Physiologically Based Pharmacokinetic Models Are Effective Support for Pediatric Drug Development

HSPiP, Computational, and Thermodynamic Model-Based Optimized Solvents for Subcutaneous Delivery of Tolterodine Tartrate and GastroPlus‑Based In Vivo Prediction in Humans: Part II

In part I, we reported Hansen solubility parameters (HSP, HSPiP program), experimental solubility at varied temperatures for TOTA delivery. Here, we studied dose volume selection, stability, pH, osmolality, dispersion, clarity, and viscosity of the explored combinations (I-VI). Ex vivo permeation and deposition studies were performed to observe relative diffusion rate from the injected site in rat skin. Confocal laser scanning microscopy (CLSM) study was conducted to support ex vivo findings. Moreover, GastroPlus predicted in vivo parameters in humans and the impact of various critical factors on pharmacokinetic parameters (PK). Immediate release product (IR) contained 60% of PEG400 whereas controlled release formulation (CR) contained PEG400 (60%), water (10%) and d-limonene (30%) to deliver 2 mg of TOTA. GastroPlus predicted the plasma drug concentration of weakly basic TOTA as function of pH (from pH 2.0 to 9). The cumulative drug permeation and drug deposition were found to be in the order as B-VI˃ C-VI˃A-VI across rat skin. This finding was further supported with CLSM. Moreover, IR and CR were predicted to achieve Cmax of 0.0038 µg/ mL and 0.00023 µg/mL, respectively, after sub-Q delivery. Added limonene in CR extended the plasma drug concentration over period of 12 h as predicted in GastroPlus. Parameters sensitivity analysis (PSA) assessment predicted that sub-Q blood flow rate is the only factor affecting PK parameters in IR formulation whereas this was insignificant for CR. Thus, sub-Q delivery CR would be promising alternative with ease of delivery to children and aged patient.

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.

Physiologically-based pharmacokinetic models versus allometric scaling for prediction of tyrosine-kinase inhibitor exposure from adults to children

PURPOSE

Model-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption.

METHODS

Pediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values.

RESULTS

Ten pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (Ctrough) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations.

CONCLUSION

Based on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection.

Challenges of pediatric pharmacotherapy: A narrative review of pharmacokinetics, pharmacodynamics, and pharmacogenetics

PURPOSE

Personalized pharmacotherapy, including for the pediatric population, provides optimal treatment and has emerged as a major trend owing to advanced drug therapeutics and diversified drug selection. However, it is essential to understand the growth and developmental characteristics of this population to provide appropriate drug therapy. In recent years, clinical pharmacogenetics has accumulated knowledge in pediatric pharmacotherapy, and guidelines from professional organizations, such as the Clinical Pharmacogenetics Implementation Consortium, can be consulted to determine the efficacy of specific drugs and the risk of adverse effects. However, the existence of a large knowledge gap hinders the use of these findings in clinical practice.

METHODS

We provide a narrative review of the knowledge gaps in pharmacokinetics (PK) and pharmacodynamics (PD) in the pediatric population, focusing on the differences from the perspective of growth and developmental characteristics. In addition, we explored PK/PD in relation to pediatric clinical pharmacogenetics.

RESULTS

The lack of direct and indirect biomarkers for more accurate assessment of the effects of drug administration limits the current knowledge of PD. In addition, incorporating pharmacogenetic insights as pivotal covariates is indispensable in this comprehensive synthesis for precision therapy; therefore, we have provided recommendations regarding the current status and challenges of personalized pediatric pharmacotherapy. The integration of clinical pharmacogenetics with the health care system and institution of educational programs for health care providers is necessary for its safe and effective implementation. A comprehensive understanding of the physiological and genetic complexities of the pediatric population will facilitate the development of effective and personalized pharmacotherapeutic strategies.

