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Abstract Background The Alpe-DPD study (NCT02324452) demonstrated that prospective genotyping and dose-individualization using four alleles in DPYD (DPYD*2A/rs3918290, c.1236G > A/rs75017182, c.2846A > T/rs67376798 and c.1679 T > G/rs56038477) can mitigate the risk of severe fluoropyrimidine toxicity. However, this could not prevent all toxicities. The goal of this study was to identify additional genetic variants, both inside and outside DPYD, that may contribute to fluoropyrimidine toxicity. Methods Biospecimens and data from the Alpe-DPD study were used. Exon sequencing was performed to identify risk variants inside DPYD. In silico and in vitro analyses were used to classify DPYD variants. A genome-wide association study (GWAS) with severe fluoropyrimidine-related toxicity was performed to identify variants outside DPYD. Association with severe toxicity was assessed using matched-pair analyses for the exon sequencing and logistic, Cox, and ordinal regression analyses for GWAS. Results Twenty-four non-synonymous, frameshift, and splice site DPYD variants were detected in ten of 986 patients. Seven of these variants (c.1670C > T, c.1913 T > C, c.1925 T > C, c.506delC, c.731A > C, c.1740 + 1G > T, c.763 − 2A > G) were predicted to be deleterious. The carriers of either of these variants showed a trend towards a 2.14-fold (95% CI, 0.41–11.3, P = 0.388) increased risk of severe toxicity compared to matched controls (N = 30). After GWAS of 942 patients, no individual single nucleotide polymorphisms achieved genome-wide significance (P ≤ 5 × 10−8), however, five variants were suggestive of association (P

Abstract Voriconazole is the cornerstone of the treatment and prevention of fungal infections. While there is a good correlation between CYP2C19 genotype and voriconazole exposure during prophylactic treatment, no correlation was found in patients with invasive aspergillosis. Proinflammatory cytokines result in inhibition of CYP2C19 enzyme activity (and may result in phenoconversion). Here we investigated the relationship between inflammation, CYP2C19 genotype‐predicted‐phenotype, and CYP2C19 activity in patients receiving voriconazole. Data were obtained from two prospective studies investigating voriconazole treatment (NCT02074462 and NCT00893555). Dose‐corrected voriconazole plasma concentration and C‐reactive protein (CRP) were used as proxies for CYP2C19 activity and inflammation, respectively. After data extraction and synthesis, data from 39 patients with paired voriconazole and CRP measurements were available. The distribution of CYP2C19 genotype‐predicted metabolizer phenotypes was 31% intermediate (IM), 41% normal (NM), and 28% rapid metabolizer (RM). During inflammation, dose‐corrected voriconazole levels were increased by 245%, 278%, and 486% for CYP2C19 NMs IMs and RMs, respectively. Patients with moderate or high CRP levels (>50 mg/L) were phenoconverted to a lower metabolizer phenotype irrespective of their CYP2C19 genotype. In a subgroup analysis of eight patients with longitudinal data available with and without inflammation, the pattern of the dose‐corrected voriconazole and CRP measurements were similar, with CYP2C19 activity following decreasing or increasing CRP levels. In conclusion, voriconazole plasma concentrations increase during inflammation due to downregulation of CYP2C19 activity. While this effect appears largest for CYP2C19 RMs, no clinically relevant differences were observed between the CYP2C19 genotypes.

BackgroundPharmacogenomics (PGx) is a well-established concept of how genes impact medication response, with many studies demonstrating reductions in medication side effects, improved efficacy and cost effectiveness. Despite these benefits, implementation of PGx in daily practice remains limited. Studies on the implementation of PGx in clinical practice have previously found that inadequate knowledge is one of the main barriers. Details regarding specifically which educational needs exist among family medicine clinicians requires further study.ObjectiveThe aim of this study was to identify both the perceived role that pharmacogenomics (PGx) could play in primary care practice, the knowledge gaps that family medicine clinicians experience, and the skills they require to use PGx in their daily practice.MethodsTo achieve this aim, the attitudes, knowledge, barriers, skills needed, and preferred educational program were explored in a family medicine clinician focus group study via a semi-structured interview and knowledge quiz. Second, multidisciplinary focus groups provided information on the level of knowledge and necessary skills to use PGx in patient care. After gathering key recorded information from both focus groups, the perceived role pharmacogenomics could possibly play in primary care, the predominant knowledge gaps, and the most appropriate educational program was determined by qualitative analysis.ResultsFour themes emerged regarding the PGx educational needs and the role of PGx in family medicine: 1) need for PGx competences, 2) insight into the roles and responsibilities of PGx services, 3) optimization of PGx workflow through artificial intelligence integrated in the electronic health record, and 4) the ethical dilemmas and psychological effects related to PGx. These themes reflect a shift in the role of PGx in family medicine with implications for education.ConclusionThe results obtained from this study will help improve the implementation of PGx in daily practice, and consequently, may result in increased utilization of PGx, thereby resulting in improved medication efficacy and reduced side effects.

