Pediatric Drug-Drug Interaction Evaluation: Drug, Patient Population, and Methodological Considerations

Transporter-mediated drug-drug interactions: regulatory guidelines, in vitro and in vivo methodologies and translation, special populations, and the blood-brain barrier

This review, part of a special issue on drug-drug interactions (DDIs) spearheaded by the International Society for the Study of Xenobiotics (ISSX) New Investigators, explores the critical role of drug transporters in absorption, disposition, and clearance in the context of DDIs. Over the past two decades, significant advances have been made in understanding the clinical relevance of these transporters. Current knowledge on key uptake and efflux transporters that affect drug disposition and development is summarized. Regulatory guidelines from the FDA, EMA, and PMDA that inform the evaluation of potential transporter-mediated DDIs are discussed in detail. Methodologies for preclinical and clinical testing to assess potential DDIs are reviewed, with an emphasis on the utility of physiologically based pharmacokinetic (PBPK) modeling. This includes the application of relative abundance and expression factors to predict human pharmacokinetics (PK) using preclinical data, integrating the latest regulatory guidelines. Considerations for assessing transporter-mediated DDIs in special populations, including pediatric, hepatic, and renal impairment groups, are provided. Additionally, the impact of transporters at the blood-brain barrier (BBB) on the disposition of CNS-related drugs is explored. Enhancing the understanding of drug transporters and their role in drug disposition and toxicity can improve efficacy and reduce adverse effects. Continued research is essential to bridge remaining gaps in knowledge, particularly in comparison with cytochrome P450 (CYP) enzymes.

Drug-Drug Interactions Involving High-Alert Medications that Lead to Interaction-Associated Symptoms in Pediatric Intensive Care Patients: A Retrospective Study

BACKGROUND

Children treated in a pediatric intensive care unit (PICU) often receive several drugs together, among them drugs defined as high-alert medications (HAMs). Those drugs carry a high risk of causing patient harm, for example, due to a higher potential for interactions. HAMs should therefore be administered with caution, especially in a PICU.

OBJECTIVES

The objective of the current study was to identify drug-drug interactions involving HAMs that increase the risk of interaction-associated symptoms in pediatric intensive care.

METHODS

In a retrospective study, we analyzed the electronic documentation of patients hospitalized for at least 48 h in a general PICU who received at least two different drugs within a 24-h interval. We assessed potential drug-drug interactions involving HAM on the basis of the two drug information databases UpToDate and drugs.com. Furthermore, we analyzed whether symptoms were observed after the administration of drug pairs that could lead to interaction-associated symptoms. For drug pairs involving HAM administered on at least 2% of patient days, and symptoms observed at least ten times after a respective drug pair, we calculated odds ratios, 95% confidence intervals, and p-values by using a univariate binary logistic regression.

RESULTS

Among 315 analyzed patients, 81.3% (256/315) received drugs defined as high-alert medication for pediatric patients. Those high-alert medications were involved in 20,150 potential drug-drug interactions. In 14.0% (2830/20,150) of these, one or more symptoms were observed that could be a possible consequence of the interaction, resulting in 3203 observed symptoms affecting 56.3% (144/256) of patients receiving high-alert medication. The odds ratios for symptoms observed after a drug-drug interaction were increased for eight specific symptoms (each p ≤ 0.05), especially hemodynamic alterations and disturbances of electrolyte and fluid balance. The odds ratio was highest for decreased blood pressure observed after the administration of the drug pair fentanyl and furosemide (OR 5.06; 95% confidence interval 3.5-7.4; p < 0.001). Increased odds ratios for specific symptoms observed after drug-drug interactions resulted from eight combinations composed of eight different drugs: digoxin, fentanyl, midazolam, phenobarbital, potassium salts and vancomycin (high-alert medications), and the diuretics furosemide and hydrochlorothiazide (non-high-alert medications). The resulting drug pairs were: potassium salts-furosemide, fentanyl-furosemide, vancomycin-furosemide, digoxin-furosemide, digoxin-hydrochlorothiazide, fentanyl-phenobarbital, potassium salts-hydrochlorothiazide, and midazolam-hydrochlorothiazide.

CONCLUSIONS

In a cohort of PICU patients, this study identified eight specific drug pairs involving high-alert medications that may increase the risk of interaction-associated symptoms, mainly hemodynamic alterations and electrolyte/fluid balance disturbances. If the administration of those drug pairs is unavoidable, patients should be closely monitored.

