Prediction of drug clearance in children: an evaluation of the predictive performance of several models

Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future

The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations. Some considerations are put forth for the future improvements of MIDD including refining regulatory considerations, improving the integrity of physiological data, applying the emerging technologies, and exploring the application of MIDD in new therapies like gene therapies for special populations.

Animal-to-Human Dose Translation of ANTHRASIL for Treatment of Inhalational Anthrax in Healthy Adults, Obese Adults, and Pediatric Subjects

Anthrax Immune Globulin Intravenous (AIGIV [ANTHRASIL]), was developed for the treatment of toxemia associated with inhalational anthrax. It is a plasma product collected from individuals vaccinated with anthrax vaccine and contains antitoxin IgG antibodies against Bacillus anthracis protective antigen. A pharmacokinetic (PK) and exposure-response model was constructed to assess the PKs of AIGIV in anthrax-free and anthrax-exposed rabbits, non-human primates and anthrax-free humans, as well as the relationship between AIGIV exposure and survival from anthrax, based on available preclinical/clinical studies. The potential effect of anthrax on the PKs of AIGIV was evaluated and estimates of survival odds following administration of AIGIV protective doses with and without antibiotic co-treatment were established. As the developed PK model can simulate exposure of AIGIV in any species for any dosing scenario, the relationship between the predicted area under the concentration curve of AIGIV in humans and the probability of survival observed in preclinical studies was explored. Based on the simulation results, the intravenous administration of 420 U (units of potency as measured by validated Toxin Neutralization Assay) of AIGIV is expected to result in a > 80% probability of survival in more than 90% of the human population. Additional simulations suggest that exposure levels were similar in healthy and obese humans, and exposure in pediatrics is expected to be up to approximately seven-fold higher than in healthy adults, allowing for doses in pediatric populations that ranged from one to seven vials. Overall, the optimal human dose was justified based on the PK/pharmacodynamic (PD) properties of AIGIV in animals and model-based translation of PK/PD to predict human exposure and efficacy.

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.

How to select the initial dose for a pediatric study?

In typical clinical development programs, a new drug is first developed for the adult use. Drugs are often approved for adult use or in the process of obtaining approval in adults in the target indication before pediatric development is initiated. In designing the first pediatric clinical trial, one of the challenges is to select the initial dose to be tested. The ICH E11 R1 guidance advises that chronologic age alone may not always be the most appropriate categorical determinant to define developmental subgroups in pediatric studies. In this manuscript, the approaches to utilize available data in adults related to those factors beyond age to inform the starting dose selection in pediatric drug development are discussed. Practical considerations and approaches are provided for informing pediatric starting dose. Additional considerations to use pre-clinical information are provided in the case when adult information is limited or not available.

A General Biphasic Bodyweight Model for Scaling Basal Metabolic Rate, Glomerular Filtration Rate, and Drug Clearance from Birth to Adulthood

The objective of this study is to propose a unified, continuous, and bodyweight-only equation to quantify the changes of human basal metabolic rate (BMR), glomerular filtration rate (GFR), and drug clearance (CL) from infancy to adulthood. The BMR datasets were retrieved from a comprehensive historical database of male and female subjects (0.02 to 64 years). The CL datasets for 17 drugs and the GFR dataset were generated from published maturation and growth models with reported parameter values. A statistical approach was used to simulate the model-generated CL and GFR data for a hypothetical population with 26 age groups (from 0 to 20 years). A biphasic equation with two power-law functions of bodyweight was proposed and evaluated as a general model using nonlinear regression and dimensionless analysis. All datasets universally reveal biphasic curves with two distinct linear segments on log-log plots. The biphasic equation consists of two reciprocal allometric terms that asymptotically determine the overall curvature. The fitting results show a superlinear scaling phase (asymptotic exponent >1; ca. 1.5-3.5) and a sublinear scaling phase (asymptotic exponent <1; ca. 0.5-0.7), which are separated at the phase transition bodyweight ranging from 5 to 20 kg with a mean value of 10 kg (corresponding to 1 year of age). The dimensionless analysis generalizes and offers quantitative realization of the maturation and growth process. In conclusion, the proposed mixed-allometry equation is a generic model that quantitatively describes the phase transition in the human maturation process of diverse human functions.

