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

Prescription Opioid Use before and after Diagnosis of Cancer Among Older Cancer Survivors With Non-Cancer Chronic Pain Conditions (NCPCs): An Application of Group-Based Trajectory Modeling (GBTM)

BACKGROUND

Prescription opioids are essential in managing pain among adults with chronic pain conditions. However, persistent use over time can lead to negative health consequences. Identifying individuals with persistent use over time and their characteristics can inform clinical decision-making and aid in reducing the risk of abuse and overdose deaths.

OBJECTIVE

This study aims to examine trajectories of prescription opioid use over time and factors associated with these trajectories among older cancer survivors with any non-cancer pain conditions (NCPC).

METHODS

We conducted a retrospective cohort study design with longitudinal data of older (age at cancer diagnosis ≥67 years) cancer (incident breast, colorectal, and prostate cancers, or non-Hodgkin lymphoma) survivors with any NCPC. Data were derived from the 2007-2015 linked Surveillance, Epidemiology, and End Results (SEER)-Medicare dataset (N = 35,071). Group-Based Trajectory Modeling (GBTM) was used to identify homogeneous subgroups (distinct trajectories) of individuals based on every 90-day prescription opioid use during pre-cancer diagnosis (t1-t4), acute cancer treatment (t5-t8), and post-cancer treatment (t9-t12) periods. Biological factors, social determinants of health (SDoH), physical and mental health, medication use, health care use, and external factors associated with a trajectory membership were analyzed with multivariable multinomial logistic regressions.

RESULTS

Four distinct trajectories of opioid use were identified: (1) increase-decrease use (6.1%); (2) short-term use after cancer diagnosis (40.6%); (3) low-use (41.0%); and (4) persistent use (12.3%). In the fully-adjusted multinomial logistic regression, the SDoH such as Non-Hispanic Black [adjusted odds ratios (AOR) = 1.69; 95%CI = 1.48, 1.93)] and rural residence (AOR = 1.49; 95%CI = 1.15, 1.94)], comorbid anxiety (AOR = 1.33; 95%CI = 1.18, 1.51), and medication use (NSAIDs - AOR = 1.20; 95%CI = 1.10, 1.30) were associated with membership in the persistent use group. Persistent use was less likely among those with higher fragmented care index (AOR = 0.95, 95%CI = 0.93, 0.97) and those living in counties with higher Medicare advantage penetration (AOR = 0.96; 95%CI = 0.95, 0.97).

CONCLUSIONS

One in eight older adults had persistent opioid use over time. The profile characteristics of this group were different from the other trajectory groups. Policies and programs to reduce chronic opioid use need to consider the intra- and inter-individual variability to reduce opioid-related morbidity and mortality.

Hydrocodone, Oxycodone, and Morphine Metabolism and Drug-Drug Interactions

Awareness of drug interactions involving opioids is critical for patient treatment as they are common therapeutics used in numerous care settings, including both chronic and disease-related pain. Not only do opioids have narrow therapeutic indexes and are extensively used, but they have the potential to cause severe toxicity. Opioids are the classical pain treatment for patients who suffer from moderate to severe pain. More importantly, opioids are often prescribed in combination with multiple other drugs, especially in patient populations who typically are prescribed a large drug regimen. This review focuses on the current knowledge of common opioid drug-drug interactions (DDIs), focusing specifically on hydrocodone, oxycodone, and morphine DDIs. The DDIs covered in this review include pharmacokinetic DDI arising from enzyme inhibition or induction, primarily due to inhibition of cytochrome p450 enzymes (CYPs). However, opioids such as morphine are metabolized by uridine-5'-diphosphoglucuronosyltransferases (UGTs), principally UGT2B7, and glucuronidation is another important pathway for opioid-drug interactions. This review also covers several pharmacodynamic DDI studies as well as the basics of CYP and UGT metabolism, including detailed opioid metabolism and the potential involvement of metabolizing enzyme gene variation in DDI. Based upon the current literature, further studies are needed to fully investigate and describe the DDI potential with opioids in pain and related disease settings to improve clinical outcomes for patients. SIGNIFICANCE STATEMENT: A review of the literature focusing on drug-drug interactions involving opioids is important because they can be toxic and potentially lethal, occurring through pharmacodynamic interactions as well as pharmacokinetic interactions occurring through inhibition or induction of drug metabolism.

