Physiologically-Based Pharmacokinetic Modeling vs. Allometric Scaling for the Prediction of Infliximab Pharmacokinetics in Pediatric Patients

Recent Progress on Physiologically Based Pharmacokinetic (PBPK) Model: A Review Based on Bibliometrics

Physiologically based pharmacokinetic/toxicokinetic (PBPK/PBTK) models are designed to elucidate the mechanism of chemical compound action in organisms based on the physiological, biochemical, anatomical, and thermodynamic properties of organisms. After nearly a century of research and practice, good results have been achieved in the fields of medicine, environmental science, and ecology. However, there is currently a lack of a more systematic review of progress in the main research directions of PBPK models, especially a more comprehensive understanding of the application in aquatic environmental research. In this review, a total of 3974 articles related to PBPK models from 1996 to 24 March 2024 were collected. Then, the main research areas of the PBPK model were categorized based on the keyword co-occurrence maps and cluster maps obtained by CiteSpace. The results showed that research related to medicine is the main application area of PBPK. Four major research directions included in the medical field were "drug assessment", "cross-species prediction", "drug-drug interactions", and "pediatrics and pregnancy drug development", in which "drug assessment" accounted for 55% of the total publication volume. In addition, bibliometric analyses indicated a rapid growth trend in the application in the field of environmental research, especially in predicting the residual levels in organisms and revealing the relationship between internal and external exposure. Despite facing the limitation of insufficient species-specific parameters, the PBPK model is still an effective tool for improving the understanding of chemical-biological effectiveness and will provide a theoretical basis for accurately assessing potential risks to ecosystems and human health. The combination with the quantitative structure-activity relationship model, Bayesian method, and machine learning technology are potential solutions to the previous research gaps.

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.

Physiologically-Based Pharmacokinetic Modeling of Anti-Tumor Necrosis Factor Agents for Inflammatory Bowel Disease Patients to Predict the Withdrawal Time in Pregnancy and Vaccine Time in Infants

Anti-tumor necrosis factor (anti-TNF) agents are widely applied for patients with inflammatory bowel disease (IBD); however, the timing of the last dosing for IBD pregnancy and time to elimination in anti-TNF agent-exposed infants is controversial. This study aimed to determine the optimal timing for the last dosing of anti-TNF agents (infliximab, adalimumab, and golimumab) in pregnant women with IBD, as well as to investigate the recommended vaccine schedules for infants exposed to these drugs. A physiologically-based pharmacokinetic (PBPK) model of anti-TNF agents was built for adults and extrapolated to pregnant patients, fetuses, and infants. The PBPK models successfully predicted and verified the pharmacokinetics (PKs) of infliximab, adalimumab, and golimumab in pregnancy, fetuses, and infants. The predicted PK data were within two-fold of the observed data. The simulated results were used as timing advice. According to the dose of administration, the suggested timing of the last dosing for infliximab, adalimumab, and golimumab is successfully provided based on PBPK predictions. PBPK models indicated that, for infants, the advocated timing of vaccination is 12, 8, and 5 months after birth for infliximab, adalimumab, and golimumab, respectively. Our study illustrated that PBPK models can provide a valuable tool to predict the PKs of large macromolecules in pregnant women, fetuses, and infants, ultimately informing drug-treatment decisions for pregnancy and vaccination regimens for infants.

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.

Application of Modelling and Simulation Approaches to Predict Pharmacokinetics of Therapeutic Monoclonal Antibodies in Pediatric Population

Ethical regulations and limited paediatric participants are key challenges that contribute to a median delay of 6 years in paediatric mAb approval. To overcome these barriers, modelling and simulation methodologies have been adopted to design optimized paediatric clinical studies and reduce patient burden. The classical modelling approach in paediatric pharmacokinetic studies for regulatory submissions is to apply body weight-based or body surface area-based allometric scaling to adult PK parameters derived from a popPK model to inform the paediatric dosing regimen. However, this approach is limited in its ability to account for the rapidly changing physiology in paediatrics, especially in younger infants. To overcome this limitation, PBPK modelling, which accounts for the ontogeny of key physiological processes in paediatrics, is emerging as an alternative modelling strategy. While only a few mAb PBPK models have been published, PBPK modelling shows great promise demonstrating a similar prediction accuracy to popPK modelling in an Infliximab paediatric case study. To facilitate future PBPK studies, this review consolidated comprehensive data on the ontogeny of key physiological processes in paediatric mAb disposition. To conclude, this review discussed different use-cases for pop-PK and PBPK modelling and how they can complement each other to increase confidence in pharmacokinetic predictions.

