with opioid tolerance. Specifically in blood, differentially expressed miRNAs were identified in OUD patients with targets enriched in signatures of brain differentially expressed genes. Importantly, among the differentially expressed miRNAs are two members of the Let-7 family, known to be involved in opioid tolerance. Further, the identified miRNA targets included genes associated with OUD, including the immediate early gene EGR1, as well as inflammatory genes and potassium channels involved in opioid tolerance (Figure 2).

Establishing a biobehavioral profile for opioid misuse risk.

A series of studies were conducted aimed at demonstrating the feasibility of tracking patients with trauma upon admission to a Level I trauma center and for up to 1 year following discharge.

Initially, risk of opioid misuse was assessed using the ORT, a standard psychometric screening tool. The findings identified 15% of hospitalized trauma patients as “high risk” for opioid-related aberrant behavior. Moreover, ORT risk classification predicted injury-related stress reported at 2 weeks post-discharge.

In an effort to improve the ability to identify individuals at risk of opioid misuse, the utility of assessing the behavioral economic opioid demand as a measure of opioid valuation and potential abuse liability was demonstrated. Drug demand is a behavioral economic measure assessing changes in drug consumption as a function of increasing price, which maps onto modem conceptualizations of chronic drug use being compulsive, persisting in the face of increasing negative consequences, ’rhe present disclosure administered a brief 3-item demand task along with various pain-related self-report measures in 103 trauma- surgery patients at 4 weeks post-discharge. Opioid demand was significantly associated with pain (i.e., average pain, pain preventing daily activities, pain-related stress, need for additional pain management services) and opioid-related measures (i.e., number of pills taken, took any pills, obtained a refill). Opioid demand was also significantly associated with hospital measures of MME but not the ORT, showing that the ORT and opioid demand may assess different domains of opioid misuse risk (Figure 3).

In a follow-up study among a subset of participants (N = 34), opioid demand assessed at 4-week follow-up was associated with significantly worse self-reported pain outcomes and greater opioid use at 1-year post-discharge. Although pain-related measures decreased for the group as a whole, the magnitude of improvement at 1-year post-discharge was less for those reporting greater opioid demand at 4-weeks post-discharge. Taken together, the present disclosure demonstrates the ability to: 1) recruit and enroll sufficient samples of trauma patients; 2) apply longitudinal study designs to track patients following hospitalization; 3) improve upon traditional screening tools (ORT) for identifying patients at risk.

Most recently, similar outcomes were assessed, but also including more standard measures of pain (Brief Pain Inventory, BPI) and opioid use (Current Opioid Misuse Measure, COMM). Additionally, participants (N = 60) were provided compensation ($40 gift card) for completing REDCap-based follow-up assessments at 7-days, 1-month, and 3-months post discharge. The enrollment target of approximately 10 participants per month was achieved with nearly 70% compliance rates across follow-up timepoints. Primary outcomes revealed that opioid demand was significantly associated with measures of opioid use (i.e., past 7-day use, opioid medication refill), pain, and COMM scores at the 7-day and 1-month follow-up visits. Additionally, opioid demand assessed at 7-days predicted measures of opioid use (i.e., past 7- day use, opioid medication refill) and COMM scores. This work demonstrates the ability to collect longitudinal data from trauma patients and, in doing so, identify a high-risk subgroup of patients who report continuous pain, stress and opioid use over an extended follow up period.

Establishing a genetic/epigenetic signature of opioid misuse risk

Aiming to establish a genetic/epigenetic signature of opioid misuse risk, miRNA sequencing was performed on blood obtained from a subset of 82 trauma patients (from the total of 107 above) for which ORT data was available, prior to discharge and opioid prescription, and classified these patients as high or low risk for opioid dependence based on PRS, calculated using the summary statistics from the largest discovery GWAS for opioid dependence available. Leveraging information of the biological functions of 2,600 human miRNAs available from the miRbase (v.22), cluster analysis of normalized miRNA expression data with age and sex regression was performed using the Louvain network detection methods, as implemented in the Seurat R package. Three unique clusters of patients based on miRNA function were identified, of which cluster 2 was more strongly associated with PRS compared to clusters 1 and 2 (Figure 4). Using machine learning analysis, miRNA signatures were identified that predicted cluster membership in trauma patients, including 25 miRNAs in the PRS-associated cluster 2 that target genes previously identified to be involved in opioid tolerance and addiction (Figure 5).

Machine learning for individual clusters was performed using three methods, k- nearest neighbor (KNN) analysis, Random Forest (RF), and Support Vector Machines (SVM), as implemented in the R package caret v6.0-94. Eighty percent of the observations were used for the training data set and 20% of observations for the testing data set. Classifier performance was evaluated using Receiver Operating Characteristics (ROC) curve analysis and Area Under the Curve (AUC) analysis.

