Pediatric Physiologically Based Pharmacokinetic Models Research Articles

Physiologically Based Pharmacokinetic Modeling for Selumetinib to Evaluate Drug-Drug Interactions and Pediatric Dose Regimens.

Selumetinib (ARRY‐142886), an oral, potent and highly selective allosteric mitogen‐activated protein kinase kinase 1/2 inhibitor, is approved by the US Food and Drug Administration for the treatment of pediatric patients aged ≥2 years with neurofibromatosis type 1 with symptomatic, inoperable plexiform neurofibromas. A physiologically based pharmacokinetic (PBPK) model was constructed to predict plasma concentration–time profiles of selumetinib, and to evaluate the impact of coadministering moderate cytochrome P450 (CYP) 3A4/2C19 inhibitors/inducers. The model was also used to extrapolate pharmacokinetic exposures from older children with different body surface area to guide dosing in younger children. This model was built based on physiochemical data and clinical in vivo drug‐drug interaction (DDI) studies with itraconazole and fluconazole, and verified against data from an in vivo rifampicin DDI study and an absolute bioavailability study. The pediatric model was updated by changing system‐specific input parameters using the Simcyp pediatric module. The model captured the observed selumetinib pharmacokinetic profiles and the interactions with CYP inhibitors/inducers. The predictions from the PBPK model showed a DDI effect of 30% to 40% increase or decrease in selumetinib exposure when coadministered with moderate CYP inhibitors or inducers, respectively, which was used to inform dose management and adjustments. The pediatric PBPK model was applied to simulate exposures in specific body surface area brackets that matched those achieved with a 25 mg/m2 dose in SPRINT clinical trials. The pediatric PBPK model was used to guide the dose for younger patients in a planned pediatric clinical study.

A best practice framework for applying physiologically-based pharmacokinetic modeling to pediatric drug development.

Pediatric physiologically‐based pharmacokinetic (PBPK) models have broad application in the drug development process and are being used not only to project doses for clinical trials but increasingly to replace clinical studies. However, the approach has yet to become fully integrated in regulatory submissions. Emerging data support an expanded integration of the PBPK model informed approach in regulatory guidance on pediatrics. Best practice standards are presented for further development through interaction among regulators, industry, and model providers.

Leveraging Physiologically Based Pharmacokinetic Modeling and Experimental Data to Guide Dosing Modification of CYP3A-Mediated Drug-Drug Interactions in the Pediatric Population.

Solithromycin is a novel fluoroketolide antibiotic that is both a substrate and time-dependent inhibitor of CYP3A. Solithromycin has demonstrated efficacy in adults with community-acquired bacterial pneumonia and has also been investigated in pediatric patients. The objective of this study was to develop a framework for leveraging physiologically based pharmacokinetic (PBPK) modeling to predict CYP3A-mediated drug-drug interaction (DDI) potential in the pediatric population using solithromycin as a case study. To account for age, we performed in vitro metabolism and time-dependent inhibition studies for solithromycin for CYP3A4, CYP3A5, and CYP3A7. The PBPK model included CYP3A4 and CYP3A5 metabolism and time-dependent inhibition, glomerular filtration, P-glycoprotein transport, and enterohepatic recirculation. The average fold error of simulated and observed plasma concentrations of solithromycin in both adults (1966 plasma samples) and pediatric patients from 4 days to 17.9 years (684 plasma samples) were within 0.5- to 2.0-fold. The geometric mean ratios for the simulated area under the concentration versus time curve (AUC) extrapolated to infinity were within 0.75- to 1.25-fold of observed values in healthy adults receiving solithromycin with midazolam or ketoconazole. DDI potential was simulated in pediatric patients (1 month to 17 years of age) and adults. Solithromycin increased the simulated midazolam AUC 4- to 6-fold, and ketoconazole increased the simulated solithromycin AUC 1- to 2-fold in virtual subjects ranging from 1 month to 65 years of age. This study presents a systematic approach for incorporating CYP3A in vitro data into adult and pediatric PBPK models to predict pediatric CYP3A-mediated DDI potential. SIGNIFICANCE STATEMENT: Using solithromycin, this study presents a framework for investigating and incorporating CYP3A4, CYP3A5, and CYP3A7 in vitro data into adult and pediatric physiologically based pharmacokinetic models to predict CYP3A-mediated DDI potential in adult and pediatric subjects during drug development. In this study, minor age-related differences in inhibitor concentration resulted in differences in the magnitude of the DDI. Therefore, age-related differences in DDI potential for substrates metabolized primarily by CYP3A4 can be minimized by closely matching adult and pediatric inhibitor concentrations.

