Evidence map›Paper›PMID 27449411›Full record

ReviewClinical therapeutics2016

Pharmacokinetic and Pharmacodynamic Principles of Anti-infective Dosing.

Nikolas J Onufrak, Alan Forrest, Daniel Gonzalez

Abstract readReview
In one paragraph

Review in Clinical therapeutics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed, 3 pooled it
–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

71 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Article
  6. Review
  7. Comparative Pharmacokinetics of Gentamicin in Healthy Young-Adult and Geriatric Horses.Journal of veterinary pharmacology and therapeutics · 2026
    Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Co-evolution of resistance and virulence inFrontiers in cellular and infection microbiology · 2026
    Review
  14. Article
  15. Review
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article

11 more citing papers are in PubMed but not listed here.

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.

Nikolas J OnufrakDivision of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Alan ForrestDivision of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Daniel GonzalezDivision of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina. Electronic address: daniel.gonzalez@unc.edu.

Funding

UNC-Duke Collaborative Clinical Pharmacology Postdoctoral Training Program-Evaluation SupplementT32GM086330 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KIM L.R. BROUWER, Daniel Gonzalez · 2011 to 2026
$8.5M
Use of Physiologically-Based PK/PD Models to Streamline Drug ApprovalsK23HD083465 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GONZALEZ, DANIEL · 2015 to 2019
$727k
NICHD NIH HHS K23 HD083465NIGMS NIH HHS T32 GM086330
6 · The paper itself

Abstract

purposeAn understanding of the pharmacokinetic (PK) and pharmacodynamic (PD) principles that determine response to antimicrobial therapy can provide the clinician with better-informed dosing regimens. Factors influential on antibiotic disposition and clinical outcome are presented, with a focus on the primary site of infection. Techniques to better understand antibiotic PK and optimize PD are acknowledged.

methodsPubMed (inception-April 2016) was reviewed for relevant publications assessing antimicrobial exposures within different anatomic locations and clinical outcomes for various infection sites.

findingsA limited literature base indicates variable penetration of antibiotics to different target sites of infection, with drug solubility and extent of protein binding providing significant PK influences in addition to the major clearing pathway of the agent. PD indices derived from in vitro studies and animal models determine the optimal magnitude and frequency of dosing regimens for patients. PK/PD modeling and simulation has been shown an efficient means of assessing these PD endpoints against a variety of PK determinants, clarifying the unique effects of infection site and patient characteristics to inform the adequacy of a given antibiotic regimen. IMPLICATIONS: Appreciation of the PK properties of an antibiotic and its PD measure of efficacy can maximize the utility of these life-saving drugs. Unfortunately, clinical data remain limited for a number of infection site-antibiotic exposure relationships. Modeling and simulation can bridge preclinical and patient data for the prescription of optimal antibiotic dosing regimens, consistent with the tenets of personalized medicine.

Indexed as

AnimalsAnti-Bacterial AgentsBacteremiaBacterial InfectionsBone and BonesDose-Response Relationship, DrugHumansLungSoft Tissue InfectionsAnti-Bacterial Agentsantibioticdosingexposurepharmacodynamicspharmacokinetics

Identifiers

PMID27449411
PMCPMC5039113

What OpenQuestion holds

Textmetadata
LicenceTDM
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.