Publikation: Low-dimensional neural ODEs and their application in pharmacokinetics
Dateien
Datum
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
DOI (zitierfähiger Link)
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Sammlungen
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
Erschienen in
Zusammenfassung
Machine Learning (ML) is a fast-evolving field, integrated in many of today’s scientific disciplines. With the recent development of neural ordinary differential equations (NODEs), ML provides a new tool to model dynamical systems in the field of pharmacology and pharmacometrics, such as pharmacokinetics (PK) or pharmacodynamics. The novel and conceptionally different approach of NODEs compared to classical PK modeling creates challenges but also provides opportunities for its application. In this manuscript, we introduce the functionality of NODEs and develop specific low-dimensional NODE structures based on PK principles. We discuss two challenges of NODEs, overfitting and extrapolation to unseen data, and provide practical solutions to these problems. We illustrate concept and application of our proposed low-dimensional NODE approach with several PK modeling examples, including multi-compartmental, target-mediated drug disposition, and delayed absorption behavior. In all investigated scenarios, the NODEs were able to describe the data well and simulate data for new subjects within the observed dosing range. Finally, we briefly demonstrate how NODEs can be combined with mechanistic models. This research work enhances understanding of how NODEs can be applied in PK analyses and illustrates the potential for NODEs in the field of pharmacology and pharmacometrics.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
BRÄM, Dominic Stefan, Uri NAHUM, Johannes SCHROPP, Marc PFISTER, Gilbert KOCH, 2024. Low-dimensional neural ODEs and their application in pharmacokinetics. In: Journal of Pharmacokinetics and Pharmacodynamics. Springer. 2024, 51(2), S. 123-140. ISSN 1567-567X. eISSN 1573-8744. Verfügbar unter: doi: 10.1007/s10928-023-09886-4BibTex
@article{Bram2024-04Lowdi-68037, year={2024}, doi={10.1007/s10928-023-09886-4}, title={Low-dimensional neural ODEs and their application in pharmacokinetics}, number={2}, volume={51}, issn={1567-567X}, journal={Journal of Pharmacokinetics and Pharmacodynamics}, pages={123--140}, author={Bräm, Dominic Stefan and Nahum, Uri and Schropp, Johannes and Pfister, Marc and Koch, Gilbert} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/68037"> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/39"/> <dc:contributor>Schropp, Johannes</dc:contributor> <dc:creator>Schropp, Johannes</dc:creator> <dc:creator>Nahum, Uri</dc:creator> <dc:contributor>Bräm, Dominic Stefan</dc:contributor> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-11-03T08:30:30Z</dc:date> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/> <dc:creator>Pfister, Marc</dc:creator> <dc:creator>Koch, Gilbert</dc:creator> <dcterms:issued>2024-04</dcterms:issued> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/39"/> <dcterms:title>Low-dimensional neural ODEs and their application in pharmacokinetics</dcterms:title> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/68037/1/Braem_2-o8lznlcjam6k3.pdf"/> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/68037"/> <dcterms:abstract>Machine Learning (ML) is a fast-evolving field, integrated in many of today’s scientific disciplines. With the recent development of neural ordinary differential equations (NODEs), ML provides a new tool to model dynamical systems in the field of pharmacology and pharmacometrics, such as pharmacokinetics (PK) or pharmacodynamics. The novel and conceptionally different approach of NODEs compared to classical PK modeling creates challenges but also provides opportunities for its application. In this manuscript, we introduce the functionality of NODEs and develop specific low-dimensional NODE structures based on PK principles. We discuss two challenges of NODEs, overfitting and extrapolation to unseen data, and provide practical solutions to these problems. We illustrate concept and application of our proposed low-dimensional NODE approach with several PK modeling examples, including multi-compartmental, target-mediated drug disposition, and delayed absorption behavior. In all investigated scenarios, the NODEs were able to describe the data well and simulate data for new subjects within the observed dosing range. Finally, we briefly demonstrate how NODEs can be combined with mechanistic models. This research work enhances understanding of how NODEs can be applied in PK analyses and illustrates the potential for NODEs in the field of pharmacology and pharmacometrics.</dcterms:abstract> <dc:language>eng</dc:language> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-11-03T08:30:30Z</dcterms:available> <dc:contributor>Pfister, Marc</dc:contributor> <dc:rights>Attribution 4.0 International</dc:rights> <dc:creator>Bräm, Dominic Stefan</dc:creator> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/68037/1/Braem_2-o8lznlcjam6k3.pdf"/> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dc:contributor>Nahum, Uri</dc:contributor> <dc:contributor>Koch, Gilbert</dc:contributor> </rdf:Description> </rdf:RDF>