The present disclosure builds upon and extends findings by: (1) adding clinically relevant time points to coincide with standard opioid prescribing practices at discharge and CDC guidelines for defining persistent opioid use after surgery (> 90 days); (2) taking advantage of new remote data collection methods by using REDCap to assess pain and pill taking behavior in the daily life of the patient following hospitalization; (3) providing compensation to enhancing study retention, compliance, and representativeness of the sample; (4) including standardized outcome measures of opioid use/misuse, psychiatric symptoms, and social support to inform treatment needs; (5) performing repeated longitudinal assessments of miRNA levels to validate a miRNA signature of opioid misuse risk.

A prospective cohort observational design is utilized to assess associations between opioid demand and clinically /biologically relevant outcomes measures (i.e., pain, opioid use, PRS, miRNA levels) and to assess if miRNA signatures at discharge predict a patient’s likelihood to exhibit continued opioid use at more than 90 days post-discharge. Table 1 illustrates an outline of study -related procedures and assessments. Initial hospital-based measures are taken following informed consent and based on patient records during their hospital stay. Following discharge, the first assessment is conducted virtually on Day 7. This time-point was chosen in order to provide participants with enough time to experience their opioid medication. This visit is conducted virtually as not all participants may have recuperated enough for an in-person visit. Follow-up visits at months 1 to 12 are conducted in person to provide the opportunity to collect critical biological measures. Assessments are administered in REDCap, a HIPAA-compliant and cost-effective method used worldwide for conducting assessments and collecting data remotely as well as in the clinic through convenient interfaces such as an iPad. At the time of consent, participants are asked their preferred modality (i.e., smartphone or email) by which to send them a link to complete Day 7 follow-up surveys. The present disclosure identifies a miRNA signature associated with risk for opioid misuse in patients following exposure to opiate medication after trauma surgery. The present disclosure also demonstrates improved predictive precision with the miRNA signature compared to current tools alone (PRS, ORT). The present disclosure also provides the go signal for a subsequent implementation study to develop a miRNA test kit and optimize the use of this screening tool and informs targeted interventions to prevent opioid misuse and OUD after traumatic injury.

Participants

Participants are recruited and provided consent (or assented for individuals under 18 years old) following admission to the RDTI. Eligible participants are 16 years of age or older and have a mobile phone. Based on the previous pilot study, monolingual Spanish speaking individuals represented only a small percentage of participants. However, the current study provides appropriate language support for individuals who are not fluent in English. Pregnant women, prisoners, patients placed in observation, and non-acute trauma admissions, including readmissions, are excluded.

Hospital Measures (BL)

Following consent, participants complete the ORT, a brief, clinician-administered, selfreport tool designed to be administered in primary care populations to assess risk of opioid misuse among individuals being considered for opioid therapy assessing family history of substance use, age, history of pre-adolescent sexual abuse, and presence of psychiatric diseases. A revised, 9-item unweighted version of the ORT is utilized that has been found to be superior in identifying at-risk individuals. The NPRS is well-validated in clinical populations and measures participants’ self-reported pain on a 0 (“no pain”) to 10 (“most severe pain”) scale. MME is calculated based on best available evidence, converting opioids to oral MME.

Biological Measures (BL, Ml, M3, M6, M9, M12) The blood draw for miRNA sequencing, DNA genotyping, and bioinformatics analysis is conducted at baseline and at subsequent in-person time points. miRNA sequencing. RNA is extracted from plasma using miRNeasy micro kit by Qiagen. As a post-extraction quality control, the RNA is quantified using Qubit Fluorometer (Thermo Fisher). Small RNA libraries are generated using the Illumina small RNA protocol, sequenced on the Illumina Genome Analyzer NextSeq 2000, generating -10-20 million 75 base pair reads per sample, and analyzed using published bioinformatics pipeline. Briefly, RNA- Seq libraries are constructed using the Takara SMARTer Universal Low Input RNA Kit designed to handle 2-100 ng of total RNA and retain strand-specific information. Illumina small RNA adapter sequences are trimmed from the reads, and reads of length below lOnt or ending in homopolymers of length 9 nucleotides or above are discarded. Total usable number of reads for each sample are calculated and mapped to the miRbase using BLAST; the abundance of each expressed miRNA is quantified as a fraction of the usable reads and expressed as parts per million.

Urinary drug test are assessed via a standard NIDA 5-Panel Drug Test Kit, testing for opioid metabolites in addition to cocaine, amphetamine, methamphetamine, and THC (Arham International, Inc., Greenville, SC).

OUD Polygenic Risk Scores (PRS)

Genotyping of all individuals is performed with the Infinium Global Screening Array- 24 v2.0 Kit (Illumina), according to the manufacturer’s instructions. Raw genotyping data is pre-processed in PLINK v. 1.9 to exclude samples with high rates of genotype missingness (>10%) and filter out single nucleotide polymorphisms (SNPs) that are missing in a large proportion of subjects (locus missingness >10%), that have a minor frequency allele (MAF) lower than 0.01, and that deviate from the Hardy-Weinberg equilibrium (p < IxlO'⁶). BCFtools are used to fix strand orientation prior to imputation using the TOPMed Imputation Server and TOPMed reference panel (Version R2, 194,512 haplotypes). PRS for OUD is calculated from the summary statistics of the largest discovery GWAS for opioid dependence available at the time using a high-dimensional Bayesian regression framework, which is robust to varying genetic architectures, and enables multivariate modeling of local linkage disequilibrium patterns.

Opioid- and Drug-Related Assessments (BL, D7, Ml, M3, M6, M9, M12)

Opioid demand is assessed using a hypothetical purchasing task, assessing opioid purchase and consumption at different opioid price points. Data is fit to current models of demand in order to derive indices of demand commonly assessed in the literature and successfully used. Primary demand indices include Qo - maximum drug consumption at price 0; Omax - maximum output (money spent); P_max - price at which O_m<zx is observed; breakpoint - price at which drug is no longer purchased; and essential value - the rate of change in the slope of the curve. Among these metrics, Qo and Omax are typically the most often associated with abuse liability in human research. The time-line follow-back is used to assess self-reported recent opioid use as well as other commonly used drugs of abuse. The COMM is presented only at 90-day follow-up and consist of 17 questions presented on a 5-pt Likert scale assessing opioid misuse in the past 30 days. The COMM is a valid and reliable measure of opioid misuse and have been previously used to monitor opioid misuse among chronic pain patients.

Pain-Related Assessments (BL, D7, Ml, M3, M6, M9, M12)

The BPI was developed for use in cancer patients, but has been validated in non-cancer patient populations, with improved scores reflecting treatment impact. The NPRS, described above, measures participants’ self-reported pain on a 0 (“no pain”) to 10 (“most severe pain”) scale.

Psychological & Psychiatric Assessments (BL, D7, Ml, M3, M6, M9, M12)

Need for additional support is assessed using the PROMIS Instrumental Support and PROMIS Emotional Support assessments. Both assessments consist of 8 items asking various questions on a 5-pt Likert scale (Never to Always). Instrumental support consists of practical aid (e.g., “Do you have someone to take you to the doctor if you it?”), whereas emotional support assesses psychological aid (e.g., “I have someone who will listen to me when I need to talk.”). Stress and anxiety are assessed using the HAM-A and PSS. The HAM-A is a clinicianrated 14-item assessment with acceptable inter-rate reliability. Items are scored on a 0 (not present) to 4 (severe) scale with clinical cutoffs for mild (<18), mild to moderate (18 to 24), and severe anxiety (>25). The PSS is a 10-item questionnaire assessing the occurrence of stressful events in the past month using a 5-pt Likert scale (i.e, “never” to “very often”). The PCL-5 is used to screen for PTSD based on DSM-5 criteria, and to monitor symptom changes over the course of the study. Changes in sleep quality are assessed via the PSQI, a standard assessment of past month sleep quality. The first 7 questions are scored from 0 to 3, with higher scores indicative of more disturbed sleep, and a clinical cut-off score of 5 indicating poor sleep quality. Depressive symptoms and risk of suicidality are assessed using the BDI and CSSR. In the case of responses indicative of suicidal ideation, trained research therapists assess suicide risk and refer participants to appropriate medical support as needed. Compensation (D7, Ml, M3, M6, M9, M12)

Participants received $50 for completing the 7-day follow-up assessment via REDCap. Participants received $100 for completing the in-person follow-up visits at months 1, 3, 6, 9, and 12 (total = $550). Participants also received $5 compensation providing urinalysis samples at each of the in-person visits. On an individual basis, compensation for either parking, busfare, or transportation is provided, averaging $32.50 per participant for each of the in-person followup visits.

Data Analysis

Generalized linear mixed modeling (GLMM) is used to evaluate relationships between predictors and outcomes with random effects for longitudinal data (e.g., level 2 intercept terms). Data is partitioned into sets for training and testing. Model performance is quantified by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Model assumptions are evaluated via graphical evidence (e.g., residual plots) and formal statistical tests. Violations are handled via re-specification, variable transformation, robust estimation, or stratification. Missing data is handled via robust methods via maximum likelihood, explicit modeling of missingness, and/or imputation. Each model is evaluated at the significance level a=0.05. Multiple comparisons use false discovery rate to control for Type I error for any post hoc models. GLM evaluates relationships between baseline sample characteristics, predictors, and outcomes. Confounders are defined as any characteristics that demonstrate a relationship with both the predictor and outcome in a given model. Models are tested with and without confounders; if inferences are different, both models are reported; otherwise, the simpler model is retained. Analyses also evaluate sample characteristics (e.g., sex) as moderators. Statistical inference for the frequentist models described to this point are complemented by Bayesian models with weakly informative priors to directly yield the probability of the alternative situation. Sensitivity analyses evaluate robustness to different prior distributions. Evaluating assumptions of Bayesian inference rely on effective sample size, scale reduction factors (“rhat”), and posterior predictive checking. Regularization for Bayesian analyses rely on horseshoe priors.

Machine learning

PRS, clinical measures and miRNA signatures are incorporated into a machine learning algorithm (linear and non-linear) to build a classification model for opioid misuse risk. Linear algorithms include penalized linear regression algorithms (i.e., least absolute shrinkage selection operator (LASSO) and Elastic Net) and a linear kernel learning algorithm (i.e., linear support vector machine). Mboost is also used for its ability to account for correlations between repeated observations by explicitly including random effects for participant and time. A nonlinear support vector machine is also examined using a polynomial kernel function and a gaussian radial basis function (RBF). Penalized linear regression algorithms are optimal in mitigating the curse-of-dimensionality or small-n-large-p problem. A kernel-based non-linear support vector machine is employed and both polynomial and RBF kernel functions are evaluated. In both penalized regression and kernel-based learning approaches, optimal hyperparameters are selected using a 10-fold cross-validation process while the overall model are validated using leave-one-out cross-validation process to establish the utility of the multimarker biosignatures in predicting groups.

Power analyses

For gene expression analyses, performed either using bulk RNA-Seq or pseudobulk aggregates, assuming a standard deviation of 50% of the mean in the population, with at least N=20 per group detects a fold change of 2x using a parametric test at the significance level of alpha=0.05 with a power of over 97%. For clinical correlation analyses, assuming N = 180 and r = 0.50 for correlated observations within person over time, a moderate average correlation r = 0.30 between baseline demand and a given outcome increasing linearly over time was found in 87.6% of simulations. A less conservative model assuming a static correlation r = 0.30 yielded a significant main effect of baseline demand in 96.3% of simulations, controlling for time.

Example 2: miRTracker - microRNA signature for predicting susceptibility of individuals to addiction and quantifying addiction risk and progression during prescription drug use miRTracker, consists of a novel microRNA signature, including the Eet-7 family, extracted through machine learning based behavioral and bioinformatics pipelines designed and developed to characterize opioid-induced miRNA expression dynamics through the integration of Polygenic Risk Scores (PRS) and microRNA sequencing data from trauma patients undergoing surgery prescribed opioids at discharge. The use of miRTracker at monthly intervals to monitor and identify patients at high risk for addiction to the drugs prescribed to mitigate pain to alter the dose of opioids personalized to the patient and/or the duration of drug use can significantly decrease death from drug overdose in patients due to Opioid Overuse Disorder (OUD). miRTracker Diagnostic Panel:

1. hsa-miR-let-i-5p

2. hsa-miR-15a-5p

3. hsa-miR-15b-3p

4. hsa-miR-16-5p

5. hsa-miR-25p-3p

6. hsa-miR-103a-3p

7. hsa-miR-130b-3p

8. hsa-miR-140-3p

9. hsa-miR-185-5p

10. hsa-miR-483-3p

11. hsa-miR-550a-5p

12. hsa-miR-942-5p

13. hsa-miR-5010-5p

14. hsa-miR-4732-5p

Behavioral bioinformatics pipeline: using machine learning and Al, this pipeline extracts polygenic risk score (PRS)-associated microRNA signatures for assessing susceptibility to addiction and quantifying addiction risk and progression during prescription drug use. microRNA selection filter: This filter is based on target genes associated with addiction to opioids and other substances.

Based on the work described herein, a microRNA-based diagnostic biomarker was developed to predict risk for OUD through a blood test. It is contemplated that miRNAs serve as powerful bio- signatures for predicting long-term clinical outcomes in patients treated with opioids for pain management following trauma injury.

Herein, the miRTracker, a microRNA biomarker signature to predict risk for Opioid Use Disorder (OUD) through a blood test, is disclosed. Using a behavioral bioinformatics platform incorporating machine learning and artificial intelligence (Al) tools, polygenic risk scores (PRS) are integrated with microRNA changes in the blood of individuals to extract a microRNA signature that is predictive of the susceptibility of individuals to addiction and therefore can be used to quantify addiction risk and progression during prescription drug use. Using minimum features necessary for building random forest model for each classification, 11 microRNAs that are down-regulated in individuals at high risk for addiction were extracted from Cluster 2 vs. Cluster 1 at resolution 1; 2 microRNAs from Custer 0 vs. Cluster 1 at resolution 0.4 (Figure 5); and 1 microRNA that is upregulated in individuals at high risk for addiction from Cluster 2 vs. Cluster 1 at resolution 1 (Figure 7). The microRNA panel (version 1) consisting of 13 microRNAs that are downregulated and one microRNA that is upregulated in individuals at high risk is shown above.

