Individualizing Pharmacotherapy: Genetic factors and co-prescribed drugs affecting pharmacotherapy

Drug therapies may result in adverse drug reactions or in ineffective therapy. A better prediction which patients will not respond to drug therapy or will develop adverse drug reactions may avoid these events. In this thesis we studied the effect of drug-drug interactions and genetic variation. The exposure to potential life-threatening drug-drug interactions in the elderly general population (≥ 70 years) increased from 1.5 percent in 1992 to 2.9 percent in 2005. About half a percent of all hospital admissions were due to drug-drug interactions. The polymorphism rs622342 in the Organic Cation Transporter 1 and rs2289669 in the Multidrug And Toxin Extrusion 1 were both associated with the glucose lowering effect of metformin. The predictability of the response to metformin increased substantially if also the interaction between both polymorphisms was included. The CYP3A4*1B polymorphisms was associated with adverse drug reactions during simvastatin and atorvastatin therapy. This effect was stronger in women and in patients with the ABCB1 CT or TT genotype. These differences are most likely explained by a lower ABCB1 expression and a higher statin concentration in the hepatocyte. We also studied the association between genetic variation in the CYP2C9 gene and sulphonylurea response, the rs10494366 polymorphisms and sulphonylurea and calcium channel blocker response, genetic variation in the ABCB1 gene and the cholesterol lowering effect of simvastatin, and the association between the rs622342 polymorphism and the response to antiparkinson drugs. The response to drug therapy is better predictable if also the interaction between factors is taken into account

Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice

BACKGROUND: The exact risk of developing QTc-prolongation when using a combination of QTc-prolonging drugs is still unknown, making it difficult to interpret these QT drug-drug interactions (QT-DDIs). A tool to identify high-risk patients is needed to support healthcare providers in handling automatically generated alerts in clinical practice. The main aim of this study was to develop and validate a tool to assess the risk of QT-DDIs in clinical practice. METHODS: A model was developed based on risk factors associated with QTc-prolongation determined in a prospective study on QT-DDIs in a university medical center inthe Netherlands. The main outcome measure was QTc-prolongation defined as a QTc interval > 450 ms for males and > 470 ms for females. Risk points were assigned to risk factors based on their odds ratios. Additional risk factors were added based on a literature review. The ability of the model to predict QTc-prolongation was validated in an independent dataset obtained from a general teaching hospital against QTc-prolongation as measured by an ECG as the gold standard. Sensitivities, specificities, false omission rates, accuracy and Youden's index were calculated. RESULTS: The model included age, gender, cardiac comorbidities, hypertension, diabetes mellitus, renal function, potassium levels, loop diuretics, and QTc-prolonging drugs as risk factors. Application of the model to the independent dataset resulted in an area under the ROC-curve of 0.54 (95% CI 0.51-0.56) when QTc-prolongation was defined as > 450/470 ms, and 0.59 (0.54-0.63) when QTc-prolongation was defined as > 500 ms. A cut-off value of 6 led to a sensitivity of 76.6 and 83.9% and a specificity of 28.5 and 27.5% respectively. CONCLUSIONS: A clinical decision support tool with fair performance characteristics was developed. Optimization of this tool may aid in assessing the risk associated with QT-DDIs. TRIAL REGISTRATION: No trial registration, MEC-2015-368

A gene variant near ATM is significantly associated with metformin treatment response In type 2 diabetes: A replication and meta-analysis of five cohorts

_Aims/hypothesis:_ In this study we aimed to replicate the previously reported association between the glycaemic response to metformin and the SNP rs11212617 at a locus that includes the ataxia telangiectasia mutated (ATM) gene in multiple additional populations. _Methods:_ Incident users of metformin selected from the Diabetes Care System West-Friesland (DCS, n=929) and the Rotterdam Study (n=182) from the Netherlands, and the CARDS Trial (n=254) from the UK were genotyped for rs11212617 and tested for an association with both HbA1c reduction and treatment success, defined as the ability to reach the treatment target of an HbA1c ≤7 % (53 mmol/mol). Finally, a meta-analysis including data from literature was performed. _Results:_ In the DCS cohort, we observed an association between rs11212617 genotype and treatment success on metformin (OR 1.27, 95% CI 1.03, 1.58, p=0.028); in the smaller Rotterdam Study cohort, a numerically similar but non-significant trend was observed (OR 1.45, 95% CI 0.87, 2.39, p=0.15); while in the CARDS cohort there was no significant association. In meta-analyses of these three cohorts separately or combined with the previously published cohorts, rs11212617 genotype is associated with metformin treatment success (OR 1.24, 95% CI 1.04, 1.49, p=0.016 and OR 1.25, 95% CI 1.33, 1.38, p=7.8×10-6, respectively). _ Conclusions/inte

Switching to different generic medicines: A checklist for safety issues

Competition between commercially available generics results in lower prices and reduction of healthcare costs. However, the introduction of a new generic product may result in problems with the application in clinical practice. We composed a checklist to survey the differences between products. With this checklist we aim to detect properties of th

Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes

OBJECTIVE - Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired b-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology. RESEARCH DESIGN AND METHODS - We have conducted a meta-analysis of genome-wide association tests of ;2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates. RESULTS - Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10-8). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/ C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 3 10-4), improved b-cell function (P = 1.1 × 10-5), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10-6). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets. CONCLUSIONS - We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis