Evidence map›Paper›PMID 39832006›Full record

ArticleEuropean journal of clinical pharmacology2025

Validating the accuracy of mathematical model-based pharmacogenomics dose prediction with real-world data.

Yolande Saab, Zahi Nakad

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in European journal of clinical pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Yolande Saab *Pharmaceutical Sciences Department, School of Pharmacy, Lebanese American University, Byblos, Lebanon. ysaab2011@hotmail.com.ORCID http://orcid.org/0000-0002-6432-582X
Zahi Nakad *Electrical and Computer Engineering Department, School of Engineering, Lebanese American University, P.O. Box: 36, Byblos, F-19, Lebanon. zahi.nakad@lau.edu.lb.ORCID http://orcid.org/0000-0002-5714-6487

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe study aims to verify the usage of mathematical modeling in predicting patients' medication doses in association with their genotypes versus real-world data.

methodsThe work relied on collecting, extracting, and using real-world data on dosing and patients' genotypes. Drug metabolizing enzymes, i.e., cytochrome CYP 450, were the focus. A total number of 1914 subjects from 26 studies were considered, and CYP2D6 and CYP2C19 gene polymorphisms were used for the verification.

resultsResults show that the mathematical model was able to predict the reported optimal dosing of the values provided in the considered studies. Predicting patients' optimal doses circumvents trial and error in patients' treatments. DISCUSSION: The authors discussed the advantages of using a mathematical model in patients' dosing and identified multiple issues that would hinder the usability of raw data in the future, especially in the era of artificial intelligence (AI). The authors recommend that researchers and healthcare professionals use simple descriptive metabolic activity terms for patients and use allele activity scores for drug dosing rather than phenotype/genotype classifications.

conclusionThe authors verified that a mathematical model could assist in providing data for better-informed decision-making in clinical settings and drug research and development.

Indexed as

Cytochrome P-450 CYP2C19Cytochrome P-450 CYP2D6Models, TheoreticalPharmacogeneticsDose-Response Relationship, DrugGenotypeHumansPharmaceutical PreparationsPolymorphism, GeneticCYP2C19 protein, humanCytochrome P-450 CYP2C19Cytochrome P-450 CYP2D6Pharmaceutical PreparationsAllele activityArtificial intelligenceCytochrome P450Dose predictionDrug metabolismGenotypePersonalized medicine

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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.