Abstract The antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and P‐glycoprotein (P‐gp) and is therefore recommended for use in clinical drug–drug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and P‐gp, it is susceptible to DDIs involving these proteins. Physiologically‐based pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drug–drug(–gene) interaction (DD(G)I) network with a diverse set of CYP3A4 and P‐gp perpetrators as well as CYP2D6 and P‐gp victims. The quinidine parent‐metabolite model including 3‐hydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1–600 mg). The model covers efflux transport via P‐gp and metabolic transformation to either 3‐hydroxyquinidine or unspecified metabolites via CYP3A4. The 3‐hydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within two‐fold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within two‐fold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is.

Abstract Background Cardiovascular diseases and especially Acute Coronary Syndrome (ACS) constitute a major health issue impacting millions of patients worldwide. Being a leading cause of death and hospital admissions in many European countries including Spain, it accounts for enormous amounts of healthcare expenditures for its management. Clopidogrel is one of the oldest antiplatelet medications used as standard of care in ACS. Methods In this study, we performed an economic evaluation study to estimate whether a genome-guided clopidogrel treatment is cost-effective compared to conventional one in a large cohort of 243 individuals of Spanish origin suffering from ACS and treated with clopidogrel. Data were derived from the U-PGx PREPARE clinical trial. Effectiveness was measured as survival of individuals while study data on safety and efficacy, as well as on resource utilization associated with each adverse drug reaction were used to measure costs to treat these adverse drug reactions. A generalized linear regression model was used to estimate cost differences for both study groups. Results Based on our findings, PGx-guided treatment group is cost-effective. PGx-guided treatment demonstrated to have 50% less hospital admissions, reduced emergency visits and almost 13% less ADRs compared to the non-PGx approach with mean QALY 1.07 (95% CI, 1.04–1.10) versus 1.06 (95% CI, 1.03–1.09) for the control group, while life years for both groups were 1.24 (95% CI, 1.20–1.26) and 1.23 (95% CI, 1.19–1.26), respectively. The mean total cost of PGx-guided treatment was 50% less expensive than conventional therapy with clopidogrel [€883 (95% UI, €316–€1582), compared to €1,755 (95% UI, €765–€2949)]. Conclusion These findings suggest that PGx-guided clopidogrel treatment represents a cost-effective option for patients suffering from ACS in the Spanish healthcare setting.

Abstract CYP3A4 activity shows considerable interindividual variability. Although studies indicate 60%–80% is heritable, common single nucleotide variants (SNVs) in CYP3A4 together only explain ~10%. Transcriptional factors, such as the testis‐specific Y‐encoded‐like proteins (TSPYLs) family, have been reported to regulate the expression of CYP enzymes including CYP3A4 in vitro. Here, we investigated the effect of genetic variants in TSPYL on CYP3A4 activity using data from a clinical study and a human liver bank. Five SNVs (rs3828743, rs10223646, rs6909133, rs1204807, and rs1204811) in TSPYL were selected because of a reported effect on CYP3A4 expression in vitro or suggested clinical effect. For the clinical study, whole blood concentrations, clinical data, and DNA were available from 295 kidney transplant recipients participating in the prospective MECANO study. A multivariate pharmacokinetic model adjusted for body weight, steroid treatment, and CYP3A4 genotype was used to assess the effect of the genetic variants on cyclosporine clearance. In multivariate analysis, homozygous carriers of rs3828743 had a 18% lower cyclosporin clearance compared to the wild‐type and heterozygous patients (28.72 vs. 35.03 L/h, p = 0.018) indicating a lower CYP3A4 activity and an opposite direction of effect compared to the previously reported increased CYP3A4 expression. To validate, we tested associations between rs3828743 and CYP3A4 mRNA and protein expression as well as enzyme activity with data from a liver bank (n = 150). No association with any of these end points was observed. In conclusion, the totality of evidence is not in support of a significant role for TSPYL SNV rs3828743 in explaining variability in CYP3A4 activity.

