The design and development of a personalized medicine support system
Abstract
The use of a patient's genetic data to aid clinical diagnosis and drug treatment represents a major milestone in the contribution of genomic research to healthcare practices. Initial implementation of a personalized medicine system is currently possible, uniting the areas of medicine, genomics, and informatics, built upon a foundation of the latest advances in information technology. The aim of a clinical application of pharmacogenomics is to tailor therapeutic drug regimens to an individual's genetic profile, thereby maximizing the effectiveness of drug therapy and minimizing the likelihood of an adverse drug response (ADR). The fulfillment of these goals is of the utmost importance. Only half of all patients treated with conventional blockbuster drugs respond adequately and ADRs currently rank as the fourth leading cause of death in the US with more than 50% classified as dose-related (Olivier, Williams-Jones, Godard, Mikalson, & Ozdemir, 2008; Brockmöller & Tzvetkov, 2008). This represents a major financial and social burden. Though the link between allelic variants and altered drug metabolism is well established, the technology for implementing such considerations toward individualized dose adjustments does not currently exist. In order to leverage current advances in pharmacogenomics and assume a proactive role for the more rapid adoption of future technologies and advancements, a societal-scale personalized medicine system has been designed and developed. Given the very recent nature of the advances in human genomic knowledge and biotechnology methods as well as the lack of formal pharmacogenomics education, the system has been developed to serve as an education tool for physicians and pharmacists. The system represents a fully operational software system and incorporates development methodologies and technology that a business organization would use in order to implement a commercially viable product. This in particular allows the system to contribute toward the development of current data standards, exemplify an interface between genotypic analysis and its practical application to clinical decision support, allow for an analysis of current gaps within the supporting infrastructure, and enable the final dissemination of a "best practices" report with regard to real-world design, development, and implementation.
Degree
M.S.
Advisors
Kane, Purdue University.
Subject Area
Pharmacology
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