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Pharmacogenonomics (PGx) is the intersection of genomic medicine and pharmacology whereby knowledge of variation in an individual's genome is utilized to inform prescribing practice. In conclusion, PGx represents a novel piece in the jigsaw of factors that should be collectively considered when developing novel drugs and prescribing current drugs to improve the safety and efficacy of pharmacotherapy. For instance, it is possible that some clinical pharmacologists with a particular interest in PGx may become PGx specialists, similar to how some clinical pharmacologists have specialized in hypertension or clinical toxicology. [Extracted from the article]

The field of pharmacogenomics has made great strides in oncology over the last 20 years and indeed a significant number of pre‐emptive genetic tests are now routinely undertaken prior to anticancer drug administration. Many of these gene–drug interactions are the fruits of candidate gene and genome‐wide association studies, which have largely focused on common genetic variants (allele frequency>1%). Examples where there is clinical utility include genotyping or phenotyping for G6PD to prevent rasburicase‐induced RBC haemolysis, and TPMT to prevent thiopurine‐induced bone marrow suppression. Other associations such as CYP2D6 status in determining the efficacy of tamoxifen are more controversial because of contradictory evidence from different sources, which has led to variability in the implementation of testing. As genomic technology becomes ever cheaper and more accessible, we must look to the additional data our genome can provide to explain interindividual variability in anticancer drug response. Clearly genes do not act on their own and it is therefore important to investigate genetic factors in conjunction with clinical factors, interacting concomitant drug therapies and other factors such as the microbiome, which can all affect drug disposition. Taking account of all of these factors, in conjunction with the somatic genome, is more likely to provide better predictive accuracy in determining anticancer drug response, both efficacy and safety. This review summarises the existing knowledge related to the pharmacogenomics of anticancer drugs and discusses areas of opportunity for further advances in personalisation of therapy in order to improve both drug safety and efficacy. [ABSTRACT FROM AUTHOR]

Aims: The lipid‐lowering drug, atorvastatin (ATV), is 1 of the most commonly prescribed medications worldwide. The aim of this study was to comprehensively investigate and characterise the clinical factors and comedications associated with circulating levels of ATV and its metabolites in secondary prevention clinical practice. Methods: The plasma concentrations of ATV, 2‐hydroxy (2‐OH) ATV, ATV lactone (ATV L) and 2‐OH ATV L were determined in patients 1 month after hospitalisation for a non‐ST elevation acute coronary syndrome. Factors were identified using all subsets multivariable regression and model averaging with the Bayesian information criterion. Exploratory genotype‐stratified analyses were conducted using ABCG2 rs2231142 (Q141K) and CYP2C19 metaboliser status to further investigate novel associations. Results: A total of 571 patients were included; 534 and 37 were taking ATV 80 mg and 40 mg daily, respectively. Clinical factors associated with ATV and/or its metabolite levels included age, sex, body mass index and CYP3A inhibiting comedications. Smoking was newly associated with increased ATV lactonisation and reduced hydroxylation. Proton pump inhibitors (PPIs) and loop diuretics were newly associated with modestly increased levels of ATV (14% and 38%, respectively) and its metabolites. An interaction between PPIs and CYP2C19 metaboliser status on exposure to specific ATV analytes (e.g. interaction P =.0071 for 2‐OH ATV L) was observed. Overall model R2 values were 0.14–0.24.ConclusionMultiple factors were associated with circulating ATV and metabolite levels, including novel associations with smoking and drug–drug(–gene) interactions involving PPIs and loop diuretics. Further investigations are needed to identify additional factors that influence ATV exposure. [ABSTRACT FROM AUTHOR]

Aims To determine whether thiazolidinedione use is associated with a risk of bladder cancer. Methods We searched MEDLINE and EMBASE in June 2012 (with Pub Med 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. Results We 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). Conclusions A 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. [ABSTRACT FROM AUTHOR]