Abstract The immunosuppressant and narrow therapeutic index drug tacrolimus is metabolized mainly via cytochrome P450 (CYP) 3A4 and CYP3A5. For its pharmacokinetics (PK), high inter‐ and intra‐individual variability can be observed. Underlying causes include the effect of food intake on tacrolimus absorption as well as genetic polymorphism in the CYP3A5 gene. Furthermore, tacrolimus is highly susceptible to drug–drug interactions, acting as a victim drug when coadministered with CYP3A perpetrators. This work describes the development of a whole‐body physiologically based pharmacokinetic model for tacrolimus as well as its application for investigation and prediction of (i) the impact of food intake on tacrolimus PK (food–drug interactions [FDIs]) and (ii) drug–drug(−gene) interactions (DD[G]Is) involving the CYP3A perpetrator drugs voriconazole, itraconazole, and rifampicin. The model was built in PK‐Sim® Version 10 using a total of 37 whole blood concentration–time profiles of tacrolimus (training and test) compiled from 911 healthy individuals covering the administration of tacrolimus as intravenous infusions as well as immediate‐release and extended‐release capsules. Metabolism was incorporated via CYP3A4 and CYP3A5, with varying activities implemented for different CYP3A5 genotypes and study populations. The good predictive model performance is demonstrated for the examined food effect studies with 6/6 predicted FDI area under the curve determined between first and last concentration measurements (AUClast) and 6/6 predicted FDI maximum whole blood concentration (Cmax) ratios within twofold of the respective observed ratios. In addition, 7/7 predicted DD(G)I AUClast and 6/7 predicted DD(G)I Cmax ratios were within twofold of their observed values. Potential applications of the final model include model‐informed drug discovery and development or the support of model‐informed precision dosing.

Abstract In recent years, the use of artificial intelligence (AI) in health care has risen steadily, including a wide range of applications in the field of pharmacology. AI is now used throughout the entire continuum of pharmacology research and clinical practice and from early drug discovery to real‐world datamining. The types of AI models used range from unsupervised clustering of drugs or patients aimed at identifying potential drug compounds or suitable patient populations, to supervised machine learning approaches to improve therapeutic drug monitoring. Additionally, natural language processing is increasingly used to mine electronic health records to obtain real‐world data. In this mini‐review, we discuss the basics of AI followed by an outline of its application in pharmacology research and clinical practice.

Introduction: Pharmacogenetics-informed drug prescribing is increasingly applied in clinical practice. Typically, drug metabolizing phenotypes are determined based on genetic test results, whereupon dosage or drugs are adjusted. Drug-drug-interactions (DDIs) caused by concomitant medication can however cause mismatches between predicted and observed phenotypes (phenoconversion). Here we investigated the impact of CYP2C19 genotype on the outcome of CYP2C19-dependent DDIs in human liver microsomes.Methods: Liver samples from 40 patients were included, and genotyped for CYP2C19*2, *3 and *17 variants. S-mephenytoin metabolism in microsomal fractions was used as proxy for CYP2C19 activity, and concordance between genotype-predicted and observed CYP2C19 phenotype was examined. Individual microsomes were subsequently co-exposed to fluvoxamine, voriconazole, omeprazole or pantoprazole to simulate DDIs.Results: Maximal CYP2C19 activity (Vmax) in genotype-predicted intermediate metabolizers (IMs; *1/*2 or *2/*17), rapid metabolizers (RMs; *1/*17) and ultrarapid metabolizers (UMs; *17/*17) was not different from Vmax of predicted normal metabolizers (NMs; *1/*1). Conversely, CYP2C19*2/*2 genotyped-donors exhibited Vmax rates ∼9% of NMs, confirming the genotype-predicted poor metabolizer (PM) phenotype. Categorizing CYP2C19 activity, we found a 40% concordance between genetically-predicted CYP2C19 phenotypes and measured phenotypes, indicating substantial phenoconversion. Eight patients (20%) exhibited CYP2C19 IM/PM phenotypes that were not predicted by their CYP2C19 genotype, of which six could be linked to the presence of diabetes or liver disease. In subsequent DDI experiments, CYP2C19 activity was inhibited by omeprazole (−37% ± 8%), voriconazole (−59% ± 4%) and fluvoxamine (−85% ± 2%), but not by pantoprazole (−2 ± 4%). The strength of CYP2C19 inhibitors remained unaffected by CYP2C19 genotype, as similar percental declines in CYP2C19 activity and comparable metabolism-dependent inhibitory constants (Kinact/KI) of omeprazole were observed between CYP2C19 genotypes. However, the consequences of CYP2C19 inhibitor-mediated phenoconversion were different between CYP2C19 genotypes. In example, voriconazole converted 50% of *1/*1 donors to a IM/PM phenotype, but only 14% of *1/*17 donors. Fluvoxamine converted all donors to phenotypic IMs/PMs, but *1/*17 (14%) were less likely to become PMs than *1/*1 (50%) or *1/*2 and *2/*17 (57%).Conclusion: This study suggests that the differential outcome of CYP2C19-mediated DDIs between genotypes are primarily dictated by basal CYP2C19 activity, that may in part be predicted by CYP2C19 genotype but likely also depends on disease-related factors.

