ArticlePharmacogenomics
Pharmacogenomics and rare diseases: optimizing drug development and personalized therapeutics.
Article in Pharmacogenomics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
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Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Pharmacogenomics (PGx) is an evolving field that integrates genetic information into clinical decision-making to optimize drug therapy and minimize adverse drug reactions (ADRs). Its application in rare disease (RD) drug development is promising, given the genetic basis of many RDs and the need for precision medicine approaches. Despite significant advancements, challenges persist in developing effective therapies for RDs due to small patient populations, genetic heterogeneity, and limited surrogate biomarkers. The Orphan Drug Act in the U.S. has incentivized RD drug development. However, the traditional drug approval process is constrained by logistical and economic challenges, necessitating innovative PGx-driven strategies. Identifying genetic biomarkers in the early drug development stages can optimize dose selection, enhance therapeutic efficacy, and reduce ADRs. Case studies such as eliglustat for Gaucher disease and ivacaftor for cystic fibrosis demonstrate the efficacy of PGx-guided treatment strategies. Integrating PGx into global drug development requires the harmonization of regulatory policies and increased diversity in genetic research. Artificial intelligence (AI) tools further enhance genetic analysis, disease prediction, and clinical decision-making. Modernizing drug labeling with PGx information is critical to ensuring safe and effective drug use. Collectively, PGx offers transformative potential in RD therapeutics by facilitating personalized medicine approaches and addressing unmet medical needs.
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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.