Getting the dose right using physiologically-based pharmacokinetic modeling: dexamethasone to prevent post-extubation stridor in children as proof of concept.

Introduction: Critically ill patients show large variability in drug disposition due to e.g., age, size, disease and treatment modalities. Physiologically-based pharmacokinetic (PBPK) models can be used to design individualized dosing regimens taking this into account. Dexamethasone, prescribed for the prevention post-extubation stridor (PES), is metabolized by the drug metabolizing enzyme CYP3A. As CYP3A4 undergoes major changes during childhood, we aimed to develop age-appropriate dosing recommendations for children of dexamethasone for PES, as proof of concept for PBPK modeling to individualize dosing for critically ill patients. Methods: All simulations were conducted in Simcyp™ v21 (a population-based PBPK modeling platform), using an available dexamethasone compound model and pediatric population model in which CYP3A4 ontogeny is incorporated. Published pharmacokinetic (PK) data was used for model verification. Evidence for the dose to prevent post-extubation stridor was strongest for 2-6 year old children, hence simulated drug concentrations resulting from this dose from this age group were targeted when simulating age-appropriate doses for the whole pediatric age range. Results: Dexamethasone plasma concentrations upon single and multiple intravenous administration were predicted adequately across the pediatric age range. Exposure-matched predictions of dexamethasone PK indicated that doses (in mg/kg) for the 2-6 years olds can be applied in 3 month-2 year old children, whereas lower doses are needed in children of other age groups (60% lower for 0-2 weeks, 40% lower for 2-4 weeks, 20% lower for 1-3 months, 20% lower for 6-12 year olds, 40% lower for 12-18 years olds). Discussion: We show that PBPK modeling is a valuable tool that can be used to develop model-informed recommendations using dexamethasone to prevent PES in children. Based on exposure matching, the dose of dexamethasone should be reduced compared to commonly used doses, in infants <3 months and children ≥6 years, reflecting age-related variation in drug disposition. PBPK modeling is an promising tool to optimize dosing of critically ill patients.

Pragmatic physiologically-based pharmacokinetic modeling to support clinical implementation of optimized gentamicin dosing in term neonates and infants: proof-of-concept.

Introduction: Modeling and simulation can support dosing recommendations for clinical practice, but a simple framework is missing. In this proof-of-concept study, we aimed to develop neonatal and infant gentamicin dosing guidelines, supported by a pragmatic physiologically-based pharmacokinetic (PBPK) modeling approach and a decision framework for implementation. Methods: An already existing PBPK model was verified with data of 87 adults, 485 children and 912 neonates, based on visual predictive checks and predicted-to-observed pharmacokinetic (PK) parameter ratios. After acceptance of the model, dosages now recommended by the Dutch Pediatric Formulary (DPF) were simulated, along with several alternative dosing scenarios, aiming for recommended peak (i.e., 8-12 mg/L for neonates and 15-20 mg/L for infants) and trough (i.e., <1 mg/L) levels. We then used a decision framework to weigh benefits and risks for implementation. Results: The PBPK model adequately described gentamicin PK. Simulations of current DPF dosages showed that the dosing interval for term neonates up to 6 weeks of age should be extended to 36-48 h to reach trough levels <1 mg/L. For infants, a 7.5 mg/kg/24 h dose will reach adequate peak levels. The benefits of these dose adaptations outweigh remaining uncertainties which can be minimized by routine drug monitoring. Conclusion: We used a PBPK model to show that current DPF dosages for gentamicin in term neonates and infants needed to be optimized. In the context of potential uncertainties, the risk-benefit analysis proved positive; the model-informed dose is ready for clinical implementation.

Physiologically-Based Pharmacokinetic Modeling for Drug Dosing in Pediatric Patients: A Tutorial for a Pragmatic Approach in Clinical Care.

It is well-accepted that off-label drug dosing recommendations for pediatric patients should be based on the best available evidence. However, the available traditional evidence is often low. To bridge this gap, physiologically-based pharmacokinetic (PBPK) modeling is a scientifically well-founded tool that can be used to enable model-informed dosing (MID) recommendations in children in clinical practice. In this tutorial, we provide a pragmatic, PBPK-based pediatric modeling workflow. For this approach to be successfully implemented in pediatric clinical practice, a thorough understanding of the model assumptions and limitations is required. More importantly, careful evaluation of an MID approach within the context of overall benefits and the potential risks is crucial. The tutorial is aimed to help modelers, researchers, and clinicians, to effectively use PBPK simulations to support pediatric drug dosing.

Lamivudine and Emtricitabine Dosing Proposal for Children with HIV and Chronic Kidney Disease, Supported by Physiologically Based Pharmacokinetic Modelling.

