Current Pharmacogenomics and Personalized Medicine (Formerly Current Pharmacogenomics) - Volume 7, Issue 4, 2009

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Nanotechnology and personalized medicine are two of the most rapidly emerging areas of biomedical research, as well as two of the most promising technologies for improving health care and health outcomes. They are also rapidly converging in numerous current and future clinical applications. Examples include the use of nanotechnology for improved DNA sequencing and SNP analysis, the development of nano-therapeutics that can target specific cell and tissue types, biosensors for specific proteins and other molecules in vivo, and point-of-care molecular diagnostic devices enabled by nanotechnology. Nanotechnology offers many advantages for personalized medicine applications, including a size that matches the scale of the molecular substrates of personalized medicine, an increased sensitivity in detecting and binding with target molecules, and flexibility in the design and function of therapeutics and diagnostics at the nano scale. Yet, at the same time, the utilization of nanotechnology in personalized medicine may create uncertainties or risk relating to potential toxicity. In addition to describing the scientific and technical opportunities and challenges in applying nanotechnology to personalized medicine, this article also addresses some of the policy, legal and ethical issues raised by the convergence of nanotechnology and personalized medicine.

Since the completion of the Human Genome Project, increasing scrutiny has focused on patterns of genetic variation among global populations and their association with disease and human traits. This paper addresses emerging techniques to identify genetic differences, including admixture mapping and the use of ancestry informative markers (AIMS), towards controlling population substructure in genetic association studies. The paper discusses the need to reconcile statistical race used to determine genetic ancestry with phenotypic race in identifying and addressing ongoing health disparities among human populations. As DNA biobanking grows and standards for collecting phenotypic information are developed, clear understanding of the varied approaches to defining race and their implications will be imperative. Central to this explication will be an exploration of how genetic technologies inform current approaches to ancestry and their relevance for pharmacogenomic applications.

Dr. Young-Ki Paik directs the Yonsei Proteome Research Center in Seoul, Korea and was elected as the President of the Human Proteome Organization (HUPO) in 2009. In the December 2009 issue of the Current Pharmacogenomics and Personalized Medicine (CPPM), Dr. Paik explains the new field of pharmacoproteomics and the approaching wave of “proteomics diagnostics” in relation to personalized medicine, HUPO's role in advancing proteomics technology applications, the HUPO Proteomics Standards Initiative, and the future impact of proteomics on medicine, science, and society. Additionally, he comments that (1) there is a need for launching a Gene- Centric Human Proteome Project (GC-HPP) through which all representative proteins encoded by the genes can be identified and quantified in a specific cell and tissue and, (2) that the innovation frameworks within the diagnostics industry hitherto borrowed from the genetics age may require reevaluation in the case of proteomics, in order to facilitate the uptake of pharmacoproteomics innovations. He stresses the importance of biological/clinical plausibility driving the evolution of biotechnologies such as proteomics, instead of an isolated singular focus on the technology per se. Dr. Paik earned his Ph.D. in biochemistry from the University of Missouri-Columbia and carried out postdoctoral work at the Gladstone Foundation Laboratories of Cardiovascular Disease, University of California at San Francisco. In 2005, his research team at Yonsei University first identified and characterized the chemical structure of C. elegans dauer pheromone (daumone) which controls the aging process of this nematode. He is interviewed by a multidisciplinary team specializing in knowledge translation, technology regulation, health systems governance, and innovation analysis.

Variation in the behavior of drugs between people, and variation in drug behavior in a given patient over time, have both presented us with challenging problems in optimal description of such behavior as well as challenges of how best to act on such information. New high-throughput genotyping methods and measurement of variations in gene expression over time present us with issues of 1) how best to use such information in the overall process of planning drug dosage regimens for individual patients, especially if the drug is potentially toxic; 2) how to further refine our knowledge about the patient during the course of pharmacotherapy; and 3) how best to adjust the dosage regimen to the new information we obtain about him/her as a unique individual. Human genetic variation, in the form of gene sequence or expression variability, provides us with important covariate information to help further individualize our dosage regimen for a particular patient based on that information, just as does information about smoking status, age, gender, body weight, and renal function, for example. It helps us consider the patient as an individual rather than as a member of a larger group. Variation in gene expression over time (i.e., transcriptomic biomarkers) in an individual patient presents another problem, as it can cause significant differences in drug behavior over time. However, just as variation over time can occur in other covariates such as body weight and renal function, so can such changes in genetic expression over time be incorporated into models of drug behavior in individual patients, and used thoughtfully to optimize each patient's drug dosage regimen. The overall structure of optimally precise Bayesian adaptive control is presented in this paper, to define explicitly the context in which human genetic/genomic information can be incorporated and used to optimize drug therapy for patients.

HER2 is over-expressed in approximately 25% to 30% of human metastatic breast cancers, primarily due to gene amplification. There are currently two HER2-targeted therapies approved for clinical use, the monoclonal HER2 antibody trastuzumab and the EGFR/HER2 dual tyrosine kinase inhibitor lapatinib. Although both agents show clinical benefit in a subset of patients with metastatic breast cancer, many patients with HER2-over-expressing metastatic breast tumors do not respond to these agents. Furthermore, those who do show an initial response generally demonstrate disease progression, on average in less than one year. It has become clear that HER2 expression status alone does not adequately predict response to HER2-targeted therapy. Identification and clinical validation of molecular predictors of response to trastuzumab and lapatinib is critical for further personalizing treatment and improving clinical benefit for patients whose tumors over-express HER2. In this review, we discuss published data describing potential predictors of response or resistance to trastuzumab and lapatinib. While a discussion of the preclinical work is provided, the emphasis is placed on potential predictors that have been studied in clinical specimens such as tumor tissue or serum obtained from patients treated with HER2-targeted therapy. The present analysis and synthesis of the available literature therefore contribute towards an emerging knowledgebase to personalize breast cancer treatment taking into factors including but beyond HER2 expression.