Cytochrome P450 3A (CYP3A) subfamily enzymes are involved in the metabolism of 40% of drugs in clinical use. Twin studies have indicated that 66% of the variability in CYP3A4 activity is hereditary. Yet, the complexity of the CYP3A locus and the lack of distinct drug metabolizer phenotypes has limited the identification and clinical application of CYP3A genetic variants compared to other Cytochrome P450 enzymes. In recent years evidence has emerged indicating that a substantial part of the missing heritability is caused by low frequency genetic variation. In this review, we outline the current pharmacogenomics knowledge of CYP3A activity and discuss potential future directions to improve our genetic knowledge and ability to explain CYP3A variability.

Aim: Prospective studies support the clinical impact of pharmacogenomics (PGx)-guided prescribing to reduce severe and potentially fatal adverse effects. Drug-gene interactions (DGIs) preventing potential drug-related deaths have been categorized as “essential” by the Dutch Pharmacogenetics Working Group (DPWG). The collective clinical impact and cost-effectiveness of this sub-set is yet undetermined. Therefore, we aim to assess impact and cost-effectiveness of “essential” PGx tests for prevention of gene-drug-related deaths, when adopted nation-wide.Methods: We used a decision-analytic model to quantify the number and cost per gene-drug-related death prevented, from a 1-year Dutch healthcare perspective. The modelled intervention is a single gene PGx-test for CYP2C19, DPYD, TPMT or UGT1A1 to guide prescribing based on the DPWG recommendations among patients in the Netherlands initiating interacting drugs (clopidogrel, capecitabine, systemic fluorouracil, azathioprine, mercaptopurine, tioguanine or irinotecan).Results: For 148,128 patients initiating one of seven drugs in a given year, costs for PGx-testing, interpretation, and drugs would increase by €21.4 million. Of these drug initiators, 35,762 (24.1%) would require an alternative dose or drug. PGx-guided prescribing would relatively reduce gene-drug related mortality by 10.6% (range per DGI: 8.1–14.5%) and prevent 419 (0.3% of initiators) deaths a year. Cost-effectiveness is estimated at €51,000 per prevented gene-drug-related death (range per DGI: €-752,000–€633,000).Conclusion: Adoption of PGx-guided prescribing for “essential” DGIs potentially saves the lives of 0.3% of drug initiators, at reasonable costs.

Translating CYP2D6 genotype to metabolizer phenotype is not standardized across clinical laboratories offering pharmacogenetic (PGx) testing and PGx clinical practice guidelines, such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPWG). The genotype to phenotype translation discordance between laboratories and guidelines can cause discordant cytochrome P450 2D6 (CYP2D6) phenotype assignments and, thus lead to inconsistent therapeutic recommendations and confusion among patients and clinicians. A modified‐Delphi method was used to obtain consensus for a uniform system for translating CYP2D6 genotype to phenotype among a panel of international CYP2D6 experts. Experts with diverse involvement in CYP2D6 interpretation (clinicians, researchers, genetic testing laboratorians, and PGx implementers; n = 37) participated in conference calls and surveys. After completion of 7 surveys, a consensus (> 70%) was reached with 82% of the CYP2D6 experts agreeing to the final CYP2D6 genotype to phenotype translation method. Broad adoption of the proposed CYP2D6 genotype to phenotype translation method by guideline developers, such as CPIC and DPWG, and clinical laboratories as well as researchers will result in more consistent interpretation of CYP2D6 genotype.