Physiologically-Based Pharmacokinetic Modeling of Omalizumab to Predict the Pharmacokinetics and Pharmacodynamics in Pediatric Patients

Omalizumab is widely used in clinical practice; however, knowledge gaps in the dosage of omalizumab for children aged 2-6 years with moderate-to-severe persistent allergic asthma have been identified. The aim of this study was to explore dosing regimens for moderately-to-severely allergic pediatric patients aged 2-6 years. The physiologically-based pharmacokinetic (PBPK) model of omalizumab was developed and verified in adult patients, extrapolated to pediatric patients, and simulated for omalizumab by adding two observation chambers (free IgE and total IgE). The simulation results showed that the fold errors of the predicted and observed values of the area under the curve (AUC) and peak plasma concentration (C_max ) were between 0.5 and 2.0, and the average folding error and the absolute average folding error values for all concentration-time data points were 1.09 and 1.48, respectively. The PBPK model combined with pharmacokinetic/pharmacodynamic analysis of omalizumab demonstrated that both the model-derived dose and the original dose could control the average free IgE of 2-6-year-old children with moderate-to-severe allergic asthma below 25 ng/mL, and some of the model-derived doses were lower. This conclusion provides a basis for the selection of dosage in clinical practice reference.

The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

Early Infliximab Clearance Predicts Remission in Children with Crohn's Disease

BACKGROUND AND AIMS

Children with Crohn's disease have lower response rates to infliximab, lower infliximab levels, and higher infliximab clearance on weight-based dosing than adults. We hypothesize infliximab clearance is a predictive of later outcomes on infliximab in children with Crohn's disease.

METHODS

In this single-center retrospective study, data were collected from charts on diagnosis, anthropometry, routine labs, infliximab therapeutic drug monitoring, infliximab dosing, disease activity, and other treatments. With these data we generated a population pharmacokinetic model using non-linear mixed effects modeling and calculated infliximab clearance for each patient over time. Patients were classified as in remission, responder-only or non-responder at 5, 10 and 16 months. Regression and ROC analyses were used to assess for early predictors of remission and response to infliximab.

RESULTS

Eighty-five subjects were included, with a median follow-up of 22.3 months (IQR 10.1-36.8). Our pharmacokinetic model showed infliximab clearance was positively associated with CRP and weight, while negatively associated with albumin. In regression analyses, early infliximab clearance was the only significant, consistent predictor of remission. A 0.1 L/day increase in infliximab clearance predicted remission with an OR between 0.179 and 0.426. Differences in dosing did not account for differences in outcome. Infliximab clearance alone had moderate predictive accuracy of remission, with an AUC between 0.682 and 0.738.

CONCLUSIONS

Early infliximab clearance is strongly associated with remission in children with Crohn's disease. It may be useful as a marker of response in proactive therapeutic drug monitoring to guide early dose optimization and/or changes in treatment for betterment of long-term outcomes.

Interspecies and in vitro-in vivo scaling for quantitative modeling of whole-body drug pharmacokinetics in patients: Application to the anticancer drug oxaliplatin

Quantitative systems pharmacology holds the promises of integrating results from laboratory animals or in vitro human systems into the design of human pharmacokinetic/pharmacodynamic (PK/PD) models allowing for precision and personalized medicine. However, reliable and general in vitro-to-in vivo extrapolation and interspecies scaling methods are still lacking. Here, we developed a translational strategy for the anticancer drug oxaliplatin. Using ex vivo PK data in the whole blood of the mouse, rat, and human, a model representing the amount of platinum (Pt) in the plasma and in the red blood cells was designed and could faithfully fit each dataset independently. A "purely physiologically-based (PB)" scaling approach solely based on preclinical data failed to reproduce human observations, which were then included in the calibration. Investigating approaches in which one parameter was set as species-specific, whereas the others were computed by PB scaling laws, we concluded that allowing the Pt binding rate to plasma proteins to be species-specific permitted to closely fit all data, and guaranteed parameter identifiability. Such a strategy presenting the drawback of including all clinical datasets, we further identified a minimal subset of human data ensuring accurate model calibration. Next, a "whole body" model of oxaliplatin human PK was inferred from the ex vivo study. Its three remaining parameters were estimated, using one third of the available patient data. Remarkably, the model achieved a good fit to the training dataset and successfully reproduced the unseen observations. Such validation endorsed the legitimacy of our scaling methodology calling for its testing with other drugs.

