Literature DB >> 31502689

Predictive Pediatric Modeling and Simulation Using Ontogeny Information.

Ibrahim Ince1, Juri Solodenko1, Sebastian Frechen1, André Dallmann1, Christoph Niederalt1, Jan Schlender1, Rolf Burghaus1, Jörg Lippert1, Stefan Willmann1.   

Abstract

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.
© 2019, The American College of Clinical Pharmacology.

Entities:  

Keywords:  Modeling and Simulation; Ontogeny; Open Source; Open Systems Pharmacology; PBPK; PK-Sim; Pediatrics; Prediction; Qualification

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Substances:

Year:  2019        PMID: 31502689     DOI: 10.1002/jcph.1497

Source DB:  PubMed          Journal:  J Clin Pharmacol        ISSN: 0091-2700            Impact factor:   3.126


  9 in total

1.  Prediction of Tissue Exposures of Meropenem, Colistin, and Sulbactam in Pediatrics Using Physiologically Based Pharmacokinetic Modeling.

Authors:  Shixing Zhu; Jiayuan Zhang; Zhihua Lv; Peijuan Zhu; Charles Oo; Mingming Yu; Sherwin K B Sy
Journal:  Clin Pharmacokinet       Date:  2022-08-10       Impact factor: 5.577

2.  A generic framework for the physiologically-based pharmacokinetic platform qualification of PK-Sim and its application to predicting cytochrome P450 3A4-mediated drug-drug interactions.

Authors:  Sebastian Frechen; Juri Solodenko; Thomas Wendl; André Dallmann; Ibrahim Ince; Thorsten Lehr; Jörg Lippert; Rolf Burghaus
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-05-24

3.  Scientific and Regulatory Considerations for an Ontogeny Knowledge Base for Pediatric Clinical Pharmacology.

Authors:  Gilbert J Burckart; Shirley Seo; Aaron C Pawlyk; Susan K McCune; Lynne P Yao; George P Giacoia; Yaning Wang; Issam Zineh
Journal:  Clin Pharmacol Ther       Date:  2020-01-26       Impact factor: 6.903

Review 4.  Allometric scaling of therapeutic monoclonal antibodies in preclinical and clinical settings.

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Journal:  MAbs       Date:  2021 Jan-Dec       Impact factor: 5.857

5.  Data-driven personalization of a physiologically based pharmacokinetic model for caffeine: A systematic assessment.

Authors:  Rebekka Fendt; Ute Hofmann; Annika R P Schneider; Elke Schaeffeler; Rolf Burghaus; Ali Yilmaz; Lars Mathias Blank; Reinhold Kerb; Jörg Lippert; Jan-Frederik Schlender; Matthias Schwab; Lars Kuepfer
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-06-26

Review 6.  Physiologically Based Pharmacokinetic Models Are Effective Support for Pediatric Drug Development.

Authors:  Kefei Wang; Kun Jiang; Xiaoyi Wei; Yulan Li; Tiejie Wang; Yang Song
Journal:  AAPS PharmSciTech       Date:  2021-07-26       Impact factor: 3.246

Review 7.  Clinical pharmacology and dosing regimen optimization of neonatal opioid withdrawal syndrome treatments.

Authors:  Fei Tang; Chee M Ng; Henrietta S Bada; Markos Leggas
Journal:  Clin Transl Sci       Date:  2021-05-01       Impact factor: 4.689

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

Authors:  Ibrahim Ince; André Dallmann; Sebastian Frechen; Katrin Coboeken; Christoph Niederalt; Thomas Wendl; Michael Block; Michaela Meyer; Thomas Eissing; Rolf Burghaus; Jörg Lippert; Stefan Willmann; Jan-Frederik Schlender
Journal:  J Clin Pharmacol       Date:  2021-06       Impact factor: 3.126

9.  Population pharmacokinetic analysis of rivaroxaban in children and comparison to prospective physiologically-based pharmacokinetic predictions.

Authors:  Stefan Willmann; Katrin Coboeken; Yang Zhang; Hannah Mayer; Ibrahim Ince; Emir Mesic; Kirstin Thelen; Dagmar Kubitza; Anthonie W A Lensing; Haitao Yang; Peijuan Zhu; Wolfgang Mück; Henk-Jan Drenth; Jörg Lippert
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-08-23
  9 in total

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