Allometric Scaling in Pharmacokinetic Studies in Anesthesiology

A clinical review is presented of basic allometric scaling theory and its application to pharmacokinetic models in anesthesia and other fields in the biologic sciences.

Ontogeny of Scaling Factors for Pediatric Physiology-Based Pharmacokinetic Modeling and Simulation: Microsomal Protein Per Gram of Liver

Microsomal protein per gram of liver (MPPGL) is an important scaling factor for bottom-up physiology-based pharmacokinetic modeling and simulation, but data in pediatrics are limited. Therefore, MPPGL was determined in 160 liver samples from pediatric (n = 129) and adult (n = 31) donors obtained from four sources: the University of Maryland Brain and Tissue Bank (UMBTB), tissue retrieval services at the University of Minnesota and University of Pittsburgh, and Sekisui-Xenotech. Tissues were homogenized and subjected to differential centrifugation to prepare microsomes, and cytochrome c reductase activities in tissue homogenates and microsomes were used to estimate cytochrome P450 reductase (POR) activity as a marker of microsomal recovery; microsomal POR content was also assessed by quantitative proteomics. MPPGL values varied 5- to 10-fold within various age groups/developmental stages, and tissue source was identified as a contributing factor. Using a "trimmed" dataset comprised of samples ranging from 3 to 18 years of age common to the four sources, POR protein abundance and activity in microsomes and POR activity in homogenates was lower in UMBTB samples (autopsy) compared with other sources (perfused/flash-frozen). Regression analyses revealed that the UMBTB samples were driving an apparent age effect as no effect of age on log-transformed MPPGL values was observed when the UMBTB samples were excluded. We conclude that a mean±SD MPPGL value of 30.4±1.7 mg/g is representative between one month postnatal age and early adulthood. Potential source effects should be considered for studies involving tissue samples from multiple sources with different procurement and processing procedures. SIGNIFICANCE STATEMENT: Microsomal protein per gram of liver (MPPGL) is an important scaling factor for bottom up PBPK modeling and simulation, but data in pediatrics are limited. Although MPPGL varies 5- to 10-fold at a given developmental stage, a value of 30.4 ± 1.7 mg/g (mean ± SD) is representative between one month postnatal age and early adulthood. However, when tissue samples are obtained from multiple sources, different procurement and processing procedures may influence the results and should be taken into consideration.

Individualized precision dosing approaches to optimize antimicrobial therapy in pediatric populations

Introduction:Severe infections continue to impose a major burden on critically ill children and mortality rates remain stagnant. Outcomes rely on accurate and timely delivery of antimicrobials achieving target concentrations in infected tissue. Yet, developmental aspects, disease-related variables, and host factors may severely alter antimicrobial pharmacokinetics in pediatrics. The emergence of antimicrobial resistance increases the need for improved treatment approaches.Areas covered:This narrative review explores why optimization of antimicrobial therapy in neonates, infants, children, and adolescents is crucial and summarizes the possible dosing approaches to achieve antimicrobial individualization. Finally, we outline a roadmap toward scientific evidence informing the development and implementation of precision antimicrobial dosing in critically ill children.The literature search was conducted on PubMed using the following keywords: neonate, infant, child, adolescent, pediatrics, antimicrobial, pharmacokinetic, pharmacodynamic target, Bayes dosing software, optimizing, individualizing, personalizing, precision dosing, drug monitoring, validation, attainment, and software implementation. Further articles were sought from the references of the above searched articles.Expert opinion:Recently, technological innovations have emerged that enabled the development of individualized antimicrobial dosing approaches in adults. More work is required in pediatrics to make individualized antimicrobial dosing approaches widely operationalized in this population.

