Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder

A Study of the MTHFR Gene Prevalence in a Rural Tennessee Opioid Use Disorder Treatment Center Population

Background: Opioid Use Disorder (OUD) has been linked to dopamine and the neurological reward centers. Methylenetetrahydrofolate reductase (MTHFR) is an enzyme involved in the production of many neurotransmitters such as dopamine. As such, MTHFR variants that lead to decreased production of neurotransmitters may play a role in OUD. However, lacunae exist for characterizing the prevalence of the MTHFR mutations in an OUD population. The objective of this study was to determine prevalence of the MTHFR gene mutations in a rural Tennessean population with OUD. Methods: This study was a retrospective cohort of individuals with OUD that evaluated the prevalence of MTHFR variants. Patients were categorized as normal, homozygous C677T, heterozygous C677T, homozygous A1298C, or heterozygous A1298C. The primary outcome was a qualitative comparison of the prevalence of each of the MTHFR variants in our cohort to the publicly reported MTHR polymorphism prevalence. Secondary outcomes include race and ethnicity differences as well as stimulant use differences for each of the variants. Results: A total of 232 patients undergoing care for opioid use disorder were included in the study. Of those included, 30 patients had a normal MTHFR allele and 202 had a variant MTHFR allele. Overall, the prevalence of any MTHFR variant was 87.1% (95% CI 82.6–91.4%). When comparing those with a normal MTHFR allele to those with any MTHFR variant, there was no difference in age, sex, race and ethnicity, or stimulant use. Conclusion: The overall prevalence of MTHFR variants in patients with opioid use disorders is high.

Prospective Prediction of Lapses in Opioid Use Disorder: Protocol for a Personal Sensing Study

BACKGROUND

Successful long-term recovery from opioid use disorder (OUD) requires continuous lapse risk monitoring and appropriate use and adaptation of recovery-supportive behaviors as lapse risk changes. Available treatments often fail to support long-term recovery by failing to account for the dynamic nature of long-term recovery.

OBJECTIVE

The aim of this protocol paper is to describe research that aims to develop a highly contextualized lapse risk prediction model that forecasts the ongoing probability of lapse.

METHODS

The participants will include 480 US adults in their first year of recovery from OUD. Participants will report lapses and provide data relevant to lapse risk for a year with a digital therapeutic smartphone app through both self-report and passive personal sensing methods (eg, cellular communications and geolocation). The lapse risk prediction model will be developed using contemporary rigorous machine learning methods that optimize prediction in new data.

RESULTS

The National Institute of Drug Abuse funded this project (R01DA047315) on July 18, 2019 with a funding period from August 1, 2019 to June 30, 2024. The University of Wisconsin-Madison Health Sciences Institutional Review Board approved this project on July 9, 2019. Pilot enrollment began on April 16, 2021. Full enrollment began in September 2021.

CONCLUSIONS

The model that will be developed in this project could support long-term recovery from OUD-for example, by enabling just-in-time interventions within digital therapeutics.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/29563.

Pharmacogenomics in Pain Management: A Review of Relevant Gene-Drug Associations and Clinical Considerations

OBJECTIVE

To provide an overview of clinical recommendations regarding genomic medicine relating to pain management and opioid use disorder.

DATA SOURCES

A literature review was conducted using the search terms pain management, pharmacogenomics, pharmacogenetics, pharmacokinetics, pharmacodynamics, and opioids on PubMed (inception to February 1, 2021), CINAHL (2016 through February 1, 2021), and EMBASE (inception through February 1, 2021).

STUDY SELECTION AND DATA EXTRACTION

All relevant clinical trials, review articles, package inserts, and guidelines evaluating applicable pharmacogenotypes were considered for inclusion.

DATA SYNTHESIS

More than 300 Food and Drug Administration-approved medications contain pharmacogenomic information in their labeling. Genetic variability may alter the therapeutic effects of commonly prescribed pain medications. Pharmacogenomic-guided therapy continues to gain traction in clinical practice, but a multitude of barriers to widespread pharmacogenomic implementation exist.

RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE

Pain is notoriously difficult to treat given the need to balance safety and efficacy when selecting pharmacotherapy. Pharmacogenomic data can help optimize outcomes for patients with pain. With improved technological advances, more affordable testing, and a better understanding of genomic variants resulting in treatment disparities, pharmacogenomics continues to gain popularity. Unfortunately, despite these and other advancements, pharmacogenomic testing and implementation remain underutilized and misunderstood in clinical care, in part because of a lack of health care professionals trained in assessing and implementing test results.

CONCLUSIONS

A one-size-fits-all approach to pain management is inadequate and outdated. With increasing genomic data and pharmacogenomic understanding, patient-specific genomic testing offers a comprehensive and personalized treatment alternative worthy of additional research and consideration.

Identification of a sex-stratified genetic algorithm for opioid addiction risk

The opioid epidemic has had a devastating impact on our country, with wide-ranging effects on healthcare, corrections, employment, and social systems. Programs have been put in place for monitoring prescriptions, initiating and expanding medications for opioid use disorder, and harm reduction (i.e., naloxone distribution, needle exchanges). However, opportunities for personalization of opioid therapy based on addiction risk have been limited. The goal of the present study was to develop an objective risk assessment algorithm based on genetic markers that are correlated with opioid use disorder (OUD). A total of 180 single-nucleotide polymorphisms (SNPs) were tested in patients with and without OUD. SNPs selected for testing were associated with opioid metabolism and drug reward pathways based on previous studies. Of the 394 patients recruited, 200 had OUD and 194 served as controls without OUD but with prior opioid exposure. Logistic regression analyses stratified by sex identified ten unique SNPs in females and nine unique SNPs in males that were significantly associated with OUD. A Genetics Opioid Risk Score (GenORs) was calculated by counting the number of OUD risk-associated SNPs/genotypes for each patient. To evaluate the discrimination of the GenORs, a receiver operating characteristic (ROC) curve for each sex was generated and determined to be sensitive and specific. This represents the first published example of a sex-based genetic risk score with potential to predict OUD, and the first OUD algorithm to include opioid-associated pharmacokinetic genes.

Opioids, Polypharmacy, and Drug Interactions: A Technological Paradigm Shift Is Needed to Ameliorate the Ongoing Opioid Epidemic

Polypharmacy is a common phenomenon among adults using opioids, which may influence the frequency, severity, and complexity of drug–drug interactions (DDIs) experienced. Clinicians must be able to easily identify and resolve DDIs since opioid-related DDIs are common and can be life-threatening. Given that clinicians often rely on technological aids—such as clinical decision support systems (CDSS) and drug interaction software—to identify and resolve DDIs in patients with complex drug regimens, this narrative review provides an appraisal of the performance of existing technologies. Opioid-specific CDSS have several system- and content-related limitations that need to be overcome. Specifically, we found that these CDSS often analyze DDIs in a pairwise manner, do not account for relevant pharmacogenomic results, and do not integrate well with electronic health records. In the context of polypharmacy, existing systems may encourage inadvertent serious alert dismissal due to the generation of multiple incoherent alerts. Future technological systems should minimize alert fatigue, limit manual input, allow for simultaneous multidrug interaction assessments, incorporate pharmacogenomic data, conduct iterative risk simulations, and integrate seamlessly with normal workflow.

Individual differences in human opioid abuse potential as observed in a human laboratory study

BACKGROUND

Opioids have high abuse potential and pose a major public health concern. Yet, a large percentage of individuals exposed to opioids do not develop problematic use. Individual differences in opioid abuse potential are not well understood.

