Clinical implementation of pharmacogenetics and model-informed precision dosing to improve patient care

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.

Barriers and Facilitators for Bringing Model-Informed Precision Dosing to the Patient's Bedside: A Systematic Review

Model-informed precision dosing (MIPD) utilizes mathematical models to predict optimal medication doses for a specific patient or patient population. However, the factors influencing the implementation of MIPD have not been fully elucidated, hindering its widespread use in clinical practice. A systematic review was conducted in PubMed from inception to December 2022, aiming to identify barriers and facilitators for the implementation of MIPD into patient care. Articles with a focus on implementation of MIPD were eligible for this review. After screening titles and abstracts, full articles investigating the clinical implementation of MIPD were included for data extraction. Of 790 records identified, 15 publications were included. A total of 72 barriers and facilitators across seven categories were extracted through a hybrid thematic analysis. Barriers comprised limited data for model validation, unclear regulatory pathways for model endorsement and additional drug level measurements required for certain types of MIPD. Facilitators encompassed the development of user-friendly MIPD tools continuously updated based on user feedback and data. Collaborative efforts among diverse stakeholders for model validation and implementation, along with education of end-users, may promote the utilization of MIPD in patient care. Despite ongoing challenges, this systematic review revealed various strategies to facilitate the clinical implementation of MIPD.

Pharmacokinetic Factors Associated With Early Meropenem Target Attainment in Pediatric Severe Sepsis

OBJECTIVES

To determine the frequency of early meropenem concentration target attainment (TA) in critically ill children with severe sepsis; to explore clinical, therapeutic, and pharmacokinetic factors associated with TA; and to assess how fluid resuscitation and volume status relate to early TA.

DESIGN

Retrospective analysis of prospective observational cohort study.

SETTING

PICU in a single academic quaternary care children's hospital.

PATIENTS

Twenty-nine patients starting meropenem for severe sepsis (characterized as need for positive pressure ventilation, vasopressors, or ≥ 40 mL/kg bolused fluid), of which 17 were newly escalated to PICU level care.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Concentration-time profiles were analyzed using modeling software employing opportunistic sampling, Bayesian estimation, and a population pharmacokinetic model. Time above four times minimum inhibitory concentration (T > 4×MIC), using the susceptibility breakpoint of 1 µg/mL, was determined for each patient over the first 24 hours of meropenem therapy, as well as individual clearance and volume of distribution (Vd) estimates. Twenty-one of 29 patients met a target of 40%T > MIC 4 μg/mL. Reaching TA, vs. not, was associated with lower meropenem clearance. We failed to identify a difference in Vd or an association between the TA group and age, weight, creatinine-based estimated glomerular filtration rate (eGFR), or the amount of fluid administered. eGFR was, however, negatively correlated with overall T > MIC.

CONCLUSIONS

Eight of 29 pediatric patients with early severe sepsis did not meet the selected TA threshold within the first 24 hours of meropenem therapy. Higher clearance was associated with failure to meet targets. Identifying patients likely to have higher meropenem clearance could help with dosing regimens.

Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward

Interindividual variability in genes encoding drug-metabolizing enzymes, transporters, receptors, and human leukocyte antigens has a major impact on a patient's response to drugs with regard to efficacy and safety. Enabled by both technological and conceptual advances, the field of pharmacogenomics is developing rapidly. Major progress in omics profiling methods has enabled novel genotypic and phenotypic characterization of patients and biobanks. These developments are paralleled by advances in machine learning, which have allowed us to parse the immense wealth of data and establish novel genetic markers and polygenic models for drug selection and dosing. Pharmacogenomics has recently become more widespread in clinical practice to personalize treatment and to develop new drugs tailored to specific patient populations. In this review, we provide an overview of the latest developments in the field and discuss the way forward, including how to address the missing heritability, develop novel polygenic models, and further improve the clinical implementation of pharmacogenomics.