General clinical and methodological considerations on the extrapolation of pharmacokinetics and optimization of study protocols for small molecules and monoclonal antibodies in children

Pharmacometric modelling plays a key role in both the design and analysis of regulatory trials in paediatric drug development. Studies in adults provide a rich source of data to inform the paediatric investigation plans, including knowledge on drug pharmacokinetics (PK), safety and efficacy. In children, drug disposition differs widely from birth to adolescence but extrapolating adult to paediatric PK, safety and efficacy either with pharmacometric or physiologically based approaches can help design or in some cases reduce the need for clinical studies. Aspects to consider when extrapolating PK include the maturation of drug metabolizing enzyme expression, glomerular filtration, drug excretory systems, and the expression and activity of specific transporters in conjunction with other drug properties such as fraction unbound. Knowledge of these can be used to develop extrapolation tools such as allometric scaling plus maturation functions or physiologically based PK. PK/pharmacodynamic approaches and well-designed clinical trials in children are of key importance in paediatric drug development. In this white paper, state-of-the-art of current methods used for paediatric extrapolation will be discussed. This paper is part of a conect4children implementation of innovative methodologies including pharmacometric and physiologically based PK modelling in clinical trial design/paediatric drug development through dissemination of expertise and expert advice. The suggestions arising from this white paper should define a minimum set of standards in paediatric modelling and contribute to the regulatory science.

Application of physiologically-based pharmacokinetic models for therapeutic proteins and other novel modalities

The past two decades have seen diversification of drug development pipelines and approvals from traditional small molecule therapies to alternative modalities including monoclonal antibodies, engineered proteins, antibody drug conjugates (ADCs), oligonucleotides and gene therapies. At the same time, physiologically-based pharmacokinetic (PBPK) models for small molecules have seen increased industry and regulatory acceptance.This review focusses on the current status of the application of PBPK models to these newer modalities and give a perspective on the successes, challenges and future directions of this field.There is greatest experience in the development of PBPK models for therapeutic proteins, and PBPK models for ADCs benefit from prior experience for both therapeutic proteins and small molecules. For other modalities, the application of PBPK models is in its infancy.Challenges are discussed and a common theme is lack of availability of physiological and experimental data to characterise systems and drug parameters to enable a priori prediction of pharmacokinetics. Furthermore, sufficient clinical data are required to build confidence in developed models.The PBPK modelling approach provides a quantitative framework for integrating knowledge and data from multiple sources and can be built on as more data becomes available.

Physiologically-based pharmacokinetic modeling of immunoglobulin and antibody coadministration in patients with primary human immunodeficiency

Intravenous immunoglobulin (IVIG) (2000 mg/kg) increased the clearance of the mouse monoclonal antibody 7E3, directed against platelet integrin IIb/IIIa (alpha IIb beta 3, CD41/CD61) in rodents. We wanted to investigate the effect of IVIG on clearance of monoclonal antibodies in humans as there is extremely limited data regarding this interaction in the literature. Using the tyrosine protein kinase KIT anti-cluster of differentiation 117 (c-Kit) humanized monoclonal antibody (JSP191) as a case study, we used physiologically-based pharmacokinetic (PBPK) modeling to evaluate the pharmacokinetic interaction between monoclonal antibodies and IVIG at doses (300-600 mg/kg) administered to patients with primary human immunodeficiency (PI). We first characterized the interaction between monoclonal antibodies and IVIG in PK-Sim®/MoBi® using published literature data, including the following: IVIG plus 7E3 in mice and rats and IVIG plus the human anti-C5 monoclonal antibody tesidolumab in adults with end-stage renal disease. We next developed a PBPK model using digitized data for JSPI91 alone in older adults with myelodysplastic syndrome and acute myeloid leukemia and in pediatric patients with severe combined immunodeficiency (SCID). Finally, we simulated the impact of IVIG (300-2000 mg/kg) coadministration with JSP191 on the area under the curve of JSP191 in patients with SCID. Model predictions were within 1.5-fold of observed values for 7E3 plus IVIG and tesidolumab plus IVIG as well as for JSP191 administered alone. Based on our simulations, IVIG doses ≥500 mg exceeded the 80%-125% no-effect boundaries. IVIG treatment with monoclonal antibodies in patients with PI may result in a clinically significant interaction depending on the IVIG dose administered and the exposure-response relationship for the specific monoclonal antibody.

Development of a pediatric physiologically-based pharmacokinetic model to support recommended dosing of atezolizumab in children with solid tumors

Background: Atezolizumab has been studied in multiple indications for both pediatric and adult patient populations. Generally, clinical studies enrolling pediatric patients may not collect sufficient pharmacokinetic data to characterize the drug exposure and disposition because of operational, ethical, and logistical challenges including burden to children and blood sample volume limitations. Therefore, mechanistic modeling and simulation may serve as a tool to predict and understand the drug exposure in pediatric patients.

Objective: To use mechanistic physiologically-based pharmacokinetic (PBPK) modeling to predict atezolizumab exposure at a dose of 15 mg/kg (max 1,200 mg) in pediatric patients to support dose rationalization and label recommendations.