Development of a Virtual Chinese Pediatric Population Physiological Model Targeting Specific Metabolism and Kidney Elimination Pathways.

Background: Physiologically based pharmacokinetic (PBPK) modeling and simulating may be a powerful tool in predicting drug behaviors in specific populations. It is a mathematical model that relates the pharmacokinetic (PK) profile of a compound with human anatomical characteristics, physiological characteristics, and biochemical parameters. Predictions using PBPK models offer a promising way to guide drug development and can be used to optimize clinical dosing regimens. However, PK data of new drugs in the pediatric population are too limited to guide clinical therapy, which may lead to frequent adverse events or insufficient efficacy for pediatric patients, particularly in neonates and infants. Objective: The objective of this study was to establish a virtual Chinese pediatric population based on the physiological parameters of Chinese children that could be utilized in PBPK models. Methods: A Chinese pediatric PBPK model was developed in Simcyp Simulator by collecting published Chinese pediatric physiological and anthropometric data to use as system parameters. This pediatric population model was then evaluated in the Chinese pediatric population by predicting the pharmacokinetic characteristics of four probe drugs: theophylline (major CYP1A2 substrate), fentanyl (major CYP3A4 substrate), vancomycin, and ceftazidime (renal-eliminated). Results: The predicted maximum concentration (Cmax), area under the curve of concentration-time (AUC), and clearance (CL) for theophylline (CYP1A2 metabolism pathway) and fentanyl (CYP3A4 metabolism pathway) were within two folds of the observed data. For drugs mainly eliminated by renal clearance (vancomycin and ceftazidime) in the Chinese pediatric population, the ratio of prediction to observation for major PK parameters was within a 2-fold error range. Conclusion: The model is a supplement to the previous Chinese population PBPK model. We anticipate the model to be a better representative of the pediatric Chinese population for drugs PK, offering greater clinical precision for medication given to the pediatric population, ultimately advancing clinical development of pediatric drugs. We can refine this model further by collecting more physiological parameters of Chinese children.

Application of a Physiologically Based Pharmacokinetic Model to Characterize Time-dependent Metabolism of Voriconazole in Children and Support Dose Optimization.

Background: Voriconazole is a potent antifungal drug with complex pharmacokinetics caused by time-dependent inhibition and polymorphisms of metabolizing enzymes. It also exhibits different pharmacokinetic characteristics between adults and children. An understanding of these alterations in pharmacokinetics is essential for pediatric dose optimization. Objective: To determine voriconazole plasma exposure in the pediatric population and further investigate optimal dosage regimens. Methods: An adult and pediatric physiologically based pharmacokinetic (PBPK) model of voriconazole, integrating auto-inhibition of cytochrome P450 3A4 (CYP3A4) and CYP2C19 gene polymorphisms, was developed. The model was evaluated with visual predictive checks and quantitative measures of the predicted/observed ratio of the area under the plasma concentration-time curve (AUC) and maximum concentration (Cmax). The validated pediatric PBPK model was used in simulations to optimize pediatric dosage regimens. The probability of reaching a ratio of free drug (unbound drug concentration) AUC during a 24-h period to minimum inhibitory concentration greater than or equal to 25 (fAUC24h/MIC ≥ 25) was assessed as the pharmacokinetic/pharmacodynamic index. Results: The developed PBPK model well represented voriconazole's pharmacokinetic characteristics in adults; 78% of predicted/observed AUC ratios and 85% of Cmax ratios were within the 1.25-fold range. The model maintained satisfactory prediction performance for intravenous administration in pediatric populations after incorporating developmental changes in anatomy/physiology and metabolic enzymes, with all predicted AUC values within 2-fold and 73% of the predicted Cmax within 1.25-fold of the observed values. The simulation results of the PBPK model suggested that different dosage regimens should be administered to children according to their age, CYP2C19 genotype, and infectious fungal genera. Conclusion: The PBPK model integrating CYP3A4 auto-inhibition and CYP2C19 gene polymorphisms successfully predicted voriconazole pharmacokinetics during intravenous administration in children and could further be used to optimize dose strategies. The infectious fungal genera should be considered in clinical settings, and further research with large sample sizes is required to confirm the current findings.