Signal mining and analysis for central nervous system adverse events due to taking oxycodone based on FAERS database

OBJECTIVES

Central nervous system adverse events (AEs) occur when oxycodone is used in combination with benzodiazepines, antidepressants and anticonvulsants. There have been no reports of central nervous system AEs with oxycodone alone or in combination with oxycodone. Based on USA Food and Drug Administration Adverse Event Reporting System (FAERS) data, this study aims to explore the risk signals of central nervous system AEs with oxycodone alone or in combination with benzodiazepines, antidepressants and anticonvulsants, and to provide a reference for the safe and rational use of this drug.

METHODS

Extracted AEs data from the FAERS for oxycodone alone and in combination with benzodiazepines, antidepressants, and anticonvulsants from Q1 2004 to Q2 2021. The risk signal mining analysis of AEs was performed using the proportional imbalance method and Bayesian method. Number of reports ≥3 and lower 95% CI limit of reporting odds ratio (ROR)>1; number of reports ≥3, proportional reporting ratio (PRR)≥2 and χ2≥4; lower information components (IC) lower 95% CI limit (IC025)>0; empirical Bayes geometric mean (EBGM) lower 95% CI limit (EBGM05)>2, and N>0 were defined as positive signals.

RESULTS

A total of 5 793 reports of central nervous system AEs with oxycodone alone were tapped, and 366, 622, and 740 reports of combined benzodiazepines, antidepressants, and anticonvulsants, respectively. Consumers and physicians were the main reporting population. The age distribution of oxycodone alone was mainly from 61 to 80 years old. The age distribution of oxycodone in combination with related drugs was mainly from 46 to 60 years old. The risk of AEs was greater in women than in men, and the United States was the predominant reporting country. Oxycodone alone was strongly associated with myoclonus [ROR=2.92, 95% CI 2.28 to 3.76); PRR=2.92, χ2(77.49); IC=1.52, IC025(0.65); EBGM=2.89, EBGM05(2.33)], delirium [ROR=4.69, 95% CI 4.24 to 5.21; PRR=4.66, χ2(1 052.64); IC=2.17, IC025(1.81); EBGM=4.50, EBGM05 (4.13)], mental disorder [ROR=2.95, 95% CI 2.53 to 3.44; PRR=2.94, χ2(206.93); IC=1.56, IC025(0.96); EBGM=2.95, EBGM05(2.58)], and acute central respiratory depression [ROR=2.87, 95% CI 2.68 to 3.08); PRR=2.82, χ2(971.62); IC=1.52, IC025(1.33), EBGM=2.87, EBGM05 (2.76)]. Combination of benzodiazepines was most strongly associated with mental disorder [ROR=10.08, 95% CI 9.38 to 10.78; PRR=9.90, χ2(64.06); IC=3.33, IC025 (1.65); EBGM=10.08, EBGM05(5.61)], and tremor [ROR=3.09, 95% CI 2.76 to 3.42); PRR=3.08, χ2(48.93); IC=1.63, IC025 (1.17); EBGM=3.09, EBGM05(2.34)]. Combination of antidepressants was most strongly associated with delirium [ROR=13.23, 95% CI 12.23 to 14.23; PRR=12.87, χ2(43.86); IC=3.69, IC025(1.36); EBGM=12.23, EBGM05 (5.32)] and somnolence [ROR=6.74, 95% CI 6.15 to 7.33); PRR=6.73, χ2(53.42); IC=2.75, IC025(1.52); EBGM=6.73, EBGM05(4.10)]. Combination of anticonvulsants was most strongly associated with myoclonus [ROR=17.89, 95% CI 17.46 to 18.32; PRR=17.72, χ2(971.39); IC=4.16, IC025(2.70); EBGM=17.89, EBGM05(12.46)] and delirium [ROR=4.86, 95% CI 4.45 to 5.27); PRR=4.82, χ2(69.49); IC=2.28, IC025 (1.51); EBGM=4.86, EBGM05(3.44)].

