Application of Physiologically‐Based and Population <scp>Pharmacokinetic</scp> Modeling for Dose Finding and Confirmation During the Pediatric Development of Moxifloxacin

Willmann, Stefan; Frei, Matthias; Sutter, Gerd; Coboeken, Katrin; Wendl, Thomas; Eißing, Thomas; Lippert, Jörg; Staß, Heino

doi:10.1002/psp4.12446

Cited by 18 publications

(26 citation statements)

References 41 publications

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“…Figure shows the single‐dose exposure (expressed as area under the concentration‐time curve [AUC]) predicted for children with a PBPK model in comparison to the data observed in the first clinical trial with rivaroxaban in children . Another example is the fluoroquinolone antibiotic moxifloxacin the clearance of which was predicted in children by PBPK modeling before the conduction of pediatric trials and compared with individual data observed in 2 pediatric trials . In both case examples, PBPK modeling in children using previous ontogeny information has been proven to be very helpful to guide the development of suitable dosing regimens in pediatric development programs.…”

Section: Application Of Pbpk In Pediatric Researchmentioning

confidence: 99%

“…25 Another example is the fluoroquinolone antibiotic moxifloxacin the clearance of which was predicted in children by PBPK modeling before the conduction of pediatric trials and compared with individual data observed in 2 pediatric trials. [26][27][28] In both case examples, PBPK modeling in children using previous ontogeny information has been proven to be very helpful to guide the development of suitable dosing regimens in pediatric development programs.…”

Section: Established Ontogeny Functionsmentioning

confidence: 99%

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Predictive Pediatric Modeling and Simulation Using Ontogeny Information

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Solodenko

Frechen

et al. 2019

The Journal of Clinical Pharma

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Food and Drug Administration submissions of physiologically based pharmacokinetic (PBPK) modeling and simulation of small‐molecule drugs document the relevance of pediatric drug development and, in particular, information on dosing strategies in children. The most relevant prerequisite for reliable PBPK‐based translation of adult pharmacokinetics of a small molecule to children is knowledge of the drug‐specific absorption, distribution, metabolism, and elimination (ADME) processes in adults together with existing information about ontogeny of ADME processes relevant for the drug. All mechanisms driving a drug's clearance are of specific importance. For other drug modalities, our knowledge of ADME processes and ontogeny is still limited. More research is required, for example, to understand why some therapeutic proteins show complex differences in pharmacokinetics between adults and children, whereas other proteins seem to follow simple allometric scaling rules. Ontogeny information originates from various sources, such as (semi)quantitative mRNA expression, in vitro activity data, and deconvolution of in vivo pharmacokinetic data. The workflow for pediatric predictions is well described in several articles documenting successful translation from adults to children. The technical hurdles for PBPK modeling are low. State‐of‐the‐art PBPK modeling software tools provide integrated pediatric translation workflows. For example, PK‐Sim and MoBi are freely available as fully transparent open‐source software via Open Systems Pharmacology (OSP). With the latest 2019 software release, version 8.0, OSP even provides a fully integrated technical framework for the qualification (and requalification) of any specific intended PBPK use in line with Food and Drug Administration and European Medicines Agency PBPK guidance. Qualification packages for pediatric translation are available on the OSP platform.

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Section: Application Of Pbpk In Pediatric Researchmentioning

confidence: 99%

Section: Established Ontogeny Functionsmentioning

confidence: 99%

Predictive Pediatric Modeling and Simulation Using Ontogeny Information

Ince

Solodenko

Frechen

et al. 2019

The Journal of Clinical Pharma

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“…Additional maturational processes, particularly phase I and phase II enzyme ontogeny, may have to be considered in children younger than two years of age for small molecule drugs. This has given rise to more complex approaches, including physiologically based pharmacokinetic (PBPK) models for which there is meanwhile considerable experience with scaling PBPK models to pediatric populations for small molecule drugs (Leong et al, 2012;Templeton et al, 2018;Heimbach et al, 2019;Willmann et al, 2019). For therapeutic proteins like antibodies, however, there is currently only very limited experience with scaling PBPK models to children (Hardiansyah and Ng, 2018;Hanke et al, 2019;Malik and Edginton, 2019;Malik and Edginton, 2020), and there is currently only limited quantitative information regarding the maturation of the processes relevant for antibody PK (Shi and Derendorf, 2010;Xu et al, 2013;Edlund et al, 2015;Zhang et al, 2015;Liu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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 PKspecific 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.

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“…The PBPK models for amikacin, gadovist, and magnevist were updated to PK‐Sim version 9, 20,21 as additional simulations needed to be performed for this analysis, which is described in more detail below. As the developed PBPK models that were applied for clinical decision making have been filed for regulatory request, most of these models are also already published, whereas some of them are still part of the ongoing drug development program 3,12–17 …”

Section: Methodsmentioning

confidence: 99%

“…Both intravenous and orally administered ciprofloxacin PK data were available for analysis. To reflect the known elimination pathways of ciprofloxacin, 17 the PBPK model included renal clearance and hepatic clearance. The renal clearance processes were glomerular filtration and an unspecific tubular secretion (TS) accounting for the exceeding renal clearance 29,30 .…”

Section: Methodsmentioning

confidence: 99%

Predictive Performance of Physiology‐Based Pharmacokinetic Dose Estimates for Pediatric Trials: Evaluation With 10 Bayer Small‐Molecule Compounds in Children

Ince

Dallmann

Frechen

et al. 2021

The Journal of Clinical Pharma

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Development and guidance of dosing schemes in children have been supported by physiology‐based pharmacokinetic (PBPK) modeling for many years. PBPK models are built on a generic basis, where compound‐ and system‐specific parameters are separated and can be exchanged, allowing the translation of these models from adults to children by accounting for physiological differences. Owing to these features, PBPK modeling is a valuable approach to support clinical decision making for dosing in children. In this analysis, we evaluate pediatric PBPK models for 10 small‐molecule compounds that were applied to support clinical decision processes at Bayer for their predictive power in different age groups. Ratios of PBPK‐predicted to observed PK parameters for the evaluated drugs in different pediatric age groups were estimated. Predictive performance was analyzed on the basis of a 2‐fold error range and the bioequivalence range (ie, 0.8 ≤ predicted/observed ≤ 1.25). For all 10 compounds, all predicted‐to‐observed PK ratios were within a 2‐fold error range (n = 27), with two‐thirds of the ratios within the bioequivalence range (n = 18). The findings demonstrate that the pharmacokinetics of these compounds was successfully and adequately predicted in different pediatric age groups. This illustrates the applicability of PBPK for guiding dosing schemes in the pediatric population.

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

Cited by 18 publications

References 41 publications

Predictive Pediatric Modeling and Simulation Using Ontogeny Information

Predictive Pediatric Modeling and Simulation Using Ontogeny Information

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

Predictive Performance of Physiology‐Based Pharmacokinetic Dose Estimates for Pediatric Trials: Evaluation With 10 Bayer Small‐Molecule Compounds in Children

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