Evaluation of models for predicting pediatric fraction unbound in plasma for human health risk assessment

ABSTRACT Pediatric physiologically based pharmacokinetic (PBPK) models facilitate the prediction of PK parameters in children under specific exposure conditions. Pharmacokinetic outcomes are highly sensitive to fraction unbound in plasma (fup) as incorporated into PBPK models. Rarely is fup in children (fupchild) experimentally derived and prediction is based upon fup in adults (fupadult) as well as a ratio of plasma protein concentrations between children and adults. The objectives were to (i) evaluate protein concentration vs. age profile derived from ontogeny models, (ii) assess predictive performances of fup ontogeny models, and (iii) determine overall uncertainty in fupchild prediction resulting from a combination of quantitative structure–property relationship (QSPR) model and ontogeny models. The plasma albumin and alpha-acid glycoprotein (AAG) concentration data for pediatrics and fupchild and fupadult data were obtained from literature. The protein concentration vs. age profile derived from ontogeny models were compared to observed levels. Fupchild values were calculated according to ontogeny models using both observed and QSPR-predicted fupadult as inputs and predictive performances of ontogeny models assessed by comparing predicted fupchild to observed values. Protein concentrations vs. age profiles derived from non-linear equations were more congruent with observed albumin levels than linear or step-wise models. When observed fupadult values were used as input, the fupchild data were under-predicted with average fold error (AFE) amounts ranging 0.79–0.81 and 0.77–0.97 for albumin and AAG ontogeny models, respectively. When QSPR-predicted fupadult values were used as input, AFE of fupchild ranged 1.2–1.35 and 0.98–1.2 for albumin and AAG models, respectively. The choice of ontogeny model with respect to prediction accuracy is more important for AAG, highly bound compounds and infants. For these compounds and scenarios, experimental determination of fupchild for inclusion into a pediatric PBPK model is necessary to have confidence in PBPK model outputs.

Physiologically-Based Pharmacokinetic (PBPK) Modeling Providing Insights into Fentanyl Pharmacokinetics in Adults and Pediatric Patients.

Fentanyl is widely used for analgesia, sedation, and anesthesia both in adult and pediatric populations. Yet, only few pharmacokinetic studies of fentanyl in pediatrics exist as conducting clinical trials in this population is especially challenging. Physiologically-based pharmacokinetic (PBPK) modeling is a mechanistic approach to explore drug pharmacokinetics and allows extrapolation from adult to pediatric populations based on age-related physiological differences. The aim of this study was to develop a PBPK model of fentanyl and norfentanyl for both adult and pediatric populations. The adult PBPK model was established in PK-Sim® using data from 16 clinical studies and was scaled to several pediatric subpopulations. ~93% of the predicted AUClast values in adults and ~88% in pediatrics were within 2-fold of the corresponding value observed. The adult PBPK model predicted a fraction of fentanyl dose metabolized to norfentanyl of ~33% and a fraction excreted in urine of ~7%. In addition, the pediatric PBPK model was used to simulate differences in peak plasma concentrations after bolus injections and short infusions. The novel PBPK models could be helpful to further investigate fentanyl pharmacokinetics in both adult and pediatric populations.

Physiologically Based Pharmacokinetic Modeling of Monoclonal Antibodies in Pediatric Populations Using PK-Sim.