Background The clinical implementation of pharmacogenomics (PGx) could be one of the first milestones towards realizing personalized medicine in routine care. However, its widespread adoption requires the availability of suitable clinical decision support (CDS) systems, which is often impeded by the fragmentation or absence of adequate health IT infrastructures. We report results of CDS implementation in the large-scale European research project Ubiquitous Pharmacogenomics (U-PGx), in which PGx CDS was rolled out and evaluated across more than 15 clinical sites in the Netherlands, Spain, Slovenia, Italy, Greece, United Kingdom and Austria, covering a wide variety of healthcare settings. Methods We evaluated the CDS implementation process through qualitative and quantitative process indicators. Quantitative indicators included statistics on generated PGx reports, median time from sampled upload until report delivery and statistics on report retrievals via the mobile-based CDS tool. Adoption of different CDS tools, uptake and usability were further investigated through a user survey among healthcare providers. Results of a risk assessment conducted prior to the implementation process were retrospectively analyzed and compared to actual encountered difficulties and their impact. Results As of March 2021, personalized PGx reports were produced from 6884 genotyped samples with a median delivery time of twenty minutes. Out of 131 invited healthcare providers, 65 completed the questionnaire (response rate: 49.6%). Overall satisfaction rates with the different CDS tools varied between 63.6% and 85.2% per tool. Delays in implementation were caused by challenges including institutional factors and complexities in the development of required tools and reference data resources, such as genotype-phenotype mappings. Conclusions We demonstrated the feasibility of implementing a standardized PGx decision support solution in a multinational, multi-language and multi-center setting. Remaining challenges for future wide-scale roll-out include the harmonization of existing PGx information in guidelines and drug labels, the need for strategies to lower the barrier of PGx CDS adoption for healthcare institutions and providers, and easier compliance with regulatory and legal frameworks.

Pharmacogenomics (PGx) studies the use of genetic data to optimize drug therapy. Numerous clinical centers have commenced implementing pharmacogenetic tests in clinical routines. Next-generation sequencing (NGS) technologies are emerging as a more comprehensive and time- and cost-effective approach in PGx. This review presents the main considerations for applying NGS in guiding drug treatment in clinical practice. It discusses both the advantages and the challenges of implementing NGS-based tests in PGx. Moreover, the limitations of each NGS platform are revealed, and the solutions for setting up and management of these technologies in clinical practice are addressed.

Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance to overcome cancer treatment resistance. Model‐based approaches may help achieve this goal. In this review, we comprehensively summarized mathematical models of tumor dynamics for solid tumors and of drug resistance evolution. Models displayed by ordinary differential equations, algebraic equations, and partial differential equations for characterizing tumor burden dynamics are introduced and discussed. As for tumor resistance evolution, stochastic and deterministic models are introduced and discussed. The results may facilitate a novel model‐based analysis on anticancer treatment response and the occurrence of resistance, which incorporates both tumor dynamics and resistance evolution. The opportunities of a model‐based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.

IntroductionCertain HLA variants are associated with an increased risk of hypersensitivity reactions to specific drugs. Both the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPWG) have issued actionable HLA gene – drug interaction guidelines but diagnostic test criteria remain largely unknown. We present an overview of the diagnostic test criteria of the actionable HLA – drug pairs.MethodsA systematic literature search was conducted in PubMed, Embase, Web of Science and Cochrane Library. Original case-control and cohort studies were selected and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and number needed to genotype (NNG) were calculated for the actionable HLA-drug pairs.ResultsIn general, the HLA tests show high specificity and NPV for predicting hypersensitivity reactions. The sensitivity of HLA tests shows a wide range, from 0-33% for HLA-B*1502 testing to predict lamotrigine induced SJS/TEN up to 100% for HLA-B*5701 to predict immunologically confirmed abacavir hypersensitivity syndrome (ABC-HSR). PPV is low for all tests except for HLA-B*5701 and ABC-HSR which is approximately 50%. HLA-B*5701 to predict ABC-HSR shows the lowest NNG followed by HLA-B*5801 for allopurinol induced severe cutaneous adverse drug reactions and HLA-B*1502 for carbamazepine induced SJS/TEN.DiscussionThis is the first overview of diagnostic test criteria for actionable HLA-drug pairs. Studies researching HLA genes and hypersensitivity are scarce for some of the HLA-drug pairs in some populations and patient numbers in studies are small. Therefore, more research is necessary to calculate the diagnostic test criteria more accurately.

