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Innocent G Asiimwe, Munir Pirmohamed The Wolfson Centre for Personalized Medicine, MRC Centre for Drug Safety Science, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UKCorrespondence: Innocent G Asiimwe; Munir Pirmohamed, Tel +44 151 7955387 ; +44 151 794 5549, Fax +44 151 794 5059, Email i.asiimwe@liverpool.ac.uk; munirp@liverpool.ac.ukAbstract: Cardiovascular disease remains a leading cause of both morbidity and mortality worldwide. It is widely accepted that both concomitant medications (drug–drug interactions, DDIs) and genomic factors (drug–gene interactions, DGIs) can influence cardiovascular drug-related efficacy and safety outcomes. Although thousands of DDI and DGI (aka pharmacogenomic) studies have been published to date, the literature on drug–drug–gene interactions (DDGIs, cumulative effects of DDIs and DGIs) remains scarce. Moreover, multimorbidity is common in cardiovascular disease patients and is often associated with polypharmacy, which increases the likelihood of clinically relevant drug-related interactions. These, in turn, can lead to reduced drug efficacy, medication-related harm (adverse drug reactions, longer hospitalizations, mortality) and increased healthcare costs. To examine the extent to which DDGIs and other interactions influence efficacy and safety outcomes in the field of cardiovascular medicine, we review current evidence in the field. We describe the different categories of DDIs and DGIs before illustrating how these two interact to produce DDGIs and other complex interactions. We provide examples of studies that have reported the prevalence of clinically relevant interactions and the most implicated cardiovascular medicines before outlining the challenges associated with dealing with these interactions in clinical practice. Finally, we provide recommendations on how to manage the challenges including but not limited to expanding the scope of drug information compendia, interaction databases and clinical implementation guidelines (to include clinically relevant DDGIs and other complex interactions) and work towards their harmonization; better use of electronic decision support tools; using big data and novel computational techniques; using clinically relevant endpoints, preemptive genotyping; ensuring ethnic diversity; and upskilling of clinicians in pharmacogenomics and personalized medicine.Keywords: drug–drug interactions, drug–gene interactions, drug–drug–gene interactions, drug–gene–gene interactions, pharmacogenomics

Vanessa Fontana,1,2 Richard Myles Turner,1,2 Ben Francis,3 Peng Yin,3 Benno Pütz,4 Timo P Hiltunen,5 Sanni Ruotsalainen,6 Kimmo K Kontula,5 Bertam Müller-Myhsok,1,4 Munir Pirmohamed1,2,7 1The Wolfson Centre for Personalised Medicine, Institute of Translational Medicine, University of Liverpool, Liverpool, UK; 2MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK; 3Department of Biostatistics, Institute of Translational Medicine, University of Liverpool, Liverpool, UK; 4Max Planck Institute of Psychiatry, Munich, Germany; 5Department of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland; 6Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland; 7Royal Liverpool and Broadgreen University Hospitals NHS Trust, and Liverpool Health Partners, Liverpool, UKCorrespondence: Vanessa Fontana, MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, University of Liverpool, Block A: Waterhouse Buildings, 1-5 Brownlow Street, Liverpool, L69 3GL, UK, Fax +44 151 794 5059, Email V.Fontana@liverpool.ac.ukPurpose: Bisoprolol is a widely used beta-blocker in patients with cardiovascular diseases. As with other beta-blockers, there is variability in response to bisoprolol, but the underlying reasons for this have not been clearly elucidated. Our aim was to investigate genetic factors that affect bisoprolol pharmacokinetics (PK) and pharmacodynamics (PD), and potentially the clinical outcomes.Patients and Methods: Patients with non-ST elevation acute coronary syndrome were recruited prospectively on admission to hospital and followed up for up to 2 years. Patients from this cohort who were on treatment with bisoprolol, at any dose, had bisoprolol adherence data and a plasma sample, one month after discharge from index hospitalisation were included in the study. Individual bisoprolol clearance values were estimated using population pharmacokinetic modeling. Genome-wide association analysis after genotyping was undertaken using an Illumina HumanOmniExpressExome-8 v1.0 BeadChip array, while CYP2D6 copy number variations were determined by PCR techniques and phenotypes for CYP2D6 and CYP3A were inferred from the genotype. GWAS significant SNPs were analysed for heart rate response to bisoprolol in an independent cohort of hypertensive subjects.Results: Six hundred twenty-two patients on bisoprolol underwent both PK and genome wide analysis. The mean (IQR) of the estimated clearance in this population was 13.6 (10.0– 18.0) L/h. Bisoprolol clearance was associated with rs11029955 (p=7.17× 10− 9) mapped to the region of coiled-coil domain containing 34 region (CCDC34) on chromosome 11, and with rs116702638 (p=2.54× 10− 8). Each copy of the minor allele of rs11029955 was associated with 2.2 L/h increase in clearance. In an independent cohort of hypertensive subjects, rs11029955 was associated with 24-hour heart rate response to 4-week treatment with bisoprolol (p= 9.3× 10− 5), but not with rs116702638.Conclusion: A novel locus on the chromosomal region 11p14.1 was associated with bisoprolol clearance in a real-world cohort of patients and was validated in independent cohort with a pharmacodynamic association.Keywords: bisoprolol, pharmacokinetics, genome-wide association