Methods: A minimal mechanistic PBPK model was used which incorporated age-dependent changes in physiology and biochemistry that are related to atezolizumab disposition such as endogenous IgG concentration and lymph flow. The PBPK model was developed using both in vitro data and clinically observed data in adults and was verified across dose levels obtained from a phase I and multiple phase III studies in both pediatric patients and adults. The verified model was then used to generate PK predictions for pediatric and adult subjects ranging from 2- to 29-year-old.

Results: Individualized verification in children and in adults showed that the simulated concentrations of atezolizumab were comparable (76% within two-fold and 90% within three-fold, respectively) to the observed data with no bias for either over- or under-prediction. Applying the verified model, the predicted exposure metrics including C_min, C_max, and AUC_tau were consistent between pediatric and adult patients with a geometric mean of pediatric exposure metrics between 0.8- to 1.25-fold of the values in adults.

Conclusion: The results show that a 15 mg/kg (max 1,200 mg) atezolizumab dose administered intravenously in pediatric patients provides comparable atezolizumab exposure to a dose of 1,200 mg in adults. This suggests that a dose of 15 mg/kg will provide adequate and effective atezolizumab exposure in pediatric patients from 2- to 18-year-old.

Opportunities and Challenges for PBPK Model of mAbs in Paediatrics and Pregnancy

New drugs may in some cases need to be tested in paediatric and pregnant patients. However, it is difficult to recruit such patients and there are many ethical issues around their inclusion in clinical trials. Modelling and simulation can help to plan well-designed clinical trials with a reduced number of participants and to bridge gaps where recruitment is difficult. Physiologically based pharmacokinetic (PBPK) models for small molecule drugs have been used to aid study design and dose adjustments in paediatrics and pregnancy, with several publications in the literature. However, published PBPK models for monoclonal antibodies (mAb) in these populations are scarce. Here, the current status of mAb PBPK models in paediatrics and pregnancy is discussed. Seven mAb PBPK models published for paediatrics were found, which report good prediction accuracy across a wide age range. No mAb PBPK models for pregnant women have been published to date. Current challenges to the development of such PBPK models are discussed, including gaps in our knowledge of relevant physiological processes and availability of clinical data to verify models. As the availability of such data increases, it will help to improve our confidence in the PBPK model predictive ability. Advantages for using PBPK models to predict mAb PK in paediatrics and pregnancy are discussed. For example, the ability to incorporate ontogeny and gestational changes in physiology, prediction of maternal, placental and foetal exposure and the ability to make predictions from in vitro and preclinical data prior to clinical data being available.

Characterizing Pharmacokinetics in Children With Obesity-Physiological, Drug, Patient, and Methodological Considerations

Childhood obesity is an alarming public health problem. The pediatric obesity rate has quadrupled in the past 30 years, and currently nearly 20% of United States children and 9% of children worldwide are classified as obese. Drug distribution and elimination processes, which determine drug exposure (and thus dosing), can vary significantly between patients with and without obesity. Obesity-related physiological changes, such as increased tissue volume and perfusion, altered blood protein concentrations, and tissue composition can greatly affect a drug's volume of distribution, which might necessitate adjustment in loading doses. Obesity-related changes in the drug eliminating organs, such as altered enzyme activity in the liver and glomerular filtration rate, can affect the rate of drug elimination, which may warrant an adjustment in the maintenance dosing rate. Although weight-based dosing (i.e., in mg/kg) is commonly practiced in pediatrics, choice of the right body size metric (e.g., total body weight, lean body weight, body surface area, etc.) for dosing children with obesity still remains a question. To address this gap, the interplay between obesity-related physiological changes (e.g., altered organ size, composition, and function), and drug-specific properties (e.g., lipophilicity and elimination pathway) needs to be characterized in a quantitative framework. Additionally, methodological considerations, such as adequate sample size and optimal sampling scheme, should also be considered to ensure accurate and precise top-down covariate selection, particularly when designing opportunistic studies in pediatric drug development. Further factors affecting dosing, including existing dosing recommendations, target therapeutic ranges, dose capping, and formulations constraints, are also important to consider when undergoing dose selection for children with obesity. Opportunities to bridge the dosing knowledge gap in children with obesity include modeling and simulating techniques (i.e., population pharmacokinetic and physiologically-based pharmacokinetic [PBPK] modeling), opportunistic clinical data, and real world data. In this review, key considerations related to physiology, drug parameters, patient factors, and methodology that need to be accounted for while studying the influence of obesity on pharmacokinetics in children are highlighted and discussed. Future studies will need to leverage these modeling opportunities to better describe drug exposure in children with obesity as the childhood obesity epidemic continues.