Physiologically based pharmacokinetic (PBPK) models are increasingly used to support pediatric dose selection for small molecule drugs. In contrast, only a few pediatric PBPK models for therapeutic antibodies have been published recently, and the knowledge on the maturation of the processes relevant for antibody pharmacokinetics (PK) is limited compared to small molecules. The aim of this study was, thus, to evaluate predictions from antibody PBPK models for children which were scaled from PBPK models for adults in order to identify respective knowledge gaps. For this, we used the generic PBPK model implemented in PK-Sim without further modifications. Focusing on general clearance and distribution mechanisms, we selected palivizumab and bevacizumab as examples for this evaluation since they show simple, linear PK which is not governed by drug-specific target mediated disposition at usual therapeutic dosages, and their PK has been studied in pediatric populations after intravenous application. The evaluation showed that the PK of palivizumab was overall reasonably well predicted, while the clearance for bevacizumab seems to be underestimated. Without implementing additional ontogeny for antibody PK-specific processes into the PBPK model, bodyweight normalized clearance increases only moderately in young children compared to adults. If growth during aging at the time of the simulation was considered, the apparent clearance is approximately 20% higher compared to simulations for which growth was not considered for newborns due to the long half-life of antibodies. To fully understand the differences and similarities in the PK of antibodies between adults and children, further research is needed. By integrating available information and data, PBPK modeling can contribute to reveal the relevance of involved processes as well as to generate and test hypothesis.

Pharmacokinetics of Ticagrelor in Infants and Toddlers Aged

Pharmacokinetics of Ticagrelor in Infants and Toddlers Aged <24 Months with Sickle Cell Disease

Application of Physiologically-Based and Population Pharmacokinetic Modeling for Dose Finding and Confirmation During the Pediatric Development of Moxifloxacin.

Moxifloxacin is a widely used fluoroquinolone for the treatment of complicated intra‐abdominal infections. We applied physiologically‐based pharmacokinetic (PBPK) and population pharmacokinetic (popPK) modeling to support dose selection in pediatric patients. We scaled an existing adult PBPK model to children based on prior physiological knowledge. The resulting model proposed an age‐dependent dosing regimen that was tested in a phase I study. Refined doses were then tested in a phase III study. A popPK analysis of all clinical pediatric data confirmed the PBPK predictions, including the proposed dosing schedule in children, and supported pharmacokinetics‐related safety/efficacy questions. The pediatric PBPK model adequately predicted the doses necessary to achieve antimicrobial efficacy while maintaining safety in the phase I and III pediatric studies. Altogether, this study retroactively demonstrated the robustness and utility of modeling to support dose finding and confirmation in pediatric drug development for moxifloxacin.

Model qualification of the PK-Sim® pediatric module for pediatric exposure assessment of CYP450 metabolized compounds

ABSTRACTPediatric physiologically based pharmacokinetic (PBPK) models facilitate the estimation of pharmacokinetic (PK) parameters in children under specific exposure conditions. In human health risk assessment, PBPK modeling has been used to determine a chemical-specific human kinetic adjustment factor (HKAF). Due to increased demands in regulatory assessment, model evaluation and qualification have gained growing attention. The aim of this study was to undertake model qualification of pediatric PBPK models for compounds that are primarily metabolized by cytochrome P450 (CYP) enzymes. The objectives were to determine the appropriateness of the virtual individual creating algorithm in PK-Sim® in predicting PK parameters and their variability in children and identify critical system-specific inputs. PBPK models in adults were constructed for several pharmaceuticals (grouped by major clearance process such as CYP3A4). Several age groups of virtual individuals were created to represent children in pediatric clinical studies. The mean and variance of clearance (CL) from virtual populations were compared to observed values. Sensitivity analysis on area under the curve (AUC) was performed. System-specific parameters of virtual children that contribute to inter-individual PK properties were assessed. Eighty-one percent of the comparisons between simulated and observed clearance values were within twofold error. The mean fold errors were 1.1, 1, 0.7 and 1.8 in adolescents, children, infants and neonates, respectively. CL variability was reasonably predicted for 70% of the comparisons with comparable coefficients of variation between observed and predicted. The sensitivity analysis revealed that fraction unbound in plasma, parameters related to CYP enzyme-mediated metabolism and liver volumewere most important in the estimation of pediatric exposure. A comparison of variabilities in weight, height and liver volume in virtual children showed reliable agreement with observed data. The presented results of predictive performance and properties of virtual populations provide confidence in the use of PK-Sim for pediatric PBPK modeling in toxicological applications including PBPK-based-HKAF derivation.

Development of a physiologically-based pharmacokinetic pediatric brain model for prediction of cerebrospinal fluid drug concentrations and the influence of meningitis.

