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

• Published in: European journal of clinical pharmacology Vol. 81; no. 3; pp. 451 - 462
• Main Authors: Saab, Yolande, Nakad, Zahi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2025; Springer Nature B.V
• Subjects: Artificial intelligence; Biomedical and Life Sciences; Biomedicine; CYP2D6 protein; Cytochrome P-450 CYP2C19 - genetics; Cytochrome P-450 CYP2D6 - genetics; Cytochrome P450; Decision making; Dosage; Dose-Response Relationship, Drug; Drug dosages; Gene polymorphism; Genotype; Humans; Mathematical models; Models, Theoretical; Patients; Pharmaceutical Preparations - administration & dosage; Pharmaceutical Preparations - metabolism; Pharmacogenetics - methods; Pharmacogenomics; Pharmacology/Toxicology; Phenotypes; Polymorphism, Genetic; Allele activity; Dose prediction; Cytochrome P450; Drug metabolism; Genotype; Personalized medicine; Artificial intelligence
• ISSN: 0031-6970; 1432-1041; 1432-1041
• DOI: 10.1007/s00228-025-03805-x

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.