Pharmacogenomic testing for drug-metabolizing enzymes is heavily promoted to personalize therapy with expectations of improved therapeutic outcomes. It promises predicting an individual’s drug response by identifying genetic variants in drug-metabolizing enzymes, particularly cytochrome P450s. However, for a variety of reasons, genotype-predicted activity does not always correlate with functional activity (phenotype). This mismatch, known as phenoconversion, results from alteration of endogenous drug metabolizing enzyme activity, shifting it away from a genetically-determined metabolizer status, and effectively reassigning a patient to a different functional phenotype. Phenoconversion can occur because of co-administered clinically prescribed drugs; inflammatory co-morbidities; dietary factors including nutrients and bioactive compounds; epigenetic modification; gut microbiome; and biological aging.

In polypharmacy settings, where multiple concurrent medications are common, the likelihood of phenoconversion is high and is therefore highly relevant to drug response and overall patient care. However, clinical causes and consequences of phenoconversion are often overlooked in genotype-guided dosing studies and therapeutic strategies. Furthermore, most tools designed to support pharmacogenomic-based therapeutic decisions frequently fail to account for these dynamic shifts in drug-metabolizing status. The result is a critical gap between genotypic prediction and phenotypic reality, driving toxicity, therapeutic failure, and avoidable adverse events, especially in high-risk patient populations.

This Research Topic aims to advance the understanding of phenoconversion as a clinically distinct and actionable phenomenon, highlighting its prevalence and implications for patient safety, as well as promoting tools and strategies that integrate patient genotypes with modulators of drug metabolizing enzymes into precision prescribing.

Topics of interest include, but are not limited to:

- Mechanisms and prevalence of phenoconversion across drug-metabolizing enzymes
- Drug-drug-gene interactions and clinical outcomes in polypharmacy populations
- Inflammatory cytokines as silent accomplices of phenoconversion
- Phenoconversion in high-risk groups: elderly, oncology, and psychiatric patients
- Drug classes which are considered high risk for inducing phenoconversion
- Phenoconversion and therapeutic drug monitoring: using measured phenotype to refine genotype-guided dosing strategies
- EHR-based clinical decision support for phenoconversion detection
- Disparities in phenoconversion prevalence: geography, disease burden, and ethnicity
- Impact of biological aging on genotype-phenotype mismatch
- Microbiome-mediated phenoconversion: the role of the gut-liver axis
- Integration of phenoconversion into genotype-guided prescribing frameworks and guidelines

Please note: If patient data are analyzed, a comprehensive description of the patients including sex, age, diagnostic criteria, inclusion and exclusion criteria, disease stage, therapy received, comorbidities, as well as additional clinical information and assessment of clinical response/effects should be included. If genetic, proteomics, metabolomics, or other omics data are analyzed, a comprehensive description of the methods and the rationale for selecting the specific data studied should be provided. Studies related to natural compounds, herbal extracts, or traditional medicine products are outside the scope of the Specialty Section Pharmacogenetics and Pharmacogenomics, and should instead be submitted to the specialty section of Ethnopharmacology. Studies solely based on the analysis of public databases or published evidence, with no further experimental insights or insufficient experimental validation, will not be included in this Research Topic. Articles should fit into the journal’s mission and scope. Further information on the journal’s scope can be accessed at: https://www.frontiersin.org/journals/pharmacology/about

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Pitfalls in Genotype-Based Personalized Therapy: Phenoconversion Induced by Co-Medications and Co-Morbidities