Physiologically-based pharmacokinetic modeling of nafamostat to support dose selection for treatment of pediatric patients with COVID-19

Pediatric patients with coronavirus disease 2019 (COVID-19) are increasing, and severe cases such as multisystem inflammatory syndrome are being reported. Nafamostat, a repurposing drug, is currently being explored for the treatment of COVID-19 in adults. However, the data supporting its exposure in pediatrics remains scarce. Physiologically-based pharmacokinetic (PBPK) modeling enables the prediction of drug exposure in pediatrics based on ontogeny of metabolic enzymes and age dependent anatomical and physiological changes. The study aimed to establish a PBPK model of nafamostat in adults, then scale the adult PBPK model to children for predicting pediatric exposures of nafamostat and an optimal weight-based nafamostat dose in pediatric population. The developed model adequately described adult exposure data in healthy volunteers following i.v. administration with three doses (10, 20, and 40 mg). Scaling adult PBPK models to five pediatric groups predicted that as age advances from neonate to adult, the exposure of nafamostat slightly increased from neonate to infant, steadily decreased from infant to child, and then increased from child to adult after the administration of 0.2 mg/kg/h for 14 days, a dosing regimen being conducted in a clinical trial for COVID-19. Based on the fold change of predicted area under the curve for the respective pediatric group over those of adults, weight-based dosages for each pediatric group may be suggested. The novel PBPK model described in this study may be useful to investigate nafamostat pharmacokinetics in a pediatric subgroup further.

Knowledge gaps in late-onset neonatal sepsis in preterm neonates: a roadmap for future research

Late-onset neonatal sepsis (LONS) remains an important threat to the health of preterm neonates in the neonatal intensive care unit. Strategies to optimize care for preterm neonates with LONS are likely to improve survival and long-term neurocognitive outcomes. However, many important questions on how to improve the prevention, early detection, and therapy for LONS in preterm neonates remain unanswered. This review identifies important knowledge gaps in the management of LONS and describe possible methods and technologies that can be used to resolve these knowledge gaps. The availability of computational medicine and hypothesis-free-omics approaches give way to building bedside feedback tools to guide clinicians in personalized management of LONS. Despite advances in technology, implementation in clinical practice is largely lacking although such tools would help clinicians to optimize many aspects of the management of LONS. We outline which steps are needed to get possible research findings implemented on the neonatal intensive care unit and provide a roadmap for future research initiatives. IMPACT: This review identifies knowledge gaps in prevention, early detection, antibiotic, and additional therapy of late-onset neonatal sepsis in preterm neonates and provides a roadmap for future research efforts. Research opportunities are addressed, which could provide the means to fill knowledge gaps and the steps that need to be made before possible clinical use. Methods to personalize medicine and technologies feasible for bedside clinical use are described.

A population physiologically-based pharmacokinetic model to characterize antibody disposition in pediatrics and evaluation of the model using infliximab

AIMS

In order to better predict the pharmacokinetics (PK) of antibodies in children, and to facilitate dose optimization of antibodies in paediatric patients, there is a need to develop systems PK models that integrate ontogeny-related changes in human physiological parameters.

METHODS

A population-based physiological-based PK (PBPK) model to characterize antibody PK in paediatrics has been developed, by incorporating age-related changes in body weight, organ weight, organ blood flow rate and interstitial volumes in a previously published platform model. The model was further used to perform Monte Carlo simulations to investigate clearance vs. age and dose-exposure relationships for infliximab.

RESULTS

By estimating only one parameter and associated interindividual variability, the model was able to characterize clinical PK of infliximab from two paediatric cohorts (n = 141, 4-19 years) reasonably well. Model simulations demonstrated that only 50% of children reached desired trough concentrations when receiving FDA-labelled dosing regimen for infliximab, suggesting that higher doses and/or more frequent dosing are needed to achieve target trough concentrations of this antibody.

CONCLUSION

The paediatric PBPK model presented here can serve as a framework to characterize the PK of antibodies in paediatric patients. The model can also be applied to other protein therapeutics to advance precision medicine paradigm and optimize antibody dosing regimens in children.

A Quantitative Modeling and Simulation Framework to Support Candidate and Dose Selection of Anti-SARS-CoV-2 Monoclonal antibodies to Advance Bamlanivimab into a First-in-Human Clinical Trial

