Evidence map›Paper›PMID 33049161›Full record

ReviewAnnual review of pharmacology and toxicology2021

Engineering the Microbiome to Prevent Adverse Events: Challenges and Opportunities.

Saad Khan, Ruth Hauptman, Libusha Kelly

Abstract readReview
In one paragraph

Review in Annual review of pharmacology and toxicology, 2021. 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. Review
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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

3 authors.

Saad KhanDepartment of Systems and Computational Biology, Albert Einstein College of Medicine, New York, NY 10461, USA; email: libusha.kelly@einsteinmed.org.
Ruth HauptmanDepartment of Systems and Computational Biology, Albert Einstein College of Medicine, New York, NY 10461, USA; email: libusha.kelly@einsteinmed.org.
Libusha KellyDepartment of Systems and Computational Biology, Albert Einstein College of Medicine, New York, NY 10461, USA; email: libusha.kelly@einsteinmed.org.

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007288 · NIGMS · YESHIVA UNIVERSITY · PI AKABAS, MYLES H. · 1985 to 2022
$35.6M
Geographic Medicine and Emerging InfectionsT32AI070117 · NIAID · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI WEISS, LOUIS M. · 2008 to 2023
$5.1M
NIAID NIH HHS T32 AI070117NIGMS NIH HHS T32 GM007288
6 · The paper itself

Abstract

In the past decade of microbiome research, we have learned about numerous adverse interactions between the microbiome and medical interventions such as drugs, radiation, and surgery. What if we could alter our microbiomes to prevent these events? In this review, we discuss potential routes to mitigate microbiome adverse events, including applications from the emerging field of microbiome engineering. We highlight cases where the microbiome acts directly on a treatment, such as via differential drug metabolism, and cases where a treatment directly harms the microbiome, such as in radiation therapy. Understanding and preventing microbiome adverse events is a difficult challenge that will require a data-driven approach involving causal statistics, multiomics techniques, and a personalized means of mitigating adverse events. We propose research considerations to encourage productive work in preventing microbiome adverse events, and we highlight the many challenges and opportunities that await.

Indexed as

Gastrointestinal MicrobiomeMicrobiotaPharmaceutical PreparationsHumansPharmaceutical Preparationsadverse drug responsecausal inferencemetagenomicsmicrobiome adverse eventmicrobiome engineeringpersonalized medicine

Identifiers

PMID33049161
PMCPMC9015100

What OpenQuestion holds

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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.