CONCLUSIONS

Based on pharmacovigilance studies of the FAERS database, clinical medication monitoring of oxycodone alone and in combination with benzodiazepines, antidepressants, and anticonvulsants should be strengthened to be alert to the occurrence of central nervous system-related AEs.

National Estimates and Physician-Reported Impacts of Prescription Drug Monitoring Program Use

BACKGROUND

Despite widespread adoption of state prescription drug monitoring programs (PDMPs), it is unclear how often PDMPs are accessed through an electronic health record system (EHR-PDMP integration), or whether efforts to make PDMPs easier to access and use have improved their utility.

OBJECTIVE

To produce national-level estimates on the use of PDMPs among office-based physicians and benefits associated with their use.

DESIGN

We use nationally representative survey data to produce descriptive statistics on PDMP use and associated benefits among office-based physicians in the USA.

PARTICIPANTS

1398 office-based physicians who prescribe controlled substances.

MAIN MEASURES

We examined physician-reported ease and frequency of PDMP use, and how EHR-PDMP integration affects frequency and ease of use. Multivariate models were used to assess whether characteristics of PDMP use were related to physician-reported benefits such as reduced prescribing of controlled substances and perceived improvements in clinical decision-making.

KEY RESULTS

In 2019, two-thirds of office-based physicians in the USA reported frequent use of their state PDMP and over three-quarters reported they were easy to use. Both frequency and ease of use were positively correlated with PDMP integration status. Respondents who frequently checked their state's PDMP were 8.7 percentage points (95% CI -.4 to 17.8) more likely to report perceived benefits and reported 2.2 (95% CI 1.54 to 2.83) more benefits. Respondents who indicated their PDMP was easy to use were 12.7 percentage points (95% CI .040 to .214) more likely to report perceived benefits and reported 0.94 (95% CI 0.26 to 1.61) more benefits.

CONCLUSIONS

Our findings suggest efforts to make PDMPs easier to access and use aided physicians in making informed clinical decisions that may not be captured by reduced prescribing alone. Efforts to further increase frequency and ease of use-including advancing a standards-based approach to PDMP and EHR data interoperability-may further increase the benefit of PDMPs.

CYP2D6 pharmacogenetics and phenoconversion in personalized medicine

INTRODUCTION

CYP2D6 contributes to the metabolism of approximately 20-25% of drugs. However, CYP2D6 is highly polymorphic and different alleles can lead to impacts ranging from null to increase in activity. Moreover, there are commonly used drugs that potently inhibit the CYP2D6, thus causing 'phenoconversion' which can convert the genotypic normal metabolizer into phenotypic poor metabolizer. Despite growing literature on the clinical implications of non-normal CYP2D6 genotype and phenoconversion on patient-related outcomes, implementation of CYP2D6 pharmacogenetics and phenoconversion to guide prescribing is rare. This review focuses on providing the clinical importance of CYP2D6 pharmacogenetics and phenoconversion in precision medicine and summarizes the challenges and approaches to implement these into clinical practice.

AREAS COVERED

A literature search was performed using PubMed and clinical studies documenting the effects of CYP2D6 genotypes and/or CYP2D6 inhibitors on pharmacokinetics, pharmacodynamics or treatment outcomes of CYP2D6-metabolized drugs, and studies on implementation challenges and approaches.

EXPERT OPINION

Considering the extent and impact of genetic polymorphisms of CYP2D6, phenoconversion by the comedications, and contribution of CYP2D6 in drug metabolism, CYP2D6 pharmacogenetics is essential to ensure drug safety and efficacy. Utilization of proper guidelines incorporating both CYP2D6 pharmacogenetics and phenoconversion in clinical care assists in optimizing drug therapy.

Bioequivalence, Drugs with Narrow Therapeutic Index and the Phenomenon of Biocreep: A Critical Analysis of the System for Generic Substitution

The prescription of generic drugs represents one of the main cost-containment strategies of health systems, aimed at reducing pharmaceutical expenditure. In this context, most regulatory authorities encourage or obligate dispensing generic drugs because they are far less expensive than their brand-name alternatives. However, drug substitution can be critical in particular situations, such as the use of drugs with a narrow therapeutic index (NTI). Moreover, generics cannot automatically be considered bioequivalent with each other due to the biocreep phenomenon. In Italy, the regulatory authority has established the Transparency Lists which include the medications that will be automatically substituted for brand-name drugs, except in exceptional cases. This is a useful tool to guide prescribers and guarantee pharmaceutical sustainability, but it does not consider the biocreep phenomenon.