Defining the next generation of severe malaria treatment: a target product profile

BACKGROUND

Severe malaria is a life-threatening infection, particularly affecting children under the age of 5 years in Africa. Current treatment with parenteral artemisinin derivatives is highly efficacious. However, artemisinin partial resistance is widespread in Southeast Asia, resulting in delayed parasite clearance after therapy, and has emerged independently in South America, Oceania, and Africa. Hence, new treatments for severe malaria are needed, and it is prudent to define their characteristics now. This manuscript focuses on the target product profile (TPP) for new treatments for severe malaria. It also highlights preparedness when considering ways of protecting the utility of artemisinin-based therapies.

TARGET PRODUCT PROFILE

Severe malaria treatments must be highly potent, with rapid onset of antiparasitic activity to clear the infection as quickly as possible to prevent complications. They should also have a low potential for drug resistance selection, given the high parasite burden in patients with severe malaria. Combination therapies are needed to deter resistance selection and dissemination. Partner drugs which are approved for uncomplicated malaria treatment would provide the most rapid development pathway for combinations, though new candidate molecules should be considered. Artemisinin combination approaches to severe malaria would extend the lifespan of current therapy, but ideally, completely novel, non-artemisinin-based combination therapies for severe malaria should be developed. These should be advanced to at least phase 2 clinical trials, enabling rapid progression to patient use should current treatment fail clinically. New drug combinations for severe malaria should be available as injectable formulations for rapid and effective treatment, or as rectal formulations for pre-referral intervention in resource-limited settings.

CONCLUSION

Defining the TPP is a key step to align responses across the community to proactively address the potential for clinical failure of artesunate in severe malaria. In the shorter term, artemisinin-based combination therapies should be developed using approved or novel drugs. In the longer term, novel combination treatments should be pursued. Thus, this TPP aims to direct efforts to preserve the efficacy of existing treatments while improving care and outcomes for individuals affected by this life-threatening disease.

Potential drug-drug interactions analysis in Polish pediatric pneumonology units, including cystic fibrosis patients

The lack of data on drug-drug interactions in pediatrics represents a relevant problem in making appropriate therapeutic decisions. Our study aimed to investigate the incidence and risk factors for potential drug-drug interactions (pDDIs) in pediatric pneumonology units, including cystic fibrosis patients. We performed a 6-month prospective observational study during which clinical pharmacists, using the Lexicomp Drug Interactions checker, screened medical records to identify pDDIs. Spearman's rank coefficient, logistic regression, and the Mann-Whitney U test were used to identify correlations, analyze risk factors for pDDIs, and compare cystic fibrosis patients with the rest, respectively. Recommendations were provided for the D and X pDDIs categories. Within the 218 patients, 428 pDDIs were identified, out of which 237 were classified as clinically significant. Almost 60% of patients were exposed to at least one relevant interaction. The number of pDDIs correlated with the number of; drugs (rs = 0.53, P <  .001), hospitalization length (rs = 0.20, P <  .01), and off-label medicines (rs = 0.25, P <  .001). According to the multivariate analysis, at least 6 administered medications (OR = 4.15; 95% CI = 2.21-7.78), 4 days of hospitalization (OR = 6.41; 95% CI = 2.29-17.97), and off-label therapy (OR = 3.37; 95% CI = 1.69-6.70) were the risk factor for pDDIs. Despite significant differences in the number of medications taken, comorbidities, and off-label drugs, cystic fibrosis patients were not more exposed to pDDI. Given the lack of data on pDDIs in the pediatric population, the need for close cooperation between clinicians and clinical pharmacists to improve the safety and efficacy of pharmacotherapy is highlighted.

Deep-NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data

Noncompartmental analysis (NCA) is a model-independent approach for assessing pharmacokinetics (PKs). Although the existing NCA algorithms are very well-established and widely utilized, they suffer from low accuracies in the setting of sparse PK samples. In response, we developed Deep-NCA, a deep learning (DL) model to improve the prediction of key noncompartmental PK parameters. Our methodology utilizes synthetic PK data for model training and uses an innovative patient-specific normalization method for data preprocessing. Deep-NCA demonstrated adequate performance across six previously unseen simulated drugs under multiple dosing, showcasing effective generalization. Compared to traditional NCA, Deep-NCA exhibited superior performance for sparse PK data. This study advances the application of DL to PK studies and introduces an effective method for handling sparse PK data. With further validation and refinement, Deep-NCA could significantly enhance the efficiency of drug development by providing more accurate NCA estimates while requiring fewer PK samples.

