Current Pharmacogenomics and Personalized Medicine (Formerly Current Pharmacogenomics) - Volume 9, Issue 2, 2011

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Over evolutionary time, human mitochondrial DNA (mtDNA) has accumulated many mutations. Because mtDNA is exclusively inherited from the maternal lineage, these mtDNA mutations have given rise to different mtDNA genetic backgrounds, or haplogroups. Human mtDNA codes subunits of the oxidative phosphorylation system and the RNAs required for the expression of these subunits. As this cell pathway is significant in cell homeostasis, mtDNA population polymorphisms may affect cell function, tissue dynamics and, finally, the health status of individuals. Supporting this idea, transmitochondrial cell lines differ in cellular, biochemical and molecular-genetic properties among mtDNA haplogroups. Moreover, several epidemiological studies suggest that population genetic variation in mtDNA can have important phenotypic effects and modify the predisposition of human beings toward different disorders. Human mitogenomics, the study of mtDNAs in humans, will allow the development of mtDNA barcodes connecting particular mtDNA haplotypes with higher/lower propensity to particular diseases. Importantly, mitogenomics offers a previously neglected avenue for the development of drugs to treat individuals according their mtDNA genetic background, raising the possibility of mitochondrially-personalized medicine.

Tardive dyskinesia (TD) is a serious adverse effect often associated with the first generation antipsychotic medications used in the management of mental health disorders such as schizophrenia. Pharmacogenomics is the study of human genomic variation in relation to individual and population variability in medication response and side effects. Neuropsychiatry is one of the clinical domains in which pharmacogenomic approaches have been extensively studied. In the late 1990s, the Glycine9 (Gly9) allele of the Serine-9-Glycine (Ser9Gly) polymorphism in dopamine D3 receptor gene (DRD3) was found to be associated with both a liability to, and worsened severity of, TD in schizophrenic patients treated with typical antipsychotics. This initial discovery has been subsequently replicated and testing for the Ser9Gly polymorphism has now become commercially available. The question that currently presents itself is whether its use should be encouraged for patients who may be prescribed a typical or atypical antipsychotic medication. However, the translation of this new technology to clinical practice presents multiple social, ethical and policy challenges. Though pharmacogenomic testing holds much promise in this scenario, many important questions remain to be answered before its widespread use can be medically and ethically justified. This article highlights the key advances in our understanding of the role of human genetic variation in the D3 receptor in relation to TD. Then, issues of uncertainty, consent, confidentiality, and access are considered with respect to the use of DRD3 polymorphism testing in risk stratification for susceptibility to tardive dyskinesia. We propose three recommendations that may help bring this technology into the clinic: 1) prospective pharmacogenomic studies of DRD3 polymorphism and TD risk should be conducted; 2) the design of such studies should be influenced by scientists, ethicists and policy makers to protect potentially vulnerable patients; and 3) appropriate knowledge transfer to front-line health care workers must take place.

Although target-based approach has yielded novel drugs in many therapeutic fields, this approach remains suboptimal and slows the pace of innovations for drug discovery in central nervous system (CNS) disorders. This is because the target identification requires a hierarchical integration from in vitro cellular and functional tissue studies to animal models that reasonably predict drug effects in humans. Because the majority of CNS drugs were discovered empirically to date, the precise target site(s) for many of them remain unknown or uncertain, even after years of clinical and commercial use. This paper presents a critical review and synthesis of the emerging proteomics applications for drug target discovery with an emphasis on defining the early molecular mechanisms by which (hitherto empirically discovered) drugs or novel neurotrophic factors initiate cellular change toward physiological effects with therapeutic ramifications. Pharmacoproteomics methodology provides important qualitative information on post-translational modifications to each protein as well as quantitative data on protein expression patterns in response to a given pharmaceutical stimulus. This information is particularly important because it provides data on early cellular events such as the stimulus and signaling cascades triggered independently of protein neosynthesis. Pharmacoproteomics is still in its infancy but offers substantial promise in personalized medicine in addition to the pharmacogenomics methodologies.

Hepatitis C virus (HCV) has emerged as a major viral pandemic over the past two decades, infecting 170 million individuals worldwide. Egypt has the highest prevalence of hepatitis C in the world with prevalence rates of 13%- 15% of the population. Hepatitis C is characterized with its high tendency to induce chronic progressive liver damage in the form of hepatic fibrosis, cirrhosis or liver cancer. There is no vaccine against HCV infection because our understanding of the early interactions between the virus and the host immune system is still limited. The current standard of care for patients with chronic hepatitis C, pegylated interferon alpha and ribavirin combination therapy, has improved the rate of resolution of chronic hepatitis C infection. However, many patients still do not respond to therapy or develop adverse events. Moreover, the prohibitive cost of interferon-based regimen makes them inaccessible for many patients particularly in developing countries. Understanding the host and viral factors associated with viral clearance is necessary for individualizing therapy to maximize sustained virologic response rates, prevent progression to liver disease, and increase the overall benefits of therapy with respect to its costs. Host genetic diversity seems to contribute to the outcome of each phase of the clinical spectrum of HCV infection. Genome wide studies have recently showed significant associations between a set of polymorphisms in the region of the interleukin-28B (IL28B) gene and clearance of HCV in natural infection or after pegylated interferon-alfa and ribavirin treatment. This paper synthesizes the advances in pharmacogenetics of HCV infection, including the lessons learned in Egypt to date.

One of the most significant barriers to personalized medicine is effective linkage of scientific discoveries from bench to improved therapeutic outcomes at the bedside, and at the level of health systems and services more broadly. Translational bioinformatics is an emerging field that provides powerful new methods to bridge the gaps between biomedical sciences, clinical practice and population sciences. These objectives can be achieved both from the biomedical and the informatics sides. On the biomedical side, translational bioinformatics elucidates the structure-function associations and genotype-phenotype correlations for the identification of patient subgroups for personalized therapeutics. It enables the modeling of systemic interactions, networks, and interrelationships among genes, drugs, tissues, organs, and the environment at various systems levels. Translational bioinformatics methods facilitate the identification of systemsbased biomarkers for accurate diagnosis and prognosis, effective preventive measures and optimal treatments. Resources such as dbSNP, HapMap, and OMIM are particularly useful for analyses at different levels of a complex knowledge system such as personalized medicine. On the informatics side, data and workflow integration methods assist in understanding of the complexity in decision-making processes in both research and clinical settings. These methods can be combined with data mining techniques for pattern recognition and predictive modeling in translational systems biology studies and drug combination therapies. Knowledge discovery and knowledge representation methods can help across the domain barriers and provide effective decision support. The integration of these approaches via translational bioinformatics is timely and essential for understanding and applying personalized therapies at a systems level that is more likely to stand the test of time and rigorous scientific evidence.