Whether theranostic testing to discern person-to-person and population differences in drug metabolism pathways offers clinical guidance in oncology for fluoropyrimidines such as 5-fluorouracil (5-FU) remains an open question. Extensive basic and clinical studies have been performed over the past several decades with regard to personalizing treatment with fluoropyrimidines, optimizing patients' quality of life and reducing risks for severe and fatal toxic events. A variety of genetic variants in the dihydropyrimidine dehydrogenase (DPYD) gene were thus identified, and have been clustered according to their clinical and predictive value. However, further research is still needed as the DPYD gene is highly variable, complicating the attempts to establish genotype-phenotype correlations. Therefore, to individualise 5-FU therapy, several integrative/complementary approaches appear to be indicated to determine the dihydropyrimidine dehydrogenase metabolizer status. In principle, these diagnostic approaches in assessing dihydropyrimidine dehydrogenase activity include genotyping and phenotyping for the DPYD. This paper presents a critical summary and evaluation of the current state of research on both DPYD genotyping and phenotyping, and the pharmacogenetic syndrome of dihydropyrimidine dehydrogenase deficiency more generally. For DPYD based personalized medicine diagnostics to advance forward to become standard state of the art in routine testing, we also suggest the need for additional genetic, biological and clinical characterization of this pharmacologically significant variation across different global populations.

Across the translational research continuum from biomarker discovery to public health research, “costeffectiveness” considerations are crucial, and can significantly impact the adoption of personalized medicine innovations. Cost-effectiveness is concerned with providing evidence to compare the (economic) costs and the health outcomes of competing health interventions or technologies. This also affects translational research in all stages, including clinical trials, post-market monitoring and population health outcome assessment. Indeed, economic considerations are important in determining the development and diffusion of a new technology in any scientific field. This is particularly true in healthrelated sectors, wherein governments and regulatory agencies with a mandate and commitment to efficient and rational allocation of resources require transparent and rigorous economic evidence to support or decline the adoption of a new technology. In the context of personalized medicine and theragnostics (i.e., the fusion of therapeutics and diagnostics), the use of genomics in clinical practice can be markedly facilitated by tandem evaluation of the clinical benefits/risks of customized health interventions and their cost-effectiveness. This paper provides a synthesis of the past and emerging literature on cost-effectiveness studies that evaluate pharmacogenetics tests. We conclude that despite the recent efforts, there is still a scarcity of convincing evidence on the cost-effectiveness of genomics products that creates a barrier in the uptake of pharmacogenetics in personalized medicine. Additionally, the reasons that limit a wider development of the cost-effectiveness analyses in this field are discussed, with a view to amend the above translational gaps in the literature.

Pharmacogenetics has its roots in the 1950s with pioneering studies of monogenic variations in drug metabolism and pharmacokinetics. With the availability of high-throughput genomics technologies and the completion of the Human Genome Project in 2003, we are now in the postgenomics era. This transition is increasingly marked with study of polygenic and multifactorial traits such as common complex human diseases as well as pharmacodynamic differences among populations. Changes that emerge from postgenomics medicine are not, however, limited to seismic shifts in scale and scope of pharmacogenetics research. Importantly, many low- and middle-income countries (LMICs) of the South, Asia-Pacific, Eastern Mediterranean and the Middle-East are becoming notable contributors with rapid globalization of science and increasing access to genomics technologies. This brings about, in parallel, an acute demand for regional capacity building in LMICs so that the future evaluation and implementation of postgenomics technologies in personalized medicine take place in an integrated, sustainable and equitable manner. With this overarching vision, we herein report the founding of the Istanbul Working Group in Personalized Medicine (IWG-PM, represented by the authors of this report) that was inaugurated as a component of the 2nd Symposium on Personalized and Predictive Medicine held in Istanbul, sponsored by the Yeditepe University, and the Turkish Scientific and Technological Research Council (TUBYTAK) (10-12 September, 2009). While highlighting the applications of personalized medicine in oncology, psychiatry, nutrition, infectious diseases, occupational health, genetic testing and systems biology, the symposium also raised challenging questions in the context of LMICs. How can we best evaluate the promises, intended and unintended impacts of personalized medicine and enabling technologies in the context of Turkey, and the LMICs more generally? IWG-PM is a small but significant and necessary step to initiate regional capacity building in Turkey. We trust that the IWG-PM initiative may also provide a constructive example to further develop capacity in other LMICs in the Eastern Mediterranean region.

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Due to oversight on the part of the authors, G.I. Liou, A.B. El-Remessy, A.S. Ibrahim, R.B. Caldwell, Y.M. Khalifa, A. Gunes and J.J. Nussbaum., incomplete funding information was published in the review entitled “Cannabidiol as a Putative Novel Therapy for Diabetic Retinopathy: A Postulated Mechanism of Action as an Entry Point for Biomarker-Guided Clinical Development”, Current Pharmacogenomics and Personalized Medicine, September 2009, Vol. 7, No. 3, pp. 215-222. The correct funding information should read as: “This work was supported in part by grants from American Diabetes Association and Knights Templar Educational Foundation (GIL), Egyptian Culture and Education Bureau (ASI) and the US National Institutes of Health (R01-EY04618 and R01- EY11766) (RBC)”.