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Falls is a frequent type of adverse drug reactions causing significant morbidity and mortality in the elderly. We reviewed, with which drugs the risk of falls is relevant and might depend on genomic variation. Pharmacogenetic variability may contribute to drug-induced falls for instance mediated by impaired drug elimination due to inherited deficiency in enzymes like CYP2C9, CYP2C19 and CYP2D6. The relative role of specific genes and polymorphisms in old age may differ from younger people. Biomarkers for frailty, but also genomic biomarkers might help identifying patients at high risk for drug-induced falls. Many other factors including disease and drug–drug interactions also contribute to risk of falls. Further studies analyzing the impact of genomic variation on the medication-related fall risk in the older adult are urgently needed.

Despite strong pharmacological support, association studies using genotype-predicted phenotype as a variable have yielded conflicting or inconclusive evidence to promote personalized pharmacotherapy. Unless the patient is a genotypic poor metabolizer, imputation of patient's metabolic capacity (or metabolic phenotype), a major factor in drug exposure-related clinical response, is a complex and highly challenging task because of limited number of alleles interrogated, population-specific differences in allele frequencies, allele-specific substrate-selectivity and importantly, phenoconversion mediated by co-medications and inflammatory co-morbidities that modulate the functional activity of drug metabolizing enzymes. Furthermore, metabolic phenotype and clinical outcomes are not binary functions; there is large intragenotypic and intraindividual variability. Therefore, the ability of association studies to identify relationships between genotype and clinical outcomes can be greatly enhanced by determining phenotype measures of study participants and/or by therapeutic drug monitoring to correlate drug concentrations with genotype and actual metabolic phenotype. To facilitate improved analysis and reporting of association studies, we propose acronyms with the prefixes ‘g’ (genotype-predicted phenotype) and ‘m’ (measured metabolic phenotype) to better describe this important variable of the study subjects. Inclusion of actually measured metabolic phenotype, and when appropriate therapeutic drug monitoring, promises to reveal relationships that may not be detected by using genotype alone as the variable.

Aim: The identification of antidepressant drugs (ADs) response biomarkers in depression is of high clinical importance. We explored CHL1 and ITGB3 expression as tentative response biomarkers. Materials & methods: In vitro sensitivity to ADs, as well as gene expression and genetic variants of the candidate genes CHL1, ITGB3 and SLC6A4 were measured in lymphoblastoid cell lines (LCLs) of 58 depressed patients. Results: An association between the clinical remission of depression and the basal expression of CHL1 and ITGB3 was discovered. Individuals whose LCLs expressed higher levels of CHL1 or ITGB3 showed a significantly better remission upon AD treatment. In addition individuals with the CHL1 rs1516338 TT genotype showed a significantly better remission after 5 weeks AD treatment than those carrying a CC genotype. No association between the in vitro sensitivity of LCLs toward AD and the clinical remission could be detected. Conclusion: CHL1 expression in patient-derived LCLs correlated with the clinical outcome. Thus, it could be a valid biomarker to predict the success of an antidepressant therapy. Original submitted 8 December 2014; Revision submitted 2 March 2015

Aim: Development of a skin rash under treatment with EGF receptor (EGFR) inhibitors (EGFRIs) has been linked to a favorable prognosis in some studies, suggesting a possible immunological role for EGFRIs in addition to direct antagonistic downstream effects. The present study aimed to investigate whether particular HLA polymorphisms found in cancer patients treated with EGFRIs are associated with the development of skin rash and overall survival rates. Patients & methods: HLA typing was performed on 105 cancer patients and the development of skin rash was rated during the first 4 weeks of therapy with EGFRIs. Results: A significantly lower incidence of skin rash was found in patients carrying the HLA-A*02:01 or HLA-A*03:01 alleles (hazard ratio: 0.277; 95% CI: 0.121–0.634; p = 0.002 and hazard ratio: 0.292; 95% CI: 0.113–0.752; p = 0.011, respectively); however, no association with worse survival was seen. Conclusion: The chances of developing a skin rash in patients treated with EGFRIs may be lower in patients that carry the HLA-A*02:01 or HLA-A*03:01 alleles, while the antitumor efficacy of EGFRIs does not seem to be significantly impaired in these patients. Original submitted 20 April 2012; Revision submitted 9 July 2012

Genotyping patients for CYP2C9 and VKORC1 polymorphisms can improve the accuracy of dosing during the initiation of anticoagulation with vitamin K antagonists (coumarin derivatives). The anticipated degree of improvement in the safety of anticoagulation with coumarins through genotyping may vary depending on the quality of patient care, which varies both with and among countries. The management and the cost of anticoagulant care can therefore influence the cost–effectiveness of genotyping within any given country. In this article, we provide an overview of the cost–effectiveness of pharmacogenetics-guided dosing of coumarin derivatives. We describe the organization of anticoagulant care in the UK, Sweden, The Netherlands, Greece, Germany and Austria, where a genotype-guided dosing algorithm is currently being investigated as part of the EU-PACT trial. We also explore the costs of anticoagulant care for the treatment of atrial fibrillation in these countries.

Aims: Selective serotonin reuptake inhibitors (SSRIs) are the most commonly used class of antidepressants for treating major depression. However, approximately 30% of patients do not respond sufficiently to first-line antidepressant drug treatment and require alternative therapeutics. Genome-wide studies searching for SSRI response DNA biomarkers or studies of candidate serotonin-related genes so far have given inconclusive or contradictory results. Here, we present an alternative transcriptome-based genome-wide approach for searching antidepressant drug-response biomarkers by using drug-effect phenotypes in human lymphoblastoid cell lines (LCLs). Materials & methods: We screened 80 LCLs from healthy adult female individuals for growth inhibition by paroxetine. A total of 14 LCLs with reproducible high and low sensitivities to paroxetine (seven from each phenotypic group) were chosen for genome-wide expression profiling with commercial microarrays. Results: The most notable genome-wide transcriptome difference between LCLs displaying high versus low paroxetine sensitivities was a 6.3-fold lower (p = 0.0000256) basal expression of CHL1, a gene coding for a neuronal cell adhesion protein implicated in correct thalamocortical circuitry, schizophrenia and autism. The microarray findings were confirmed by real-time PCR (36-fold lower CHL1 expression levels in the high paroxetine sensitivity group). Several additional genes implicated in synaptogenesis or in psychiatric disorders, including ARRB1, CCL5, DDX60, DDX60L, ENDOD1, ENPP2, FLT1, GABRA4, GAP43, MCTP2 and SPRY2, also differed by more than 1.5-fold and a p-value of less than 0.005 between the two paroxetine sensitivity groups, as confirmed by real-time PCR experiments. Conclusion: Genome-wide transcriptional profiling of in vitro phenotyped LCLs identified CHL1 and additional genes implicated in synaptogenesis and brain circuitry as putative SSRI response biomarkers. This method might be used as a preliminary tool for searching for potential depression treatment biomarkers. Original submitted 7th September 2010; Revision submitted 15th November 2010.