The Opioid Risk Tool (ORT) is the most commonly used screener for assessing opioid misuse risk. The ORT has a sensitivity and specificity ranging from 0.25 to 0.83 and 0.43 to 0.88, respectively, with likelihood ratios showing low predictive accuracy. These results underscore the need for developing and implementing both improved and adjunctive assessments for measuring risk of opioid misuse. Across both studies, various pain- and opioid- related measures were remotely assessed at 4-weeks post discharge (N = 107) as well as in a subset at 1-year post discharge (N = 34). Most recently, similar outcomes were assessed, but also included more standard measures of pain (BPI) and opioid use (COMM). Additionally, participants (N = 60) completed REDCap-based follow-up assessments at 7-days, 1-month, and 3-months post discharge. The ability to validate the microRNA panel associated with opioid addiction risk to collect longitudinal data from trauma patients in a high-risk subgroup of patients who report continuous pain, stress and opioid use over an extended follow up period was also demonstrated.

Methods for miRNA/Target Gene Integration

Integration of miRNAs and genes was performed using the miRDB7 and TargetScan8 databases. The microRNA-mRNA target prediction outputs used for extracting the microRNAs linked to addiction are shown in Figures 6A, 6B, 6C, 6D, 6E, 6F, and 6G. miRNA-target gene pairs that are oppositely correlated with Spearman coefficients less than -0.3 and with at least one significant (FDR<0.05) anti-correlation are selected. The one-sided Fisher’s exact P -value gives the probability of enrichment for that particular microRNA. However, where several hundred microRNAs are simultaneously considered for enrichment, the issue of multiple testing needs to be taken into account. One way to address this is to use methods such as from Storey and Tibshirani (2003) to estimate the false discovery rate (FDR), i.e., multiply the total number of microRNAs tested by a given nominal P -value, then divide by the total number of microRNAs having P -values less than the given. Map sequence reads to the genome and identify mRNA transcript isoforms from the mRNA-seq data using reference-based assembly.

1. Calculate TargetScan 6.0 miRNA binding sites on the full-length reconstructed transcripts.

— miRBase - www.mirbase.org/ — TargetScan - www.targetscan.org/

— miRDB - mirdb.org/miRDB/

— miRanda - www.microrna.org/microma/home.do

2. Identify anti-correlated miRNA:mRNA pairs

— Spearman correlation coefficients (r) between the abundance profiles for mRNA isoforms and miRNA-5p and miRNA-3p forms

— thresholding at a q-value (FDR) < 0.05.

3. Integrate PRS

Methods for MicroRNA clusters analysis

MicroRNA expression data for 87 samples was clustered using the Louvain network detection methods, as implemented in the Seurat R package6. Data was analyzed at two resolutions: r=0.4 with two clusters, and r=1.0, with three clusters. Difference between distribution of polygenic risk score (PRS) was assessed via one-way AN0VA, using R Studio v4.2.2. Cluster signatures were derived using AN0VA. Multiple hypothesis testing correction was performed using the Benjamini & Hochberg method, with significance achieved for FDRcO.l.

Machine Learning

Machine learning for individual clusters was performed using three methods, k- nearest neighbor (KNN) analysis, Random Forest (RF), and Support Vector Machines (SVM), as implemented in the R package caret v6.0-94. Eighty percent of the observations were used for the training data set and 20% of observations for the testing data set. Classifier performance was evaluated using Receiver Operating Characteristics (ROC) curve analysis and Area Under the Curve (AUC) analysis.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present disclosure without departing from the scope or spirit of the invention. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the methods disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims. TABLES

ORT = Opioid Risk Tool; NPRS - Numeric Pain Rating Scale; MME = Morphine milligrams equivalent; TLFB = Time Line Follow Back interview of substance use; COMM = Current Opioid Misuse Measure; BPI = Brief Pain Inventory; HAM-A = Hamilton Anxiety Rating Scale; PSS = Perceived Stress Scale; PCL-5 = PTSD Checklist for DSM-5; PSQI = Pittsburgh Sleep Quality Index; BDI = Beck Depression Inventory; CSSR = Columbia-Suicide Severity Rating Scale; DD = Delayed Discounting.

Table 2. Differentially expressed miRNAs in postmortem blood from OUD patients. Potassium channel genes that are miRNA targets are highlighted in bold.

Table 4. miRNAs that target the focused list of genes (Res 0.4 Cluster 0 over 1). Differentially expressed miRNAs by t-test.

Table 5. miRNAs that target the focused list of genes (Res 0.4 Cluster 0 over 1). miRNAs important for ML after 70% iteration cutoff.

Table 6. miRNAs that target the focused list of genes (Res 1 Cluster 2 over 1). Differentially expressed miRNAs by t-test.

Table 7. miRNAs that target the focused list of genes (Res 1 Cluster 2 over 1). miRNAs important for ML after 70% iteration cutoff.

SEQUENCES

1. SEQ ID NO: 1 - Position 399-405 of OPRM1 3’ UTR CACAUAAAGUAAAUGCUACCUCU

2. SEQ ID NO: 2 - hsa-let-7i-5p UUGUCGUGUUUGAUGAUGGAGU

3. SEQ ID NO: 3 - Position 134-140 of OPRM1 3’ UTR ACAUUAAUCAAAACUUUACAGAG

4. SEQ ID NO: 4 - hsa-miR-3686

AGUAAAUGAAAGAGAAUGUCUA

5. SEQ ID NO: 5 - Position 75-82 of OPRM1 3’ UTR AAAAACGACCUCAUAACACAAAA

6. SEQ ID NO: 6 - hsa-miR-5010-3p GACCCCUUACCCUCUGUGUUUU

7. SEQ ID NO: 7 - Position 835-841 of OPRM1 3’ UTR AAGACAGAUUAAUCCAAAGAGAA

8. SEQ ID NO: 8 - hsa-miR-130b-5p CAUCACGUUGUCCCUUUCUCA

9. SEQ ID NO: 9 - Position 515-521 of OPRK1 3’ UTR CAUUUCAUGUGGCUGUGUGGUAA

10. SEQ ID NO: 10 - hsa-miR- 140-3p.2 GGCACCAAGAUGGGACACCAU

11. SEQ ID NO: 11 - Position 1372-1378 of OPRK1 3’ UTR UUCUGUAAUUUGCCUGAGAAGAA

12. SEQ ID NO: 12 - hsa-miR-942-5p

GUGUACCGGUUUUGUCUCUUCU

13. SEQ ID NO: 13 - Position 2901-2907 of OPRK1 3’ UTR UCCUAACGAGGGGUCAGAGAAGG

14. SEQ ID NO: 14 - Position 1594-1600 of OPRK1 3’ UTR UACACAUGGCUUCAGAAUGUUAC

15. SEQ ID NO: 15 - hsa-miR-3143 GCUUUCUUCGCGAAAUGUUACAAUA

16. SEQ ID NO: 16 - Position 2834-2840 of OPRK1 3’ UTR ACCUUCAGAUCCAUUCUCUACAU

17. SEQ ID NO: 17 - hsa-miR-4732-5p

UCGAAGGACGAGGGACGAGAUGU 18. SEQ ID NO: 18 - Position 295-301 of OPRD1 3’ UTR

AGGAGAGGAGCGGGACCUGUGGC

19. SEQ ID NO: 19 - hsa-miR-140-3p.l CAGGCACCAAGAUGGGACACCA

20. SEQ ID NO: 20 - Position 796-802 of KCNH1 3’ UTR CUCUGGCCCUGCUCCAUGCUGCU

21. SEQ ID NO: 21 - hsa-miR-103a-3p

AGUAUCGGGACAUGUUACGACGA

22. SEQ ID NO: 22 - hsa-miR- 107

AGUAUCGGGACAUGUUACGACGA

23. SEQ ID NO: 23 - Position 3889-3895 of KCNH1 3’ UTR GGUUUUACUAUGUCUUGCAAUAA

24. SEQ ID NO: 24 - hsa-miR-25-3p

AGUCUGGCUCUGUUCACGUUAC

25. SEQ ID NO: 25 - Position 583-590 of KCNA23’ UTR GUUUCAUAACGGAAAAUGCUGCA

26. SEQ ID NO: 26 - Position 214-221 of KCNA23’ UTR CCACAGUCUUUUGUAAAUAUUGA

27. SEQ ID NO: 27 - hsa-miR- 16-2-3p

AUUUCGUCGUGUCAUUAUAACC

28. SEQ ID NO: 28 - Position 36-43 of KCNA23’ UTR

UAUUCUGGAAGCUUUCAGCCCCA

29. SEQ ID NO: 29 - hsa-miR- 185-3p CUGGUCUCCUUUCG— GUCGGGGA

30. SEQ ID NO: 30 - Position 1205-1211 of COMT 3’ UTR UUACAAAAAUUUAGGUGUUUACC

31. SEQ ID NO: 31 - hsa-miR-30b-5p

UCGACUCACAUCCUACAAAUGU

