Novel Diagnostics R&D for Public Health and Personalized Medicine in Taiwan: Current State, Challenges and Opportunities

Eugene      Lin; Shih-Jen      Tsai

doi:10.2174/187569212802510049

Abstract

Novel diagnostics R&D in public health and personalized medicine hold great promise as tools to speed up the translation of results from bench to bedside for selecting and optimizing treatment and health outcomes for each person. Although the integration of individualized medicine into clinical decision making is still evolving, accumulating evidence reveals that genetic and genomic information could help predict clinical drug response and adverse drug reactions for drugs in patients with certain diseases. This article first gives a brief overview of selected diagnostics R&D studies in Taiwan for antipsychotic treatments, antidepressant responsiveness, sibutramine therapy, chronic hepatitis C therapy, and other various treatments of significance for public health in Taiwan, with consideration of genetic and genomic variants. For example, the interactions of two insulin-induced gene (INSIG) biomarkers, designated INSIG1 and INSIG2, were found to be associated with metabolic syndrome in schizophrenic patients treated with antipsychotics. Recent advances in pharmacogenomics and personalized medicine studies conducted in Taiwanese populations and attendant novel computational approaches are highlighted here, with a view to public and population health in the country and the Asia- Pacific region where there are extensive investments in genomics and postgenomics diagnostics R&D. Finally, we address a discussion of future directions and some of the most pressing computational challenges in data-intensive life sciences owing to the combinatorial explosion of ever larger data sets. Anticipating and addressing these emerging issues in public health genomics are of great relevance not only for Taiwan but also for the Asia-Pacific and global health.

Keywords: Antipsychotics, bioinformatics, computational biology, data-intensive life sciences, gene–gene interaction, novel diagnostics, personalized medicine, pharmacogenomics.