Dose recommendations for lamivudine or emtricitabine in children with HIV and chronic kidney disease (CKD) are absent or not supported by clinical data. Physiologically based pharmacokinetic (PBPK) models have the potential to facilitate dose selection for these drugs in this population. Existing lamivudine and emtricitabine compound models in Simcyp® (v21) were verified in adult populations with and without CKD and in non-CKD paediatric populations. We developed paediatric CKD population models reflecting subjects with a reduced glomerular filtration and tubular secretion, based on extrapolation from adult CKD population models. These models were verified using ganciclovir as a surrogate compound. Then, lamivudine and emtricitabine dosing strategies were simulated in virtual paediatric CKD populations. The compound and paediatric CKD population models were verified successfully (prediction error within 0.5- to 2-fold). The mean AUC ratios in children (GFR-adjusted dose in CKD population/standard dose in population with normal kidney function) were 1.15 and 1.23 for lamivudine, and 1.20 and 1.30 for emtricitabine, with grade-3- and -4-stage CKD, respectively. With the developed paediatric CKD population PBPK models, GFR-adjusted lamivudine and emtricitabine dosages in children with CKD resulted in adequate drug exposure, supporting paediatric GFR-adjusted dosing. Clinical studies are needed to confirm these findings.

Development of Best Evidence Dosing Recommendations for Term and Preterm Neonates (NeoDose Project).

Many drugs are used off-label in neonates which leads to large variation in prescribed drugs and dosages in neonatal intensive care units (NICUs). The NeoDose project aimed to develop best evidence dosing recommendations (DRs) for term and preterm neonates using a three-step approach: 1) drug selection, 2) establishing consensus-based DRs, and 3) establishing best evidence DRs. METHODS: The selection of drugs was based on frequency of prescribing, availability of a neonatal DR in the Dutch Pediatric Formulary, and the labeling status. Clinical need, pharmacological diversity, and Working Group Neonatal Pharmacology (WGNP) preferences were also taken into account, using a consensus-based approach. For the second step, we requested local dosing protocols from all ten Dutch NICUs and established consensus-based DRs within the WGNP, consisting of neonatologists, clinical pharmacologists, hospital pharmacists, and researchers. In the third step, the consensus-based DRs were compared with the available literature, using standardized PubMed searches. RESULTS: Fourteen drugs were selected for which the local dosing protocols were collected. These protocols differed mostly in total daily dose, dosing frequency, and/or route of administration. Strikingly, almost none of the dosing protocols of these 14 drugs distinguished between preterm and term neonates. The working group established consensus-based DRs, which after literature review needed modification in 56%, mainly in terms of a dose increase. Finally, we established 37 best evidence DRs, 22 for preterm and 15 for term neonates, representing 19 indications. CONCLUSION: This project showed the successful three-step approach for the development of DRs for term and preterm neonates.

Physiologically Based Pharmacokinetic (PBPK) Model-Informed Dosing Guidelines for Pediatric Clinical Care: A Pragmatic Approach for a Special Population.

Physiologically based pharmacokinetic (PBPK) modeling can be an attractive tool to increase the evidence base of pediatric drug dosing recommendations by making optimal use of existing pharmacokinetic (PK) data. A pragmatic approach of combining available compound models with a virtual pediatric physiology model can be a rational solution to predict PK and hence support dosing guidelines for children in real-life clinical care, when it can also be employed by individuals with little experience in PBPK modeling. This comes within reach as user-friendly PBPK modeling platforms exist and, for many drugs and populations, models are ready for use. We have identified a list of drugs that can serve as a starting point for pragmatic PBPK modeling to address current clinical dosing needs.

Demystifying physiologically based pharmacokinetic modelling among non-modelers towards model-informed medicine use in under-served populations.

Physiologically based pharmacokinetic (PBPK) models represent computational technology to characterize drug behavior within the context of detailed human physiology. Today, PBPK is routinely used in drug development and regulatory approval to support decisions on how a medicine can be used under certain clinical conditions. As such, PBPK has the potential to enhance medicine use for populations that are often under-served globally in drug development and clinical care, namely pediatric patients, pregnant and lactating women. To facilitate broader applications of PBPK for these populations, we joined force and organized five hands-on workshops primarily to non-modelers on the principles of PBPK and its potential applications in pediatric and obstetric pharmacology in 2021 and 2022. In this open letter, we report learning objectives and content of such workshops and to highlight the significant value of these educational efforts.

Feasibility of a Pragmatic PBPK Modeling Approach: Towards Model-Informed Dosing in Pediatric Clinical Care.

