Evidence map›Paper›PMID 27863172›Full record

ReviewCPT: pharmacometrics & systems pharmacology2017

Building confidence in quantitative systems pharmacology models: An engineer's guide to exploring the rationale in model design and development.

J Timmis, K Alden, P Andrews, E Clark, A Nellis, B Naylor, M Coles, P Kaye

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in CPT: pharmacometrics & systems pharmacology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Pharmacodynamic Drug-Drug Interactions.Clinical pharmacology and therapeutics · 2019
    Review
  4. 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

8 authors.

J TimmisDepartment of Electronics, The University of York, York, UK.
K AldenDepartment of Electronics, The University of York, York, UK.
P AndrewsSimOmics Limited, York, UK.
E ClarkSimOmics Limited, York, UK.
A NellisSimOmics Limited, York, UK.
B NaylorDepartment of Electronics, The University of York, York, UK.
M ColesCentre for Immunology and Infection, Hull York Medical School/University of York, York, UK.
P KayeCentre for Immunology and Infection, Hull York Medical School/University of York, York, UK.

Funding

Medical Research Council G1000230National Centre for the Replacement, Refinement and Reduction of Animals in Research NC/C013205/1
6 · The paper itself

Abstract

This tutorial promotes good practice for exploring the rationale of systems pharmacology models. A safety systems engineering inspired notation approach provides much needed rigor and transparency in development and application of models for therapeutic discovery and design of intervention strategies. Structured arguments over a model's development, underpinning biological knowledge, and analyses of model behaviors are constructed to determine the confidence that a model is fit for the purpose for which it will be applied.

Indexed as

Models, BiologicalAnimalsAntiprotozoal AgentsBiomedical EngineeringHumansImmunity, CellularLeishmaniasis, VisceralSystems BiologyAntiprotozoal Agents

Identifiers

PMID27863172
PMCPMC5351409

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.