Identification of Sociodemographic, Clinical, and Genetic Factors to Aid Alaska Native and American Indian People to Successfully Quit Smoking

INTRODUCTION

Alaska Native and American Indian (ANAI) people have a smoking prevalence of 23%. Nicotine metabolite ratio (NMR) and genetic testing may enable tailored selection of tobacco cessation medication.

AIMS AND METHODS

The purpose of this study was to evaluate the relative contributions of NMR, cessation medication, demographics, and tobacco use history to cessation. Participants were recruited into an observational cohort study consisting of a baseline visit prior to their quit date and 6-week follow-up. Demographic and tobacco use surveys and blood, urine, and breath samples were collected at each visit. Electronic health records were queried for cessation medications. NMR was categorized into slow or normal nicotine metabolism phenotypes (<0.31 and ≥ 0.31, respectively). The main outcome was cessation at 6 weeks. Analyses consisted of descriptive statistics, medication and phenotype concordance, and estimates of relative risk (RR) of quitting.

RESULTS

We enrolled 151 ANAI adults who smoked cigarettes daily. Two-thirds had normal nicotine metabolism phenotype. Retrospective medication and phenotype concordance was 39%. The overall quit rate was 25%. No demographic factors or tobacco use history were associated with quit success. Varenicline and bupropion increased the likelihood of quitting (RR = 2.93 [1.42, 6.03] and RR = 2.52 [1.12, 5.64], respectively) compared to nicotine replacement therapy. Non-optimal medication and phenotype concordance decreased likelihood of quit success (RR = 0.44 [0.22, 0.91]) compared to optimal concordance.

CONCLUSIONS

This exploratory study found associations between quit success and tobacco cessation medication as well as medication and phenotype concordance. Additional research is needed to assess use of NMR for treatment selection among ANAI people.

IMPLICATIONS

These results broadly support additional community-engaged research to improve medication and phenotype concordance in tribal health settings. Such future research on implementing meditcation and phenotype concordance holds promise to improve expectations, quit success, and health outcomes amongst individuals attempting to quit smoking.

The Critical Role of Pharmacists in the Clinical Delivery of Pharmacogenetics in the U.S

Since the rebirth of pharmacogenomics (PGx) in the 1990s and 2000s, with new discoveries of genetic variation underlying adverse drug response and new analytical technologies such as sequencing and microarrays, there has been much interest in the clinical application of PGx testing. The early involvement of pharmacists in clinical studies and the establishment of organizations to support the dissemination of information about PGx variants have naturally resulted in leaders in clinical implementation. This paper presents an overview of the evolving role of pharmacists, and discusses potential challenges and future paths, primarily focused in the U.S. Pharmacists have positioned themselves as leaders in clinical PGx testing, and will prepare the next generation to utilize PGx testing in their scope of practice.

Model-Informed Precision Dosing Guidance of Ethosuximide Developed from a Randomized Controlled Clinical Trial of Childhood Absence Epilepsy

Ethosuximide was identified as the optimal option for new-onset childhood absence epilepsy (CAE) in a randomized, two-phase dose escalation comparative effectiveness trial of ethosuximide, lamotrigine, and valproic acid. However, 47% of ethosuximide initial monotherapy participants experienced short-term treatment failure. This study aimed to characterize the initial monotherapy ethosuximide exposure-response relationship and to propose model-informed precision dosing guidance. Dose titration occurred over a 16-20-week period until patients experienced seizure freedom or intolerable side effects. Subjects with initial monotherapy failure were randomized to one of the other two medications and dose escalation was repeated. A population pharmacokinetic model was created using plasma concentration data (n = 1,320), collected at 4-week intervals from 211 unique participants during both the initial and second monotherapy phases. A logistic regression analysis was performed on the initial monotherapy cohort (n = 103) with complete exposure-response data. Eighty-four participants achieved seizure freedom with a wide range of ethosuximide area under the curves (AUC) ranging from 420 to 2,420 μg·h/mL. AUC exposure estimates for achieving a 50% and 75% probability of seizure freedom were 1,027 and 1,489 μg·h/mL, respectively, whereas the corresponding cumulative frequency of intolerable adverse events was 11% and 16%. Monte Carlo Simulation indicated a daily dose of 40 and 55 mg/kg to achieve 50% and 75% probability of seizure freedom in the overall population, respectively. We identified the need for adjusted mg/kg dosing in different body weight cohorts. This ethosuximide proposed model-informed precision dosing guidance to achieve seizure freedom carries promise to optimize initial monotherapy success for patients with CAE.

