Current Pharmacogenomics and Personalized Medicine (Formerly Current Pharmacogenomics) - Volume 12, Issue 3, 2014

Cardiovascular disease is one of the main leading causes of mortality. Approximately, 80% of cardiovascular deaths occur in low- and middle-income countries (LMICs). Current guidelines which are based on randomized controlled trials direct the cardiovascular diagnosis and treatment. Yet, such guidelines do not benefit every patient. Recent studies question the ‘one size fits all’ principle particularly in complex traits such as thrombosis. The cost and duration of genetic testing continue to decline rapidly and novel strategies are on the rise to determine individual susceptibility to diseases and responses to therapy. Multidimensional evaluation of the patient with his/her environment, genomics, detailed medical history, and compliance to treatment are crucial in preventing complications from antithrombotic treatment. In this review, we discuss some of the pharmacogenomic features of antithrombotic medications that are currently used. We believe current personalized medicine and pharmacogenomics are pivotal in elucidating contradictory results of randomized controlled trials to test the same antithrombotic regimen on all participants.

Despite the common use of antidepressant drugs for the treatment of various psychiatric illnesses, selection of initial antidepressant therapy and subsequent drug modification strategies continue to be largely based on trial and error. There are no biomarkers that can objectively guide dose and treatment selection or alteration. The approach for antidepressant therapy management is even more concerning when we consider that the effectiveness of antidepressants tend to be delayed with low response rates. Therefore, strategies aimed at improving the current standard for selecting antidepressant treatment and doses may have significant economic and clinical benefit. A promising approach towards this effort is the use of pharmacogenomics to better identify patients that are likely to have an efficacious or adverse response from antidepressant treatment. Candidate gene approaches as well as genome-wide association studies have been conducted to identify genes or loci that influence antidepressant response. In this report, we highlighted key antidepressant pharmacogenomic findings and identified candidate pharmacokinetic and pharmacodynamic genes for downstream analyses and further validation. We also provided future directions regarding study methodologies and experimental design that are aimed to move antidepressant pharmacogenomics research forward.

Assessment of added value of stratified medicine (SM) interventions is complex and depends on many factors including the performance of the diagnostic test and the utility of the test for informing patient management. Modelling studies based on decision analysis is a well-recognized method used for health technology assessments (HTA’s) of health care interventions to analyse the consequences of decisions that are made under uncertainty. In this study, we address specific modelling issues associated with SM interventions and used depression to scrutinize the modelling approach with SM in particular. The model includes the stratification of patients into poor, normal and rapid metabolisers. The analysis outlines the importance of addressing input parameters such as test sensitivity and specificity and especially false negative and false positive considerations of the diagnostic test. This requires additional structural model complexity to establish the link between the test results and the consecutive treatment changes and outcomes and lead to a higher degree of uncertainty in economic models for SM compared with traditional ones. In case of depression, CYP450 testing will aid the decision maker (e.g. GP) in dose adjustment of antidepressant treatments, and in minimizing factors (adverse events interactions) that may influence patient’s compliance, thereby potentially reducing disease management costs.

Acute myocardial infarction (AMI) is a common and complex disease; pathogenesis of AMI is not completely understood and genetic, clinical and phenotypic variables are involved in the disease’s history. The aim of this paper was to assess: 1) the predictive capacity of Artificial Neural Networks (ANNs) in consistently distinguishing the two different conditions (AMI or control). 2) the identification of the variables with the maximal amount of relevant information for AMI. Genetic variances in inflammatory genes, along with clinical and classical risk factors from 149 AMI patients and 72 healthy subjects were investigated. From the data base of this case/control study 36 genetic, clinical and phenotypic variables were selected. The TWIST system, an evolutionary algorithm able to remove redundant and noisy information from complex data sets, further selected 18 variables. Moreover, fitness, sensitivity, specificity, overall accuracy and areas under the receiver-operating curves (AUC) of the 18 selected variables associated with AMI risk were investigated. Our findings showed that ANNs are useful in distinguishing risk factors selectively associated with the disease. Finally, the new mix of variables, including classical risk factors and genetic markers, generated a new risk cluster of variables able to discriminate between AMI and control subjects with an overall accuracy of 90%. This approach may be used to assess individual AMI risk in unaffected subjects with increased risk of the disease such as, first relative with positive parental history of cardiovascular diseases.

Pharmacogenomics (PGx) knowledge and practical applications in academic medicine and public health are expanding worldwide. In the past ten years, genotyping technologies have emerged in Jordan through the establishment of several research centers and university departments. The goals of this study were to assess the extensiveness, depth, and perceived importance of PGx instruction, in addition to the knowledge and use of PGxin academic medicine and public health in Jordan.A previously published questionnaire was modified and further developed and sent to medical academicians and public health workers. A total of 200 individuals in academic medicine and public health were invited to participate in the survey. The response rate was 75%. Approximately41% of respondents studied PGx at school. Coverage of PGxin departments of genetics and faculties of pharmacy was33% for <10 hours, 8% for 10-30 hours,2% for 31-60 hours and 6% for>60 hours. In terms of medical practice, the most frequently applied PGx topics were drug side effects (43%) and drug selection (35%). Nearly21% of PGx users reported they had been asked questions that require PGx knowledge by physicians whereas29% reported such questions from patients. Respondents considered their own PGx training inadequate, with a mean (±SD) value of 2.0± 1.0 (1 = very inadequate, 5 = very adequate). More PGx training is needed in order to establish good PGx education and apply PGx services in developing countries including Jordan. If PGx applications expand in academic medicine and public health as anticipated, the need to develop training capabilities will be even larger.

The field of immuno-oncology (IO) is rapidly expanding, generating enormous interest on the part of drug developers and giving new hope to patients and physicians. Attempts to harness the immune system to fight cancer date back to the 19th century; however, it is only in recent years that immunotherapies have entered mainstream oncology and have shown promise across a broad range of tumor types. New therapeutic combinations are emerging, with companies pairing two immunotherapies, an immunotherapy and a molecularly targeted therapy, or an immunotherapy plus traditional chemotherapy in order to more effectively fight cancer. Some immunotherapies are also pairing with companion diagnostics in order to maximize efficacy in patient populations. All of these developments come with their own unique challenges that drug developers will need to confront. This Perspective article reviews developments in the IO field and highlights key challenges in successfully commercializing an IO therapy.