ReviewComputational and structural biotechnology journal2025
G protein-coupled receptor digital twins for precision and personalized medicine.
Review in Computational and structural biotechnology journal, 2025. 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.
- A bibliometric analysis of artificial intelligence applications in heart failure.Journal of thoracic disease · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision medicine has revolutionized healthcare by tailoring treatments to individual genetic, environmental, and lifestyle profiles, with G protein-coupled receptors (GPCRs) serving as prime therapeutic targets due to their role in diverse diseases. Here we explore the transformative potential of digital twin technologies in advancing precision GPCR medicines, integrating genomic, proteomic, and real-time physiological data to create patient-specific virtual models. We trace the evolution of precision medicine from early pharmacogenetics to modern multi-omics approaches, highlighting key GPCRs-Glucagon-like peptide 1 receptor (GLP1R), chemokine receptor CXCR4, and dopamine receptor D2 (DRD2)-and their personalized therapeutic applications in cardiovascular, oncological, and neurological disorders. Advanced quantitative systems pharmacology (QSP) and artificial intelligence (AI) hold the potential to enhance digital twins by simulating complex GPCR signaling and predicting drug responses, accelerating drug discovery, and optimizing patient stratification. Here we review and conceptualize how digital twin applications will likely induce a significant impact in personalizing receptor-based therapies for multiple disorders including heart failure, cancer, metabolic disorders, as well as neurodegenerative and neuropsychiatric conditions. Ethical challenges, including data privacy, equitable access, and model validation, must however also be addressed to ensure responsible ultimate implementation to the broadest clinical audience. Technical hurdles, such as data integration and computational scalability, also require the rational and intelligent creation of standardized frameworks. By bridging molecular insights with clinical applications, digital twins offer a powerful platform for precision GPCR medicine, promising improved therapeutic outcomes through individualized treatment strategies. The triumvirate of digital twins, QSP, and AI will likely revolutionize GPCR-targeted therapies, paving the way for a new era of precision medicine while highlighting the need for ethical and technical advancements to maximize their clinical impact.
Indexed as
Identifiers
What Socratic 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 Socratic graph.