BACKGROUND AND OBJECTIVE: More than half of all drugs are still prescribed off-label to children. Pharmacokinetic (PK) data are needed to support off-label dosing, however for many drugs such data are either sparse or not representative. Physiologically-based pharmacokinetic (PBPK) models are increasingly used to study PK and guide dosing decisions. Building compound models to study PK requires expertise and is time-consuming. Therefore, in this paper, we studied the feasibility of predicting pediatric exposure by pragmatically combining existing compound models, developed e.g. for studies in adults, with a pediatric and preterm physiology model. METHODS: Seven drugs, with various PK characteristics, were selected (meropenem, ceftazidime, azithromycin, propofol, midazolam, lorazepam, and caffeine) as a proof of concept. Simcyp® v20 was used to predict exposure in adults, children, and (pre)term neonates, by combining an existing compound model with relevant virtual physiology models. Predictive performance was evaluated by calculating the ratios of predicted-to-observed PK parameter values (0.5- to 2-fold acceptance range) and by visual predictive checks with prediction error values. RESULTS: Overall, model predicted PK in infants, children and adolescents capture clinical data. Confidence in PBPK model performance was therefore considered high. Predictive performance tends to decrease when predicting PK in the (pre)term neonatal population. CONCLUSION: Pragmatic PBPK modeling in pediatrics, based on compound models verified with adult data, is feasible. A thorough understanding of the model assumptions and limitations is required, before model-informed doses can be recommended for clinical use.

Off-Label, but on-Evidence? A Review of the Level of Evidence for Pediatric Pharmacotherapy.

Many drugs are still prescribed off-label to the pediatric population. Although off-label drug use not supported by high level of evidence is potentially harmful, a comprehensive overview of the quality of the evidence pertaining off-label drug use in children is lacking. The Dutch Pediatric Formulary (DPF) provides best evidence-based dosing guidelines for drugs used in children. For each drug-indication-age group combination-together compiling one record-we scored the highest available level of evidence: labeled use, systematic review or meta-analysis, randomized controlled trial (RCT), comparative research, noncomparative research, or consensus-based expert opinions. For records based on selected guidelines, the original sources were not reviewed. These records were scored as guideline. A total of 774 drugs were analyzed comprising a total of 6,426 records. Of all off-label records (n = 2,718), 14% were supported by high quality evidence (4% meta-analysis or systematic reviews, 10% RCTs of high quality), 20% by comparative research, 14% by noncomparative research, 37% by consensus-based expert opinions, and 15% by selected guidelines. Fifty-eight percent of all records were authorized, increasing with age from 30% in preterm neonates (n = 110) up to 64% in adolescents (n = 1,630). Many have advocated that off-label use is only justified when supported by a high level of evidence. We show that this prerequisite would seriously limit available drug treatment for children as the underlying evidence is low across ages and drug classes. Our data identify the drugs and therapeutic areas for which evidence is clearly missing and could drive the global research agenda.

Chloroquine Dosing Recommendations for Pediatric COVID-19 Supported by Modeling and Simulation.

As chloroquine (CHQ) is part of the Dutch Centre for Infectious Disease Control coronavirus disease 2019 (COVID-19) experimental treatment guideline, pediatric dosing guidelines are needed. Recent pediatric data suggest that existing World Health Organization (WHO) dosing guidelines for children with malaria are suboptimal. The aim of our study was to establish best-evidence to inform pediatric CHQ doses for children infected with COVID-19. A previously developed physiologically-based pharmacokinetic (PBPK) model for CHQ was used to simulate exposure in adults and children and verified against published pharmacokinetic data. The COVID-19 recommended adult dosage regimen of 44 mg/kg total was tested in adults and children to evaluate the extent of variation in exposure. Based on differences in area under the concentration-time curve from zero to 70 hours (AUC0-70h ) the optimal CHQ dose was determined in children of different ages compared with adults. Revised doses were re-introduced into the model to verify that overall CHQ exposure in each age band was within 5% of the predicted adult value. Simulations showed differences in drug exposure in children of different ages and adults when the same body-weight based dose is given. As such, we propose the following total cumulative doses: 35 mg/kg (CHQ base) for children 0-1 month, 47 mg/kg for 1-6 months, 55 mg/kg for 6 months-12 years, and 44 mg/kg for adolescents and adults, not to exceed 3,300 mg in any patient. Our study supports age-adjusted CHQ dosing in children with COVID-19 in order to avoid suboptimal or toxic doses. The knowledge-driven, model-informed dose selection paradigm can serve as a science-based alternative to recommend pediatric dosing when pediatric clinical trial data is absent.