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Generative artificial intelligence (gAI) tools and large language models (LLMs) are gaining popularity among non-specialist audiences (patients, caregivers, and the general public) as a source of plain language medical information. AI-based models have the potential to act as a convenient, customizable and easy-to-access source of information that can improve patients' self-care and health literacy and enable greater engagement with clinicians. However, serious negative outcomes could occur if these tools fail to provide reliable, relevant and understandable medical information. Herein, we review published findings on opportunities and risks associated with such use of gAI/LLMs. We reviewed 44 articles published between January 2023 and July 2024. From the included articles, we find a focus on readability and accuracy; however, only three studies involved actual patients. Responses were reported to be reasonably accurate and sufficiently readable and detailed. The most commonly reported risks were oversimplification, over-generalization, lower accuracy in response to complex questions, and lack of transparency regarding information sources. There are ethical concerns that overreliance/unsupervised reliance on gAI/LLMs could lead to the \"humanizing\" of these models and pose a risk to patient health equity, inclusiveness and data privacy. For these technologies to be truly transformative, they must become more transparent, have appropriate governance and monitoring, and incorporate feedback from healthcare professionals (HCPs), patients, and other experts. Uptake of these technologies will also need education and awareness among non-specialist audiences around their optimal use as sources of plain language medical information.

Drug-drug-gene interactions can alter drug exposure and thereby increase the risk of clinically relevant outcomes, such as concentration-dependent toxicity (eg, tacrolimus toxicity in the context of altered CYP3A4/5 activity) or reduced treatment effectiveness. Despite their emerging relevance in clinical research, drug-drug-gene interactions remain understudied and are often ignored in clinical practice. Our objective was to assess the risk of phenoconversion by identifying potential drug-drug-gene interactions involving the transporters OATP1B1 and BCRP and the enzymes CYP2B6 and CYP3A4/5 in the Swiss population. Using claims data from the Helsana basic health insurance, we identified all persons of all ages with at least one drug claim between 2017 and 2021 and with Helsana basic health insurance coverage for at least one full year. For the five-year analysis, only persons with insurance for the entire five-year period were included. Within this study population, we assessed and ranked the frequency of potential drug-drug-gene interactions of a pharmacogenetic substrate and an inhibitor/inducer of OATP1B1, BCRP, CYP2B6, or CYP3A4/5. Potential drug-drug-gene interactions were defined as the co-occurrence of a pharmacogenetic substrate and an inhibitor/inducer within a 30- or 5-days window. During the entire five-year period, 18'523 (2.1%) and 12'645 (1.4%) individuals were exposed to potential drug-drug-gene interactions using the 30-day and 5-day windows, respectively. Potential drug-drug-gene interactions most frequently involved CYP3A4/5 (81.0% and 85.3%), followed by CYP2B6 (10.9% and 8.7%) and OATP1B1 (8.7% and 13.3%). The top three drug classes involved were nervous system drugs (75.1%), cardiovascular drugs (10.6%), and dermatologicals (4.0%). Quetiapine ranked first in the number of involved drug pairs, with quetiapine - metamizole being the predominant drug pair. In Switzerland, two out of 100 persons taking drugs metabolized or transported by OATP1B1, BCRP, CYP2B6, and CYP3A4/5 are at potential risk of phenoconversion, predominantly involving CYP3A4/5. These findings quantify real-world phenoconversion potential at the population level and underscore the need for outcome- and genotype-linked studies to determine clinical relevance. As this study was not designed to assess clinical outcomes, future genotype- and outcome-linked investigations are required to determine the actual impact on adverse drug reactions or treatment effectiveness.

We aimed to determine the prevalence of interactions between PGx drugs metabolized by CYP2C9, CYP2C19, and CYP2D6 and drugs that act as inhibitors or inducers of those enzymes in the Swiss population. We defined concomitant use of PGx drugs and inhibitors/inducers as instances where a claim of a PGx drug and a claim of an inducer or inhibitor concerning the same enzyme were made within a specified temporal window, either ± 5 days or ± 30 days. We assessed concomitant drug use between 2017 and 2021, using claims data from a Swiss insurance company (Helsana). Out of 894,748 individuals continuously insured, between 17.4% (± 5-days window) and 24.8% (± 30-days window) were exposed to potentially interacting drug pairs, with 1.5% to 2.2% being exposed to potentially strong interacting drug pairs. Individuals exposed to potentially interacting drugs were more frequently female, older and took a greater number of drugs than the general population. The majority of potential interactions were associated with CYP2D6 or CYP2C19. In light of the high prevalence of the simultaneous use of PGx drugs with inhibitor and inducer drugs, it is imperative to consider non-genetic factors, such as drug-induced phenoconversions, when interpreting PGx test results.

