Morphine Dose Optimization in Critically Ill Pediatric Patients With Acute Respiratory Failure: A Population Pharmacokinetic-Pharmacogenomic Study

Scoping Review of Paediatric Population Pharmacokinetic Models of Morphine

OBJECTIVE AND PURPOSE

This scoping review aimed to summarise all available population pharmacokinetic models of morphine and its metabolites (morphine-3-glucoronide [M3G], morphine-6-glucoronide [M6G]) in children and describe how morphine exposure varies across paediatric age groups and settings. Identifying the factors that contribute to pharmacokinetic variability may improve our understanding of a patient's pharmacodynamic response to morphine.

METHODS

We searched Embase and MEDLINE databases from inception to 8 March 2024 for paediatric population pharmacokinetic models of morphine and its metabolites. Two reviewers independently screened abstracts and full texts and extracted the data. The review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines.

RESULTS

In total, 21 paediatric population pharmacokinetic models of morphine were identified; 12 studies also included morphine metabolites (M3G and/or M6G). Neonates and young children (< 6 years) were the most studied age groups (18/21; 86%), whereas older children (> 6 years) and adolescents (> 10 years) were included in only 6 of the 21 (29%) models. Morphine pharmacokinetics were most commonly described with two-compartment (52%) and one-compartment (38%) structure with first-order elimination. Several model covariates were identified: bodyweight, post-natal age for neonates, body temperature, therapeutic cooling, duration of mechanical ventilation, and genetic variation in drug transporters that mediate the uptake of morphine (e.g. OCT1).

CONCLUSION

Several population pharmacokinetic models of morphine and its metabolites in paediatrics have been published across diverse patient groups. Bodyweight and age-related covariates emerged as the most common factors affecting clearance and distribution; other covariates, including mechanical ventilation, therapeutic cooling, and genetic variation, also impacted morphine pharmacokinetics. Further research should focus on validating the predictive accuracy of paediatric morphine models in different patient populations and the combined effect of covariates, such as those related to critical illness and genetic variation, on morphine pharmacokinetics.

The Influence of Pharmacogenetic Factors on the Pharmacokinetics of Morphine and Its Metabolites in Pediatric Patients: A Systematic Review

Morphine is a potent analgesic used for treating surgical and cancer pain. Despite being the drug of choice for the management of severe pain in children, the high interindividual variability in morphine pharmacokinetics limits its clinical utility to effectively relieve pain without adverse effects. This review was conducted to identify and describe all studies that have assessed the effect of genetic factors on the pharmacokinetics of morphine and its main metabolites in children. Embase and Medline databases were used to conduct the literature search, and the systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Of the 188 articles screened and after the application of specific inclusion and exclusion criteria, the review identified 8 studies. These studies suggest that genetic variants of selected metabolic enzymes and transporters may play a role in the observed interindividual variability in morphine plasma concentrations. Variants of the genes SLC22A1 and ABCC3 had the most supporting evidence for genetic variants that influence morphine and morphine metabolites pharmacokinetics. Although the available evidence suggests a potential genetic contribution to the variability in morphine concentration, the heterogeneity of the included studies in terms of experimental design and small sample sizes in some studies makes it challenging to propose the use of genetic biomarkers to personalize morphine dosing. This underscores the need to conduct more comprehensive and large-scale pharmacokinetic-pharmacogenetic studies to determine how or if genetic testing can optimize morphine safety and effectiveness in children.

