Genetic risk signatures of opioid-induced respiratory depression following pediatric tonsillectomy

Opioid use disorder in pediatric populations: considerations for perioperative pain management and precision opioid analgesia

INTRODUCTION

Opioids are commonly used for perioperative analgesia, yet children still suffer high rates of severe post-surgical pain and opioid-related adverse effects. Persistent and severe acute surgical pain greatly increases the child's chances of chronic surgical pain, long-term opioid use, and opioid use disorder.

AREAS COVERED

Enhanced recovery after surgery (ERAS) protocols are often inadequate in treating a child's severe surgical pain. Research suggests that 'older' and longer-acting opioids such as methadone are providing better methods to treat acute post-surgical pain. Studies indicate that lower repetitive methadone doses can decrease the incidence of chronic persistent surgical pain (CPSP). Ongoing research explores genetic components influencing severe surgical pain, inadequate opioid analgesia, and opioid use disorder. This new genetic research coupled with better utilization of opioids in the perioperative setting provides hope in personalizing surgical pain management, reducing pain, opioid use, adverse effects, and helping the fight against the opioid pandemic.

EXPERT OPINION

The opioid and analgesic pharmacogenomics approach can proactively 'tailor' a perioperative analgesic plan to each patient based on underlying polygenic risks. This transition from population-based knowledge of pain medicine to individual patient knowledge can transform acute pain medicine and greatly reduce the opioid epidemic's socioeconomic, personal, and psychological strains globally.

Study on the association between adverse drug reactions to opioids and gene polymorphisms: a case-case-control study

OBJECTIVE

Adverse drug reactions (ADRs) caused by opioid drugs show individual differences. Our objective was to explore the association between gene polymorphism and ADRs induced by opioid drugs.

METHODS

Evidence-based medical data analysis was conducted for genes related to ADRs induced by opioid drugs to select target genes. Sixty patients with cancer pain who had ADRs after taking opioid drugs (morphine, codeine, oxycodone) and 60 patients without ADRs after taking opioid drugs were used as the experimental group and control group, respectively. Then, we used polymerase chain reaction (PCR) or in situ hybridization to detect target genes. By combining with clinical data such as age, sex, dosage and duration of medication, the effect of gene polymorphism on the ADR of patients after taking opioid drugs was statistically analysed.

RESULTS

Based on a database search and evidence-based medical data, we identified CYP2D6*10, CYP3A5*3, ABCB1, and OPRM1 as target genes for detection. The results of statistical analysis showed no significant difference in genotype distribution between the experimental group and the control group (p > 0.05). However, if 32 patients with ADRs after taking oxycodone and 32 controls were selected for comparison, the SPSS22.0 and SNPStats genetic models showed that the ABCB1 (062rs1045642) CT and TT genotypes correlated with the occurrence of ADRs (p < 0.05): the total number of CT + TT genotypes in the experimental group was 29 (90.62%), with 11 (34.37%) CT + TT genotypes types in the control group.

CONCLUSION

Polymorphism of ABCB1 (062rs1045642) is related to ADRs caused by oxycodone, and the incidence of ADRs is higher with the allele T. Polymorphism of ABCB1 is expected to become a clinical predictor of ADRs to oxycodone, and attention should be given to the occurrence of serious ADRs in patients with ABCB1 (062rs1045642) CT and TT genotypes.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.

Personalized pediatric anesthesia and pain management: problem-based review

Pharmacogenetics, the genetic influence on the interpersonal variability in drug response, has enabled tailored pharmacotherapy and emerging 'personalized medicine.' Although oncology spearheaded the clinical implementation of personalized medicine, other specialties are rapidly catching up. In anesthesia, classical examples of genetically mediated idiosyncratic reactions have been long known (e.g., malignant hyperthermia and prolonged apnea after succinylcholine). The last two decades have witnessed an expanding body of pharmacogenetic evidence in anesthesia. This review highlights some of the prominent pharmacogenetic associations studied in anesthesia and pain management, with special focus on pediatric anesthesia.

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

Impact of genetic polymorphisms on opioid misuse: a scoping review

We conducted a scoping review on genetic polymorphisms associated with opioid intake-related adverse patient outcomes including behavioral, physiological and clinical outcomes. We searched for studies on Medline®, EMBASE®, CINAHL®, Psychinfo® and SNPedia® from January 2006 to January 2018. Our study identified 33 genes and 71 SNPs associated with opioid-intake related adverse patient outcomes: four studies showing associations of nine SNPs with clinical events (e.g., arrhythmia, length of stay and deaths); six studies showing associations of 13 SNPs with respiratory depression and 25 studies showing associations of 50 SNPs with opioid misuse behaviors. Available pharmacogenetic-tests covered polymorphisms associated with opioids metabolism and ignored polymorphisms associated with opioids transport, receptor-binding and signaling that were linked with respiratory depression and misuse behaviors.

Development of an AmpliSeqTM Panel for Next-Generation Sequencing of a Set of Genetic Predictors of Persisting Pain

Background: Many gene variants modulate the individual perception of pain and possibly also its persistence. The limited selection of single functional variants is increasingly being replaced by analyses of the full coding and regulatory sequences of pain-relevant genes accessible by means of next generation sequencing (NGS).

Methods: An NGS panel was created for a set of 77 human genes selected following different lines of evidence supporting their role in persisting pain. To address the role of these candidate genes, we established a sequencing assay based on a custom AmpliSeq^TM panel to assess the exomic sequences in 72 subjects of Caucasian ethnicity. To identify the systems biology of the genes, the biological functions associated with these genes were assessed by means of a computational over-representation analysis.

