Despite scientific and clinical advances in the field of pharmacogenomics (PGx), application into routine care remains limited. Opportunely, several implementation studies and programs have been initiated over recent years. This article presents an overview of these studies and identifies current research gaps. Importantly, one such gap is the undetermined collective clinical utility of implementing a panel of PGx-markers into routine care, because the evidence base is currently limited to specific, individual drug-gene pairs. The Ubiquitous Pharmacogenomics (U-PGx) Consortium, which has been funded by the European Commission's Horizon-2020 program, aims to address this unmet need. In a prospective, block-randomized, controlled clinical study (PREemptive Pharmacogenomic testing for prevention of Adverse drug REactions [PREPARE]), pre-emptive genotyping of a panel of clinically relevant PGx-markers, for which guidelines are available, will be implemented across healthcare institutions in seven European countries. The impact on patient outcomes and cost-effectiveness will be investigated. The program is unique in its multicenter, multigene, multidrug, multi-ethnic, and multihealthcare system approach.

Statins reduce cholesterol, prevent cardiovascular disease, and are among the most commonly prescribed medications in the world. Statin‐associated musculoskeletal symptoms (SAMS) impact statin adherence and ultimately can impede the long‐term effectiveness of statin therapy. There are several identified pharmacogenetic variants that impact statin disposition and adverse events during statin therapy. SLCO1B1 encodes a transporter (SLCO1B1; alternative names include OATP1B1 or OATP‐C) that facilitates the hepatic uptake of all statins. ABCG2 encodes an efflux transporter (BCRP) that modulates the absorption and disposition of rosuvastatin. CYP2C9 encodes a phase I drug metabolizing enzyme responsible for the oxidation of some statins. Genetic variation in each of these genes alters systemic exposure to statins (i.e., simvastatin, rosuvastatin, pravastatin, pitavastatin, atorvastatin, fluvastatin, lovastatin), which can increase the risk for SAMS. We summarize the literature supporting these associations and provide therapeutic recommendations for statins based on SLCO1B1, ABCG2, and CYP2C9 genotype with the goal of improving the overall safety, adherence, and effectiveness of statin therapy. This document replaces the 2012 and 2014 Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for SLCO1B1 and simvastatin‐induced myopathy.

Statins are a cornerstone in the pharmacological prevention of cardiovascular disease. Although generally well tolerated, a small subset of patients experience statin-related myotoxicity (SRM). SRM is heterogeneous in presentation; phenotypes include the relatively more common myalgias, infrequent myopathies, and rare rhabdomyolysis. Very rarely, statins induce an anti-HMGCR positive immune-mediated necrotizing myopathy. Diagnosing SRM in clinical practice can be challenging, particularly for mild SRM that is frequently due to alternative aetiologies and the nocebo effect. Nevertheless, SRM can directly harm patients and lead to statin discontinuation/non-adherence, which increases the risk of cardiovascular events. Several factors increase systemic statin exposure and predispose to SRM, including advanced age, concomitant medications, and the nonsynonymous variant, rs4149056, in SLCO1B1, which encodes the hepatic sinusoidal transporter, OATP1B1. Increased exposure of skeletal muscle to statins increases the risk of mitochondrial dysfunction, calcium signalling disruption, reduced prenylation, atrogin-1 mediated atrophy and pro-apoptotic signalling. Rare variants in several metabolic myopathy genes including CACNA1S, CPT2, LPIN1, PYGM and RYR1 increase myopathy/rhabdomyolysis risk following statin exposure. The immune system is implicated in both conventional statin intolerance/myotoxicity via LILRB5 rs12975366, and a strong association exists between HLA-DRB1*11:01 and anti-HMGCR positive myopathy. Epigenetic factors (miR-499-5p, miR-145) have also been implicated in statin myotoxicity. SRM remains a challenge to the safe and effective use of statins, although consensus strategies to manage SRM have been proposed. Further research is required, including stringent phenotyping of mild SRM through N-of-1 trials coupled to systems pharmacology omics- approaches to identify novel risk factors and provide mechanistic insight.