Physiologically Based Pharmacokinetic Models Are Effective Support for Pediatric Drug Development

Pediatric drug development faces many difficulties. Traditionally, pediatric drug doses are simply calculated linearly based on the body weight, age, and body surface area of adults. Due to the ontogeny of children, this simple linear scaling may lead to drug overdose in pediatric patients. The physiologically based pharmacokinetic (PBPK) model, as a mathematical model, contributes to the research and development of pediatric drugs. An example of a PBPK model guiding drug dose selection in pediatrics has emerged and has been approved by the relevant regulatory agencies. In this review, we discuss the principle of the PBPK model, emphasize the necessity of establishing a pediatric PBPK model, introduce the absorption, distribution, metabolism, and excretion of the pediatric PBPK model, and understand the various applications and related prospects of the pediatric PBPK model.

Model-Informed Drug Development in Pediatric Dose Selection

Model-informed drug development (MIDD) has been a powerful and efficient tool applied widely in pediatric drug development due to its ability to integrate and leverage existing knowledge from different sources to narrow knowledge gaps. The dose selection is the most common MIDD application in regulatory submission related to pediatric drug development. This article aims to give an overview of the 3 broad categories of use of MIDD in pediatric dose selection: leveraging from adults to pediatric patients, leveraging from animals to pediatric patients, and integrating mechanism in infants and neonates. Population pharmacokinetic analyses with allometric scaling can reasonably predict the clearance in pediatric patients aged >5 years. A mechanistic-based approach, such as physiologically based pharmacokinetic accounting for ontogeny, or an allometric model with age-dependent exponent, can be applied to select the dose in pediatric patients aged ≤2 years. The exposure-response relationship from adults or from other drugs in the same class may be useful in aiding the pediatric dose selection and benefit-risk assessment. Increasing application and understanding of use of MIDD have contributed greatly to several policy developments in the pediatric field. With the increasing efforts of MIDD under the Prescription Drug User Fee Act VI, bigger impacts of MIDD approaches in pediatric dose selection can be expected. Due to the complexity of model-based analyses, early engagement between drug developers and regulatory agencies to discuss MIDD issues is highly encouraged, as it is expected to increase the efficiency and reduce the uncertainty.

Prediction of Clearance in Children from Adults Following Drug-Drug Interaction Studies: Application of Age-Dependent Exponent Model

Background and Objective

Pharmacokinetic drug–drug interaction (DDI) studies are conducted in adult subjects during drug development but there are limited studies that have characterized pharmacokinetic DDI studies in children. The objective of this study was to evaluate if the DDI clearance values from adults can be allometrically extrapolated from adults to children.

Methods

Fifteen drugs were included in this study and the age of the children ranged from premature neonates to adolescents (30 observations across the age groups). The age-dependent exponent (ADE) model was used to predict the clearance of drugs in children from adults following DDI studies.

Results

The prediction error of drug clearances following DDIs in children ranged from 4 to 67%. Of 30 observations, 17 (57%) and 27 (90%) observations had a prediction error ≤ 30% and ≤ 50%, respectively.

Conclusion

This study indicates that it is possible to predict the clearance of drugs with reasonable accuracy in children from adults following DDI studies using an ADE model. The method is simple, robust, and reliable and can replace other complex empirical models.

Extrapolation of Drug Clearance in Children ≤ 2 Years of Age from Empirical Models Using Data from Children (> 2 Years) and Adults

BACKGROUND

The application of modeling and simulation approaches in clinical pharmacology studies has gained momentum over the last 20 years.

OBJECTIVES

The objective of this study was to develop six empirical models from clearance data obtained from children aged > 2 years and adults to evaluate the suitability of the models to predict drug clearance in children aged ≤ 2 years (preterm, term, and infants).

