Evidence map›Paper›PMID 30522365›Full record

ReviewCritical reviews in microbiology2019

Underscoring interstrain variability and the impact of growth conditions on associated antimicrobial susceptibilities in preclinical testing of novel antimicrobial drugs.

David A Sanchez, Luis R Martinez

Abstract readReview
In one paragraph

Review in Critical reviews in microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. South African Honey: Anti-International journal of food science · 2026
    Article
  4. Article
  5. Article
  6. Surface Design for Antibacterial Materials: From Fundamentals to Advanced Strategies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021
    Review
  7. Preparation of Poly(MTZ)ACS omega · 2020
    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

2 authors.

David A Sancheza Howard University College of Medicine , Washington , DC , USA.
Luis R Martinezc Department of Biological Sciences , The Border Biomedical Research Center, University of Texas at El Paso , El Paso , TX , USA.

Funding

TCCG12MD007592 · NIMHD · UNIVERSITY OF TEXAS EL PASO · PI KIRKEN, ROBERT A. · 2012 to 2018
$19.4M
Impact of Methamphetamine Induced IL-6 Production on Wound Healing and InflammationR15GM117501 · NIGMS · NEW YORK INST OF TECHNOLOGY · PI RAMOS, RADDY LEONARDO · 2016 to 2016
$432k
NIGMS NIH HHS R15 GM117501NIMHD NIH HHS G12 MD007592
6 · The paper itself

Abstract

In the era of multidrug resistant (MDR) organisms, reliable efficacy testing of novel antimicrobials during developmental stages is of paramount concern prior to introduction in clinical trials. Unfortunately, interstrain variability is often underappreciated when appraising the efficacy of innovative antimicrobials as preclinical testing of a limited number of standardized strains in unvarying conditions does not account for the vastness and potential for hyperdiversity among and within microbial populations. In this review, the importance of accounting for interstrain variability's potential to impact breadth of novel drug efficacy evaluation in the early stages of drug development will be discussed. Additionally, testing under varying microenvironmental conditions that may influence drug efficacy will be discussed. Biofilm growth, the influence of polymicrobial growth, mechanisms of antimicrobial resistance, pH, anaerobic conditions, and other virulence factors are some of critical issues that require more attention and standardization during preclinical drug efficacy evaluation. Furthermore, potential solutions for addressing this issue in pre-clinical antimicrobial development are proposed via centralization of microbial characterization and drug target databases, testing of a large number of clinical strains, inclusion of mutator strains in testing and the use of growth parameter mathematical models for testing.

Indexed as

Anti-Infective AgentsBacteriaBiological Variation, PopulationDrug Evaluation, PreclinicalFungiMicrobial Sensitivity TestsAnti-Infective AgentsAntimicrobialsinfectionresistancestrainsvariability

Identifiers

PMID30522365
PMCPMC6905375

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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.