ArticleJournal of pharmacokinetics and pharmacodynamics2022
Application of different approaches to generate virtual patient populations for the quantitative systems pharmacology model of erythropoiesis.
Article in Journal of pharmacokinetics and pharmacodynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model.CPT: pharmacometrics & systems pharmacology · 2025Article
- Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice.Journal of pharmacokinetics and pharmacodynamics · 2024Review
- Applying quantitative and systems pharmacology to drug development and beyond: An introduction to clinical pharmacologists.Indian journal of pharmacology · 2024Review
- Quantitative systems pharmacology model of erythropoiesis to simulate therapies targeting anemia due to chronic kidney disease.Frontiers in pharmacology · 2023Article
- Accelerating robust plausible virtual patient cohort generation by substituting ODE simulations with parameter space mapping.Journal of pharmacokinetics and pharmacodynamics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
In a standard situation, a quantitative systems pharmacology model describes a "reference patient," and the model parameters are fixed values allowing only the mean values to be described. However, the results of clinical trials include a description of variability in patients' responses to a drug, which is typically expressed in terms of conventional statistical parameters, such as standard deviations (SDs) from mean values. Therefore, in this study, we propose and compare four different approaches: (1) Monte Carlo Markov Chain (MCMC); (2) model fitting to Monte Carlo sample; (3) population of clones; (4) stochastically bounded selection to generate virtual patient populations based on experimentally measured mean data and SDs. We applied these approaches to generate virtual patient populations in the QSP model of erythropoiesis. According to the results of our research, stochastically bounded selection showed slightly better results than the other three methods as it allowed the description of any number of patients from clinical trials and could be applied in the case of complex models with a large number of variable parameters.
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Registered trials
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