METHODS

Ten drugs were included in this study and administered intravenously: alfentanil, amikacin, busulfan, cefetamet, meperidine, oxycodone, propofol, sufentanil, theophylline, and tobramycin. These drugs were selected according to the availability of individual subjects' weight, age, and clearance data (concentration-time data for these drugs were not available to the author). The chosen drugs are eliminated by extensive metabolism by either the renal route or both the renal and hepatic routes. The six empirical models were (1) age and body weight-dependent sigmoidal maximum possible effect (E_max) maturation model, (2) body weight-dependent sigmoidal E_max model, (3) uridine 5'-diphospho [body weight-dependent allometric exponent model (BDE)], (4) age-dependent allometric exponent model (ADE), (5) a semi-physiological model, and (6) an allometric model developed from children aged > 2 years to adults. The model-predicted clearance values were compared with observed clearance values in an individual child. In this analysis, a prediction error of ≤ 50% for mean or individual clearance values was considered acceptable.

RESULTS

Across all age groups and the ten drugs, data for 282 children were compared between observed and model-predicted clearance values. The validation data consisted of 33 observations (sum of different age groups for ten drugs). Only three of the six models (body weight-dependent sigmoidal E_max model, ADE, and semi-physiological model) provided reasonably accurate predictions of clearance (> 80% observation with ≤ 50% prediction error) in children aged ≤ 2 years. In most instances, individual predicted clearance values were erratic (as indicated by % error) and were not in agreement with the observed clearance values.

CONCLUSIONS

The study indicated that simple empirical models can provide more accurate results than complex empirical models.

Prediction of Clearance of Monoclonal and Polyclonal Antibodies and Non-Antibody Proteins in Children: Application of Allometric Scaling

Allometric scaling can be used for the extrapolation of pharmacokinetic parameters from adults to children. The objective of this study was to predict clearance of therapeutic proteins (monoclonal and polyclonal antibodies and non-antibody proteins) allometrically in preterm neonates to adolescents. There were 13 monoclonal antibodies, seven polyclonal antibodies, and nine therapeutic proteins (non-antibodies) in the study. The clearance of therapeutic proteins was predicted using the age dependent exponents (ADE) model and then compared with the observed clearance values. There were in total 29 therapeutic proteins in this study with 75 observations. The number of observations with ≤30%, ≤50%, and >50% prediction error was 60 (80%), 72 (96%), and 3 (4%), respectively. Overall, the predicted clearance values of therapeutic proteins in children was good. The allometric method proposed in this manuscript can be used to select first-in-pediatric dose of therapeutic proteins in pediatric clinical trials.

The Ontogeny of UDP-glucuronosyltransferase Enzymes, Recommendations for Future Profiling Studies and Application Through Physiologically Based Pharmacokinetic Modelling

Limited understanding of drug pharmacokinetics in children is one of the major challenges in paediatric drug development. This is most critical in neonates and infants owing to rapid changes in physiological functions, especially in the activity of drug-metabolising enzymes. Paediatric physiologically based pharmacokinetic models that integrate ontogeny functions for cytochrome P450 enzymes have aided our understanding of drug exposure in children, including those under the age of 2 years. Paediatric physiologically based pharmacokinetic models have consequently been recognised by the European Medicines Agency and the US Food and Drug Administration as innovative tools in paediatric drug development and regulatory decision making. However, little is currently known about age-related changes in UDP-glucuronosyltransferase-mediated metabolism, which represents the most important conjugation reaction for xenobiotics. Therefore, the objective of the review was to conduct a thorough literature survey to summarise our current understanding of age-related changes in UDP-glucuronosyltransferases as well as associated clinical and experimental sources of variance. Our findings indicate that there are distinct differences in UDP-glucuronosyltransferase expression and activity between isoforms for different age groups. In addition, there is substantial variability between individuals and laboratories reported for human liver microsomes, which results in part from a lack of standardised experimental conditions. Therefore, we provide a number of best practice recommendations for experimental conditions, which ultimately may help improve the quality of data used for quantitative clinical pharmacology approaches, and thus for safe and effective pharmacotherapy in children.