Enhancing immune regulation in vitro: the synergistic impact of 3'-sialyllactose and osteopontin in a nutrient blend following influenza virus infection

Natural components of breast milk, human milk oligosaccharides (HMOs) and osteopontin (OPN) have been shown to have a variety of functional activities and are widely used in infant formulas. However, the preventive and therapeutic effects of both on influenza viruses are not known. In this study, antiviral assays using a human laryngeal carcinoma cell line (HEP-2) showed that 3'-sialyllactose (3'-SL) and OPN had the best antiviral ability with IC50 values of 33.46 μM and 1.65 μM, respectively. 3'-SL (10 μM) and OPN (4 μM) were used in combination to achieve 75% inhibition. Further studies found that the combination of 200 μg/mL of 3'-SL with 500 μg/mL of OPN exerted the best antiviral ability. The reason for this was related to reduced levels of the cytokines TNF-α, IL-6, and iNOS in relation to mRNA expression. Plaque assay and TCID50 assay found the same results and verified synergistic effects. Our research indicates that a combination of 3'-SL and OPN can effectively reduce inflammatory storms and exhibit anti-influenza virus effects through synergistic action.

Major Drug-Drug Interaction Exposure Among Medicaid-Insured Children in the Outpatient Setting

BACKGROUND AND OBJECTIVES

Drug-drug interactions (DDIs) can cause adverse drug events, but little is known about DDI exposure in children in the outpatient setting. This study aimed to determine the prevalence of major DDI exposure and factors associated with higher DDI exposure rates among children in an outpatient setting.

METHODS

We performed a cross-sectional study of children aged 0 to 18 years with ≥1 ambulatory encounter, and ≥2 dispensed outpatient prescriptions study using the 2019 Marketscan Medicaid database. DDIs (exposure to a major DDI for ≥1 day) and the adverse physiologic effects of each DDI were identified using DrugBank's interaction database. Primary outcomes included the prevalence and rate of major DDI exposure. We used logistic regression to assess patient characteristics associated with DDI exposure. We examined the rate of DDI exposures per 100 children by adverse physiologic effects category, and organ-level effects (eg, heart rate-corrected QT interval prolongation).

RESULTS

Of 781 019 children with ≥2 medication exposures, 21.4% experienced ≥1 major DDI exposure. The odds of DDI exposure increased with age and with medical and mental health complexity. Frequently implicated drugs included: Clonidine, psychiatric medications, and asthma medications. The highest adverse physiologic effect exposure rate per 100 children included: Increased drug concentrations (14.6), central nervous system depression (13.6), and heart rate-corrected QT interval prolongation (9.9).

CONCLUSIONS

One in 5 Medicaid-insured children with ≥2 prescription medications were exposed to major DDIs annually, with higher exposures in those with medical or mental health complexity. DDI exposure places children at risk for negative health outcomes and adverse drug events, especially in the harder-to-monitor outpatient setting.

Analgesia and sedation in critically ill pediatric patients: an update from the recent guidelines and point of view

In the last decades, the advancement of knowledge in analgesia and sedation for critically ill pediatric patients has been conspicuous and relevant. Many recommendations have changed to ensure patients' comfort during their intensive care unit (ICU) stay and prevent and treat sedation-related complications, as well as improve functional recovery and clinical outcomes. The key aspects of the analgosedation management in pediatrics have been recently reviewed in two consensus-based documents. However, there remains a lot to be researched and understood. With this narrative review and authors' point of view, we aimed to summarize the new insights presented in these two documents to facilitate their interpretation and application in clinical practice, as well as to outline research priorities in the field.    Conclusion: With this narrative review and authors' point of view, we aimed to summarize the new insights presented in these two documents to facilitate their interpretation and application in clinical practice, as well as to outline research priorities in the field. What is Known: • Critically ill pediatric patients receiving intensive care required analgesia and sedation to attenuate painful and stressful stimuli. •Optimal management of analgosedation is a challenge often burdened with complications such as tolerance, iatrogenic withdrawal syndrome, delirium, and possible adverse outcomes. What is New: •The new insights on the analgosedation treatment for critically ill pediatric patients delineated in the recent guidelines are summarized to identify strategies for changes in clinical practice. •Research gaps and potential for quality improvement projects are also highlighted.