Physiologically Based Pharmacokinetic Modeling in Neonates: Current Status and Future Perspectives

Rational drug use in special populations is a clinical problem that doctors and pharma-cists must consider seriously. Neonates are the most physiologically immature and vulnerable to drug dosing. There is a pronounced difference in the anatomical and physiological profiles be-tween neonates and older people, affecting the absorption, distribution, metabolism, and excretion of drugs in vivo, ultimately leading to changes in drug concentration. Thus, dose adjustments in neonates are necessary to achieve adequate therapeutic concentrations and avoid drug toxicity. Over the past few decades, modeling and simulation techniques, especially physiologically based pharmacokinetic (PBPK) modeling, have been increasingly used in pediatric drug development and clinical therapy. This rigorously designed and verified model can effectively compensate for the deficiencies of clinical trials in neonates, provide a valuable reference for clinical research design, and even replace some clinical trials to predict drug plasma concentrations in newborns. This review introduces previous findings regarding age-dependent physiological changes and pathological factors affecting neonatal pharmacokinetics, along with their research means. The application of PBPK modeling in neonatal pharmacokinetic studies of various medications is also reviewed. Based on this, we propose future perspectives on neonatal PBPK modeling and hope for its broader application.

Physiologically based mechanistic insight into differential risk of valproate hepatotoxicity between children and adults: A focus on ontogeny impact

The anticonvulsant valproic acid (VPA) despite complex pharmacokinetics has been in clinical use for nearly 6 decades. Previous reports indicated neonates, infants, and toddlers/preschoolers had higher risk of valproate hepatotoxicity than adults. However, dosing recommendations for those less than 10 years of age are lacking. To decipher clinical puzzles, physiologically-based pharmacokinetic (PBPK) models of VPA and its hepatotoxic metabolite 4-ene-VPA were constructed and simulated with particularly integrated information of drug-metabolizing enzyme ontogeny. Adult and pediatric PK data of VPA (n = 143 subjects) and 4-ene-VPA (n = 8 subjects) collected from previous reports were used for model development and validation. Sensitivity analyses were performed to characterize ontogeny impacts of CYP2C9 and UGT2B7 on dispositions of VPA and 4-ene-VPA across age groups. Optimal VPA dosing for each pediatric age group was also predicted and objectively judged by ensuring VPA efficacy and avoiding 4-ene-VPA hepatotoxicity. The study revealed UGT2B7 ontogeny was quite influential on VPA clearance even in neonates and small children. Intrinsic clearance of CYP2C9 was the most prominent determinant for areas under the concentration-time curve of VPA and 4-ene-VPA in infants, and toddlers/preschoolers, reflecting higher hepatotoxicity risk due to noxious 4-ene-VPA accumulation in these groups. The ontogeny-based PBPK approach complements conventional allometric methods in dosing estimation for the young by providing more mechanistic insight of the processes changing with age. The established ontogeny-based PBPK approach for VPA therapy deserves further corroboration by real-world therapeutic data to affirm its clinical applicability.

Challenges, approaches and enablers: effectively triangulating towards dose selection in pediatric rare diseases

Dose selection is an integral part of a molecule's journey to become medicine. On top of typical challenges faced in dose selection for more common diseases, pediatric rare disease has additional unique challenges due to the combination of 'rare' and 'pediatric' populations. Using the central theme of maximizing 'relevant' information to overcome information paucity, dose selection strategy in pediatric rare diseases is discussed using a triangulation concept involving challenges, approaches and very importantly, enablers. Using actual examples, unique scenarios are discussed where specific enablers allowed certain approaches to be used to overcome the challenges. The continued need for model-informed drug development is also discussed using examples of where modeling and simulation tools have been successfully used in bridging available information to select pediatric doses in rare disease. Additionally, challenges with translation and associated dose selection of new modalities such as gene therapy in rare diseases are examined with the lens of continuous learning and knowledge development that will enable pediatric dose selection of these modalities with confidence.

In silico prediction of bioequivalence of atorvastatin tablets based on GastroPlus™ software

The prediction of intestinal absorption of various drugs based on computer simulations has been a reality. However, in vivo pharmacokinetic simulations and virtual bioequivalence evaluation based on GastroPlus™ have not been found. This study aimed to simulate plasma concentrations with different dissolution profiles and run population simulations to evaluate the bioequivalence of test and reference products of atorvastation using GastroPlus software. The dissolution profiles of the reference and test products of atorvastatin (20 mg tablets), and clinical plasma concentration-time data of the reference product were used for the simulations. The results showed that the simulated models were successfully established for atorvastatin tablets. Population simulation results indicated that the test formulation was bioequivalent to the reference formulation. The findings suggest that modelling is an essential tool to demonstrating the possibility of pharmacokinetic and bioequivalence for atorvastatin. It will contribute to understanding the potential risks during the development of generic products.

