ArticleJournal of pharmacokinetics and pharmacodynamics2025
Identification and characterization of virtual sub-populations through phenotype-guided filtering. The challenging case of nonidentifiable models in the context of therapeutic evaluation.
Article in Journal of pharmacokinetics and pharmacodynamics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- On the structural identifiability of nonlinear models of ligand binding dynamics.Journal of pharmacokinetics and pharmacodynamics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The usefulness of mathematical modeling of biological systems and their responses to exogenous products is now well recognized. However, this recognition is marred by problems of unreliability of representations of real populations and predictions of responses to treatments. To remedy this, the generation of virtual populations combined with quantitative systems pharmacology models is increasingly being adopted. However, the complexity of these models and the large number of parameters they involve, generally within a context of lack of information or data, raise the question of nonidentifiability as a potential source affecting the quality of model predictions. This article attempts to present a vision that confronts the management of nonidentifiability with the concerns linked to the classification of virtual populations and their corresponding parametric signatures, as a potential tool for the evaluation of therapeutic interventions.
Indexed as
Identifiers
41361061What Socratic 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 Socratic graph.