Modeling Clinical Phenotype Variability: Consideration of Genomic Variations, Computational Methods, and Quantitative Proteomics

Advances in biomedical and computer technologies have presented the modeling community the opportunity for mechanistically modeling and simulating the variability in a disease phenotype or in a drug response. The capability to quantify response variability can inform a drug development program. Quantitative systems pharmacology scientists have published various computational approaches for creating virtual patient populations (VPops) to model and simulate drug response variability. Genomic variations can impact disease characteristics and drug exposure and response. Quantitative proteomics technologies are increasingly used to facilitate drug discovery and development and inform patient care. Incorporating variations in genomics and quantitative proteomics may potentially inform creation of VPops to model and simulate virtual patient trials, and may help account for, in a predictive manner, phenotypic variations observed clinically.

β-lactam precision dosing in critically ill children: Current state and knowledge gaps

There has been emerging interest in implementing therapeutic drug monitoring and model-informed precision dosing of β-lactam antibiotics in critically ill patients, including children. Despite a position paper endorsed by multiple international societies that support these efforts in critically ill adults, implementation of β-lactam precision dosing has not been widely adopted. In this review, we highlight what is known about β-lactam antibiotic pharmacokinetics and pharmacodynamics in critically ill children. We also define the knowledge gaps that present barriers to acceptance and implementation of precision dosing of β-lactam antibiotics in critically ill children: a lack of consensus on which subpopulations would benefit most from precision dosing and the uncertainty of how precision dosing changes outcomes. We conclude with opportunities for further research to close these knowledge gaps.

Applying machine learning to the pharmacokinetic modeling of cyclosporine in adult renal transplant recipients: a multi-method comparison

Objective: The aim of this study was to identify the important factors affecting cyclosporine (CsA) blood concentration and estimate CsA concentration using seven different machine learning (ML) algorithms. We also assessed the predictability of established ML models and previously built population pharmacokinetic (popPK) model. Finally, the most suitable ML model and popPK model to guide precision dosing were determined.

Methods: In total, 3,407 whole-blood trough and peak concentrations of CsA were obtained from 183 patients who underwent initial renal transplantation. These samples were divided into model-building and evaluation sets. The model-building set was analyzed using seven different ML algorithms. The effects of potential covariates were evaluated using the least absolute shrinkage and selection operator algorithms. A separate evaluation set was used to assess the ability of all models to predict CsA blood concentration. R squared (R2) scores, median prediction error (MDPE), median absolute prediction error (MAPE), and the percentages of PE within 20% (F20) and 30% (F30) were calculated to assess the predictive performance of these models. In addition, previously built popPK model was included for comparison.

Results: Sixteen variables were selected as important covariates. Among ML models, the predictive performance of nonlinear-based ML models was superior to that of linear regression (MDPE: 3.27%, MAPE: 34.21%, F20: 30.63%, F30: 45.03%, R2 score: 0.68). The ML model built with the artificial neural network algorithm was considered the most suitable (MDPE: −0.039%, MAPE: 25.60%, F20: 39.35%, F30: 56.46%, R2 score: 0.75). Its performance was superior to that of the previously built popPK model (MDPE: 5.26%, MAPE: 29.22%, F20: 33.94%, F30: 51.22%, R2 score: 0.68). Furthermore, the application of the most suitable model and the popPK model in clinic showed that most dose regimen recommendations were reasonable.

Conclusion: The performance of these ML models indicate that a nonlinear relationship for covariates may help to improve model predictability. These results might facilitate the application of ML models in clinic, especially for patients with unstable status or during initial dose optimization.

