ReviewJournal of personalized medicine2026
New Personalized Medicine Model for Medication Management.
Review in Journal of personalized medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Nurses' Knowledge of Insensible Water Loss and the Evolution of a Digital Decision-Support Tool for Personalized Fluid Balance Assessment: A Cross-Sectional Study.Journal of personalized medicine · 2026Article
- Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions.Journal of clinical medicine · 2026Review
- Mechanistic Artificial Intelligence for Personalized Drug Therapy: Integrating Pharmacokinetics, Pharmacodynamics, Therapeutic Drug Monitoring, and Multiomic Systems Biology.Pharmaceutics · 2026Review
- Blood biomarker trajectories in ICU-directed prediction models - A scoping review.The EPMA journal · 2026Review
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
When using traditional approaches, such as pharmacokinetics and pharmacodynamics, the entire cellular or molecular response to drugs in the body cannot be fully ascertained or established. The oral medication process involves pharmacokinetics, followed by oral microbiomics and then gut microbiomics and pharmacodynamics. Recently, there has been increasing interest in the role of genetics (pharmacogenetics and pharmacogenomics) in both humans and microbiomes, as well as omics alterations (e.g., epigenetic, transcriptomic, proteomic, and metabolomic alterations as a consequence of drug exposure), which can help to ascertain the cellular responses to medications. Both the efficacy and toxicity of a drug are influenced by these factors. To assess these at an individual level, an integrative Personalized Medicine Model may be needed to help with medication management. Two example application cases for SSRIs and statins demonstrate the clinical usefulness of such a model, which can guide clinicians during drug selection and dosing to reduce reliance on trial-and-error, thus potentially improving patient outcomes and safety. Integrating this framework into practical clinical workflows requires the capture, analysis, and translation of multi-omics data in order to realize decision support protocols and actionable drug recommendations. This review also discusses IT requirements and different stakeholder roles. Although the proposed model can guide the treatment of diseases at the individual patient level, further research is still needed before it can be implemented as part of drug development research, clinical care, and healthcare delivery systems.
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.