32. SEQ ID NO: 32 - hsa-miR-30c-5p

CGACUCUCACAUCCUACAAUGU

33. SEQ ID NO: 33 - hsa-miR-30a-5p

GAAGGUCAGCUCCUACAAAUGU

34. SEQ ID NO: 34 - hsa-miR-30e-5p

GAAGGUCAGUUCCUACAAAUGU

35. SEQ ID NO: 35 - hsa-miR-30d-5p GAAGGUCAGCCCCUACAAAUGU

36. SEQ ID NO: 36 - Position 234-240 of DRD23’ UTR GGCUGGGCCCCCCAGCUCAGGGG

37. SEQ ID NO: 37 - hsa-miR-125b-5p

AGUGUUCAAUCCCAGAGUCCCU

38. SEQ ID NO: 38 - Position 1081-1088 of COMT 3’ UTR CCUCCACCCAGGGCCCUGCCCCA

39. SEQ ID NO: 39 - hsa-miR-486-3p

UAGGACAUGACUCGACGGGGC

40. SEQ ID NO: 40 - Position 1873-1880 of SLC6A4 3’ UTR AAACCAUGAUUACUUUUGCACUA

41. SEQ ID NO: 41 - hsa-miR-130b-3p

UACGGGAAAGUAGUAACGUGAC

42. SEQ ID NO: 42 - Position 504-510 of SLC6A4 3’ UTR ACCUUCUAAUCCAUG— UGCUGCUG

43. SEQ ID NO: 43 - hsa-miR-15a-5p

GUGUUUGGUAAUACACGACGAU

44. SEQ ID NO: 44 - hsa-miR-15b-5p

ACAUUUGGUACUACACGACGAU

45. SEQ ID NO: 45 - hsa-miR-424-5p

UGUUUGGUAAUACACGACGAU

46. SEQ ID NO: 46 - hsa-miR-424-5p

AAGUUUUGUACUUAACGACGAC

47. SEQ ID NO: 47 - hsa-miR-6838-5p

UCCUCAGAACGGUGACGACGAA

48. SEQ ID NO: 48 - hsa-miR-16-5p

GCGGUUAUAAAUGCACGACGAU

49. SEQ ID NO: 49 - hsa-miR-195-5p

CGGUUAUAAAGACACGACGAU

50. SEQ ID NO: 50 - hsa-miR-135b-5p

AGUGUAUCCUUACUUUUCGGUAU

51. SEQ ID NO: 51 - hsa-miR-135a-5p

AGUGUAUCCUUAUUUUUCGGUAU

52. SEQ ID NO: 52 - Position 205-211 of GAL 3’ UTR

UUGCAAUUGUCUUUUUCUUCCAN 53. SEQ ID NO: 53 - hsa-miR-7-5p UGUUGUUUUAGUGAUC— AGAAGGU

54. SEQ ID NO: 54 - Position 47-53 of CYP2D6 3’ UTR AGCCAGAGGCUCUAAUGUACAAU

55. SEQ ID NO: 55 - hsa-let-7a-2-3p

CCUUUCGAUCCUCCGACAUGUC

56. SEQ ID NO: 56 - hsa-let-7c-3p

CCUUUCGAUCUUCCAACAUGUC

57. SEQ ID NO: 57 - Position 47-53 of CYP2D6 3’ UTR AGCCAGAGGCUCAA-UGUACAAU

58. SEQ ID NO: 58 - hsa-let-7g-3p

CGUUCCGUCACCGGACAUGUC

59. SEQ ID NO: 59 - Position 50-56 of CYP2D6 3’ UTR CAGAGGCUCUAAUGUACAAUAAA

60. SEQ ID NO: 60 - hsa-miR-5701

UUAGUCUUGCACUGUUAUU

61. SEQ ID NO: 61 - Position 62-68 of CYP2D6 3’ UTR UGUACAAUAAAGCAAUGUGGUAG

62. SEQ ID NO: 62 - Position 86-92 of TPH23’ UTR

CGCAAAUAACCUUCUGUGUCAUG

63. SEQ ID NO: 63 - hsa-miR-425-5p

AGUUGCCCUCACUAGCACAGUAA

64. SEQ ID NO: 64 - Position 36-42 of ABCB 1 3’ UTR

UAAAUACUUUUUAAUAUUUGUUU

65. SEQ ID NO: 65 - hsa-miR-7-l-3p

AUACCGUCUGACACUAAACAAC

66. SEQ ID NO: 66 - hsa-miR-7-2-3p

AAUCCAUCUGACCCUAAACAAC

67. SEQ ID NO: 67 - Position 49-55 of ABCB 1 3’ UTR AUAUUUGUUUAGAUAUGACAUUU

68. SEQ ID NO: 68 - hsa-miR-513b-3p

AGGAGAGUUUUUCCACUGUAAA

69. SEQ ID NO: 69 - Position 824-830 of ABCB1 3’ UTR UGCCAGUAAUUGGCC-UCUUCCAA 70. SEQ ID NO: 70 - hsa-miR-7-5p UGUUGUUUUAGUGAUCAGAAGGU