Opioid Drug-Drug-Drug Interactions and Unintentional Traumatic Injury: Screening to Detect Three-Way Drug Interaction Signals

Growing evidence suggests that drug interactions may be responsible for much of the known association between opioid use and unintentional traumatic injury. While prior research has focused on pairwise drug interactions, the role of higher-order (i.e., drug-drug-drug) interactions (3DIs) has not been examined. We aimed to identify signals of opioid 3DIs with commonly co-dispensed medications leading to unintentional traumatic injury, using semi-automated high-throughput screening of US commercial health insurance data. We conducted bi-directional, self-controlled case series studies using 2000-2015 Optum Data Mart database. Rates of unintentional traumatic injury were examined in individuals dispensed opioid-precipitant base pairs during time exposed vs unexposed to a candidate interacting precipitant. Underlying cohorts consisted of 16-90-year-olds with new use of opioid-precipitant base pairs and ≥1 injury during observation periods. We used conditional Poisson regression to estimate rate ratios adjusted for time-varying confounders, and semi-Bayes shrinkage to address multiple estimation. For hydrocodone, tramadol, and oxycodone (the most commonly used opioids), we examined 16,024, 8185, and 9330 drug triplets, respectively. Among these, 75 (0.5%; hydrocodone), 57 (0.7%; tramadol), and 42 (0.5%; oxycodone) were significantly positively associated with unintentional traumatic injury (50 unique base precipitants, 34 unique candidate precipitants) and therefore deemed potential 3DI signals. The signals found in this study provide valuable foundations for future research into opioid 3DIs, generating hypotheses to motivate crucially needed etiologic investigations. Further, this study applies a novel approach for 3DI signal detection using pharmacoepidemiologic screening of health insurance data, which could have broad applicability across drug classes and databases.

Assessing the Impact of an Advanced Clinical Decision Support System on Medication Safety and Hospital Readmissions in an Innovative Transitional Care Model: A Pilot Study

(1) Background: Adverse drug events and inappropriate use of medications lead to hospitalizations, medication-related morbidity, and mortality. This study examined whether a novel medication risk prediction tool, the MedWise Risk Score™, was associated with medication safety-related problem (MRP) identification and whether integration into an existing innovative transitions of care (TOC) service could decrease readmissions. (2) Methods: This retrospective comparator group study assessed patients discharged from a hospital in southern Arizona between January and December 2020. Participants were included in the study if they were 18 years of age or older, referred to the pharmacist for TOC services, and received a pharmacist consultation within one-week post discharge. Patients were categorized into two groups: (1) medication safety review (MSR)-TOC service (intervention) or (2) existing innovative TOC service (control). (3) Results: Of 164 participants, most were male (57%) and were between 70−79 years of age. Overall, there were significantly more drug-drug interactions (DDI) MRPs identified per patient in the intervention vs. control group for those who were readmitted (3.7 ± 1.5 vs. 0.9 ± 0.6, p < 0.001) and those who were not readmitted (2 ± 1.3 vs. 1.3 ± 1.2, p = 0.0120). Furthermore, of those who were readmitted, the average number of identified MRPs per patient was greater in the intervention group compared to the control (6.3 vs. 2.5, respectively, p > 0.05). Relative to the control, the readmission frequency was 30% lower in the treatment group; however, there was insufficient power to detect significant differences between groups. (4) Conclusions: The integration of a medication risk prediction tool into this existing TOC service identified more DDI MRPs compared to the previous innovative TOC service, which lends evidence that supports its ability to prevent readmissions. Future work is warranted to demonstrate the longitudinal impact of this intervention in a larger sample size.

