ArticleJournal of pharmacokinetics and pharmacodynamics2021
Reduction of quantitative systems pharmacology models using artificial neural networks.
Article in Journal of pharmacokinetics and pharmacodynamics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Tackling High Dimensionality in QSP: Guiding Model Order Reduction With Index Analysis.CPT: pharmacometrics & systems pharmacology · 2026Article
- Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model.CPT: pharmacometrics & systems pharmacology · 2025Article
- Low-dimensional neural ODEs and their application in pharmacokinetics.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Deriving mechanism-based pharmacodynamic models by reducing quantitative systems pharmacology models: An application to warfarin.CPT: pharmacometrics & systems pharmacology · 2023Article
- A pilot study of ion current estimation by ANN from action potential waveforms.Journal of biological physics · 2022Article
- Pharmacokinetics of Vancomycin among Patients with Chemotherapy-Associated Febrile Neutropenia: Which Would Be the Best Dosing to Obtain Appropriate Exposure?Antibiotics (Basel, Switzerland) · 2022Article
- Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis.Pharmaceutics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative systems pharmacology models are often highly complex and not amenable to further simulation and/or estimation analyses. Model-order reduction can be used to derive a mechanistically sound yet simpler model of the desired input-output relationship. In this study, we explore the use of artificial neural networks for approximating an input-output relationship within highly dimensional systems models. We illustrate this approach using a model of blood coagulation. The model consists of two components linked together through a highly dimensional discontinuous interface, which creates a difficulty for model reduction techniques. The proposed approach enables the development of an efficient approximation to complex models with the desired level of accuracy. The technique is applicable to a wide variety of models and provides substantial speed boost for use of such models in simulation and control purposes.
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33651241What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.