Age- and Bodyweight-dependent Allometric Exponent Model for Scaling Clearance and Maintenance Dose of Theophylline From Neonates to Adults

BACKGROUND

In population pharmacokinetic modeling, bodyweight is often incorporated as an important covariate using fixed (0.75) or single-exponent model. In recent years, several variations of allometric models have been suggested for the prediction of drug clearance across a wide age range. The objective of this study is to develop and evaluate single-exponent, bodyweight-dependent allometric exponent (BDE), age-dependent exponent (ADE), and segmented regression models for predicting clearance and maintenance dose of theophylline.

METHODS

The BDE model was described by the following equation: (Equation is included in full-text article.), where L × BW defines the BDE for clearance. The coefficient and the exponents L and M were estimated. The ADE model consisted of several empirical exponents based on age and ranged from 0.75 (children >5 years and adults) to 1.2 (premature neonates). Data for model development and validation were based on 52 subjects each.

RESULTS

All structural and statistical parameters were estimated with acceptable precision for single-exponent and BDE models (<30%); however, the BDE model was superior in describing theophylline clearance across a wide age range for the training data. The segmented regression model on log-transformed data also adequately described theophylline clearance. When models were evaluated with validation data, a single-exponent model overpredicted clearance and dosing rate in premature neonates and adults with a mean prediction error of ≥50%. For premature neonates and adults, mean clearance and dosing rate were predicted within a 30% prediction error using the BDE, ADE, and segmented models.

CONCLUSIONS

This study demonstrates that the BDE, ADE, and segmented models performed better than a single-exponent model for predicting clearance and dose of theophylline across a wide age range.

A Retrospective Evaluation of Allometry, Population Pharmacokinetics, and Physiologically-Based Pharmacokinetics for Pediatric Dosing Using Clearance as a Surrogate

Physiologically-based pharmacokinetic models are increasingly applied for pediatric dose selection along with traditional methods such as allometry and population pharmacokinetic models. We report a retrospective evaluation of the three methods. Pediatric population pharmacokinetic models sourced from literature for a subset of eight compounds were used to predict clearances for children < 2 years when they were within the modeled age range (interpolation, N = 11) or including those outside the modeled age range (interpolation and extrapolation, N = 18). Pediatric/adult clearance ratios were evaluated with a strict performance criterion of 0.8-1.25 and with twofold criteria. For children > 2 years, 58-75% of the clinical studies (N = 10) met the strict criteria, and > 80% of the clinical studies were predicted within twofold by all three methods. For children < 2 years, physiologically-based pharmacokinetic, allometry with age-dependent exponents, and pediatric population pharmacokinetic models predict 54%, 82%, and 64% within twofold of the observed, respectively.

Scaling Drug Clearance from Adults to the Young Children for Drugs Undergoing Hepatic Metabolism: A Simulation Study to Search for the Simplest Scaling Method

Previous research showed that scaling drug clearance from adults to children based on body weight alone is not accurate for all hepatically cleared drugs in very young children. This study systematically assesses the accuracy of scaling methods that, in addition to body weight, also take age-based variables into account for drugs undergoing hepatic metabolism in children younger than five years, namely scaling with (1) a body weight-based function using an age-dependent exponent (ADE) and (2) a body weight-based function with fixed exponent of 0.75 (AS0.75) combined with isoenzyme maturation functions (MFPBPK) similar to those implemented in physiologically based pharmacokinetic (PBPK) models (AS0.75 + MFPBPK). A PBPK-based simulation workflow was used, including hypothetical drugs with a wide range of properties and metabolized by different isoenzymes. Adult clearance values were scaled to seven typical children between one day and four years. Prediction errors of ± 50% were considered reasonably accurate. Isoenzyme maturation was found to be an important driver of changes in hepatic metabolic clearance in children younger than five years, which prevents the systematic accuracy of ADE scaling. AS0.75 + MFPBPK, when accounting for maturation of isoenzymes and microsomal protein per gram of liver (MPPGL), can reasonably accurately scale hepatic metabolic clearance for all low and intermediate extraction ratio drugs except for drugs binding to alpha-1-acid glycoprotein in neonates. As differences in the impact of changes in system-specific parameters on drugs with different properties yield differences in clearance ontogeny, it is unlikely that for the remaining drugs, scaling methods that do not take drug properties into account will be systematically accurate.

