ArticleJournal of pharmacokinetics and pharmacodynamics2021
Delay differential equations based models in NONMEM.
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 4 papers.
What it found
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Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- Neural Controlled Differential Equation and Its Application in Pharmacokinetics and Pharmacodynamics.CPT: pharmacometrics & systems pharmacology · 2026Article
- Tutorial for Modeling Delays in Biological Systems in the NONMEM Software.CPT: pharmacometrics & systems pharmacology · 2025Article
- Implementation of non-linear mixed effects models defined by fractional differential equations.Journal of pharmacokinetics and pharmacodynamics · 2023Article
- Computing optimal drug dosing with OptiDose: implementation in NONMEM.Journal of pharmacokinetics and pharmacodynamics · 2023Article
Corrections and comments
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Authors and funding
6 authors at 6 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Delay differential equations (DDEs) are commonly used in pharmacometric models to describe delays present in pharmacokinetic and pharmacodynamic data analysis. Several DDE solvers have been implemented in NONMEM 7.5 for the first time. Two of them are based on algorithms already applied elsewhere, while others are extensions of existing ordinary differential equations (ODEs) solvers. The purpose of this tutorial is to introduce basic concepts underlying DDE based models and to show how they can be developed using NONMEM. The examples include previously published DDE models such as logistic growth, tumor growth inhibition, indirect response with precursor pool, rheumatoid arthritis, and erythropoiesis-stimulating agents. We evaluated the accuracy of NONMEM DDE solvers, their ability to handle stiff problems, and their performance in parameter estimation using both first-order conditional estimation (FOCE) and the expectation-maximization (EM) method. NONMEM control streams and excerpts from datasets are provided for all discussed examples. All DDE solvers provide accurate and precise solutions with the number of significant digits controlled by the error tolerance parameters. For estimation of population parameters, the EM method is more stable than FOCE regardless of the DDE solver.
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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.