Tacrolimus is metabolized by CYP3A4 and CYP3A5 enzymes. Patients expressing CYP3A5 (in Caucasian patients about 15% of the population but more frequent in African Americans and Asians) have a dose requirement that is around 50% higher than non-expressers to reach the target concentration. CYP3A5 expressers can be considered fast metabolizers. The trough concentration/dose (C0/D) ratio of tacrolimus has recently been proposed as a prognostic marker for poor outcome after kidney transplantation. Patients with a low C0/D ratio (also referred to as fast metabolizers) seem to have more tacrolimus-related nephrotoxicity, more BK-viremia, and a lower graft survival. At first sight, the expression of CYP3A5 and a low C0/D ratio seem to be overlapping factors, both pointing towards patients in whom a higher tacrolimus dose is needed to reach the tacrolimus target concentration. However, there are important differences, and these differences may explain why the impact of the C0/D ratio on long term outcome is stronger than for CYP3A5 genotype status. Patients with a low C0/D ratio require a high tacrolimus dose and are exposed to high tacrolimus peak concentrations. The higher peak exposure to tacrolimus (and/or its metabolites) may explain the higher incidence of nephrotoxicity, BK-viremia and graft loss. A potential confounder is the concurrent maintenance treatment of corticosteroids, as steroids are sometimes continued in patients at high immunological risk. Steroids induce the metabolism of tacrolimus via pregnane X receptor mediated increased CYP3A4 expression, resulting in lower tacrolimus C0/D ratio in high risk patients. Also non-adherence may result in lower C0/D ratio which is also associated with poor outcome. The C0/D ratio of tacrolimus does seem to identify a group of patients with increased risk of poor outcome after kidney transplantation. Our recommendation is to monitor tacrolimus peak concentrations in these patients, and if these are high then target slightly lower pre-dose concentrations. Another possibility would be to switch to a prolonged release formulation or to dose the drug more frequently, in smaller doses, to avoid high peak concentrations.

BackgroundThe clinical implementation of pharmacogenomics (PGx) could be one of the first milestones towards realizing personalized medicine in routine care. However, its widespread adoption requires the availability of suitable clinical decision support (CDS) systems, which is often impeded by the fragmentation or absence of adequate health IT infrastructures. We report results of CDS implementation in the large-scale European research project Ubiquitous Pharmacogenomics (U-PGx), in which PGx CDS was rolled out and evaluated across more than 15 clinical sites in the Netherlands, Spain, Slovenia, Italy, Greece, United Kingdom and Austria, covering a wide variety of healthcare settings.MethodsWe evaluated the CDS implementation process through qualitative and quantitative process indicators. Quantitative indicators included statistics on generated PGx reports, median time from sampled upload until report delivery and statistics on report retrievals via the mobile-based CDS tool. Adoption of different CDS tools, uptake and usability were further investigated through a user survey among healthcare providers. Results of a risk assessment conducted prior to the implementation process were retrospectively analyzed and compared to actual encountered difficulties and their impact.ResultsAs of March 2021, personalized PGx reports were produced from 6884 genotyped samples with a median delivery time of twenty minutes. Out of 131 invited healthcare providers, 65 completed the questionnaire (response rate: 49.6%). Overall satisfaction rates with the different CDS tools varied between 63.6% and 85.2% per tool. Delays in implementation were caused by challenges including institutional factors and complexities in the development of required tools and reference data resources, such as genotype-phenotype mappings.ConclusionsWe demonstrated the feasibility of implementing a standardized PGx decision support solution in a multinational, multi-language and multi-center setting. Remaining challenges for future wide-scale roll-out include the harmonization of existing PGx information in guidelines and drug labels, the need for strategies to lower the barrier of PGx CDS adoption for healthcare institutions and providers, and easier compliance with regulatory and legal frameworks.

Pharmacogenomics is one of the first clinical applications of the postgenomic era. It promises personalized medicine rather than the established "one size fits all" approach to drugs and dosages. The expected reduction in trial and error should ultimately lead to more efficient and safer drug therapy. In recent years, commercially available pharmacogenomic tests have been approved by the Food and Drug Administration (FDA), but their application in patient care remains very limited. More generally, the implementation of pharmacogenomics in routine clinical practice presents significant challenges. This article presents specific clinical examples of such challenges and discusses how obstacles to implementation of pharmacogenomic testing can be addressed.