Different pediatric physiologically-based pharmacokinetic (PBPK) models have been described incorporating developmental changes that influence plasma drug concentrations. Drug disposition into cerebrospinal fluid (CSF) is also subject to age-related variation and can be further influenced by brain diseases affecting blood-brain barrier integrity, like meningitis. Here, we developed a generic pediatric brain PBPK model to predict CSF concentrations of drugs that undergo passive transfer, including age-appropriate parameters. The model was validated for the analgesics paracetamol, ibuprofen, flurbiprofen and naproxen, and for a pediatric meningitis population by empirical optimization of the blood-brain barrier penetration of the antibiotic meropenem. Plasma and CSF drug concentrations derived from the literature were used to perform visual predictive checks and to calculate ratios between simulated and observed area under the concentration curves (AUCs) in order to evaluate model performance. Model-simulated concentrations were comparable to observed data over a broad age range (3 months–15 years postnatal age) for all drugs investigated. The ratios between observed and simulated AUCs (AUCo/AUCp) were within 2-fold difference both in plasma (range 0.92–1.09) and in CSF (range 0.64–1.23) indicating acceptable model performance. The model was also able to describe disease-mediated changes in neonates and young children (<3m postnatal age) related to meningitis and sepsis (range AUCo/AUCp plasma: 1.64–1.66, range AUCo/AUCp CSF: 1.43–1.73). Our model provides a new computational tool to predict CSF drug concentrations in children with and without meningitis and can be used as a template model for other compounds that passively enter the CNS.

Physiologically Based Pharmacokinetic Modeling for Trimethoprim and Sulfamethoxazole in Children.

The aims of this study were to (1) determine whether opportunistically collected data can be used to develop physiologically based pharmacokinetic (PBPK) models in pediatric patients; and (2) characterize age-related maturational changes in drug disposition for the renally eliminated and hepatically metabolized antibiotic trimethoprim (TMP)-sulfamethoxazole (SMX). We developed separate population PBPK models for TMP and SMX in children after oral administration of the combined TMP-SMX product and used sparse and opportunistically collected plasma concentration samples to validate our pediatric model. We evaluated predictability of the pediatric PBPK model based on the number of observed pediatric data out of the 90% prediction interval. We performed dosing simulations to target organ and tissue (skin) concentrations greater than the methicillin-resistant Staphylococcus aureus (MRSA) minimum inhibitory concentration (TMP 2mg/L; SMX 9.5mg/L) for at least 50% of the dosing interval. We found 67-87% and 71-91% of the observed data for TMP and SMX, respectively, were captured within the 90% prediction interval across five age groups, suggesting adequate fit of our model. Our model-rederived optimal dosing of TMP at the target tissue was in the range of recommended dosing for TMP-SMX in children in all age groups by current guidelines for the treatment of MRSA. We successfully developed a pediatric PBPK model of the combination antibiotic TMP-SMX using sparse and opportunistic pediatric pharmacokinetic samples. This novel and efficient approach has the potential to expand the use of PBPK modeling in pediatric drug development.

Physiologically Based Pharmacokinetic Modeling to Supplement Nilotinib Pharmacokinetics and Confirm Dose Selection in Pediatric Patients

In adult patients, nilotinib is indicated for chronic myeloid leukemia at an approved oral dose of 300 or 400 mg BID. Physiologically based pharmacokinetic (PBPK) model was developed to describe and supplement limited PK data in the pediatric population ranging from 2 to less than 6 years of age and ultimately inform dosing regimen. An adult Simcyp PBPK model was established and verified with clinical pharmacokinetic data after a single or multiple oral doses of 400 mg nilotinib (230 mg/m2). The model was then applied to a pediatric PBPK model, taking account of ontogeny profiles of metabolizing enzymes and pediatric physiological parameters. The model was further verified using observed pediatric PK data in 12- to <18-year-old and from 6- to <12-year-old patients. The PBPK models were able to recover, describe, and supplement the limited nilotinib concentration-time data profile in 2- to <6-year-old patients after a single dose and Cmin,ss after BID dosing. The exposure (Cmax,ss, Cmin,ss, and AUCtau,ss) was predicted to be similar across age groups. PBPK model simulations confirmed that body surface area–normalized dosing regimen of 230 mg/m2 is considered appropriate for pediatric patients >2 to <18 years of age.