Neutralizing monoclonal antibodies (mAb), novel therapeutics for the treatment of coronavirus disease 2019 (COVID‐19) caused by severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2), have been urgently researched from the start of the pandemic. The selection of the optimal mAb candidate and therapeutic dose were expedited using open‐access in silico models. The maximally effective therapeutic mAb dose was determined through two approaches; both expanded on innovative, open‐science initiatives. A physiologically‐based pharmacokinetic (PBPK) model, incorporating physicochemical properties predictive of mAb clearance and tissue distribution, was used to estimate mAb exposure that maintained concentrations above 90% inhibitory concentration of in vitro neutralization in lung tissue for up to 4 weeks in 90% of patients. To achieve fastest viral clearance following onset of symptoms, a longitudinal SARS‐CoV‐2 viral dynamic model was applied to estimate viral clearance as a function of drug concentration and dose. The PBPK model‐based approach suggested that a clinical dose between 175 and 500 mg of bamlanivimab would maintain target mAb concentrations in the lung tissue over 28 days in 90% of patients. The viral dynamic model suggested a 700 mg dose would achieve maximum viral elimination. Taken together, the first‐in‐human trial (NCT04411628) conservatively proceeded with a starting therapeutic dose of 700 mg and escalated to higher doses to evaluate the upper limit of safety and tolerability. Availability of open‐access codes and application of novel in silico model‐based approaches supported the selection of bamlanivimab and identified the lowest dose evaluated in this study that was expected to result in the maximum therapeutic effect before the first‐in‐human clinical trial.

A Physiological Approach to Pharmacokinetics in Chronic Kidney Disease

The conventional approach to approximating the pharmacokinetics of drugs in patients with chronic kidney disease (CKD) only accounts for changes in the estimated glomerular filtration rate. However, CKD is a systemic and multifaceted disease that alters many body systems. Therefore, the objective of this exercise was to develop and evaluate a whole-body mechanistic approach to predicting pharmacokinetics in patients with CKD. Physiologically based pharmacokinetic models were developed in PK-Sim v8.0 (www.open-systems-pharmacology.org) to mechanistically represent the disposition of 7 compounds in healthy human adults. The 7 compounds selected were eliminated by glomerular filtration and active tubular secretion by the organic cation transport system to varying degrees. After a literature search, the healthy adult models were adapted to patients with CKD by numerically accounting for changes in glomerular filtration rate, kidney volume, renal perfusion, hematocrit, plasma protein concentrations, and gastrointestinal transit. Literature-informed interindividual variability was applied to the physiological parameters to facilitate a population approach. Model performance in CKD was evaluated against pharmacokinetic data from 8 clinical trials in the literature. Overall, integration of the CKD parameterization enabled exposure predictions that were within 1.5-fold error across all compounds and patients with varying stages of renal impairment. Notable improvement was observed over the conventional approach to scaling exposure, which failed in all but 1 scenario in patients with advanced CKD. Further research is required to qualify its use for first-in-CKD dose selection and clinical trial planning for a wider selection of renally eliminated compounds, including those subject to anion transport.

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.

Comparison between physiologically based pharmacokinetic and population pharmacokinetic modelling to select paediatric doses of gepotidacin in plague

Aims

To develop physiologically based pharmacokinetic (PBPK) and population pharmacokinetic (PopPK) models to predict effective doses of gepotidacin in paediatrics for the treatment of pneumonic plague (Yersinia pestis).

Methods

A gepotidacin PBPK model was constructed using a population‐based absorption, distribution, metabolism and excretion simulator, Simcyp®, with physicochemical and in vitro data, optimized with clinical data from a dose‐escalation intravenous (IV) study and a human mass balance study. A PopPK model was developed with pooled PK data from phase 1 studies with IV gepotidacin in healthy adults.

Results

For both the PopPK and PBPK models, body weight was found to be a key covariate affecting gepotidacin clearance. With PBPK, ~90% of the predicted PK for paediatrics fell between the 5th and 95th percentiles of adult values except for subjects weighing ≤5 kg. PopPK‐simulated paediatric means for C_max and AUC_(0‐τ) were similar to adult exposures across various weight brackets. The proposed dosing regimens were weight‐based for subjects ≤40 kg and fixed‐dose for subjects >40 kg. Comparison of observed and predicted exposures in adults indicated that both PBPK and PopPK models achieved similar AUC and C_max for a given dose, but the C_max predictions with PopPK were slightly higher than with PBPK. The two models differed on dose predictions in children <3 months old. The PopPK model may be suboptimal for low age groups due to the absence of maturation characterization of drug‐metabolizing enzymes involved with clearance in adults.

Conclusions

Both PBPK and PopPK approaches can reasonably predict gepotidacin exposures in children.

Spreadsheet-Based Minimal Physiological Models for the Prediction of Clearance of Therapeutic Proteins in Pediatric Patients

