Prediction variability in physiologically based pharmacokinetic modeling of tissue disposition under deep uncertainty.

Mustafa Farahat, D T Flaherty, Zachary R Fox, Belinda S Akpa

Abstract read

In one paragraph

Article in NPJ systems biology and applications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mustafa FarahatDepartment of Chemical and Biomolecular Engineering, University of Tennessee-Knoxville, Knoxville, TN, USA.

D T FlahertyDepartment of Chemical and Biomolecular Engineering, University of Tennessee-Knoxville, Knoxville, TN, USA.

Zachary R FoxComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.

Belinda S AkpaDepartment of Chemical and Biomolecular Engineering, University of Tennessee-Knoxville, Knoxville, TN, USA. bakpa1@utk.edu.

Funding

Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy 10493University of Tennessee, Knoxville Institutional start-up support

6 · The paper itself

Abstract

Physiologically based pharmacokinetic (PBPK) models are increasingly invoked in virtual screening workflows for therapeutics. These mechanistic models project pharmacokinetic (PK) outcomes from molecular properties, with data-driven models acting as intermediaries to map molecular structure to PBPK input parameters. Errors in predicted parameters and unvalidated assumptions within PBPK models expose PK predictions to deep uncertainty. Herein, we examine how these uncertainties affect the prediction variability of dynamic, tissue-specific exposure. We validated four PBPK models against 1854 experimental datapoints-to establish their predictive fidelity before introducing parameter uncertainty typical of property-prediction models. Depending on molecule properties and model choice, the coefficient of variation under parameter uncertainty ranged from 10

Indexed as

Models, BiologicalPharmacokineticsAnimalsComputer SimulationHumansTissue DistributionUncertainty

Identifiers

PMID42251051

DOI10.1038/s41540-026-00739-7

PMCPMC13562628

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.