Assessing CYP2C19 Ontogeny in Neonates and Infants Using Physiologically Based Pharmacokinetic Models: Impact of Enzyme Maturation Versus Inhibition.

The objective of this study was to develop pediatric physiologically based pharmacokinetic (PBPK) models for pantoprazole and esomeprazole. Pediatric PBPK models were developed by Simcyp version 15 by incorporating cytochrome P450 (CYP)2C19 maturation and auto‐inhibition. The predicted‐to‐observed pantoprazole clearance (CL) ratio ranged from 0.96–1.35 in children 1–17 years of age and 0.43–0.70 in term infants. The predicted‐to‐observed esomeprazole CL ratio ranged from 1.08–1.50 for children 6–17 years of age, and 0.15–0.33 for infants. The prediction was markedly improved by assuming no auto‐inhibition of esomeprazole in infants in the PBPK model. Our results suggested that the CYP2C19 auto‐inhibition model was appropriate for esomeprazole in adults and older children but could not be directly extended to infants. A better understanding of the complex interplay of enzyme maturation, inhibition, and compensatory mechanisms for CYP2C19 is necessary for PBPK modeling in infants.

Pediatric Physiologically Based Pharmacokinetic Model Development: Current Status and Challenges

This article provides a brief overview of the development of pediatric physiologically based pharmacokinetic (PPBPK) models, the challenges of uncertain systems information, and finally performance verification considering recent regulatory guidance. Pediatric PBPK (PPBPK) model can incorporate varied manifolds of drug and developmental system information to predict drug PK in children. Health authorities have been receiving a growing number of drug submissions that have used PPBPK. According to a recent review of the FDA office of Clinical Pharmacology, PPBPK modeling has been readily applied for dose regimen selection in a variety of pediatric patient groups via a “learn and confirm” approach. Before applying to pediatrics, a PBPK model is developed using physicochemical, biopharmaceutical, and metabolic parameters of drug and then verified by pharmacokinetics (PK) data in adults. Once the drug parameters are optimized, they can be used in the PPBPK model which contains the ‘systemic’ data of physiology and biochemistry in children. The drug absorption, distribution, metabolism, and excretion (ADME) parameters can be estimated according to developmental changes in physiology and biochemistry in various age groups of children. The model is then applied to simulate the exposure in children. The current challenge is the paucity of available pediatric systems information and oral biopharmaceutics. They are essential to verify PPBPK models and predict PK particularly in neonates and young infants. The recent research on intestinal and hepatic transporter ontogeny, via mRNA and proteomic data, gives us increased understanding of pediatric drug absorption, hepatic uptake, and biliary excretion. Nevertheless, more validated developmental ‘system’ data is needed in this field. Another gap is the lack of good quality pediatric drug studies performed across the age range, which currently is a limitation to the performance verification of PPBPK models. PPBPK models have already improved the pediatric drug development process but the important challenges lie ahead. Further development and verification are constantly required. It may be possible to minimize the number of pediatric subjects used in PK studies and avoid some studies altogether.

Pharmacokinetics of rivaroxaban in children using physiologically based and population pharmacokinetic modelling: an EINSTEIN-Jr phase I study

BackgroundThe EINSTEIN-Jr program will evaluate rivaroxaban for the treatment of venous thromboembolism (VTE) in children, targeting exposures similar to the 20 mg once-daily dose for adults. A physiologically based pharmacokinetic (PBPK) model for pediatric rivaroxaban dosing has been constructed.MethodsWe quantitatively assessed the pharmacokinetics (PK) of a single rivaroxaban dose in children using population pharmacokinetic (PopPK) modelling and assessed the applicability of the PBPK model. Plasma concentration–time data from the EINSTEIN-Jr phase I study were analysed by non-compartmental and PopPK analyses and compared with the predictions of the PBPK model. Two rivaroxaban dose levels, equivalent to adult doses of rivaroxaban 10 mg and 20 mg, and two different formulations (tablet and oral suspension) were tested in children aged 0.5–18 years who had completed treatment for VTE.ResultsPK data from 59 children were obtained. The observed plasma concentration–time profiles in all subjects were mostly within the 90% prediction interval, irrespective of dose or formulation. The PopPK estimates and non-compartmental analysis-derived PK parameters (in children aged ≥6 years) were in good agreement with the PBPK model predictions.ConclusionsThese results confirmed the applicability of the rivaroxaban pediatric PBPK model in the pediatric population aged 0.5–18 years, which in combination with the PopPK model, will be further used to guide dose selection for the treatment of VTE with rivaroxaban in EINSTEIN-Jr phase II and III studies.Trial registrationClinicalTrials.gov number, NCT01145859; registration date: 17 June 2010.