METHODS

This within-subject (N = 16), double-blind, double-dummy, human laboratory study evaluated individual differences in response to dose (placebo, low, medium, high) following administration of heroin and hydromorphone through intravenous and subcutaneous routes, in opioid-experienced but non physically-dependent participants. Outcomes were self-reported visual analog scale (VAS) ratings (High, Liking, Drug Effect, Good Effect, Rush), pupil diameter change from baseline, and crossover point on the Drug vs. Money questionnaire. The degree to which results were consistent across measures within an individual was assessed using a mixed-effects model from which an intraclass correlation coefficient measure of between and within-subject variance was derived.

RESULTS

The mixed effects model fit was significant (p < 0.0001) and revealed that 85.5% of the explainable variance was due to between-subject effects, suggesting the responses within an individual were highly consistent. Visual inspection reveals a myriad response pattern across participants, with some demonstrating classic dose-effect responses and others not differentiating any active doses from placebo.

CONCLUSIONS

Data suggest the abuse potential of opioids is significantly different between individuals but that the experience within an individual is highly consistent. Research to prospectively characterize and evaluate mechanisms underlying these differences is warranted and may provide a foundation to help identify persons at heightened risk of transitioning from opioid exposure to misuse and/or opioid use disorder.

On the utilization of polygenic risk scores for therapeutic targeting

The promise of personalized genomic medicine is that knowledge of a person's gene sequences and activity will facilitate more appropriate medical interventions, particularly drug prescriptions, to reduce the burden of disease. Early successes in oncology and pediatrics have affirmed the power of positive diagnosis and are mostly based on detection of one or a few mutations that drive the specific pathology. However, genetically more complex diseases require the development of polygenic risk scores (PRSs) that have variable accuracy. The rarity of events often means that they have necessarily low precision: many called positives are actually not at risk, and only a fraction of cases are prevented by targeted therapy. In some situations, negative prediction may better define the population at low risk. Here, I review five conditions across a broad spectrum of chronic disease (opioid pain medication, hypertension, type 2 diabetes, major depression, and osteoporotic bone fracture), considering in each case how genetic prediction might be used to target drug prescription. This leads to a call for more research designed to evaluate genetic likelihood of response to therapy and a call for evaluation of PRS, not just in terms of sensitivity and specificity but also with respect to potential clinical efficacy.

Precision Psychiatry and Dual Disorders

OBJECTIVE

Addiction co-occurs with distinct pathological personality traits, other psychiatric disorders or symptoms and cognitive impairment, which are known as dual disorders or co-occurring disorders. This symptomatic high concurrency suggests that both conditions are in some ways causally linked. Research is ongoing to identify distinctive neurobehavioral mechanisms and endophenotypes that predispose individuals to compulsive drug use and other mental disorders. Research is also providing new revelations about the diverse effects of substances on individuals, including differences according to sex. Today we know that the same substance may give rise to different behavioral, affective, cognitive, and sensory effects across different individuals.

METHODS

This state-of the art review tends to address the concept of precision psychiatry and dual disorders. The PubMed database was searched for the last 15 years to identify those articles that reported neurobiological perspectives on dual disorders, addiction and other mental disorders, precision medicine, and precision psychiatry.

RESULTS

There has been considerable progress made in recent years in relation to the study of addiction and dual disorders. The concept of dual disorders attempts to capture not only the persistence of substance use and substance seeking but also the evident vulnerability of specific subpopulations to switch from controlled to compulsive drug use. Precision medicine is focused on identifying this individual vulnerability to illness as much as the individual response to treatment. Psychiatry is fully committed to this goal. Regarding addiction, essential precision medicine advances will be possible if concerted efforts are made in the discovery of biological variations and environmental factors that contribute to individual vulnerability to addictive disorders and dual disorders, together with the identification of moderators of treatment response.

CONCLUSIONS

Here we survey the discoveries, future research directions, and translational relevance of the concept of precision psychiatry for dual disorders. The review may offer new perspectives on this issue and highlight a new way to see and to think about dual disorders.

A prospective, longitudinal study to evaluate the clinical utility of a predictive algorithm that detects risk of opioid use disorder

Purpose

The purpose of this study was to determine the clinical utility of an algorithm-based decision tool designed to assess risk associated with opioid use. Specifically, we sought to assess how physicians were using the profile in patient care and how its use affected patient outcomes.

