Evidence map›Paper›PMID 41617209›Full record

ArticleMolecular pharmaceutics2026

High-Throughput Physiologically Based Pharmacokinetic Model for Rodent Pharmacokinetics Prediction Using Machine Learning-Predicted Inputs and a Large

Davide Bassani, Andrea Andrews-Morger, Jin Zhang, Luca Docci, Giuseppe Cecere, Axel Pähler, Tejashree Belubbi, Pierre Laye, Iris Shih, Neil John Parrott

Abstract read
In one paragraph

Article in Molecular pharmaceutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

10 authors.

Davide BassaniPharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland.ORCID 0000-0001-9057-6297
Andrea Andrews-MorgerPharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland.ORCID 0000-0003-4774-6291
Jin ZhangPharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland.
Luca DocciPharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland.
Giuseppe CecerePharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland.ORCID 0000-0001-5004-8932
Axel PählerPharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland.
Tejashree BelubbiPharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland.
Pierre LayePharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland.
Iris ShihPharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland.
Neil John ParrottPharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland.ORCID 0000-0001-6821-7714

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate prediction of the pharmacokinetic (PK) properties of small-molecule drug candidates is a critical aspect of pharmaceutical research. Fast and reliable PK predictions can accelerate compound optimization cycles, reduce animal testing, and enhance the quality of molecules advancing to human studies. Although physiologically based PK (PBPK) models are well-established for compound selection, their application in early discovery faces limitations due to low throughput and the requirement for substantial

Indexed as

Machine LearningModels, BiologicalPharmacokineticsAnimalsComputer SimulationDrug DiscoveryHigh-Throughput Screening AssaysHumansPharmaceutical PreparationsPrediction AlgorithmsPredictive Learning ModelsRatsSoftwarePharmaceutical Preparationscompound optimizationdrug discoveryhigh-throughput PBPK (HT-PBPK)machine learning (ML)PK prediction

Identifiers

PMID41617209
PMCPMC12958287

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.