Cytochrome P450 2D6 (CYP2D6) is a critical pharmacogene involved in the metabolism of ~20% of commonly used drugs across a broad spectrum of medical disciplines including psychiatry, pain management, oncology and cardiology. Nevertheless, CYP2D6 is highly polymorphic with single-nucleotide polymorphisms, small insertions/deletions and larger structural variants including multiplications, deletions, tandem arrangements, and hybridisations with non-functional CYP2D7 pseudogenes. The frequency of these variants differs across populations, and they significantly influence the drug-metabolising enzymatic function of CYP2D6. Importantly, altered CYP2D6 function has been associated with both adverse drug reactions and reduced drug efficacy, and there is growing recognition of the clinical and economic burdens associated with suboptimal drug utilisation. To date, pharmacogenomic clinical guidelines for at least 48 CYP2D6-substrate drugs have been developed by prominent pharmacogenomics societies, which contain therapeutic recommendations based on CYP2D6-predicted categories of metaboliser phenotype. Novel algorithms to interpret CYP2D6 function from sequencing data that consider structural variants, and machine learning approaches to characterise the functional impact of novel variants, are being developed. However, CYP2D6 genotyping is yet to be implemented broadly into clinical practice, and so further effort and initiatives are required to overcome the implementation challenges and deliver the potential benefits to the bedside.

On-line mass spectrometry allowed the flow conditions to be quickly tuned for safe operation and optimal generation of the desired product. The validity of this approach was corroborated by off-line liquid chromatography/mass spectrometry (LC/MS) analysis of flow samples.

AIMSTo determine whether thiazolidinedione use is associated with a risk of bladder cancer. METHODSWe searched MEDLINE and EMBASE in June 2012 (with PubMed update to July 2013) and conducted meta-analysis on the overall risks of bladder cancer with pioglitazone or rosiglitazone and the risk with different categories of cumulative dose or duration of drug use. RESULTSWe screened 230 citations and included 18 studies, comprising five randomized controlled trials (RCTs) and 13 observational studies. Meta-analysis showed a significantly higher overall risk of bladder cancer with pioglitazone in RCTs [7878 participants; odds ratio (OR) 2.51, 95% confidence interval (CI) 1.09-5.80] and observational studies (>2.6 million patients; OR for 'ever' users vs. non-users 1.21, 95% CI 1.09-1.35). Subgroup analysis of observational studies by cumulative dose showed the risk of bladder cancer to be greatest with >28.0 g of pioglitazone (OR 1.64, 95% CI 1.28-2.12). A significantly increased risk was found with both 12-24 months (OR 1.41, 95% CI 1.16-1.71) and >24 months (OR 1.51, 95% CI 1.26-1.81) cumulative durations of pioglitazone exposure. No significant risk was seen with rosiglitazone in RCTs (OR 0.84, 95% CI 0.35-2.04) or 'ever' users vs. non-users in observational studies (OR 1.03, 95% CI 0.94-1.12); the evidence for any relationship between bladder cancer risk and rosiglitazone cumulative duration is limited and inconsistent. Direct comparison of pioglitazone to rosiglitazone 'ever' users yielded an OR of 1.25 (95% CI 0.91-1.72). CONCLUSIONSA modest but clinically significant increase in the risk of bladder cancer with pioglitazone was found, which appears to be related to cumulative dose and duration of exposure. We recommend that prescribers limit pioglitazone use to shorter durations.

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Flow chemistry and continuous processing techniques are beginning to have a profound impact on the production of functional materials ranging from quantum dots, nanoparticles and metal organic frameworks to polymers and dyes. These techniques provide robust procedures which not only enable accurate control of the product material's properties but they are also ideally suited to conducting experiments on scale. The modular nature of flow and continuous processing equipment rapidly facilitates reaction optimisation and variation in function of the products.