Patients and methods

A prospective, longitudinal study was conducted to assess the utility of precision medicine testing in 5,397 patients across 100 clinics in the USA. Using a patent-protected, validated algorithm combining specific genetic risk factors with phenotypic traits, patients were categorized into low-, moderate-, and high-risk patients for opioid abuse. Physicians who ordered precision medicine testing were asked to complete patient evaluations and document their actions, decisions, and perceptions regarding the utility of the precision medicine tests. The patient outcomes associated with each treatment action were carefully documented.

Results

Physicians used the profile to guide treatment decisions for over half of the patients. Of those, guided treatment decisions for 24.5% of the patients were opioid related, including changing the opioid prescribed, starting an opioid, or titrating a patient off the opioid. Treatment guidance was strongly influenced by profile-predicted opioid use disorder (OUD) risk. Most importantly, patients whose physicians used the profile to guide opioid-related treatment decisions had improved clinical outcomes, including better pain management by medication adjustments, with an average pain decrease of 3.4 points on a scale of 1–10.

Conclusion

Patients whose physicians used the profile to guide opioid-related treatment decisions had improved clinical outcomes, as measured by decreased pain levels resulting from better pain management with prescribed medications. The clinical utility of the profile is twofold. It provides clinically actionable recommendations that can be used to 1) prevent OUD through limiting initial opioid prescriptions and 2) reduce pain in patients at low risk of developing OUD.

A Predictive Algorithm to Detect Opioid Use Disorder: What Is the Utility in a Primary Care Setting?

PURPOSE

The purpose of this study was to determine the clinical utility of an algorithm-based decision tool designed to assess risk associated with opioid use in the primary care setting.

METHODS

A prospective, longitudinal study was conducted to assess the utility of precision medicine testing in 1822 patients across 18 family medicine/primary care clinics in the United States. Using the profile, patients were categorized into low, moderate, and high risk for opioid use. Physicians who ordered testing were asked to complete patient evaluations and document their actions, decisions, and perceptions regarding the utility of the precision medicine tests.

RESULTS

Approximately 47% of primary care physicians surveyed used the profile to guide clinical decision-making. These physicians rated the benefit of the profile on patient care an average of 3.6 on a 5-point scale (1 indicating no benefit and 5 indicating significant benefit). Eighty-eight percent of all clinicians surveyed felt the test exhibited some benefit to their patient care. The most frequent utilization for the profile was to guide a change in opioid prescribed. Physicians reported greater benefit of profile utilization for minority patients. Patients whose treatment was guided by the profile had pain levels that were reduced, on average, 2.7 levels on the numeric rating scale.

CONCLUSIONS

The profile provided primary care physicians with a useful tool to stratify the risk of opioid use disorder and was rated as beneficial for decision-making and patient improvement by the majority of physicians surveyed. Physicians reported the profile resulted in greater clinical improvement for minorities, highlighting the objective use of this profile to guide judicial use of opioids in high-risk patients. Significantly, when physicians used the profile to guide treatment decisions, patient-reported pain was greatly reduced.

Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting

BACKGROUND

Opioid abuse in chronic pain patients is a major public health issue. Primary care providers are frequently the first to prescribe opioids to patients suffering from pain, yet do not always have the time or resources to adequately evaluate the risk of opioid use disorder (OUD).

PURPOSE

This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm ("profile") incorporating phenotypic and, more uniquely, genotypic risk factors.

METHODS AND RESULTS

In a validation study with 452 participants diagnosed with OUD and 1237 controls, the algorithm successfully categorized patients at high and moderate risk of OUD with 91.8% sensitivity. Regardless of changes in the prevalence of OUD, sensitivity of the algorithm remained >90%.

CONCLUSION

The algorithm correctly stratifies primary care patients into low-, moderate-, and high-risk categories to appropriately identify patients in need for additional guidance, monitoring, or treatment changes.