There is a growing interest in the use of physiologically based pharmacokinetic (PBPK) models as clinical pharmacology drug development tools. In PBPK modeling, not every organ or physiological parameter is required, leading to the development of a minimal PBPK (mPBPK) model, which is simple and efficient. The objective of this study was to streamline mPBPK modeling approaches and enable straightforward prediction of clearance of protein-based products in children. Four mPBPK models for scaling clearance from adult to children were developed and evaluated on Excel spreadsheets using (1) liver and kidneys; (2) liver, kidneys, and skin; (3) liver, kidneys, skin, and lymph; and (4) interstitial, lymph, and plasma volume. There were 35 therapeutic proteins with a total of 113 observations across different age groups (premature neonates to adolescents). For monoclonal and polyclonal antibodies, more than 90% of observations were within a 0.5- to 2-fold prediction error for all 4 methods. For nonantibodies, 79% to 100% of observations were within the 0.5- to 2-fold prediction error for the 4 different methods. Methods 1 and 4 provided the best results, >90% of the total observations were within the 0.5- to 2-fold prediction error for all 3 classes of protein-based products across a wide age range. The precision of clearance prediction was comparatively lower in children ≤2 years of age vs older children (>2 years of age) with methods 1 and 4 predicting 80% to 100% and 75% to 90% of observations within the 0.5- to 2-fold prediction error, respectively. The results of the study indicated that mPBPK models can be developed on spreadsheets, with acceptable performance for prediction of clearance.

Physiologically Based Pharmacokinetic Modeling and Allometric Scaling in Pediatric Drug Development: Where Do We Draw the Line?

Developing medicines for children is now established in legislation in both the United States and Europe; new drugs require pediatric study or investigation plans as part of their development. Particularly in early age groups, many developmental processes are not reflected by simple scalars such as body weight or body surface area, and even projecting doses based on simple allometric scaling can lead to significant overdoses in certain age groups. Modeling and simulation methodology, including physiologically based modeling, has evolved as part of the drug development toolkit and is being increasingly applied to various aspects of pediatric drug development. Pediatric physiologically based pharmacokinetic (PBPK) models account for the development of organs and the ontogeny of specific enzymes and transporters that determine the age-related pharmacokinetic profiles. However, when should this approach be used, and when will simpler methods such as allometric scaling suffice in answering specific problems? The aim of this review article is to illustrate the application of allometric scaling and PBPK in pediatric drug development and explore the optimal application of the latter approach with reference to case examples. In reality, allometric scaling included as part of population pharmacokinetic and PBPK approaches are all part of a model-informed drug development toolkit helping with decision making during the process of drug discovery and development; to that end, they should be viewed as complementary.

Physiologically-Based Pharmacokinetic Modeling of Remdesivir and Its Metabolites to Support Dose Selection for the Treatment of Pediatric Patients With COVID-19

Severe coronavirus disease 2019 (COVID‐19) disease, including multisystem inflammatory syndrome, has been reported in children. This report summarizes development of a remdesivir physiologically‐based pharmacokinetic (PBPK) model that accurately describes observed adult remdesivir and metabolites exposure and predicts pediatric remdesivir and metabolites exposure. The adult PBPK model was applied to predict pediatric remdesivir and metabolites steady‐state exposures using the Pediatric Population Model in SimCYP and incorporated the relevant physiologic and mechanistic information. Model development was based on adult phase I exposure data in healthy volunteers who were administered a 200‐mg loading dose of remdesivir intravenous (IV) over 0.5 hours on Day 1, then 100‐mg daily maintenance doses of IV over 0.5 hours starting on Day 2 and continuing through Days 5 or 10. Simulations indicated that use of the adult therapeutic remdesivir dosage regimen (200‐mg loading dose on Day 1 then 100‐mg daily maintenance dose starting on Day 2) in pediatric patients ≥ 40 kg and a weight‐based remdesivir dosage regimen (5‐mg/kg loading dose on Day 1 then 2.5‐mg/kg daily maintenance dose starting on Day 2) in pediatric patients weighing 2.5 to < 40 kg is predicted to maintain therapeutic exposures of remdesivir and its metabolites. The comprehensive PBPK model described in this report supported remdesivir dosing in planned pediatric clinical studies and dosing in the emergency use authorization and pediatric compassionate use programs that were initiated to support remdesivir as a treatment option during the pandemic.

Allometric scaling of therapeutic monoclonal antibodies in preclinical and clinical settings

Constant technological advancement enabled the production of therapeutic monoclonal antibodies (mAbs) and will continue to contribute to their rapid expansion. Compared to small-molecule drugs, mAbs have favorable characteristics, but also more complex pharmacokinetics (PK), e.g., target-mediated nonlinear elimination and recycling by neonatal Fc-receptor. This review briefly discusses mAb biology, similarities and differences in PK processes across species and within human, and provides a detailed overview of allometric scaling approaches for translating mAb PK from preclinical species to human and extrapolating from adults to children. The approaches described here will remain vital in mAb drug development, although more data are needed, for example, from very young patients and mAbs with nonlinear PK, to allow for more confident conclusions and contribute to further growth of this field. Improving mAb PK predictions will facilitate better planning of (pediatric) clinical studies and enable progression toward the ultimate goal of expediting drug development.

