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Current Pharmacogenomics

Editor-in-Chief

ISSN (Print): 1570-1603
ISSN (Online): 1570-1603

Pharmacogenetic and Pharmacogenomic Research in Psychiatry: Current Advances and Clinical Applications

Author(s): Maria J. Arranz, Dalu T. Mancama and Robert W. Kerwin

Volume 1, Issue 3, 2003

Page: [151 - 158] Pages: 8

DOI: 10.2174/1570160033476287

Price: $65

Abstract

After more than 50 years of investigations, pharmacogenetic efforts have crystallized in several findings relating genetically determined pharmacokinetic and pharmacodynamic factors to treatment response. Metabolic enzymes and neurotransmitter proteins contain genetic polymorphisms that alter their interaction with psychotropic drugs and contribute to response variability. This knowledge can be used to predict clinical results and adverse reactions. Current clinical applications include rapid methods for the characterisation of metabolic status that is used in clinical trials for the identification of individuals susceptible to side-effects. This practice is being extended to clinical laboratories to avoid toxic reactions to specific treatments. Pharmacogenetics methods for the pre-treatment prediction of clinical response to the antipsychotic drugs clozapine, risperidone, olanzapine and haloperidol are in development and expected to be available for clinical use in the next decade. However, much is still expected from the wealth of information produced by pharmacogenomic research. Pharmacogenomic strategies, including large scale functional studies in brain areas related to the aetiology of mental disorders, will increase the knowledge on therapeutic mechanisms and identify novel targets. Pharmacogenomic advances will be translated into more specific and safer drugs and tailoring of drug prescription according to the patient ’ s genetic susceptibilities. Pharmacogenetic and pharmacogenomic investigations have the potential to transform psychiatric treatment in the next decades.

Keywords: pharmacogenetics, pharmacogenomics, antipsychotic drugs, genetic prediction of response

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