Pharmacogenetics of antipsychotic-induced weight gain: review and clinical implications.
ABSTRACT Second-generation antipsychotics (SGAs), such as risperidone, clozapine and olanzapine, are the most common drug treatments for schizophrenia. SGAs presented an advantage over first-generation antipsychotics (FGAs), particularly regarding avoidance of extrapyramidal symptoms. However, most SGAs, and to a lesser degree FGAs, are linked to substantial weight gain. This substantial weight gain is a leading factor in patient non-compliance and poses significant risk of diabetes, lipid abnormalities (that is, metabolic syndrome) and cardiovascular events including sudden death. The purpose of this article is to review the advances made in the field of pharmacogenetics of antipsychotic-induced weight gain (AIWG). We included all published association studies in AIWG from December 2006 to date using the Medline and ISI web of knowledge databases. There has been considerable progress reaffirming previous findings and discovery of novel genetic factors. The HTR2C and leptin genes are among the most promising, and new evidence suggests that the DRD2, TNF, SNAP-25 and MC4R genes are also prominent risk factors. Further promising findings have been reported in novel susceptibility genes, such as CNR1, MDR1, ADRA1A and INSIG2. More research is required before genetically informed, personalized medicine can be applied to antipsychotic treatment; nevertheless, inroads have been made towards assessing genetic liability and plausible clinical application.
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ABSTRACT: Antipsychotic drugs are tranquilizing psychiatric medications primarily used in the treatment of schizophrenia and similar severe mental disorders. So far, most of these drugs have been discovered without knowing much on the molecular mechanisms of their actions. The available large amount of pharmacogenetics, pharmacometabolomics, and pharmacoproteomics data for many drugs makes it possible to systematically explore the molecular mechanisms underlying drug actions. In this study, we applied a unique network-based approach to investigate antipsychotic drugs and their targets. We first retrieved 43 antipsychotic drugs, 42 unique target genes, and 46 adverse drug interactions from the DrugBank database and then generated a drug-gene network and a drug-drug interaction network. Through drug-gene network analysis, we found that seven atypical antipsychotic drugs tended to form two clusters that could be defined by drugs with different target receptor profiles. In the drug-drug interaction network, we found that three drugs (zuclopenthixol, ziprasidone, and thiothixene) tended to have more adverse drug interactions than others, while clozapine had fewer adverse drug interactions. This investigation indicated that these antipsychotics might have different molecular mechanisms underlying the drug actions. This pilot network-assisted investigation of antipsychotics demonstrates that network-based analysis is useful for uncovering the molecular actions of antipsychotics.Chemistry & Biodiversity 05/2012; 9(5):900-10. DOI:10.1002/cbdv.201100356 · 1.80 Impact Factor
- Neuropsychiatric Disease and Treatment 01/2015; DOI:10.2147/NDT.S79327 · 2.15 Impact Factor
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ABSTRACT: Evidence for involvement of DNA methylation in psychosis forms the focus of this perspective. Of interest are results from two independent sets of experiments including rats treated with antipsychotic drugs and monozygotic twins discordant for schizophrenia. The results show that DNA methylation is increased in rats treated with antipsychotic drugs, reflecting the global effect of the drugs. Some of these changes are also seen in affected schizophrenic twins that were treated with antipsychotics. The genes and pathways identified in the unrelated experiments are relevant to neurodevelopment and psychiatric disorders. The common cause is hypothesized to be aberrations resulting from medication use. However, this needs to be established by future studies that address the origin of methylation changes in psychosis.Epigenomics 02/2015; 7(1):67-74. DOI:10.2217/epi.14.66 · 5.22 Impact Factor