Aims: To update our previously reported systematic review and meta‐analysis of observational studies on cardiovascular drug exposure and COVID‐19 clinical outcomes by focusing on newly published randomized controlled trials (RCTs). Methods: More than 500 databases were searched between 1 November 2020 and 2 October 2021 to identify RCTs that were published after our baseline review. One reviewer extracted data with other reviewers verifying the extracted data for accuracy and completeness. Results: After screening 22 414 records, we included 24 and 21 RCTs in the qualitative and quantitative syntheses, respectively. The most investigated drug classes were angiotensin‐converting enzyme inhibitors (ACEIs)/angiotensin receptor blocker (ARBs) and anticoagulants, investigated by 10 and 11 studies respectively. In meta‐analyses, ACEI/ARBs did not affect hospitalization length (mean difference −0.42, 95% confidence interval [CI] −1.83; 0.98 d, n = 1183), COVID‐19 severity (risk ratio/RR 0.90, 95% CI 0.71; 1.15, n = 1661) or mortality (risk ratio [RR] 0.92, 95% CI 0.58; 1.47, n = 1646). Therapeutic anticoagulation also had no effect (hospitalization length mean difference −0.29, 95% CI −1.13 to 0.56 d, n = 1449; severity RR 0.86, 95% CI 0.70; 1.04, n = 2696; and, mortality RR 0.93, 95% CI 0.77; 1.13, n = 5689). Other investigated drug classes were antiplatelets (aspirin, 2 trials), antithrombotics (sulodexide, 1 trial), calcium channel blockers (amlodipine, 1 trial) and lipid‐modifying drugs (atorvastatin, 1 trial). Conclusion: Moderate‐ to high‐certainty RCT evidence suggests that cardiovascular drugs such as ACEIs/ARBs are not associated with poor COVID‐19 outcomes, and should therefore not be discontinued. These cardiovascular drugs should also not be initiated to treat or prevent COVID‐19 unless they are needed for an underlying currently approved therapeutic indication. [ABSTRACT FROM AUTHOR]

Aims: The aim of this study was to continually evaluate the association between cardiovascular drug exposure and COVID‐19 clinical outcomes (susceptibility to infection, disease severity, hospitalization, hospitalization length, and all‐cause mortality) in patients at risk of/with confirmed COVID‐19. Methods: Eligible publications were identified from more than 500 databases on 1 November 2020. One reviewer extracted data with 20% of the records independently extracted/evaluated by a second reviewer. Results: Of 52 735 screened records, 429 and 390 studies were included in the qualitative and quantitative syntheses, respectively. The most‐reported drugs were angiotensin‐converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) with ACEI/ARB exposure having borderline association with confirmed COVID‐19 infection (OR 1.14, 95% CI 1.00–1.31). Among COVID‐19 patients, unadjusted estimates showed that ACEI/ARB exposure was associated with hospitalization (OR 1.76, 95% CI 1.34–2.32), disease severity (OR 1.40, 95% CI 1.26–1.55) and all‐cause mortality (OR 1.22, 95% CI 1.12–1.33) but not hospitalization length (mean difference −0.27, 95% CI −1.36–0.82 days). After adjustment, ACEI/ARB exposure was not associated with confirmed COVID‐19 infection (OR 0.92, 95% CI 0.71–1.19), hospitalization (OR 0.93, 95% CI 0.70–1.24), disease severity (OR 1.05, 95% CI 0.81–1.38) or all‐cause mortality (OR 0.84, 95% CI 0.70–1.00). Similarly, subgroup analyses involving only hypertensive patients revealed that ACEI/ARB exposure was not associated with confirmed COVID‐19 infection (OR 0.93, 95% CI 0.79–1.09), hospitalization (OR 0.84, 95% CI 0.58–1.22), hospitalization length (mean difference −0.14, 95% CI −1.65–1.36 days), disease severity (OR 0.92, 95% CI 0.76–1.11) while it decreased the odds of dying (OR 0.76, 95% CI 0.65–0.88). A similar trend was observed for other cardiovascular drugs. However, the validity of these findings is limited by a high level of heterogeneity and serious risk of bias. Conclusion: Cardiovascular drugs are not associated with poor COVID‐19 outcomes in adjusted analyses. Patients should continue taking these drugs as prescribed. [ABSTRACT FROM AUTHOR]