Evidence map›Paper›PMID 29700814›Full record

ArticleClinical pharmacology and therapeutics2018

Forecasting Clinical Dose-Response From Preclinical Studies in Tuberculosis Research: Translational Predictions With Rifampicin.

Sebastian G Wicha, Oskar Clewe, Robin J Svensson, Stephen H Gillespie, Yanmin Hu, Anthony R M Coates, Ulrika S H Simonsson

Abstract read
In one paragraph

Article in Clinical pharmacology and therapeutics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Trial
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  13. Pharmacologic management ofExpert review of clinical pharmacology · 2020
    Review
  14. Intracellular PD Modelling (Pharmaceutics · 2019
    Article
  15. Article
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

7 authors.

Sebastian G WichaDepartment of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
Oskar CleweDepartment of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
Robin J SvenssonDepartment of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
Stephen H GillespieSchool of Medicine, University of St Andrews, St Andrews, UK.
Yanmin HuInstitute for Infection and Immunity, St George's University of London, London, UK.
Anthony R M CoatesInstitute for Infection and Immunity, St George's University of London, London, UK.
Ulrika S H SimonssonDepartment of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A crucial step for accelerating tuberculosis drug development is bridging the gap between preclinical and clinical trials. In this study, we developed a preclinical model-informed translational approach to predict drug effects across preclinical systems and early clinical trials using the in vitro-based Multistate Tuberculosis Pharmacometric (MTP) model using rifampicin as an example. The MTP model predicted rifampicin biomarker response observed in 1) a hollow-fiber infection model, 2) a murine study to determine pharmacokinetic/pharmacodynamic indices, and 3) several clinical phase IIa early bactericidal activity (EBA) studies. In addition, we predicted rifampicin biomarker response at high doses of up to 50 mg/kg, leading to an increased median EBA

Indexed as

Computer SimulationModels, BiologicalAnimalsAntibiotics, AntitubercularBacterial LoadClinical Trials, Phase II as TopicDisease Models, AnimalDose-Response Relationship, DrugHumansMiceMicrobial Sensitivity TestsMycobacterium tuberculosisRifampinTranslational Research, BiomedicalTreatment OutcomeTuberculosis, PulmonaryAntibiotics, AntitubercularRifampin

Identifiers

PMID29700814
PMCPMC6282494

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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.