Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Mar;81(3):451-462.
doi: 10.1007/s00228-025-03805-x. Epub 2025 Jan 17.

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

Yolande Saab #  1 , Zahi Nakad #  2
Affiliations

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

Yolande Saab et al. Eur J Clin Pharmacol. 2025 Mar.

Abstract

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

Methods: The 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.

Results: Results 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.

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

Keywords: Allele activity; Artificial intelligence; Cytochrome P450; Dose prediction; Drug metabolism; Genotype; Personalized medicine.

PubMed Disclaimer

Conflict of interest statement

Declarations. Human Ethics and Consent to Participate Declarations: Not applicable. There was no direct human data collection by the authors. There is no need for either a Consent to Participate Declaration or a Human Ethics Declaration because the authors utilized data that is readily available in the literature. All sources are referenced in the manuscript. Competing Interests: The authors declare no competing interests.

Similar articles

See all similar articles

References

    1. Lin E, Lin CH, Lane HY (2021) Machine learning and deep learning for the pharmacogenomics of antidepressant treatments. Clin Psychopharmacol Neurosci 19(4):577–588 - DOI - PubMed
    1. McInnes G, Dalton R, Sangkuhl K, Whirl-Carrillo M, Lee SB, Tsao PS et al (2020) Transfer learning enables prediction of CYP2D6 haplotype function. PLoS Comput Biol 16(11):e1008399 - DOI - PubMed - PMC
    1. van der Lee M, Allard WG, Vossen RHAM, Baak-Pablo RF, Menafra R, Deiman BALM et al (2021) Toward predicting CYP2D6-mediated variable drug response from CYP2D6 gene sequencing data. Sci Transl Med 13(603):eabf3637 - DOI - PubMed
    1. Saab Y, Nakad Z (2023) A software tool to adjust codeine dose based on CYP2D6 gene-pair polymorphisms and drug-drug interactions. Pharmacogenomics J 23(6):217–224 - DOI - PubMed
    1. Nakad Z, Saab Y (2024) Precision dosing for patients on tricyclic antidepressants. Pharmacogenet Genomics 34(4):117–125 - PubMed

Publication types

MeSH terms

Substances

LinkOut - more resources