Vincent Lai Ming Yip,1,2 Munir Pirmohamed1,2 1MRC Centre for Drug Safety Science, Institute of Translational Medicine, Department of Molecular and Clinical Pharmacology, University of Liverpool, 2Department of Clinical Pharmacology,The Royal Liverpool and Broadgreen University Hospital NHS Trust, Liverpool, UK Abstract: Carbamazepine (CBZ) is an effective anticonvulsant that can sometimes cause hypersensitivity reactions that vary in frequency and severity. Strong associations have been reported between specific human leukocyte antigen (HLA) alleles and susceptibility to CBZ hypersensitivity reactions. Screening for HLA-B*15:02 is mandated in patients from South East Asia because of a strong association with Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). HLA-A*31:01 predisposes to multiple phenotypes of CBZ hypersensitivity including maculopapular exanthema, hypersensitivity syndrome, and SJS/TEN in a range of populations including Europeans, Japanese, South Koreans and Han Chinese, although the effect size varies between the different phenotypes and populations. Between 47 Caucasians and 67 Japanese patients would need to be tested for HLA-A*31:01 in order to avoid a single case of CBZ hypersensitivity. A cost-effectiveness study has demonstrated that HLA-A*31:01 screening would be cost-effective. Patient preference assessment has also revealed that patients prefer pharmacogenetic screening and prescription of alternative anticonvulsants compared to current standard of practice without pharmacogenetic testing. For patients who test positive for HLA-A*31:01, alternative treatments are available. When alternatives have failed or are unavailable, HLA-A*31:01 testing can alert clinicians to 1) patients who are at increased risk of CBZ hypersensitivity who can then be targeted for more intensive monitoring and 2) increase diagnostic certainty in cases where hypersensitivity has already occurred, so patients can be advised to avoid structurally related drugs in the future. On the basis of the current evidence, we would favor screening all patients for HLA-A*31:01 and HLA-B*15:02 prior to starting CBZ therapy. Keywords: carbamazepine, oxcarbazepine, hypersensitivity, adverse drug reaction, pharmacogenetics, HLA

BackgroundThe clinical response to inhaled corticosteroids (ICS) is associated with single nucleotide polymorphisms (SNPs) in various genes. This study aimed to relate variations in genes in the steroid pathway and asthma susceptibility genes to exacerbations in children and young adults treated with ICS.MethodsWe performed a meta-analysis of three cohort studies: Pharmacogenetics of Asthma Medication in Children: Medication with Anti-Inflammatory effects (n = 357, age: 4-12 years, the Netherlands), BREATHE (n = 820, age: 3-22 years, UK) and Paediatric Asthma Gene Environment Study (n = 391, age: 2-16 years, UK). Seventeen genes were selected based on a role in the glucocorticoid signalling pathway or a reported association with asthma. Two outcome parameters were used to reflect exacerbations: hospital visits and oral corticosteroid (OCS) use in the previous year. The most significant associations were tested in three independent validation cohorts; the Childhood Asthma Management Programme (clinical trial, n = 172, age: 5-12 years, USA), the Genes- environment and Mixture in Latino Americans II- study (n = 745, age: 8-21, USA) and the Pharmacogenetics of adrenal suppression cohort (n = 391, age: 5-18, UK) to test the robustness of the findings. Finally, all results were meta-analysed.ResultsTwo SNPs in ST13 (rs138335 and rs138337), but not in the other genes, were associated at a nominal level with an increased risk of exacerbations in asthmatics using ICS in the three cohorts studied. In a meta-analysis of all six studies, ST13 rs138335 remained associated with an increased risk of asthma-related hospital visits and OCS use in the previous year; OR = 1.22 (P = 0.013) and OR = 1.22 (P = 0.0017), respectively.Conclusion and clinical relevanceA novel susceptibility gene, ST13, coding for a cochaperone of the glucocorticoid receptor, is associated with exacerbations in asthmatic children and young adults despite their ICS use. Genetic variation in the glucocorticoid signalling pathway may contribute to the interindividual variability in clinical response to ICS treatment in children and young adults.

The Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for HLA-B Genotype and Abacavir Dosing were originally published in April 2012. We reviewed recent literature and concluded that none of the evidence would change the therapeutic recommendations in the original guideline; therefore, the original publication remains clinically current. However, we have updated the Supplementary Material online and included additional resources for applying CPIC guidelines to the electronic health record. Up-to-date information can be found at PharmGKB (http://www.pharmgkb.org).

Objectives: To use text mining approaches to identify instances of suspected adverse drug reactions recorded in first opinion veterinary free‐text clinical narratives, and to evaluate whether these were also reported to either the Veterinary Medicines Directorate or the relevant Marketing Authorisation holder in order to derive an estimate of the suspected adverse drug reaction (sADR) minimum under‐reporting rate. To characterise sADR reports and explore whether particular features are associated with report submission. Materials and Methods: Two regular expressions were developed to identify mentions of "adverse drug reactions" and "side effects" in the free‐text clinical narratives of electronic health records contained within the Small Animal Veterinary Surveillance Network database. Consultations containing a match for the developed regular expressions were manually reviewed for inclusion and further classified to determine the suspected product, seriousness and expectedness of the event, and an indication of whether the event had been reported. The associations between event characteristics and reporting were explored using Fisher's exact tests. Results: A total of 10,565 records were manually reviewed from which 827 sADRs were identified. Approximately 90% of these sADRs were not recorded as reported. Suspected adverse drug reactions that were not considered "expected" were recorded as reported more frequently than "expected" sADRs. However, clinical severity did not appear to impact on whether there was a record of reporting. Clinical Significance: This is the first estimate of under reporting sADRs based on real world evidence from veterinary clinical records. The under‐reporting rate implied by this study highlights that further interventions are required to improve reporting rate within the veterinary profession in order to support pharmacovigilance activities and improve drug safety. [ABSTRACT FROM AUTHOR]