Evidence map›Paper›PMID 26857559›Full record

Trial reportBMC cancer2016

A pharmacogenetic pilot study reveals MTHFR, DRD3, and MDR1 polymorphisms as biomarker candidates for slow atorvastatin metabolizers.

Rafael B R León-Cachón, Jorge A Ascacio-Martínez, María E Gamino-Peña, Ricardo M Cerda-Flores, Irene Meester, Hugo L Gallardo-Blanco, Magdalena Gómez-Silva, Everardo Piñeyro-Garza, Hugo A Barrera-Saldaña

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in BMC cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.0field-weighted citation impact, top 12% of its field
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

14 citing papers in PubMed, 28 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Evaluating genotype-treatment interactions for high-risk medications in British general practice: a retrospective cohort study using UK Biobank.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Effect ofBiomedical reports · 2017
    Article
  14. 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

9 authors at 2 institutions in 1 country.

Rafael B R León-CachónCentro de Diagnóstico Molecular y Medicina Personalizada, Departamento de Ciencias Básicas, División Ciencias de la Salud, Universidad de Monterrey, San Pedro Garza García, NL, México.
Jorge A Ascacio-MartínezDepartamento de Bioquímica y Medicina Molecular, Facultad de Medicina, Universidad Autónoma de Nuevo León, Monterrey, NL, México.
María E Gamino-PeñaIpharma S.A., Monterrey, NL, México.
Ricardo M Cerda-FloresFacultad de Enfermería, Universidad Autónoma de Nuevo León, Monterrey, NL, México.
Irene MeesterCentro de Diagnóstico Molecular y Medicina Personalizada, Departamento de Ciencias Básicas, División Ciencias de la Salud, Universidad de Monterrey, San Pedro Garza García, NL, México.
Hugo L Gallardo-BlancoDepartamento de Genética, Universidad Autónoma de Nuevo León, Monterrey, NL, México.
Magdalena Gómez-SilvaIpharma S.A., Monterrey, NL, México.
Everardo Piñeyro-GarzaIpharma S.A., Monterrey, NL, México.
Hugo A Barrera-SaldañaDepartamento de Bioquímica y Medicina Molecular, Facultad de Medicina, Universidad Autónoma de Nuevo León, Monterrey, NL, México. habarrera@gmail.com.
Universidad Autónoma de Nuevo León · MXUniversity of Monterrey · MX

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe genetic variation underlying atorvastatin (ATV) pharmacokinetics was evaluated in a Mexican population. Aims of this study were: 1) to reveal the frequency of 87 polymorphisms in 36 genes related to drug metabolism in healthy Mexican volunteers, 2) to evaluate the impact of these polymorphisms on ATV pharmacokinetics, 3) to classify the ATV metabolic phenotypes of healthy volunteers, and 4) to investigate a possible association between genotypes and metabolizer phenotypes.

methodsA pharmacokinetic study of ATV (single 80-mg dose) was conducted in 60 healthy male volunteers. ATV plasma concentrations were measured by high-performance liquid chromatography mass spectrometry. Pharmacokinetic parameters were calculated by the non-compartmental method. The polymorphisms were determined with the PHARMAchip® microarray and the TaqMan® probes genotyping assay.

resultsThree metabolic phenotypes were found in our population: slow, normal, and rapid. Six gene polymorphisms were found to have a significant effect on ATV pharmacokinetics: MTHFR (rs1801133), DRD3 (rs6280), GSTM3 (rs1799735), TNFα (rs1800629), MDR1 (rs1045642), and SLCO1B1 (rs4149056). The combination of MTHFR, DRD3 and MDR1 polymorphisms associated with a slow ATV metabolizer phenotype.

conclusionFurther studies using a genetic preselection method and a larger population are needed to confirm these polymorphisms as predictive biomarkers for ATV slow metabolizers.

trial registrationAustralian New Zealand Clinical Trials Registry: ACTRN12614000851662, date registered: August 8, 2014.

Indexed as

AdolescentAdultAtorvastatinATP Binding Cassette Transporter, Subfamily BBiomarkers, PharmacologicalHumansInactivation, MetabolicMaleMethylenetetrahydrofolate Reductase (NADPH2)Middle AgedPolymorphism, Single NucleotideReceptors, Dopamine D3ABCB1 protein, humanAtorvastatinATP Binding Cassette Transporter, Subfamily BBiomarkers, PharmacologicalDRD3 protein, humanMethylenetetrahydrofolate Reductase (NADPH2)MTHFR protein, humanReceptors, Dopamine D3

Identifiers

PMID26857559
PMCPMC4746878
OpenAlexW2262046188

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.