Informing Development of Bispecific Antibodies Using Physiologically Based Pharmacokinetic-Pharmacodynamic Models: Current Capabilities and Future Opportunities

Antibody therapeutics continue to represent a significant portion of the biotherapeutic pipeline, with growing promise for bispecific antibodies (BsAbs). BsAbs can target 2 different antigens at the same time, such as simultaneously binding tumor-cell receptors and recruiting cytotoxic immune cells. This simultaneous engagement of 2 targets can be potentially advantageous, as it may overcome disadvantages posed by a monotherapy approach, like the development of resistance to treatment. Combination therapy approaches that modulate 2 targets simultaneously offer similar advantages, but BsAbs are more efficient to develop. Unlike combination approaches, BsAbs can facilitate spatial proximity of targets that may be necessary to induce the desired effect. Successful development of BsAbs requires understanding antibody formatting and optimizing activity for both targets prior to clinical trials. To realize maximal efficacy, special attention is required to fully define pharmacokinetic (PK)/pharmacodynamic (PD) relationships enabling selection of dose and regimen. The application of physiologically based pharmacokinetics (PBPK) has been evolving to inform the development of novel treatment modalities such as bispecifics owing to the increase in our understanding of pharmacology, utility of multiscale models, and emerging clinical data. In this review, we discuss components of PBPK models to describe the PK characteristics of BsAbs and expand the discussion to integration of PBPK and PD models to inform development of BsAbs. A framework that can be adopted to build PBPK-PD models to inform the development of BsAbs is also proposed. We conclude with examples that highlight the application of PBPK-PD and share perspectives on future opportunities for this emerging quantitative tool.

A GFR-Based Method to Predict the Effect of Renal Impairment on the Exposure or Clearance of Renally Excreted Drugs: A Comparative Study Between a Simple GFR Method and a Physiologically Based Pharmacokinetic Model

Objective

The objective of this study was to compare the predictive performances of a glomerular filtration rate (GFR) model with a physiologically based pharmacokinetic (PBPK) model to predict total or renal clearance or area under the curve of renally excreted drugs in subjects with varying degrees of renal impairment.

Methods

From the literature, 11 studies were randomly selected in which total or renal clearance or area under the curve of drugs in subjects with different degrees of renal impairment were predicted by PBPK models. In these published studies, drugs were given to subjects intravenously or orally. The PBPK model was generally a whole-body model whereas the GFR model was as follows: Predicted total clearance (CL_T) = CL_T in healthy subjects × (GFR in RI/GFR in H), Predicted AUC = AUC in healthy subjects × (GFR in H/GFR in RI), where H is the healthy subjects and RI is renal impairment. The predicted clearance or area under the curve values using PBPK and GFR models were compared with the observed (experimental pharmacokinetic) values. The acceptable prediction error was within the 0.5- to 2-fold or 0.5- to 1.5-fold prediction error.

Results

There were 33 drugs with a total number of 101 observations (area under the curve, total and renal clearance in subjects with mild, moderate, and severe renal impairment). From PBPK and GFR models, out of 101 observations, 94 (93.1%) and 96 (95.0%) observations were within the 0.5- to 2-fold prediction error, respectively.

Conclusions

This study indicates that the predictive power of a simple GFR model is similar to a PBPK model for the prediction of clearance or area under the curve in subjects with renal impairment. The GFR method is simple, robust, and reliable and can replace complex empirical PBPK models.

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.

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 and Clinical Pharmacology of Monoclonal Antibodies in Pediatric Patients

Monoclonal antibodies (mAbs) and their derivatives are increasingly used in pediatric pharmacotherapy, and the number of antibody-based drug products with approved pediatric indications is continuously growing. In most instances, pediatric use is being pursued after the efficacy and safety of novel antibody medications have been established in adult indications. The pediatric extrapolation exercise that is frequently used in this context to bridge efficacy and safety from adults to children is oftentimes challenged through uncertainties and knowledge gaps in how to reliably extrapolate pharmacokinetics and clinical pharmacology of mAbs to different pediatric age groups, and how to derive age-appropriate dosing regimens that strike a balance between precision dosing and practicability. The article highlights some of the pharmacokinetic and clinical pharmacology challenges with regard to therapeutic use of mAbs and antibody derivatives in children, including immunogenicity events. Although considering body size-based differences in drug disposition can account for many of the perceived and actual differences in the distribution and elimination of antibody-based therapeutics between children and adults, increasing evidence suggests potential or actual age-associated differences beyond size differences, especially for young pediatric patients such as newborns and infants. To overcome age-associated differences in antibody disposition, various different dosing approaches have been applied to ensure safe and efficacious antibody exposure for pediatric populations of different ages. The development of such dosing regimens and the associated pathway to pediatric indication approval is illustrated in more detail for two antibody-based biologics, the fusion protein abatacept and the mAb tocilizumab.