Pharmacogenetic Gene-Drug Associations in Pediatric Burn and Surgery Patients

Management of critically ill patients requires simultaneous administration of many medications. Treatment for patient comorbidities may lead to drug-drug interactions which decrease drug efficacy or increase adverse reactions. Current practices rely on a one-size-fits-all dosing approach. Pharmacogenetic testing is generally reserved for addressing problems rather than used proactively to optimize care. We hypothesized that burn and surgery patients will have one or more genetic variants in drug metabolizing pathways used by one or more medications administered during the patient's hospitalization. The aim of this study was to determine the frequency of variants with abnormal function in the primary drug pathways and identify which medications may be impacted. Genetic (19 whole exome and 11 whole genome) and medication data from 30 pediatric burn and surgery patients were analyzed to identify pharmacogene-drug associations. Nineteen patients were identified with predicted altered function in one or more of the following genes: CYP2C9, CYP2C19, CYP2D6, and CYP3A4. The majority had decreased function, except for several patients with CYP2C19 rapid or ultrarapid variants. Some drugs administered during hospitalization that rely on these pathways include hydrocodone, oxycodone, methadone, ibuprofen, ketorolac, celecoxib, diazepam, famotidine, diphenhydramine, and glycopyrrolate. Approximately one-third of the patients tested had functionally impactful genotypes in each of the primary drug metabolizing pathways. This study suggests that genetic variants may in part explain the vast variability in drug efficacy and suggests that future pharmacogenetics research may optimize dosing regimens.

Recommendations for analgesia and sedation in critically ill children admitted to intensive care unit

We aim to develop evidence-based recommendations for intensivists caring for children admitted to intensive care units and requiring analgesia and sedation. A panel of national paediatric intensivists expert in the field of analgesia and sedation and other specialists (a paediatrician, a neuropsychiatrist, a psychologist, a neurologist, a pharmacologist, an anaesthesiologist, two critical care nurses, a methodologist) started in 2018, a 2-year process. Three meetings and one electronic-based discussion were dedicated to the development of the recommendations (presentation of the project, selection of research questions, overview of text related to the research questions, discussion of recommendations). A telematic anonymous consultation was adopted to reach the final agreement on recommendations. A formal conflict-of-interest declaration was obtained from all the authors. Eight areas of direct interest and one additional topic were considered to identify the best available evidence and to develop the recommendations using the Evidence-to-Decision framework according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. For each recommendation, the level of evidence, the strength of the recommendation, the benefits, the harms and the risks, the benefit/harm balance, the intentional vagueness, the values judgement, the exclusions, the difference of the opinions, the knowledge gaps, and the research opportunities were reported. The panel produced 17 recommendations. Nine were evaluated as strong, 3 as moderate, and 5 as weak. Conclusion: a panel of national experts achieved consensus regarding recommendations for the best care in terms of analgesia and sedation in critically ill children.

The horizon of pediatric cardiac critical care

Pediatric Cardiac Critical Care (PCCC) is a challenging discipline where decisions require a high degree of preparation and clinical expertise. In the modern era, outcomes of neonates and children with congenital heart defects have dramatically improved, largely by transformative technologies and an expanding collection of pharmacotherapies. Exponential advances in science and technology are occurring at a breathtaking rate, and applying these advances to the PCCC patient is essential to further advancing the science and practice of the field. In this article, we identified and elaborate on seven key elements within the PCCC that will pave the way for the future.

An independently validated, portable algorithm for the rapid identification of COPD patients using electronic health records

Electronic health records (EHR) provide an unprecedented opportunity to conduct large, cost-efficient, population-based studies. However, the studies of heterogeneous diseases, such as chronic obstructive pulmonary disease (COPD), often require labor-intensive clinical review and testing, limiting widespread use of these important resources. To develop a generalizable and efficient method for accurate identification of large COPD cohorts in EHRs, a COPD datamart was developed from 3420 participants meeting inclusion criteria in the Mass General Brigham Biobank. Training and test sets were selected and labeled with gold-standard COPD classifications obtained from chart review by pulmonologists. Multiple classes of algorithms were built utilizing both structured (e.g. ICD codes) and unstructured (e.g. medical notes) data via elastic net regression. Models explicitly including and excluding spirometry features were compared. External validation of the final algorithm was conducted in an independent biobank with a different EHR system. The final COPD classification model demonstrated excellent positive predictive value (PPV; 91.7%), sensitivity (71.7%), and specificity (94.4%). This algorithm performed well not only within the MGBB, but also demonstrated similar or improved classification performance in an independent biobank (PPV 93.5%, sensitivity 61.4%, specificity 90%). Ancillary comparisons showed that the classification model built including a binary feature for FEV1/FVC produced substantially higher sensitivity than those excluding. This study fills a gap in COPD research involving population-based EHRs, providing an important resource for the rapid, automated classification of COPD cases that is both cost-efficient and requires minimal information from unstructured medical records.