Evaluating the Impact of Medication Risk Mitigation Services in Medically Complex Older Adults

Adverse drug events (ADEs) represent an expensive societal burden that disproportionally affects older adults. Therefore, value-based organizations that provide care to older adults—such as the Program of All-Inclusive Care for the Elderly (PACE)—should be highly motivated to identify actual or potential ADEs to mitigate risks and avoid downstream costs. We sought to determine whether PACE participants receiving medication risk mitigation (MRM) services exhibit improvements in total healthcare costs and other outcomes compared to participants not receiving structured MRM. Data from 2545 PACE participants from 19 centers were obtained for the years 2018 and 2019. We compared the year-over-year changes in outcomes between patients not receiving (control) or receiving structured MRM services. Data were adjusted based on participant multimorbidity and geographic location. Our analyses demonstrate that costs in the MRM cohort exhibited a significantly smaller year-to-year increase compared to the control (MRM: USD 4386/participant/year [95% CI, USD 3040−5732] vs. no MRM: USD 9410/participant/year [95% CI, USD 7737−11,084]). Therefore, receipt of structured MRM services reduced total healthcare costs (p < 0.001) by USD 5024 per participant from 2018 to 2019. The large majority (75.8%) of the reduction involved facility-related expenditures (e.g., hospital admission, emergency department visits, skilled nursing). In sum, our findings suggest that structured MRM services can curb growing year-over-year healthcare costs for PACE participants.

Illustrative and historic cases of phenoconversion

Intersubject variability in drug response, whether related to efficacy or toxicity, is well recognized clinically. Over the years, drug selection from our pharmacologic armamentarium has moved from providers' preferred choices to more personalized treatments as clinicians' decisions are guided by data from clinical trials. Since the advent of more accessible and affordable pharmacogenomic (PGx) testing, the promise of precise pharmacotherapy has been made. Results have accumulated in the literature with numerous examples demonstrating the value of PGx to improve drug response or prevent drug toxicity. Unfortunately, limited availability of reimbursement policies has dampened the enthusiasm of providers and organizations. The clinical application of PGx knowledge remains difficult for most clinicians under real-world conditions in patients with numerous chronic conditions and polypharmacy. This may be due to phenoconversion, a condition where there is a discrepancy between the genotype-predicted phenotype and the observed phenotype. This condition complicates the interpretation of PGx results and may lead to inappropriate recommendations and clinical decision making. For this reason, regulatory agencies have limited the transfer of information from PGx laboratories directly to consumers, especially recommendations about the use of certain drugs. This mini-review presents cases (mexiletine, propafenone, clopidogrel, warfarin, codeine, procainamide) from historical observations where drug response was modified by phenoconversion. The cases illustrate, from decades ago, that we are still in great need of advanced clinical decision systems that cope with conditions associated with phenoconversion, especially in patients with polypharmacy.

Feasibility and Acceptability of a Pharmacogenomic Decision Support System in Palliative Care

Context: Pharmacogenomic analysis may improve the efficacy or safety of the drugs used in palliative care. Decision support systems may promote clinical integration of this information. Objectives: To determine the feasibility and acceptability of a pharmacist-directed pharmacogenomic decision support system in the care of patients with advanced illness and explore the drug-gene and drug-drug interactions that occur in this population. Methods: Physicians or nurse practitioners from two U.S. hospice agencies identified opioid-treated patients receiving multiple other drugs. Buccal samples and clinical data were obtained from consenting patients. A pharmacist used the proprietary MedWise™ platform to evaluate the current medications in terms of genotype and phenotype, created a standardized report describing potential interactions and recommended actions that may reduce the associated risk. Clinicians could access the report online and completed Likert-type scales to assess use and satisfaction with the system. Results: Twenty clinicians and 100 patients participated. The reports revealed that 74 drugs were subject to 462 drug-gene interactions and 77 were involved in 691 drug-drug interactions; only 4 and 16 patients, respectively, had no drug-gene or drug-drug interactions. Clinicians routinely checked the reports and used the information to change ≥1 treatments in 55 (55%) patients. Almost all clinicians rated the system likely to improve the quality of care and all "agreed" or "strongly agreed" to recommend the system to colleagues. Conclusion: This pharmacist-directed pharmacogenomic decision support system was perceived positively and was integrated into practice. Further studies are warranted to its clinical integration and its outcomes.