Evidence map›Paper›PMID 30189667›Full record

ReviewJournal of personalized medicine2018

A Metabolomics Approach to Pharmacotherapy Personalization.

Elena E Balashova, Dmitry L Maslov, Petr G Lokhov

Abstract readReview
In one paragraph

Review in Journal of personalized medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing 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

40 citing papers in PubMed.

  1. New Personalized Medicine Model for Medication Management.Journal of personalized medicine · 2026
    Review
  2. Gender Differences and Comorbidities in Major Depression.Advances in experimental medicine and biology · 2026
    Review
  3. Article
  4. Article
  5. Drug response in the era of precision medicine: A methodological review.Computational and structural biotechnology journal · 2025
    Review
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Observational
  12. Article
  13. Article
  14. Review
  15. Review
  16. Article
  17. Review
  18. Comparative Metabolomic Study ofInternational journal of molecular sciences · 2021
    Article
  19. Review
  20. 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

3 authors.

Elena E BalashovaInstitute of Biomedical Chemistry, Pogodinskaya St. 10, Moscow 119121, Russia. balashlen@mail.ru.
Dmitry L MaslovInstitute of Biomedical Chemistry, Pogodinskaya St. 10, Moscow 119121, Russia. dlmaslov@mail.ru.
Petr G LokhovInstitute of Biomedical Chemistry, Pogodinskaya St. 10, Moscow 119121, Russia. lokhovpg@rambler.ru.

Funding

Program for Basic Research of Russia State Academies of Sciences for 2013-2020. N/A
6 · The paper itself

Abstract

The optimization of drug therapy according to the personal characteristics of patients is a perspective direction in modern medicine. One of the possible ways to achieve such personalization is through the application of "omics" technologies, including current, promising metabolomics methods. This review demonstrates that the analysis of pre-dose metabolite biofluid profiles allows clinicians to predict the effectiveness of a selected drug treatment for a given individual. In the review, it is also shown that the monitoring of post-dose metabolite profiles could allow clinicians to evaluate drug efficiency, the reaction of the host to the treatment, and the outcome of the therapy. A comparative description of pharmacotherapy personalization (pharmacogenomics, pharmacoproteomics, and therapeutic drug monitoring) and personalization based on the analysis of metabolite profiles for biofluids (pharmacometabolomics) is also provided.

Indexed as

mass spectrometrymetabolomicspersonalized medicinepharmacogenomicspharmacometabolomicstherapeutic drug monitoring

Identifiers

PMID30189667
PMCPMC6164342

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.