Model-informed Estimation of Acutely Decreased Tacrolimus Clearance and Subsequent Dose Individualization in a Pediatric Renal Transplant Patient with Posterior Reversible Encephalopathy Syndrome

BACKGROUND

Considerable inter-patient and inter-occasion variability has been reported in tacrolimus pharmacokinetics (PK) in the pediatric renal transplant population. The present study investigated tacrolimus PK in a 2-year-old post-renal transplant patient and a known CYP3A5 expresser who developed posterior reversible encephalopathy syndrome (PRES) and had significantly elevated tacrolimus blood concentrations during tacrolimus treatment. A model-informed PK assessment was performed to assist with precision dosing. Tacrolimus clearance was evaluated both before and after the development of PRES on post-transplant day (PTD) 26.

METHODS

A retrospective chart review was conducted to gather dosing data and tacrolimus concentrations, as part of a clinical pharmacology consultation service. Individual PK parameters were estimated by Bayesian estimation using a published pediatric PK model. Oral clearance (CL/F) was estimated for three distinct time periods-before CNS symptoms (PTD 25), during the PRES event (PTD 27-30), and after oral tacrolimus was re-started (PTD 93).

RESULTS

Bayesian estimation showed an estimated CL/F of 15.0 L/h in the days preceding the PRES event, compared to a population mean of 16.3 L/h (95% confidence interval 14.9-17.7 L/h) for CYP3A5 expressers of the same age and weight. Samples collected on PTD 27-30 yielded an estimated CL/F of 3.6 L/h, a reduction of 76%, coinciding with clinical confirmation of PRES and therapy discontinuation. On PTD 93, an additional assessment showed a stable CL/F value of 14.5 L/h one month after re-initiating tacrolimus and was used to recommend a continued maintenance dose.

CONCLUSION

This is the first report to demonstrate acutely decreased tacrolimus clearance in PRES, likely caused by the downregulation of metabolizing enzymes in response to inflammatory cytokines. The results suggest the ability of model-informed Bayesian estimation to characterize an acute decline in oral tacrolimus clearance after the development of PRES, and the role that PK estimation may play in supporting dose selection and individualization.

Population pharmacokinetic analysis of dexmedetomidine in children using real-world data from electronic health records and remnant specimens

AIMS

Our objectives were to perform a population pharmacokinetic analysis of dexmedetomidine in children using remnant specimens and electronic health records (EHRs) and explore the impact of patient's characteristics and pharmacogenetics on dexmedetomidine clearance.

METHODS

Dexmedetomidine dosing and patient data were gathered from EHRs and combined with opportunistically sampled remnant specimens. Population pharmacokinetic models were developed using nonlinear mixed-effects modelling. Stage 1 developed a model without genotype variables; Stage 2 added pharmacogenetic effects.

RESULTS

Our final study population included 354 post-cardiac surgery patients aged 0-22 years (median 16 mo). The data were best described with a 2-compartment model with allometric scaling for weight and Hill maturation function for age. Population parameter estimates and 95% confidence intervals were 27.3 L/h (24.0-31.1 L/h) for total clearance, 161 L (139-187 L) for central compartment volume of distribution, 26.0 L/h (22.5-30.0 L/h) for intercompartmental clearance and 7903 L (5617-11 119 L) for peripheral compartment volume of distribution. The estimate for postmenstrual age when 50% of adult clearance is achieved was 42.0 weeks (41.5-42.5 weeks) and the Hill coefficient estimate was 7.04 (6.99-7.08). Genotype was not statistically or clinically significant.

CONCLUSION

Our study demonstrates the use of real-world EHR data and remnant specimens to perform a population pharmacokinetic analysis and investigate covariate effects in a large paediatric population. Weight and age were important predictors of clearance. We did not find evidence for pharmacogenetic effects of UGT1A4 or UGT2B10 genotype or CYP2A6 risk score.

