ReviewInvestigational new drugs2014
Contributions from emerging transcriptomics technologies and computational strategies for drug discovery.
Review in Investigational new drugs, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Drug repurposing: a systematic review on root causes, barriers and facilitators.BMC health services research · 2022Pooled it
- Drug repurposing using transcriptome sequencing and virtual drug screening in a patient with glioblastoma.Investigational new drugs · 2021Article
- Multifactorial Modes of Action of Arsenic Trioxide in Cancer Cells as Analyzed by Classical and Network Pharmacology.Frontiers in pharmacology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug discovery involves various steps and is a long process being even more demanding for complex diseases such as cancer. Tumors are ensembles of subpopulations with different mutations, require very specific and effective strategies. Conventional drug screening technologies may not be adequate and efficient anymore. Drug repositioning is a useful strategy to identify new uses for previously failed drugs. High throughput and deep sequencing technologies provide valuable support by yielding enormous amounts of "-omics" data and contribute to understanding the molecular mechanisms responsible for drug action. Computational methods coupled with systems biology represent a promising step to interpret pharmacogenomic data and establish strong connections with drug discovery. Genomic variations have been found to be linked with differential drug response among individuals. Large genome wide association studies are necessary to identify reliable connections between genomic variations and drug response since personalized medicine has been accepted as an important phenomenon in the drug discovery and development process post approval.
Indexed as
Identifiers
24633650What OpenQuestion holds
Registered trials
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.