Factors impeding the progress in this direction have to do with i

Factors impeding the progress in this direction have to do with infrastructure as well as data showing efficacy and cost-effectiveness of the pharmacogenomic approach. Development of pharmacogenomic panel(s) Although for some drug-metabolizing enzymes, such as CYP2D6 and CYP2C19, allelic variations

could lead to dramatic functional and health consequences, in the majority of the “candidate genes” for antidepressant response, the influence is partial and may be cumulative. This means that many genes may influence treatment response, but each with only a small effect. This is especially true with genes encoding Inhibitors,research,lifescience,medical potential therapeutic targets. Although this has been the consensus in the field for a number of years, the extant pharmacogenetic literature is predominantly based on single genotype or a combination of only a few genotypes. In order for pharmacogenetic Inhibitors,research,lifescience,medical data to be clinically useful, multiple relevant, genotypes need to be tested simultaneously, and the results need to be available for Inhibitors,research,lifescience,medical clinicians in a timely manner (preferably within 24 hours), such that the data could be included in the HSP90 inhibitor review clinical decisions made prior to the initiation of pharmacotherapy. With the

advent of high-throughput genotyping technologies, this is no longer out of reach. Thus, the next. generation of pharmacogenomic research should include the development of specific pharmacogenomic panel(s) for different disease categories and treatment methods. Developing user-friendly tools for interpreting pharmacogenomic results Since for any disease/treatment category, such a Inhibitors,research,lifescience,medical panel will likely include a large number of “candidate genes,” whose function likely is influenced by multiple alleles, the results of the panel will be exceedingly complex and may not be easily interprétable by typical clinicians,

much less readily incorporated into Inhibitors,research,lifescience,medical the clinical decision making process. To solve such a problem, a number of modeling through programs have been developed. Of these, the most promising appears to be the neural network model or neural fuzzy model. Using such a model, relevant genetic data as well as clinical, sociodemographic, and lifestyle variables (past medication response history, concurrent use of other medications, dietary practices, and exposure to other drug-inducing or inhibiting agents, such as cigarette smoking) could be simultaneously incorporated into the estimations for the probability of efficacy and dosing strategy for different medications. Further, a unique feature of such a model is that it. is “trainable,” in that, as additional relevant data become available, they could be readily incorporated to improve the prediction model.

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