Single Nucleotide Polymorphisms (SNPs) in PRKG1 & SPATA13-AS1 are associated with bronchodilator response: a pilot study during acute asthma exacerbations in African American children

OBJECTIVE

Inhaled bronchodilators are the first-line treatment for asthma exacerbations, but individual bronchodilator response (BDR) varies by race and ethnicity. Studies have examined BDR's genetic underpinnings, but many did not include children or were not conducted during an asthma exacerbation. This pilot study tested single-nucleotide polymorphisms' (SNPs') association with pediatric African American BDR during an acute asthma exacerbation.

METHODS

This was a study of pediatric asthma patients in the age group 2-18 years treated in the emergency department for an asthma exacerbation. We measured BDR before and after inhaled bronchodilator treatments using both the Pediatric Asthma Severity Score (PASS) and asthma severity score. We collected genomic DNA and examined whether 21 candidate SNPs from a review of the literature were associated with BDR using crude odds ratios (OR) and adjusted analysis.

RESULTS

The final sample population was 53 children, with an average age of 7.2 years. The average initial PASS score (scale of ascending severity from 0 to 6) was 2.5. After adjusting for BMI, age category, gender and smoke exposure, rs912142 was associated with decreased odds of having low BDR (OR, 0.20; 95% confidence interval (CI), 0.02-0.92), and rs7081864 and rs7903366 were associated with decreased odds of having high BDR (OR, 0.097; 95% CI, 0.009-0.62).

CONCLUSIONS

We found three SNPs significantly associated with pediatric African American BDR that provide information regarding a child's potential response to emergency asthma exacerbation treatment. Once validated in larger studies, such information could guide pharmacogenomic evidence-based emergency asthma treatment to improve patient outcomes.

Therapeutic Dilemma in personalized medicine

The practice of medicine depends over a long time on identifying therapies that target an entire population. The increase in scientific knowledge over the years has led to the gradual change towards individualization and personalization of drug therapy. The hope of this change is to achieve a better clinical response to given medications and reduction of their adverse effects. Tailoring of medicine on the road of personalized medicine considers molecular and genetic mapping of the individual. However, many factors still impede the smooth application of personalized medicine and represent challenges or limitations in its achievement. In this article, we put some clinical examples that show dilemmas in the application of personalized medicine such as opioids in pain control, fluoropyrimidines in malignancy, clopidogrel as antiplatelet therapy and oral hypoglycemic drugs in Type2 diabetes in adults. Shaping the future of medicine through the application of personalized medicine for a particular patient needs to put into consideration many factors such as patient's genetic makeup and life style, pathology of the disease and dynamic changes in its course as well as interactions between administered drugs and their effects on metabolizing enzymes. We hope in the coming years, the personalized medicine will foster changes in health care system in the way not only to treat patients but also to prevent diseases.

Resolving discordant CYP2D6 genotyping results in Thai subjects: platform limitations and novel haplotypes

Aim: Several CYP2D6 Luminex xTAG genotype calls were identified as inconsistent or suspicious among Thai subjects and further characterized to identify the root causes. Material & methods: Forty-eight subjects were followed-up with long-range-PCR, quantitative copy number assays and/or Sanger sequencing. Results: Most of the Luminex-duplication calls were either negative or had hybrid structures involving CYP2D6*36 in various configurations. Ten samples were inaccurately called as CYP2D6*2, *29 or *35 alleles. Sequencing revealed three novel haplotypes, CYP2D6*142, *143 and *144 of which two are nonfunctional. Conclusion: The Luminex platform produced a relatively high number of false genotype calls for Thai subjects. Our findings underscore the need for the systematic characterization of the CYP2D6 locus in diverse populations and rigorous platform validation.

