Evidence map›Paper›PMID 29455429›Full record

ArticleClinical pharmacokinetics2018

Predictive Performance of Physiologically-Based Pharmacokinetic Models in Predicting Drug-Drug Interactions Involving Enzyme Modulation.

Chia-Hsiang Hsueh, Vicky Hsu, Yuzhuo Pan, Ping Zhao

Abstract read
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Article in Clinical pharmacokinetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Drug Dosing Recommendations for All Patients: A Roadmap for Change.Clinical pharmacology and therapeutics · 2021
    Review
  8. Article
  9. Article
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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.

Chia-Hsiang HsuehOffice of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993, USA. sean.hsueh@gilead.com.
Vicky HsuOffice of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993, USA.
Yuzhuo PanOffice of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993, USA.
Ping ZhaoOffice of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPhysiologically-based pharmacokinetic (PBPK) modeling in predicting metabolic drug-drug interactions (mDDIs) is routinely used in drug development. Currently, the US FDA endorses the use of PBPK to potentially support dosing recommendations for investigational drugs as enzyme substrates of mDDIs, and to inform a lack of mDDIs for investigational drugs as enzyme modulators.

methodsWe systematically evaluated the performance of PBPK modeling in predicting mDDIs published in the literature. Models developed to assess both investigational drugs as enzyme substrates (Groups 1 and 2, as being inhibited and induced, respectively) or enzyme modulators (Groups 3 and 4, as inhibitors and inducers, respectively) were evaluated. Predicted ratios of the area under the curve (AUCRs) and/or maximum plasma concentration (C

resultsFor Groups 1, 2, 3, and 4, 62, 50, 44, and 43% of model-predicted AUCRs, respectively, were within a predefined threshold of 1.25-fold of observed values (0.8-1.25x). When the threshold was widened to twofold, the values increased to 100, 80, 81, and 86% (0.5-2.0x). For Groups 3 and 4, prediction for mDDI liability (the existence or lack of mDDIs) using PBPK appears to be satisfactory.

conclusionOur analysis supports the FDA's current recommendations on the use of PBPK to predict mDDIs.

Indexed as

Drug InteractionsModels, BiologicalArea Under CurveComputer SimulationDrug DevelopmentDrugs, InvestigationalEnzyme InductionEnzyme InhibitorsUnited StatesUnited States Food and Drug AdministrationDrugs, InvestigationalEnzyme Inhibitors

Identifiers

PMID29455429

What OpenQuestion holds

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Registered trials

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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.