Evidence map›Paper›PMID 31628859›Full record

ReviewClinical pharmacology and therapeutics2020

Physiologically-Based Pharmacokinetic Models for Evaluating Membrane Transporter Mediated Drug-Drug Interactions: Current Capabilities, Case Studies, Future Opportunities, and Recommendations.

Kunal S Taskar, Venkatesh Pilla Reddy, Howard Burt, Maria M Posada, Manthena Varma, Ming Zheng, Mohammed Ullah, Arian Emami Riedmaier, Ken-Ichi Umehara, Jan Snoeys and 10 more

Open access · hybridAbstract readReview
In one paragraph

Review in Clinical pharmacology and therapeutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
72citing papers in PubMed, 1 pooled it
7.0field-weighted citation impact, top 2% of its field
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

72 citing papers in PubMed, 1 synthesis or guideline pooled it, 139 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Best Practices in Physiologically Based Pharmacokinetic (PBPK) Modeling.CPT: pharmacometrics & systems pharmacology · 2026
    Review
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12 more citing papers are in PubMed but not listed here.

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

20 authors at 15 institutions in 6 countries.

Kunal S TaskarGlaxoSmithKline, DMPK, In Vitro In Vivo Translation, GSK R&D, Ware, UK.
Venkatesh Pilla ReddyAstraZeneca, Modelling and Simulation, Early Oncology DMPK, Oncology R&D, AstraZeneca, Cambridge, UK.
Howard BurtSimcyp-Division, Certara UK Ltd., Sheffield, UK.
Maria M PosadaEli Lilly & Company, Indianapolis, Indiana, USA.
Manthena VarmaPfizer Inc., Groton, Connecticut, USA.
Ming ZhengBristol-Myers Squibb Company, Princeton, New Jersey, USA.
Mohammed UllahRoche Innovation Center, Basel, Switzerland.
Arian Emami RiedmaierAbbVie Inc., North Chicago, Illinois, USA.
Ken-Ichi UmeharaRoche Innovation Center, Basel, Switzerland.
Jan SnoeysJanssen Research and Development, Beerse, Belgium.
Masanori NakakariyaTakeda Pharmaceutical Company Ltd., Kanagawa, Japan.
Xiaoyan ChuMerck Sharp & Dohme Corp., Kenilworth, New Jersey, USA.
Maud BenetonServier, Centre-Val de Loire, France.
Yuan ChenGenentech, San Francisco, California, USA.
Felix HuthNovartis, Basel, Switzerland.
Rangaraj NarayananShire, Lexington, Massachusetts, USA.
Dwaipayan MukherjeeAbbVie Inc., North Chicago, Illinois, USA.
Vaishali DixitEisai Inc., Woodliff Lake, New Jersey, USA.
Yuichi SugiyamaSugiyama Laboratory, Riken, Yokohama, Japan.
Sibylle NeuhoffSimcyp-Division, Certara UK Ltd., Sheffield, UK.
AbbVie (United States) · USRoche (Switzerland) · CHSimcyp (United Kingdom) · GBAstraZeneca (United Kingdom) · GBBristol-Myers Squibb (United States) · USEisai (United States) · USEli Lilly (United States) · USGlaxoSmithKline (United Kingdom) · GBJanssen (Belgium) · BEMerck & Co., Inc., Rahway, NJ, USA (United States) · USNovartis Foundation · CHPfizer (United States) · USServier (France) · FRTakeda (Japan) · JPTakeda (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Physiologically-based pharmacokinetic (PBPK) modeling has been extensively used to quantitatively translate in vitro data and evaluate temporal effects from drug-drug interactions (DDIs), arising due to reversible enzyme and transporter inhibition, irreversible time-dependent inhibition, enzyme induction, and/or suppression. PBPK modeling has now gained reasonable acceptance with the regulatory authorities for the cytochrome-P450-mediated DDIs and is routinely used. However, the application of PBPK for transporter-mediated DDIs (tDDI) in drug development is relatively uncommon. Because the predictive performance of PBPK models for tDDI is not well established, here, we represent and discuss examples of PBPK analyses included in regulatory submission (the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Pharmaceuticals and Medical Devices Agency (PMDA)) across various tDDIs. The goal of this collaborative effort (involving scientists representing 17 pharmaceutical companies in the Consortium and from academia) is to reflect on the use of current databases and models to address tDDIs. This challenges the common perceptions on applications of PBPK for tDDIs and further delves into the requirements to improve such PBPK predictions. This review provides a reflection on the current trends in PBPK modeling for tDDIs and provides a framework to promote continuous use, verification, and improvement in industrialization of the transporter PBPK modeling.

Indexed as

Drug InteractionsModels, BiologicalCytochrome P-450 Enzyme SystemHumansMembrane Transport ProteinsPharmaceutical PreparationsPharmacokineticsCytochrome P-450 Enzyme SystemMembrane Transport ProteinsPharmaceutical Preparations

Identifiers

PMID31628859
PMCPMC7232864
OpenAlexW2981151354

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.