Development of a Pediatric Physiologically-Based Pharmacokinetic Model of Clindamycin Using Opportunistic Pharmacokinetic Data.

Physiologically-based pharmacokinetic (PBPK) modeling is a powerful tool used to characterize maturational changes in drug disposition to inform dosing across childhood; however, its use is limited in pediatric drug development. Access to pediatric pharmacokinetic data is a barrier to widespread application of this model, which impedes its development and optimization. To support the development of a pediatric PBPK model, we sought to leverage opportunistically-collected plasma concentrations of the commonly used antibiotic clindamycin. The pediatric PBPK model was optimized following development of an adult PBPK model that adequately described literature data. We evaluated the predictability of the pediatric population PBPK model across four age groups and found that 63-93% of the observed data were captured within the 90% prediction interval of the model. We then used the pediatric PBPK model to optimize intravenous clindamycin dosing for a future prospective validation trial. The optimal dosing proposed by this model was 9mg/kg/dose in children ≤5months of age, 12mg/kg/dose in children >5months-6years of age, and 10mg/kg/dose in children 6-18years of age, all administered every 8h. The simulated exposures achieved with the dosing regimen proposed were comparable with adult plasma and tissue exposures for the treatment of community-acquired methicillin-resistant Staphylococcus aureus infections. Our model demonstrated the feasibility of using opportunistic pediatric data to develop pediatric PBPK models, extending the reach of this powerful modeling tool and potentially transforming the pediatric drug development field.

Bottom-up Meets Top-down: Complementary Physiologically Based Pharmacokinetic and Population Pharmacokinetic Modeling for Regulatory Approval of a Dosing Algorithm of Valganciclovir in Very Young Children.

Population pharmacokinetic (PopPK) and physiologically based pharmacokinetic (PBPK) models are frequently used to support pediatric drug development. Both methods have strengths and limitations and we used them complementarily to support the regulatory approval of a dosing algorithm for valganciclovir (VGCV) in children <4 months old. An existing pediatric PBPK model was extended to neonates and showed that potential physiological differences compared with older children are minor. The PopPK model was used to simulate ganciclovir (GCV) exposures in children with population typical combinations of body size and renal function and to assess the effectiveness of an alternative dosing algorithm suggested by the US Food and Drug Administration. PBPK and PopPK confirmed that the proposed VGCV dosing algorithm achieves similar GCV exposures in children of all ages and that the alternative dosing algorithm leads to underexposure in a substantial fraction of patients. Our approach raised the confidence in the VGCV dosing algorithm for children <4 months old and supported the regulatory approval.

Physiologically Based Pharmacokinetic Prediction of Linezolid and Emtricitabine in Neonates and Infants.

Modeling and simulation approaches are increasingly being utilized in pediatric drug development. Physiologically based pharmacokinetic (PBPK) modeling offers an enhanced ability to predict age-related changes in pharmacokinetics in the pediatric population. In the current study, adult PBPK models were developed for the renally excreted drugs linezolid and emtricitabine. PBPK models were then utilized to predict pharmacokinetics in pediatric patients for various age groups from the oldest to the youngest patients in a stepwise approach. Pharmacokinetic predictions for these two drugs in the pediatric population, including infants and neonates, were within a twofold range of clinical observations. Based on this study, linezolid and emtricitabine pediatric PBPK models incorporating the ontogeny in renal maturation describe the pharmacokinetic differences between adult and pediatric populations, even though the contribution of renal clearance to the total clearance of two drugs was very different (30% for linezolid vs. 86% for emtricitabine). These results suggest that PBPK modeling may provide one option to help predict the pharmacokinetics of renally excreted drugs in neonates and infants.