Results: Sequencing generated a median of 2.85 ⋅ 10⁶ reads per run with a mean depth close to 200 reads, mean read length of 205 called bases and an average chip loading of 71%. A total of 3,185 genetic variants were called. A computational functional genomics analysis indicated that the proposed NGS gene panel covers biological processes identified previously as characterizing the functional genomics of persisting pain.

Conclusion: Results of the NGS assay suggested that the produced nucleotide sequences are comparable to those earned with the classical Sanger sequencing technique. The assay is applicable for small to large-scale experimental setups to target the accessing of information about any nucleotide within the addressed genes in a study cohort.

Update of the Scientific Opinion on opium alkaloids in poppy seeds

Poppy seeds are obtained from the opium poppy (Papaver somniferum L.). They are used as food and to produce edible oil. The opium poppy plant contains narcotic alkaloids such as morphine and codeine. Poppy seeds do not contain the opium alkaloids, but can become contaminated with alkaloids as a result of pest damage and during harvesting. The European Commission asked EFSA to provide an update of the Scientific Opinion on opium alkaloids in poppy seeds. The assessment is based on data on morphine, codeine, thebaine, oripavine, noscapine and papaverine in poppy seed samples. The CONTAM Panel confirms the acute reference dose (ARfD) of 10 μg morphine/kg body weight (bw) and concluded that the concentration of codeine in the poppy seed samples should be taken into account by converting codeine to morphine equivalents, using a factor of 0.2. The ARfD is therefore a group ARfD for morphine and codeine, expressed in morphine equivalents. Mean and high levels of dietary exposure to morphine equivalents from poppy seeds considered to have high levels of opium alkaloids (i.e. poppy seeds from varieties primarily grown for pharmaceutical use) exceed the ARfD in most age groups. For poppy seeds considered to have relatively low concentrations of opium alkaloids (i.e. primarily varieties for food use), some exceedance of the ARfD is also seen at high levels of dietary exposure in most surveys. For noscapine and papaverine, the available data do not allow making a hazard characterisation. However, comparison of the dietary exposure to the recommended therapeutical doses does not suggest a health concern for these alkaloids. For thebaine and oripavine, no risk characterisation was done due to insufficient data. However, for thebaine, limited evidence indicates a higher acute lethality than for morphine and the estimated exposure could present a health risk.

Electronic medical records as a replacement for prospective research data collection in postoperative pain and opioid response studies

BACKGROUND AND AIM

Many clinical research studies claim to collect data that are also captured in the electronic medical record (EMR). We evaluate the potential for EMR data to replace prospective research data collection.

METHODS

Using a dataset of 358 surgical patients enrolled in a prospective study, we examined the completeness and agreement of EMR and study entries for several variables, including the patient's stay in the post-operative care unit (PACU), surgical pain relief and pain medication side effects.

RESULTS

For all variables with a completeness percentage, values were greater than 96%. For the adverse event variables, we found slight to substantial agreement (Cohen's kappa), ranging from 0.19 (nausea) to 0.48 (respiratory depression) to 0.73 (emesis).

CONCLUSION

The potential to use EMR data as a replacement for prospective research data collection shows promise, but for now, should be evaluated on a variable-by-variable basis.

Endogenous Opiates and Behavior: 2015

This paper is the thirty-eighth consecutive installment of the annual review of research concerning the endogenous opioid system. It summarizes papers published during 2015 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides, opioid receptors, opioid agonists and opioid antagonists. The particular topics that continue to be covered include the molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors related to behavior, and the roles of these opioid peptides and receptors in pain and analgesia, stress and social status, tolerance and dependence, learning and memory, eating and drinking, drug abuse and alcohol, sexual activity and hormones, pregnancy, development and endocrinology, mental illness and mood, seizures and neurologic disorders, electrical-related activity and neurophysiology, general activity and locomotion, gastrointestinal, renal and hepatic functions, cardiovascular responses, respiration and thermoregulation, and immunological responses.

Development of Human Membrane Transporters: Drug Disposition and Pharmacogenetics

Membrane transporters play an essential role in the transport of endogenous and exogenous compounds, and consequently they mediate the uptake, distribution, and excretion of many drugs. The clinical relevance of transporters in drug disposition and their effect in adults have been shown in drug–drug interaction and pharmacogenomic studies. Little is known, however, about the ontogeny of human membrane transporters and their roles in pediatric pharmacotherapy. As they are involved in the transport of endogenous substrates, growth and development may be important determinants of their expression and activity. This review presents an overview of our current knowledge on human membrane transporters in pediatric drug disposition and effect. Existing pharmacokinetic and pharmacogenetic data on membrane substrate drugs frequently used in children are presented and related, where possible, to existing ex vivo data, providing a basis for developmental patterns for individual human membrane transporters. As data for individual transporters are currently still scarce, there is a striking information gap regarding the role of human membrane transporters in drug therapy in children.

Fatty acid amide hydrolase-morphine interaction influences ventilatory response to hypercapnia and postoperative opioid outcomes in children

AIM

Fatty acid amide hydrolase (FAAH) degrades anandamide, an endogenous cannabinoid. We hypothesized that FAAH variants will predict risk of morphine-related adverse outcomes due to opioid-endocannabinoid interactions.

PATIENTS & METHODS

In 101 postsurgical adolescents receiving morphine analgesia, we prospectively studied ventilatory response to 5% CO₂ (HCVR), respiratory depression (RD) and vomiting. Blood was collected for genotyping and morphine pharmacokinetics.

RESULTS

We found significant FAAH-morphine interaction for missense (rs324420) and several regulatory variants, with HCVR (p < 0.0001) and vomiting (p = 0.0339). HCVR was more depressed in patients who developed RD compared with those who did not (p = 0.0034), thus FAAH-HCVR association predicts risk of impending RD from morphine use.

CONCLUSION

FAAH genotypes predict risk for morphine-related adverse outcomes.