Genetic predisposition is one factor influencing interindividual drug response. Pharmacogenetic information can be used to guide the selection and dosing of certain drugs. However, the implementation of pharmacogenetics (PGx) in clinical practice remains challenging. Defining a formal structure, as well as concrete procedures and clearly defined responsibilities, may facilitate and increase the use of PGx in clinical practice. Over 140 patient cases from an observational study in Switzerland formed the basis for the design and refinement of a pharmacist-led pharmacogenetics testing and counselling service (PGx service) in an interprofessional setting. Herein, we defined a six-step approach, including: (1) patient referral; (2) pre-test-counselling; (3) PGx testing; (4) medication review; (5) counselling; (6) follow-up. The six-step approach supports the importance of an interprofessional collaboration and the role of pharmacists in PGx testing and counselling across healthcare settings.

Purpose: Pharmacogenetics (PGx) is an emerging aspect of personalized medicine with the potential to increase efficacy and safety of pharmacotherapy. However, PGx testing is still not routinely integrated into clinical practice. We conducted an observational case series study where PGx information from a commercially available panel test covering 30 genes was integrated into medication reviews. The aim of the study was to identify the drugs that are most frequently object of drug-gene-interactions (DGI) in the study population. Patients and Methods: In out-patient and in-patient settings, we recruited 142 patients experiencing adverse drug reaction (ADR) and/or therapy failure (TF). Collected anonymized data from the individual patient was harmonized and transferred to a structured database. Results: The majority of the patients had a main diagnosis of a mental or behavioral disorder (ICD-10: F, 61%), of musculoskeletal system and connective tissue diseases (ICD-10: M, 21%), and of the circulatory system (ICD-10: I, 11%). The number of prescribed medicines reached a median of 7 per person, resulting in a majority of patients with polypharmacy (≥ 5 prescribed medicines, 65%). In total, 559 suspected DGI were identified in 142 patients. After genetic testing, an association with at least one genetic variation was confirmed for 324 suspected DGI (58%) caused by 64 different drugs and 21 different genes in 141 patients. After 6 months, PGx-based medication adjustments were recorded for 62% of the study population, whereby differences were identified in subgroups. Conclusion: The data analysis from this study provides valuable insights for the main focus of further research in the context of PGx. The results indicate that most of the selected patients in our sample represent suitable target groups for PGx panel testing in clinical practice, notably those taking drugs for mental or behavioral disorder, circulatory diseases, immunological diseases, pain-related diseases, and patients experiencing polypharmacy.

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Pharmacogenetic (PGx) testing is a promising approach for optimizing drug therapies. However, there is limited knowledge regarding its real-world utilization and long-term impact in clinical practice. This study assessed how often PGx information informs treatment decisions and evaluated patients’ perspectives on its use and non-use, identifying enablers for PGx implementation. A mixed-methods study was conducted with 24 patients with a median of 1 year after PGx testing. Medication and health-related data were collected at enrollment and at the follow-up 1 year later using a semi-structured questionnaire. At the follow-up, 62 medication changes were identified in 18 patients. A median of four medication changes per patient were initiated mainly by medical specialists (58%). PGx information was considered for 15 patients in 39 medication changes (63%). Patient-reported factors contributing to the non-use of PGx information included a lack of knowledge and interest among healthcare professionals (HCPs), structural and administrative barriers, and an over-reliance on patient advocacy. Potential enablers should address targeted PGx education, interprofessional collaboration, awareness among policymakers, and concise recommendations focused on PGx-actionable drugs from testing providers. By implementing these interdependent enablers, PGx can evolve into a long-term, clinically integrated cornerstone of individualized pharmacotherapy.

Coproporphyrin I (CPI) is an established endogenous biomarker for detecting drug–drug interactions (DDIs) involving the hepatic uptake transporter Organic Anion Transporting Polypeptide 1B1 (OATP1B1, gene SLCO1B1 ). While CPI has been extensively studied in healthy volunteers using controlled pre‐ and post‐OATP1B1‐inhibitor sampling, its applicability in samples obtained in clinical routine from multimorbid, polymedicated patients remains poorly characterized. In this study, we evaluated whether single timepoint CPI concentrations obtained in clinical routine can be used to assess in vivo OATP1B1 activity by integrating genetic and pharmacological information. Serum CP levels were measured in patients undergoing pharmacist‐led medication reviews following therapy failure or adverse drug reactions. CPI concentrations were analyzed in relation to genotype‐predicted OATP1B1 phenotypes and exposure to drugs with potential impact on OATP1B1, which were identified following a previously published work by Stäuble et al ., and their interaction with OATP1B1 was further evaluated with the herein presented in vitro transporter assay. CPI levels showed an association with drug intake and genotype‐predicted phenotypes. Moreover, patients with decreased‐ or normal function phenotypes taking putative OATP1B1 inhibitors exhibited CPI levels comparable to patients carrying the poor‐function or decreased‐function phenotype without intake of such drugs, respectively, consistent with phenoconversion. These findings provide initial evidence that CPI may serve as an endogenous biomarker for identifying drug–drug‐gene interactions in clinical routine to support a safer and more effective drug therapy. Furthermore, our data emphasize the need for further investigations in larger and more diverse patient populations to validate the findings on CPI.

Objective: This study evaluates the performance of 7 synthetic data generation (SDG) methods—synthpop, avatar, copula, copulagan, ctgan, tvae, and the large language models-based tabula—for supporting pharmacogenetics (PGx) research. Materials and Methods: We used PGx profiles from 142 patients with adverse drug reactions or therapeutic failures, considering 2 scenarios: (1) a high-dimensional genotype dataset (104 variables) and (2) a phenotype dataset (24 variables). Models were assessed for (1) broad utility using propensity score mean squared error (pMSE), (2) specific utility via weighted F1 score in a Train-Synthetic-Test-Real framework, and (3) privacy risk as ε-identifiability. Results: Copula and synthpop consistently achieved strong performance across both datasets, combining low ε-identifiability (0.25-0.35) with competitive utility. Deep learning models like tabula and tvae trained for 10 000 epochs achieved lower pMSE but had higher ε-identifiability (>0.4) and limited gains in predictive performance. Specific utility was only weakly correlated with broad utility, indicating that distributional fidelity does not ensure predictive relevance. Copula and synthpop often outperformed original data in weighted F1 scores, especially under noise or data imbalance. Discussion: While deep learning models can achieve high distributional fidelity (pMSE), they often incur elevated ε-identifiability, raising privacy concerns. Traditional methods like copula and synthpop consistently offer robust utility and lower re-identification risk, particularly for high-dimensional data. Importantly, general utility does not predict specific utility (F1 score), emphasizing the need for multimetric evaluation. Conclusion: No single SDG method dominated across all criteria. For privacy-sensitive PGx applications, classical methods such as copula and synthpop offer a reliable trade-off between utility and privacy, making them preferable for high-dimensional, limited-sample settings.