Initiation of Pediatric Clinical Trials for Coagulation Factors: Application of Pharmacokinetics and Allometry to First-in-Pediatric Dose Selection

Allometric scaling is a method that can be used for the extrapolation of pharmacokinetic parameters from adults to children. Subsequently, these allometrically predicted PK parameters can be used to project a suitable dose to initiate a pediatric clinical trial. The objective of this study was to predict clearance and in vivo recovery of coagulation factors VIII and IX allometrically in children from 1 year of age to adolescents and then project the first-in-children dose. The values for observed clearance, in vivo recovery, and dose for pediatric as well as adult subjects for 7 factor VIII and 3 factor IX products were obtained from the US Food and Drug Administration package insert and the allometrically predicted clearance, in vivo recovery, and dose were compared with the observed values. The clearance of factors VIII and IX were predicted with reasonable accuracy. Of 27 predicted clearance values, all 27 (100%) were predicted with ≤30% prediction error (range, 5%-28%). Of 27 predicted in vivo recovery values, 26 (96%) were predicted with ≤30% prediction error (range, 0%-45%). The projected doses of factor VIII or IX, either by clearance or in vivo recovery, were generally within the recommended dose range indicating an accurate prediction of pediatric dose for both factors. The proposed allometric methods can be used to select first-in-children dose in pediatric clinical trials.

Population Pharmacokinetics of Intravenous Linezolid in Premature Infants

BACKGROUND

The emergence of coagulase-negative staphylococci with reduced vancomycin susceptibility in some neonatal intensive care units has resulted in an increase of linezolid use. Linezolid pharmacokinetics (PK) and safety in premature infants still need to be better established.

METHODS

This was a retrospective PK study. All infants who received intravenous linezolid and had linezolid plasma concentrations per standard of care were included. Linezolid concentrations were measured by high performance liquid chromatography. A population PK model was developed using nonlinear mixed effects modeling. Optimal dosing was determined based on achievement of the surrogate pharmacodynamics target for efficacy: a ratio of the area under the concentration-time curve to minimum inhibitory concentration >80. We assessed the occurrence of thrombocytopenia and lactic acidosis in relation with drug exposure.

RESULTS

A total of 78 plasma concentrations were collected from 26 infants, with a median postnatal age (PNA) of 24 days (8-88) and weight of 1423 g (810-3256). A 1-compartment model described linezolid data well. The final model included PNA and weight on clearance and weight on volume of distribution. Considering an MIC90 of 1 mg/L, all infants reached an area under the concentration-time curve/minimum inhibitory concentration > 80. Although thrombocytopenia and hyperlactatemia occurred frequently, they were not sustained and were not considered related to linezolid.

CONCLUSION

and was well tolerated in critically ill premature infants. PNA was the main determinant of clearance.

Misconceptions and issues regarding allometric scaling during the drug development process

INTRODUCTION

Allometry is the study of size and its consequences. The simple hypothesis of allometric scaling is that all physiological parameters are proportional to body size or body mass. This review examines the development of theory-based allometry or fixed exponents (0.75 and 1.0 for basal metabolic rate and volume, respectively) and the evidence for or against the theory. The main focus of this report is to show the readers that there is enough evidence from experimental data that negate the concept of theory-based allometry in biology, physiology, and pharmacokinetics. Areas covered: In this review, the history of the development of theoretical allometry and the strong evidence against theory-based allometry demonstrated by experimental data is provided. During drug development, allometry is applied to both inter-species (from animals to humans) and intra-species (adults to children) scaling. These two forms of allometric scaling in the context of theory-based allometry are discussed and provide insight on scientific progress that refute theory-based allometry. Expert opinion: Theory-based allometry is a mere theory and experimental data and real-life observations strongly negate the existence of such a theory. Pharmacostatistical and physiological models based on theory-based allometry can be misleading and incorrect because the theory-based allometric exponent 0.75 is not universal. The exponents of allometry are data dependent and are not fixed in the universe.