Physiologically Based Pharmacokinetics Modeling in the Neonatal Population-Current Advances, Challenges, and Opportunities

Physiologically based pharmacokinetic (PBPK) modeling is an approach to predicting drug pharmacokinetics, using knowledge of the human physiology involved and drug physiochemical properties. This approach is useful when predicting drug pharmacokinetics in under-studied populations, such as pediatrics. PBPK modeling is a particularly important tool for dose optimization for the neonatal population, given that clinical trials rarely include this patient population. However, important knowledge gaps exist for neonates, resulting in uncertainty with the model predictions. This review aims to outline the sources of variability that should be considered with developing a neonatal PBPK model, the data that are currently available for the neonatal ontogeny, and lastly to highlight the data gaps where further research would be needed.

Physiologically based pharmacokinetic modeling to characterize enterohepatic recirculation and predict food effect on the pharmacokinetics of hyzetimibe

BACKGROUND AND OBJECTIVE

Hyzetimibe is a cholesterol absorption inhibitor indicated for the treatment of hypercholesterolemia. This study aims to describe the multiple-peak pharmacokinetics (PK) of hyzetimibe and its active metabolite M1 through physiologically-based pharmacokinetic (PBPK) modeling, and to compare the model predictions of a virtual food effect study with the results of a clinical food effect study.

METHODS

The plasma concentration data used for PBPK modeling were obtained from a single-dose, two-period crossover bioequivalence study in the fasted state. Advanced Compartmental Absorption and Transit model was used for absorption. Enterohepatic recirculation process was modeled by changing the gut physiological state from fasted to fed at meal time. Based on the established PBPK models, a virtual food effect study was simulated. A clinical food effect study was used for model external validation.

RESULTS

PK profiles of hyzetimibe and M1 under fasting condition could be well described by the PBPK model, and the errors of Cmax, AUC0-∞, and AUC0-t were within the two-fold range. Simulated geometric mean ratios (GMRs, fed/fasted) showed that a high-fat breakfast slightly affected the PK of hyzetimibe, expressed as increased Cmax of hyzetimibe (130.6%). Simulated GMRs and 90% confidence intervals of AUC were within the preset bioequivalent range. The results of the simulated virtual food effect trial were consistent with those of the clinical food effect trial.

CONCLUSIONS

The established PBPK model could describe the concentration-time profiles of hyzetimibe and M1 well with good prediction performance. A fully mechanistic model of enterohepatic recirculation warrants further investigation.

Development and Validation of Physiologically Based Pharmacokinetic Model of Levetiracetam to Predict Exposure and Dose Optimization in Pediatrics

Levetiracetam (Lev) is an antiepileptic drug that has been increasingly used in the epilepsy pediatric population in recent years, but its pharmacokinetic behavior in pediatric population needs to be characterized clearly. Clinical trials for the pediatric drug remain difficult to conduct due to ethical and practical factors. The purpose of this study was to use the physiologically based pharmacokinetic (PBPK) model to predict changes in plasma exposure of Lev in pediatric patients and to provide recommendations for dose adjustment. A PBPK model of Lev in adults was developed using PK-Sim® software and extrapolated to the entire age range of the pediatric population. The model was evaluated using clinical pharmacokinetic data. The results showed the good fit between predictions and observations of the adult and pediatric models. The recommended doses for neonates, infants and children are 0.78, 1.67 and 1.22 times that of adults, respectively. Moreover, at the same dose, plasma exposure in adolescents was similar to that of adults. The PBPK models of Lev for adults and pediatrics were successfully developed and validated to provide a reference for the rational administration of drugs in the pediatric population.

Adapting physiologically-based pharmacokinetic models for machine learning applications

Both machine learning and physiologically-based pharmacokinetic models are becoming essential components of the drug development process. Integrating the predictive capabilities of physiologically-based pharmacokinetic (PBPK) models within machine learning (ML) pipelines could offer significant benefits in improving the accuracy and scope of drug screening and evaluation procedures. Here, we describe the development and testing of a self-contained machine learning module capable of faithfully recapitulating summary pharmacokinetic (PK) parameters produced by a full PBPK model, given a set of input drug-specific and regimen-specific information. Because of its widespread use in characterizing the disposition of orally administered drugs, the PBPK model chosen to demonstrate the methodology was an open-source implementation of a state-of-the-art compartmental and transit model called OpenCAT. The model was tested for drug formulations spanning a large range of solubility and absorption characteristics, and was evaluated for concordance against predictions of OpenCAT and relevant experimental data. In general, the values predicted by the ML models were within 20% of those of the PBPK model across the range of drug and formulation properties. However, summary PK parameter predictions from both the ML model and full PBPK model were occasionally poor with respect to those derived from experiments, suggesting deficiencies in the underlying PBPK model.