Characterizing Pharmacokinetics in Children With Obesity-Physiological, Drug, Patient, and Methodological Considerations

Childhood obesity is an alarming public health problem. The pediatric obesity rate has quadrupled in the past 30 years, and currently nearly 20% of United States children and 9% of children worldwide are classified as obese. Drug distribution and elimination processes, which determine drug exposure (and thus dosing), can vary significantly between patients with and without obesity. Obesity-related physiological changes, such as increased tissue volume and perfusion, altered blood protein concentrations, and tissue composition can greatly affect a drug's volume of distribution, which might necessitate adjustment in loading doses. Obesity-related changes in the drug eliminating organs, such as altered enzyme activity in the liver and glomerular filtration rate, can affect the rate of drug elimination, which may warrant an adjustment in the maintenance dosing rate. Although weight-based dosing (i.e., in mg/kg) is commonly practiced in pediatrics, choice of the right body size metric (e.g., total body weight, lean body weight, body surface area, etc.) for dosing children with obesity still remains a question. To address this gap, the interplay between obesity-related physiological changes (e.g., altered organ size, composition, and function), and drug-specific properties (e.g., lipophilicity and elimination pathway) needs to be characterized in a quantitative framework. Additionally, methodological considerations, such as adequate sample size and optimal sampling scheme, should also be considered to ensure accurate and precise top-down covariate selection, particularly when designing opportunistic studies in pediatric drug development. Further factors affecting dosing, including existing dosing recommendations, target therapeutic ranges, dose capping, and formulations constraints, are also important to consider when undergoing dose selection for children with obesity. Opportunities to bridge the dosing knowledge gap in children with obesity include modeling and simulating techniques (i.e., population pharmacokinetic and physiologically-based pharmacokinetic [PBPK] modeling), opportunistic clinical data, and real world data. In this review, key considerations related to physiology, drug parameters, patient factors, and methodology that need to be accounted for while studying the influence of obesity on pharmacokinetics in children are highlighted and discussed. Future studies will need to leverage these modeling opportunities to better describe drug exposure in children with obesity as the childhood obesity epidemic continues.

Increasing application of pediatric physiologically based pharmacokinetic models across academic and industry organizations

There has been a significant increase in the use of physiologically based pharmacokinetic (PBPK) models during the past 20 years, especially for pediatrics. The aim of this study was to give a detailed overview of the growth and areas of application of pediatric PBPK (P‐PBPK) models. A total of 181 publications and publicly available regulatory reviews were identified and categorized according to year, author affiliation, platform, and primary application of the P‐PBPK model (in clinical settings, drug development or to advance pediatric model development in general). Secondary application areas, including dose selection, biologics, and drug interactions, were also assessed. The growth rate for P‐PBPK modeling increased 33‐fold between 2005 and 2020; this was mainly attributed to growth in clinical and drug development applications. For primary applications, 50% of articles were classified under clinical, 18% under drug development, and 33% under model development. The most common secondary applications were dose selection (75% drug development), pharmacokinetic prediction and covariate identification (47% clinical), and model parameter identification (68% model development), respectively. Although population PK modeling remains the mainstay of approaches supporting pediatric drug development, the data presented here demonstrate the widespread application of P‐PBPK models in both drug development and clinical settings. Although applications for pharmacokinetic and drug–drug interaction predictions in pediatrics is advocated, this approach remains underused in areas such as assessment of pediatric formulations, toxicology, and trial design. The increasing number of publications supporting the development and refinement of the pediatric model parameters can only serve to enhance optimal use of P‐PBPK models.

Physiologically-based pharmacokinetic modeling of oxcarbazepine and levetiracetam during adjunctive antiepileptic therapy in children and adolescents

Oxcarbazepine (OXZ) and levetiracetam (LEV) are two new generation anti‐epileptic drugs, often co‐administered in children with enzyme‐inducing antiepileptic drugs (EIAEDs). The anti‐epileptic effect of OXZ and LEV are linked to the exposure of OXZ’s active metabolite 10‐monohydroxy derivative (MHD) and (the parent) LEV, respectively. However, little is known about the confounding effect of age and EIAEDs on the pharmacokinetics (PKs) of MHD and LEV. To address this knowledge gap, physiologically‐based pharmacokinetic (PBPK) modeling was performed in the PK‐Sim software using literature data from children greater than or equal to 2 years of age. Age‐related changes in clearance (CL) of MHD and LEV were characterized, both in the presence (group 1) and absence (group 2) of concomitant EIAEDs. The drug‐drug interaction effect of EIAEDs was estimated as the difference in CL estimates between groups 1 and 2. PBPK modeling suggests that bodyweight normalized CL (ml/min/kg) is higher in younger children than their older counterparts (i.e., due to an influence of age). Concomitant EIAEDs further increase MHD’s CL to a fixed extent of 25% at any age, but EIAEDs’ effect on LEV’s CL increases with age from 20% (at 2 years) to 30% (at adolescence). Simulations with the maximum recommended doses (MRDs) revealed that children between 2 and 4 years and greater than 4 years, who are not on EIAEDs, are at risk of exceeding the reference exposure range for OXZ and LEV, respectively. This analysis demonstrates the use of PBPK modeling in understanding the confounding effect of age and comedications on PKs in children and adolescents.