Prediction of Clearance, Volume of distribution, and Half-life of Drugs in Extremely Low to Low Birth Weight Neonates: An Allometric Approach

BACKGROUND AND OBJECTIVES

More than 20 million infants worldwide (15.5 % of all births) are born with low birth weight. Low birth weight is associated with poor growth in childhood and a higher incidence of adult diseases, such as type 2 diabetes, hypertension and cardiovascular disease. The objective of this study was to evaluate the predictive performance of allometric models to predict clearance, volume of distribution, and half-life in extremely low to low birth weight neonates (<1 to 2.5 kg body weight).

METHODS

Several allometric models were used to predict clearance (4 models), volume of distribution (2 models), and half-life (2 models) in extremely low to low birth weight neonates. From the literature, clearance, volume of distribution, and half-life values for 16 drugs for these neonates were obtained. The predictive performance of these allometric models was evaluated by comparing the predicted values of the aforementioned pharmacokinetic parameters with the observed pharmacokinetic parameters in an individual neonate. For the evaluation of the predictive performance of these allometric models, there were 16 drugs with 36 (n = 279), 34 (n = 261), and 31 (n = 197) weight groups for clearance, volume of distribution, and half-life, respectively.

RESULTS

The prediction error of ≤50 % for mean clearance, volume of distribution, and half-life values were 69, 79, and 58 %, respectively, by proposed allometric models. The prediction error of ≤50 % for mean clearance, volume of distribution, and half-life values by theoretical allometric exponents were 0 % (exponent = 0.75), 71 % (exponent = 1.0), and 0 % (exponent = 0.25), respectively. In this analysis, out of 16 drugs, there were three drugs (ibuprofen, zidovudine, and buprenorphine) which are metabolized by glucuronidation and one drug (furosemide) is renally secreted. The predicted clearances of these four drugs were substantially higher by the proposed allometric methods. It seems that drugs of these physiological characteristics may require different method(s) to improve the prediction of clearance in the neonates.

CONCLUSIONS

Overall, the proposed allometric methods can predict mean pharmacokinetic parameters of drugs in extremely low to low birth weight neonates with reasonable accuracy and are of practical value during neonatal drug development.

Multistep Unified Models Using Prior Knowledge for the Prediction of Drug Clearance in Neonates and Infants

Allometric approaches are widely used for interspecies scaling for the prediction of pharmacokinetic (PK) parameters during drug development. The concept of allometry can also be extended to predict PK parameters from adults to children. Three methods for extrapolating pediatric clearance were developed and evaluated using the clearance values of 4 drugs. The first method was established using a simple allometric (SA) model with estimated coefficient and exponent based on data ranging from children older than 2 years to adult. Then we developed a unified multistep single-exponent (MSE) and multistep body-weight-dependent exponent (MBDE) models. The major steps in these 2 new methods include generating pseudopredicted clearance for unobserved new populations such as preterm neonates, term neonates, and infants. Subsequent steps involve incorporating the pseudopredicted clearance with the actual PK data from older children and adults. All 3 models were then used to predict drug clearance in children ≤2 years old (N = 278). Drug clearance was predicted with mean absolute error of 29.6, 14.2, and 12.9 using SA, MSE, MBDE, respectively. The root mean square error was 65.9, 29.8, 24.7 for SA, MSE, MBDE, respectively. Approximately 41%, 72%, and 74% of the children's clearance data were within 0.5 to 1.5-fold of the observed values when drug clearance was extrapolated using SA, MSE, and MBDE models, respectively. The present multistep unified extrapolation approaches improved the prediction of clearance from preterm neonates to 2 years of age and may have practical use for first-in-pediatric dose selection.

