Applications in Individual Prescription

The characterization of an individual's metabolic status is the first and most used clinical application of pharmacogenetics. Most antipsychotic drugs are metabolized by four cyto-chrome CYP enzymes, namely CYP1A2, CYP2D6, CYP2C9, and CYP3A4 (90,91). Detection of the most common polymorphisms in the genes coding for these enzymes will facilitate the identification of individuals likely to present adverse reactions or treatment failure related to deficient metabolism. Pretreatment genotyping of CYP

polymorphisms will allow for the adjustment of therapeutic doses for slow metabolizers and UMs. This practice has already been recommended for a number of antidepressant drugs (92) and will be particularly beneficial in cases where the use of drugs with narrow therapeutic ranges is considered. Several protocols are already standardized for the rapid detection of metabolic polymorphisms, including simple polychain reaction (PCR) techniques (93,94) and DNA arrays for the simultaneous genotyping of the most frequent polymorphisms (95,96).

Preliminary studies incorporating information in drug targets have also proved their value as predictors of therapeutic response. A combination of mutations in a relatively small number of genes resulted in the successful prediction of clozapine response in 76% of the cases (80). Similar strategies are being developed for other antipsychotic drugs, such as olanzapine (47). Tailoring of antipsychotic treatment will incorporate genetic information for the prediction of therapeutic response and drug-induced side effects. Although still under investigation, these strategies will form the basis of future tailoring of antipsychotic treatment according to the individual's requirements.

Applications in Drug Development and Clinical Trials

Pharmacogenomic research will have an obvious impact in drug development. High-throughput technologies involving DNA chips, robotized handling of samples, and computerized analyses permit the investigation of a large number of genes simultaneously and will greatly accelerate the identification of therapeutic targets. These high-throughput technologies can be used to investigate functional alterations (i.e., differential gene expression) in tissue samples from brains of probands, thus identifying putative therapeutic areas. The same high-throughput technologies can be used to investigate drug-induced expression changes of neurotransmitter and receptor proteins in animal and human brains, thus helping to confirm/discard current targets. In addition, taking advantage of the sequence knowledge produced by the human genome mapping project (97), novel targets can be fished out by comparing genomic sequences with proteins of proven therapeutic relevance (98).

Pharmacogenetic applications in clinical trials are already in use. Routine CYP genotyping is carried out to select patients for clinical trials according to their metabolic characteristics. In this way, adverse reactions caused by poor metabolism of the studied drug can be easily detected and prescription could be restricted to population groups with favorable genetic profiles. Although the economic gains may not be substantial as it can result in a reduction of market share, this practice can help to obtain the optimal therapeutic dose for each patient and to avoid adverse reactions caused by deficient metabolism, therefore contributing to the success of the drug and facilitating their approval by regulatory bodies. Further applications of pharmacogenetics, namely identifying patients likely to respond according to their pharmacodynamic profile, may take longer to be introduced in clinical trials. However, this practice can be very beneficial in cases where the use of successful antipsychotics have been found to cause severe side effects in a number of cases (e.g., clozapine). Pretreatment pharmacogenotyping will identify patients likely to show positive response without developing side effects. This practice may help patients to access beneficial treatments, which otherwise would not have been considered.

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