Anna Bollinger,1 Céline K Stäuble,1,2 Isabelle O Urdieux,1 Henriette E Meyer zu Schwabedissen,1 Samuel S Allemann1 1Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland; 2Institute of Hospital Pharmacy, Stadtspital Zürich, Zürich, SwitzerlandCorrespondence: Anna Bollinger, Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland, Tel +41612076631, Email a.bollinger@unibas.chBackground: Chronic pain is a prevalent and complex condition that often results in inadequate pharmacotherapy due to interindividual variability in drug response. Pharmacogenetics (PGx) offers a promising approach to personalize pain management, particularly since many analgesic drugs are PGx actionable. However, knowledge about the clinical relevance and patient perspective on PGx in Swiss chronic pain care remains limited.Methods: We conducted a cross-sectional online survey among chronic pain patients in the German-speaking regions of Switzerland. The questionnaire was developed to (1) assess the proportion of patients currently or previously treated with PGx actionable drugs, (2) evaluate therapy satisfaction and the perception of being taken seriously by healthcare professionals (HCPs), and (3) explore patients’ awareness of PGx and their interest in genetic pain predisposition.Results: Among the 725 participants who completed the survey, most reported current or past use of PGx actionable drugs: 85% non-steroidal anti-inflammatory drugs (NSAIDs), 54% opioids, 38% co-analgesics (antidepressants), and 73% proton-pump-inhibitors (PPIs) used as adjunctive therapy. Over one-third of participants reported no use of any analgesic drug. Therapy dissatisfaction was reported by 33%, and 28% felt not taken seriously by HCPs. Notably, 97% had never been offered PGx testing by an HCP. Despite this, 60% expressed interest in knowing their genetic pain predisposition, even if it would not affect their treatment. This interest was significantly higher among younger participants and those who were dissatisfied or felt not taken seriously by HCPs.Conclusion: This study provides the first large-scale, representative insights into the use of PGx actionable drugs and treatment patterns in Swiss chronic pain care. In particular, the high prevalence of PGx actionable drug use and the strong patient interest in genetic information support not only the clinical, but also the biopsychosocial potential of PGx for chronic pain management.Keywords: pharmacogenetics, PGx testing, pain sensitivity, therapy satisfaction, chronic pain care, Switzerland

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. 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. 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. 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.

Pharmacogenetic (PGx) panel testing could help to determine the heritable component of a rheumatoid arthritis (RA) patient's susceptibility for therapy failure and/or adverse drug reactions (ADRs) from methotrexate (MTX). Considering the literature mentioning the potential applicability of PGx panel testing within MTX regimens, we discuss the case of a patient who was treated with MTX, suffered from ADRs, and obtained a reactive PGx panel testing. We used a commercial PGx panel test involving the ABC-transporters P-glycoprotein (P-gp; gene: ), and breast cancer resistance protein (BCRP; gene: ), the solute carriers reduced folate carrier 1 (RFC1; gene: ), and organic anion transporting polypeptide 1B1 (OATP1B1; gene: ), and the enzymes inosine triphosphatase (ITPA), and glutathione transferase P1 (GSTP1). In addition, we genotyped the patient for the enzymes 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase (AICAR)/inosine monophosphate (IMP) cyclohydrolase (gene name: ), gamma-glutamyl hydrolase (gene name: ) and methylenetetrahydrofolate reductase (gene name: ). The PGx profile of the patient revealed genetic variants in SLC19A1, ABCB1, and MTHFR, which may explain the ADRs experienced during the treatment with MTX and a potentially lower efficacy of MTX. Based on our interpretation of the PGx profile, we recommended the patient to avoid MTX in the future. The MTX pathway is complex, which makes the interpretation of genetic variants affecting metabolism challenging. A reactive PGx panel test was applicable to explain ADRs experienced during MTX treatment for a patient with RA. However, the clinical utility of PGx-guided MTX treatment in a primary care setting is still limited. In order to base a recommendation for MTX on PGx data, we need genome-wide association studies, large prospective multicenter studies and PGx studies, which analyze different multi-gene haplotypes and gene-drug-drug interactions for MTX.

To collect opinions on medication management aids (MMAs) in general and on an electronic MMA (e-MMA) dispensing prepackaged polypharmacy in sealed pouches. The setting involved community-dwelling older adults in Basel, Switzerland, in 2013. The study involved 1) a 14-day trial with the e-MMA and 2) a focus group to identify general attributes of MMAs, their applicability to the e-MMA, and possible target groups for the e-MMA. Six participants using long-term polypharmacy and willing to try new technologies completed the 14-day trial and participated in the focus group. Inductive content analysis was performed to extract data. Participants rated ten of 17 general attributes as clearly applicable to the e-MMA and five as unsuitable. Attributes pertained to three interrelating themes: product design, patient support, and living conditions. Envisaged target groups were patients with time-sensitive medication regimens, patients with dementia, the visually impaired, and several patients living together to prevent accidental intake of the wrong medication. The evaluated e-MMA for prepackaged polypharmacy met the majority of the requirements set for an MMA. Patients' living conditions, such as mobility, remain the key determinants for acceptance of an e-MMA.