71. SEQ ID NO: 71 - Position 655-662 of DRD1 3’ UTR CUGGCCAUUUAACUA— GCACUUUA

72. SEQ ID NO: 72 - hsa-miR-20b-5p

GAUGGACGUGAUACUCGUGAAAC

73. SEQ ID NO: 73 - hsa-miR-17-5p

GAUGGACGUGACAUUCGUGAAAC

74. SEQ ID NO: 74 - Position 41-48 of DRD43’ UTR GCCUGAUGGCCAGGCCUCAGGGA

75. SEQ ID NO: 75 - Position 88-94 of DRD5 3’UTR UACAUGCCUUUCCAGUGCUGCUC

Claims

CLAIMS What is claimed is:

1. A method of treating a subject with high-risk opioid addiction, the method comprising: collecting a sample from the subject; detecting and quantifying a panel of microRNAs (miRNAs) in the sample, wherein the panel comprises hsa-miR-25p-3p, hsa-miR-130b-3p, hsa-miR-185-5p, hsa-miR-483-3p, hsa- miR550a-5p, hsa-miR-942-5p, hsa-5010-5p, and hsa-miR-4732-5p; generating a profile of the subject for at least 6 months, wherein the profile comprises an expression level of the panel of miRNAs; and treating the subject with an opioid when the profile comprises the panel of miRNAs being expressed for at least 6 months, wherein the opioid is administered at a lower dose relative to a control subject or a subject with low-risk opioid addiction, or the opioid is administered less frequently relative to a control subject or a subject with low-risk opioid addiction.

2. The method of claim 1, wherein the panel of miRNAs further comprises hsa-miR-let-i- 5p, hsa-miR-15a-5p, hsa-miR-15b-3p, hsa-miR-16-5p, hsa-miR-103a-3p, or hsa-miR-140-3p.

3. The method of claim 1 or 2, wherein the profile is generated for 6 months, 9 months, 12 months, or more.

4. The method of any one of claims 1-3, wherein the opioid comprises oxycodone, hydrocodone, morphine, methadone, fentanyl, codeine, tramadol, or a combination thereof.

5. The method of any one of claims 1-4, wherein the subject is further treated with a nonopioid treatment.

6. The method of claim 5, wherein the non-opioid treatment comprises physical therapy, exercise, acetaminophen, ibuprofen, naproxen, or a combination thereof.

7. The method of any one of claims 1-6, wherein the subject has a traumatic injury, a chronic disease, or a combination thereof.

8. The method of claim 7, wherein the traumatic injury comprises a blunt force injury, a penetrating injury, a fracture, a bum, or combinations thereof.

9. The method of claim 7, wherein the chronic disease comprises a microbial infection, an autoimmune disease, an inflammatory disease, a cancer, or combinations thereof.

10. A method of predicting and quantifying high-risk opioid addiction in a subject, the method comprising: collecting a sample from the subject; detecting and quantifying a panel of microRNAs (miRNAs) in the sample, wherein the panel comprises hsa-miR-25p-3p, hsa-miR-130b-3p, hsa-miR-185-5p, hsa-miR-483-3p, hsa- miR550a-5p, hsa-miR-942-5p, hsa-5010-5p, and hsa-miR-4732-5p; generating a profile of the subject for at least 6 months, wherein the profile comprises an expression level of the panel of miRNAs; diagnosing a subject with high-risk opioid addiction, wherein the subject with high-risk opioid addiction expresses the panel of miRNAs for at least 6 months; and treating the subject with high-risk opioid addiction with an opioid at a lower dose relative to a control subject or a subject with low-risk opioid addiction, or treating the subject with high- risk opioid addiction with an opioid administered less frequently relative to a control subject or a subject with low-risk opioid addiction.

11. The method of claim 10, wherein the panel of miRNAs further comprises hsa-miR-let- i-5p, hsa-miR-15a-5p, hsa-miR-15b-3p, hsa-miR-16-5p, hsa-miR-103a-3p, or hsa-miR-140- 3p.

12. The method of claim 10 or 11, wherein the profile is generated for 6 months, 9 months, 12 months, or more.

13. The method of any one of claims 10-12, wherein the opioid comprises oxycodone, hydrocodone, morphine, methadone, fentanyl, codeine, tramadol, or a combination thereof.

14. The method of any one of claims 10-13, wherein the subject with high-risk opioid addiction is further treated with a non-opioid treatment.

15. The method of claim 14, wherein the non-opioid treatment comprises physical therapy, exercise, acetaminophen, ibuprofen, naproxen, or a combination thereof.