Evidence map›Paper›PMID 25341854›Full record

ReviewCurrent pharmaceutical design2015

Antihypertensive drugs metabolism: an update to pharmacokinetic profiles and computational approaches.

Aikaterini Zisaki, Ljubisa Miskovic, Vassily Hatzimanikatis

Abstract readReview
In one paragraph

Review in Current pharmaceutical design, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

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  13. The Expression of GenesBiomedicines · 2024
    Article
  14. Comparison between the ECG Outcomes of Metoprolol and Bisoprolol.Cardiovascular & hematological agents in medicinal chemistry · 2024
    Article
  15. Review
  16. Article
  17. Cardiovascular precision medicine - A pharmacogenomic perspective.Cambridge prisms. Precision medicine · 2023
    Review
  18. Review
  19. Gut microbiota and cardiac arrhythmia: a pharmacokinetic scope.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2022
    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.

Aikaterini Zisaki
Ljubisa Miskovic
Vassily HatzimanikatisLaboratory of Computational Systems Biotechnology (LCSB), Ecole Polytechnique Federale de Lausanne, EPFL/SB/ISIC/LCSB, CH H4 624/ Station 6/ CH-1015 Lausanne/ Switzerland. aikaterini.zisaki@epfl.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drug discovery and development is a high-risk enterprise that requires significant investments in capital, time and scientific expertise. The studies of xenobiotic metabolism remain as one of the main topics in the research and development of drugs, cosmetics and nutritional supplements. Antihypertensive drugs are used for the treatment of high blood pressure, which is one the most frequent symptoms of the patients that undergo cardiovascular diseases such as myocardial infraction and strokes. In current cardiovascular disease pharmacology, four drug clusters - Angiotensin Converting Enzyme Inhibitors, Beta-Blockers, Calcium Channel Blockers and Diuretics - cover the major therapeutic characteristics of the most antihypertensive drugs. The pharmacokinetic and specifically the metabolic profile of the antihypertensive agents are intensively studied because of the broad inter-individual variability on plasma concentrations and the diversity on the efficacy response especially due to the P450 dependent metabolic status they present. Several computational methods have been developed with the aim to: (i) model and better understand the human drug metabolism; and (ii) enhance the experimental investigation of the metabolism of small xenobiotic molecules. The main predictive tools these methods employ are rule-based approaches, quantitative structure metabolism/activity relationships and docking approaches. This review paper provides detailed metabolic profiles of the major clusters of antihypertensive agents, including their metabolites and their metabolizing enzymes, and it also provides specific information concerning the computational approaches that have been used to predict the metabolic profile of several antihypertensive drugs.

Indexed as

Computational BiologyAntihypertensive AgentsHumansHypertensionAntihypertensive Agents

Identifiers

PMID25341854
PMCPMC4435036

What OpenQuestion holds

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Registered trials

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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.