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.

There is growing evidence that pharmacogenetic analysis can improve drug therapy for individual patients. In Switzerland, pharmacists are legally authorized to initiate pharmacogenetic tests. However, pharmacogenetic tests are rarely conducted in Swiss pharmacies. Therefore, we aimed to identify implementation strategies that facilitate the integration of a pharmacist-led pharmacogenetic service into clinical practice. To achieve this, we conducted semi-structured interviews with pharmacists and physicians regarding the implementation process of a pharmacist-led pharmacogenetic service. We utilized the Consolidated Framework for Implementation Research (CFIR) to identify potential facilitators and barriers in the implementation process. Additionally, we employed Expert Recommendations for Implementing Change (ERIC) to identify strategies mentioned in the interviews and used the CFIR-ERIC matching tool to identify additional strategies. We obtained interview responses from nine pharmacists and nine physicians. From these responses, we identified 7 CFIR constructs as facilitators and 12 as barriers. Some of the most commonly mentioned barriers included unclear procedures, lack of cost coverage by health care insurance, insufficient pharmacogenetics knowledge, lack of interprofessional collaboration, communication with the patient, and inadequate e-health technologies. Additionally, we identified 23 implementation strategies mentioned by interviewees using ERIC and 45 potential strategies using the CFIR-ERIC matching tool. In summary, we found that significant barriers hinder the implementation process of this new service. We hope that by highlighting potential implementation strategies, we can advance the integration of a pharmacist-led pharmacogenetic service in Switzerland.

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.

Patients suffering from chronic pain may respond differently to analgesic medications. For some, pain relief is insufficient, while others experience side effects. Although pharmacogenetic testing is rarely performed in the context of analgesics, response to opiates, non-opioid analgesics, and antidepressants for the treatment of neuropathic pain can be affected by genetic variants. We describe a female patient who suffered from a complex chronic pain syndrome due to a disc hernia. Due to insufficient response to oxycodone, fentanyl, and morphine in addition to non-steroidal anti-inflammatory drug (NSAID)-induced side effects reported in the past, we performed panel-based pharmacogenotyping and compiled a medication recommendation. The ineffectiveness of opiates could be explained by a combined effect of the decreased activity in cytochrome P450 2D6 (CYP2D6), an increased activity in CYP3A, and an impaired drug response at the µ-opioid receptor. Decreased activity for CYP2C9 led to a slowed metabolism of ibuprofen and thus increased the risk for gastrointestinal side effects. Based on these findings we recommended hydromorphone and paracetamol, of which the metabolism was not affected by genetic variants. Our case report illustrates that an in-depth medication review including pharmacogenetic analysis can be helpful for patients with complex pain syndrome. Our approach highlights how genetic information could be applied to analyze a patient’s history of medication ineffectiveness or poor tolerability and help to find better treatment options.

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.

Suspected adverse drug reactions (ADRs) during treatment with clozapine often prompt therapeutic drug monitoring (TDM) in clinical practice. Currently, there is no official recommendation for pharmacogenetic (PGx) testing in the context of clozapine therapy. In this case report, we demonstrate and discuss the challenges of interpreting PGx and TDM results highlighting the possibilities and limitations of both analytical methods. A 36-year-old male patient with catatonic schizophrenia was treated with clozapine. He experienced multiple hospitalizations due to elevated creatine kinase (CK) levels (up to 9000 U/L, reference range: 30-200 U/L). With no other medical explanation found, physicians suspected clozapine-induced ADRs. However, plasma levels of clozapine were consistently low or subtherapeutic upon admission, prompting us to conduct a PGx analysis and retrospectively review the patient’s TDM data, progress notes, and discharge reports. We investigated two possible hypotheses to explain the symptoms despite low clozapine plasma levels: Hypothesis i. suggested the formation and accumulation of a reactive intermediate metabolite due to increased activity in cytochrome P450 3A5 and reduced activity in glutathione S-transferases 1, leading to myotoxicity. Hypothesis ii. proposed under-treatment with clozapine, resulting in ineffective clozapine levels, leading to a rebound effect with increased catatonic symptoms and CK levels. After considering both data sources (PGx and TDM), hypothesis ii. appeared more plausible. By comprehensively assessing all available TDM measurements and examining them in temporal correlation with the drug dose and clinical symptoms, we observed that CK levels normalized when clozapine plasma levels were raised to the therapeutic range. This was achieved through hospitalization and closely monitored clozapine intake. Therefore, we concluded that the symptoms were not an ADR due to altered clozapine metabolism but rather the result of under-treatment. Interpreting TDM and PGx results requires caution. Relying solely on isolated PGx or single TDM values can result in misinterpretation of drug reactions. We recommend considering the comprehensive patient history, including treatment, dosages, laboratory values, clinic visits, and medication adherence.