Model-Informed Precision Dosing of Antibiotics in Pediatric Patients: A Narrative Review

Optimal pharmacotherapy in pediatric patients with suspected infections requires understanding and integration of relevant data on the antibiotic, bacterial pathogen, and patient characteristics. Because of age-related physiological maturation and non-maturational covariates (e.g., disease state, inflammation, organ failure, co-morbidity, co-medication and extracorporeal systems), antibiotic pharmacokinetics is highly variable in pediatric patients and difficult to predict without using population pharmacokinetics models. The intra- and inter-individual variability can result in under- or overexposure in a significant proportion of patients. Therapeutic drug monitoring typically covers assessment of pharmacokinetics and pharmacodynamics, and concurrent dose adaptation after initial standard dosing and drug concentration analysis. Model-informed precision dosing (MIPD) captures drug, disease, and patient characteristics in modeling approaches and can be used to perform Bayesian forecasting and dose optimization. Incorporating MIPD in the electronic patient record system brings pharmacometrics to the bedside of the patient, with the aim of a consisted and optimal drug exposure. In this narrative review, we evaluated studies assessing optimization of antibiotic pharmacotherapy using MIPD in pediatric populations. Four eligible studies involving amikacin and vancomycin were identified from 418 records. Key articles, independent of year of publication, were also selected to highlight important attributes of MIPD. Although very little research has been conducted until this moment, the available data on vancomycin indicate that MIPD is superior compared to conventional dosing strategies with respect to target attainment. The utility of MIPD in pediatrics needs to be further confirmed in frequently used antibiotic classes, particularly aminoglycosides and beta-lactams.

Continuous Learning in Model-Informed Precision Dosing: A Case Study in Pediatric Dosing of Vancomycin

Model‐informed precision dosing (MIPD) leverages pharmacokinetic (PK) models to tailor dosing to an individual patient’s needs, improving attainment of therapeutic drug exposure targets and thus potentially improving drug efficacy or reducing adverse events. However, selection of an appropriate model for supporting clinical decision making is not trivial. Error or bias in dose selection may arise if the selected model was developed in a population not fully representative of the intended MIPD population. One previously proposed approach is continuous learning, in which an initial model is used in MIPD and then updated as additional data becomes available. In this case study of pediatric vancomycin MIPD, the potential benefits of the continuous learning approach are investigated. Five previously published models were evaluated and found to perform adequately in a data set of 273 pediatric patients in the intensive care unit. Additionally, two predefined simple PK models were fitted on separate populations of 50–350 patients in an approach mimicking clinical implementation of automated continuous learning. With these continuous learning models, prediction error using population PK parameters could be reduced by 2–13% compared with previously published models. Sample sizes of at least 200 patients were found suitable for capturing the interindividual variability in vancomycin at this institution, with limited benefits of larger data sets. Although comprised mostly of trough samples, these sparsely sampled routine clinical data allowed for reasonable estimation of simulated area under the curve (AUC). Together, these findings lay the foundations for a continuous learning MIPD approach.

Pharmacotherapy of neonatal opioid withdrawal syndrome: a review of pharmacokinetics and pharmacodynamics

INTRODUCTION

Neonatal opioid withdrawal syndrome (NOWS) often arises in infants born to mothers who used opioids during pregnancy. Morphine, methadone, and buprenorphine are the most common first-line treatments, whereas clonidine and phenobarbital are generally reserved for adjunctive therapy. These drugs exhibit substantial pharmacokinetic (PK) and pharmacodynamic (PD) variability. Current pharmacological treatments for NOWS are based on institutional protocols and largely rely on empirical treatment of patient symptoms.

AREAS COVERED

This article reviews the PK/PD of NOWS pharmacotherapies with a focus on the implication of physiological development and maturation. Body size-standardized clearance is consistently low in neonates, except for methadone. This can be ascribed to underdeveloped metabolic and elimination pathways. The effects of pharmacogenetics have been clarified especially for morphine. The PK/PD relationship of medications used in the treatment of NOWS is generally understudied.

EXPERT OPINION

Providing an appropriate opioid dose in neonates is challenging. Advancements in quantitative pharmacology and PK/PD modeling approaches facilitate identification of key factors driving PK/PD variability and characterization of exposure-response relationships. PK/PD model-informed simulations have been widely employed to define age-appropriate pediatric dosing regimens. The model-informed approach holds promise to aid more rational use of medications in the treatment of NOWS.