Evidence map›Paper›PMID 32347548›Full record

ReviewClinical pharmacology and therapeutics2020

The Microbiome and the Gut-Liver-Brain Axis for Central Nervous System Clinical Pharmacology: Challenges in Specifying and Integrating In Vitro and In Silico Models.

Kyle G Hawkins, Caleb Casolaro, Jacquelyn A Brown, David A Edwards, John P Wikswo

Abstract 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 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Review
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  8. Review
  9. Article
  10. Impact of Contaminants on Microbiota: Linking the Gut-Brain Axis with Neurotoxicity.International journal of environmental research and public health · 2022
    Review
  11. Review
  12. Bioengineering Approaches for the Advanced Organoid Research.Advanced materials (Deerfield Beach, Fla.) · 2021
    Review
  13. Review
  14. Microphysiological systems: What it takes for community adoption.Experimental biology and medicine (Maywood, N.J.) · 2021
    Review
  15. Review
  16. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Kyle G HawkinsDepartment of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, USA.ORCID 0000-0002-7890-980X
Caleb CasolaroDepartment of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.ORCID 0000-0002-9569-2550
Jacquelyn A BrownDepartment of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, USA.ORCID 0000-0002-2002-3010
David A EdwardsDepartment of Anesthesiology and Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.ORCID 0000-0003-0123-4805
John P WikswoDepartment of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, USA.ORCID 0000-0003-2790-1530

Funding

Vanderbilt Biomedical Informatics Training ProgramT15LM007450 · NLM · VANDERBILT UNIVERSITY · PI Jessica S. Ancker, Bradley A. Malin · 2002 to 2026
$19.7M
TRAINING PROGRAM IN ENVIRONMENTAL TOXICOLOGYT32ES007028 · NIEHS · VANDERBILT UNIVERSITY · PI F PETER Guengerich, Fiona Edith Harrison · 1985 to 2026
$15.7M
Harnessing human brain and liver microphysiological systems for testing therapeutics for metastatic melanomaU01TR002383 · NCATS · VANDERBILT UNIVERSITY · PI MURPHY, WILLIAM L., TAYLOR, D. LANSING · 2018 to 2022
$7.6M
Drug development for tuberous sclerosis complex and other pediatric epileptogenic diseases using neurovascular and cardiac microphysiological modelsUH3TR002097 · NCATS · VANDERBILT UNIVERSITY · PI ESS, KEVIN C, NEELY, MAJA DIANA · 2019 to 2021
$4.6M
CFD Research CorporationEunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)National Center for Advancing Translational Sciences (NCATS)National Institutes of Health (NIH) UG3/UH3TR002097NCATS NIH HHS U01 TR002383NCATS NIH HHS UH3 TR002097NIDA NIH HHS HHSN271201700044CNIEHS NIH HHS HHSN271201700044CNIEHS NIH HHS T32 ES007028NIH/NCATS U01TR002383NIH Training Programs in Biomedical Informatics and Environmental Toxicology T15LM007450NIH Training Programs in Biomedical Informatics and Environmental Toxicology T32ES007028NLM NIH HHS T15 LM007450U.S. Environmental Protection Agency (EPA) 83573601
6 · The paper itself

Abstract

The complexity of integrating microbiota into clinical pharmacology, environmental toxicology, and opioid studies arises from bidirectional and multiscale interactions between humans and their many microbiota, notably those of the gut. Hosts and each microbiota are governed by distinct central dogmas, with genetics influencing transcriptomics, proteomics, and metabolomics. Each microbiota's metabolome differentially modulates its own and the host's multi-omics. Exogenous compounds (e.g., drugs and toxins), often affect host multi-omics differently than microbiota multi-omics, shifting the balance between drug efficacy and toxicity. The complexity of the host-microbiota connection has been informed by current methods of in vitro bacterial cultures and in vivo mouse models, but they fail to elucidate mechanistic details. Together, in vitro organ-on-chip microphysiological models, multi-omics, and in silico computational models have the potential to supplement the established methods to help clinical pharmacologists and environmental toxicologists unravel the myriad of connections between the gut microbiota and host health and disease.

Indexed as

GenomicsMetabolomicsMicrochip Analytical ProceduresAnimalsBacteriaBrainComputer SimulationDietGastrointestinal MicrobiomeHost-Pathogen InteractionsHumansIntestinesLab-On-A-Chip DevicesLiverMetabolomeModels, Animal

Identifiers

PMID32347548
PMCPMC7572575

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.