Physiologically Based Pharmacokinetic Modeling of Extracellular Vesicles

Extracellular vesicles (EVs) are lipid membrane bound-cell-derived structures that are a key player in intercellular communication and facilitate numerous cellular functions such as tumor growth, metastasis, immunosuppression, and angiogenesis. They can be used as a drug delivery platform because they can protect drugs from degradation and target specific cells or tissues. With the advancement in the technologies and methods in EV research, EV-therapeutics are one of the fast-growing domains in the human health sector. Therapeutic translation of EVs in clinics requires assessing the quality, safety, and efficacy of the EVs, in which pharmacokinetics is very crucial. We report here the application of physiologically based pharmacokinetic (PBPK) modeling as a principal tool for the prediction of absorption, distribution, metabolism, and excretion of EVs. To create a PBPK model of EVs, researchers would need to gather data on the size, shape, and composition of the EVs, as well as the physiological processes that affect their behavior in the body. The PBPK model would then be used to predict the pharmacokinetics of drugs delivered via EVs, such as the rate at which the drug is absorbed and distributed throughout the body, the rate at which it is metabolized and eliminated, and the maximum concentration of the drug in the body. This information can be used to optimize the design of EV-based drug delivery systems, including the size and composition of the EVs, the route of administration, and the dose of the drug. There has not been any dedicated review article that describes the PBPK modeling of EV. This review provides an overview of the absorption, distribution, metabolism, and excretion (ADME) phenomena of EVs. In addition, we will briefly describe the different computer-based modeling approaches that may help in the future of EV-based therapeutic research.

Virtual Clinical Trials Guided Design of an Age-Appropriate Formulation and Dosing Strategy of Nifedipine for Paediatric Use

The rapid onset of action of nifedipine causes a precipitous reduction in blood pressure leading to adverse effects associated with reflex sympathetic nervous system (SNS) activation, including tachycardia and worsening myocardial and cerebrovascular ischemia. As a result, short acting nifedipine preparations are not recommended. However, importantly, there are no modified release preparations of nifedipine authorised for paediatric use, and hence a paucity of clinical studies reporting pharmacokinetics data in paediatrics. Pharmacokinetic parameters may differ significantly between children and adults due to anatomical and physiological differences, often resulting in sub therapeutic and/or toxic plasma concentrations of medication. However, in the field of paediatric pharmacokinetics, the use of pharmacokinetic modelling, particularly physiological-based pharmacokinetics (PBPK), has revolutionised the ability to extrapolate drug pharmacokinetics across age groups, allowing for pragmatic determination of paediatric plasma concentrations to support drug licensing and clinical dosing. In order to pragmatically assess the translation of resultant dissolution profiles to the paediatric populations, virtual clinical trials simulations were conducted. In the context of formulation development, the use of PBPK modelling allowed the determination of optimised formulations that achieved plasma concentrations within the target therapeutic window throughout the dosing strategy. A 5 mg sustained release mini-tablet was successfully developed with the duration of release extending over 24 h and an informed optimised dosing strategy of 450 µg/kg twice daily. The resulting formulation provides flexible dosing opportunities, improves patient adherence by reducing frequent administration burden and enhances patient safety profiles by maintaining efficacious levels of consistent drug plasma levels over a sustained period of time.

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.

In-Depth Analysis of Physiologically Based Pharmacokinetic (PBPK) Modeling Utilization in Different Application Fields Using Text Mining Tools

In the past decade, only a small number of papers have elaborated on the application of physiologically based pharmacokinetic (PBPK) modeling across different areas. In this review, an in-depth analysis of the distribution of PBPK modeling in relation to its application in various research topics and model validation was conducted by text mining tools. Orange 3.32.0, an open-source data mining program was used for text mining. PubMed was used for data retrieval, and the collected articles were analyzed by several widgets. A total of 2699 articles related to PBPK modeling met the predefined criteria. The number of publications per year has been rising steadily. Regarding the application areas, the results revealed that 26% of the publications described the use of PBPK modeling in early drug development, risk assessment and toxicity assessment, followed by absorption/formulation modeling (25%), prediction of drug-disease interactions (20%), drug-drug interactions (DDIs) (17%) and pediatric drug development (12%). Furthermore, the analysis showed that only 12% of the publications mentioned model validation, of which 51% referred to literature-based validation and 26% to experimentally validated models. The obtained results present a valuable review of the state-of-the-art regarding PBPK modeling applications in drug discovery and development and related fields.