Population Pharmacokinetics: Some Observations in Pediatric Modeling for Drug Clearance

The objective of this study is to evaluate the predictive performance of several models to predict drug clearance in preterm and term neonates. Five models using different types of allometric and linear models were developed. Two sets of data were used to develop these models (data from preterm neonates to adults and data from preterm and term neonates). Models were also developed with (normalized to 70 kg) or without body weight normalization (body weight 1 kg). From the literature, clearance values for four drugs from neonates to adults were obtained. External data were used to evaluate the predictive performance of these models in preterm and term neonates. The results of the study indicated that (1) normalization to a standard body weight had no impact on the predictive performance of the models, (2) the model developed from preterm neonates to adults using fixed exponent 0.75 provided inaccurate estimate (overestimation) of drug clearance in neonates, (3) a far superior prediction of clearance was observed with the model when the exponents of allometry were estimated than the model using exponent 0.75, (4) linear models with the exception of the model with intercept provided comparable results to the estimated exponent model and were superior in their predictive performance to the model using exponent 0.75, and (5) when the models were developed from neonate data, the predictive performance of all models were similar. Overall, the study indicated that body weight normalization had no impact on the performance of model prediction, the exponents of allometry in pharmacostatistical models should be estimated rather than fixed, and more studies are needed to evaluate the suitability of linear models for the prediction of drug clearance in neonates.

Allometric Scaling of Clearance in Paediatric Patients: When Does the Magic of 0.75 Fade?

Allometric scaling on the basis of bodyweight raised to the power of 0.75 (AS0.75) is frequently used to scale size-related changes in plasma clearance (CLp) from adults to children. A systematic assessment of its applicability is undertaken for scenarios considering size-related changes with and without maturation processes. A physiologically-based pharmacokinetic (PBPK) simulation workflow was developed in R for 12,620 hypothetical drugs. In scenario one, only size-related changes in liver weight, hepatic blood flow, and glomerular filtration were included in simulations of 'true' paediatric CLp. In a second scenario, maturation in unbound microsomal intrinsic clearance (CLint,mic), plasma protein concentration, and haematocrit were also included in these simulated 'true' paediatric CLp values. For both scenarios, the prediction error (PE) of AS0.75-based paediatric CLp predictions was assessed, while, for the first scenario, an allometric exponent was also estimated based on 'true' CLp. In the first scenario, the PE of AS0.75-based paediatric CLp predictions reached up to 278 % in neonates, and the allometric exponent was estimated to range from 0.50 to 1.20 depending on age and drug properties. In the second scenario, the PE sensitivity to drug properties and maturation was higher in the youngest children, with AS0.75 resulting in accurate CLp predictions above 5 years of age. Using PBPK principles, there is no evidence for one unique allometric exponent in paediatric patients, even in scenarios that only consider size-related changes. As PE is most sensitive to the allometric exponent, drug properties and maturation in younger children, AS0.75 leads to increasingly worse predictions with decreasing age.

Prediction of Human Glomerular Filtration Rate from Preterm Neonates to Adults: Evaluation of Predictive Performance of Several Empirical Models

The objective of this study was to evaluate the predictive performance of several allometric empirical models (body weight dependent, age dependent, fixed exponent 0.75, a data-dependent single exponent, and maturation models) to predict glomerular filtration rate (GFR) in preterm and term neonates, infants, children, and adults without any renal disease. In this analysis, the models were developed from GFR data obtained from inulin clearance (preterm neonates to adults; n = 93) and the predictive performance of these models were evaluated in 335 subjects (preterm neonates to adults). The primary end point was the prediction of GFR from the empirical allometric models and the comparison of the predicted GFR with measured GFR. A prediction error within ±30% was considered acceptable. Overall, the predictive performance of the four models (BDE, ADE, and two maturation models) for the prediction of mean GFR was good across all age groups but the prediction of GFR in individual healthy subjects especially in neonates and infants was erratic and may be clinically unacceptable.