Evidence map›Paper›PMID 36330677›Full record

ReviewCPT: pharmacometrics & systems pharmacology2023

Predicting the correct dose in children: Role of computational Pediatric Physiological-based pharmacokinetics modeling tools.

Xu Zhou, Jiening Dun, Xiao Chen, Bai Xiang, Yunjie Dang, Deying Cao

Abstract readReview
In one paragraph

Review in CPT: pharmacometrics & systems pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
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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

6 authors.

Xu ZhouCollege of Pharmacy, Hebei Medical University, Shijiazhuang, China.
Jiening DunCollege of Pharmacy, Hebei Medical University, Shijiazhuang, China.
Xiao ChenCollege of Pharmacy, Hebei Medical University, Shijiazhuang, China.
Bai XiangCollege of Pharmacy, Hebei Medical University, Shijiazhuang, China.
Yunjie DangCollege of Pharmacy, Hebei Medical University, Shijiazhuang, China.
Deying CaoCollege of Pharmacy, Hebei Medical University, Shijiazhuang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The pharmacokinetics (PKs) and safety of medications in particular groups can be predicted using the physiologically-based pharmacokinetic (PBPK) model. Using the PBPK model may enable safe pediatric clinical trials and speed up the process of new drug research and development, especially for children, a population in which it is relatively difficult to conduct clinical trials. This review summarizes the role of pediatric PBPK (P-PBPK) modeling software in dose prediction over the past 6 years and briefly introduces the process of general P-PBPK modeling. We summarized the theories and applications of this software and discussed the application trends and future perspectives in the area. The modeling software's extensive use will undoubtedly make it easier to predict dose prediction for young patients.

Indexed as

Models, BiologicalSoftwareChildHumansPharmacokinetics

Identifiers

PMID36330677
PMCPMC9835135

What Socratic 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 Socratic graph.