Predicting the correct dose in children: Role of computational Pediatric Physiological-based pharmacokinetics modeling tools

The pharmacokinetics (PKs) and safety of medications in particular groups can be predicted using the physiologically-based pharmacokinetic (PBPK) model. Using the PBPK model may enable safe pediatric clinical trials and speed up the process of new drug research and development, especially for children, a population in which it is relatively difficult to conduct clinical trials. This review summarizes the role of pediatric PBPK (P-PBPK) modeling software in dose prediction over the past 6 years and briefly introduces the process of general P-PBPK modeling. We summarized the theories and applications of this software and discussed the application trends and future perspectives in the area. The modeling software's extensive use will undoubtedly make it easier to predict dose prediction for young patients.

The Impact of Low Cardiac Output on Propofol Pharmacokinetics across Age Groups-An Investigation Using Physiologically Based Pharmacokinetic Modelling

BACKGROUND

pathophysiological changes such as low cardiac output (LCO) impact pharmacokinetics, but its extent may be different throughout pediatrics compared to adults. Physiologically based pharmacokinetic (PBPK) modelling enables further exploration.

METHODS

A validated propofol model was used to simulate the impact of LCO on propofol clearance across age groups using the PBPK platform, Simcyp^® (version 19). The hepatic and renal extraction ratio of propofol was then determined in all age groups. Subsequently, manual infusion dose explorations were conducted under LCO conditions, targeting a 3 µg/mL (80-125%) propofol concentration range.

RESULTS

Both hepatic and renal extraction ratios increased from neonates, infants, children to adolescents and adults. The relative change in clearance following CO reductions increased with age, with the least impact of LCO in neonates. The predicted concentration remained within the 3 µg/mL (80-125%) range under normal CO and LCO (up to 30%) conditions in all age groups. When CO was reduced by 40-50%, a dose reduction of 15% is warranted in neonates, infants and children, and 25% in adolescents and adults.

CONCLUSIONS

PBPK-driven, the impact of reduced CO on propofol clearance is predicted to be age-dependent, and proportionally greater in adults. Consequently, age group-specific dose reductions for propofol are required in LCO conditions.

Current situation of pediatric clinical trials in China: focus on trials for drug marketing application and administrative approval

Background

Research and development of pediatric drug faces many difficulties and pediatric clinical trials remain a challenge. Since 2011, a series of measures have been taken to encourage research, development of drugs for pediatric patients in China. In this study, we analyzed pediatric clinical trials conducted in China to provide reference for research and development of pediatric drugs and formulation of relevant policies.

Methods

We conducted a cross-sectional observational study of pediatric trials registered in the Drug Trial Registration and Information Publication Platform before Oct. 31, 2021. All trials that recruited children (under 18 years old as defined in China) were retrieved and general characteristics of the trials and the research drugs were extracted and analyzed. The data were extracted and statistically analyzed by excel 2010 and SPSS 22.0, respectively.

Results

There were 588 registered pediatric clinical trials, which accounted for 3.94% of the total registered trials. The overall average annual growth rate of the number of trials from 2013 to 2020 was 14.47% (P < 0.01). Of the 588 trials included, there were 312 trials (53.06%) with only children as subjects, 127 trials (21.60%) with research drugs only for children use, and the median of target subject number was 320 with the range of 8 to 600,000. The sponsors and the principal investigators were mainly located in the eastern and northern China. 325 trials were vaccine trials, and the dosage form was mainly injection. There were 98 non-vaccine biological product trials (mainly injections), 135 chemical compound drug trials (mainly tablets), 30 traditional Chinese medicine/natural drugs (mainly granules). Indications of the non-vaccine drugs were mainly diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism.

Conclusion

The number of pediatric clinical trials in China has increased these years. To further promote pediatric clinical trials and motivate pediatric appropriate drug marketing application and administrative approval, conducting large pediatric clinical trials, further development of dosage forms suitable for children with special attention to neonates and prematurity, and improving uneven geographical distribution of sponsors and researchers are the current challenges.

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.