Evidence map›Paper›PMID 32123669›Full record

ArticleMethodsX2020

PHARMIP: An insilico method to predict genetics that underpin adverse drug reactions.

Ahmad M Zidan, Eman A Saad, Nasser E Ibrahim, Amal Mahmoud, Medhat H Hashem, Alaa A Hemeida

Abstract read
In one paragraph

Article in MethodsX, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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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.

Ahmad M ZidanDepartment of Bioinformatics, Genetic Engineering & Biotechnology Research Institute, University of Sadat City, Egypt.
Eman A SaadDepartment of Bioinformatics, Genetic Engineering & Biotechnology Research Institute, University of Sadat City, Egypt.
Nasser E IbrahimDepartment of Bioinformatics, Genetic Engineering & Biotechnology Research Institute, University of Sadat City, Egypt.
Amal MahmoudDepartment of Biology, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, 31441, Dammam, Saudi Arabia.
Medhat H HashemDepartment of Animal Biotechnology, Genetic Engineering & Biotechnology Research Institute, University of Sadat City, Egypt.
Alaa A HemeidaDepartment of Bioinformatics, Genetic Engineering & Biotechnology Research Institute, University of Sadat City, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pharmacovigilance is the pharmacological science that focuses on the safe and appropriate use of drugs.Variability in response to drug therapy in both terms of safety and efficacy is highly related to patient's personal genomics. Hence, pharmacovigilance considers pharmacogenomics methodologies in the evaluation of medicinal products. The aim of this work is to introduce the pharmacovigilance/ pharmacogenomics insilico pipeline (PHARMIP) that uses the drug (or drug candidate) digital structure and the advances in bioinformatics tools and databases to figure-out the genetic factors underlying the drug reported adverse reactions (ADRs).PHARMIP uses user-friendly freely available bioinformatics resources to help pharmacovigilance and pharmacogenomics scientists with minimal bioinformatics experience to retrieve helpful information for their daily basis activities. Also, PHARMIP could help the advances in precision medicine in a drug-centric approach as it can be used to reveal genetic risk factors for certain drug ADRs. Domperidone was used as an example to the application of PHARMIP as the pipeline was initially developed during the insilico exploration of domperidone cardiotoxic ADRs. Method is composed of 3 main steps: •Preparing the drug off-label targets (OLT) list.•Retrieving the related diseases/ adverse reactions (DA) list.•Analysis of DA list to get answers.

Indexed as

BioinformaticsMolecular dockingPharmacogenomicsPharmacophore mappingPharmacovigilancePharmacovigilance/ pharmacogenomics insilico pipeline (PHARMIP)Precision medicineSimilarity ensemble approach

Identifiers

PMID32123669
PMCPMC7036477

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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.