471Usually genetic profi les cannot predict a large percentage of variation in response
to citalopram. Data available through the Sequenced Treatment Alternatives to
Relieve Depression database was used to create three boosted Classifi cation and
Regression Trees to identify 16 subgroups of patients, among whom anticipation of
positive or negative response to citalopram was signifi cantly different from 0.5
(Alemi et al. 2011 ). In a 10-fold cross-validation, this ensemble of trees made no
predictions in 33 % of cases. In the remaining 67 % of cases, it accurately classifi ed
response to citalopram in 78 % of cases. The authors concluded that for the majority
of the patients, genetic biomarkers can be used to guide selection of citalopram. The
rules identifi ed in this study can help personalize prescription of antidepressants.
International guidelines for rational therapeutic drug monitoring (TDM) are rec-
ognized for personalized treatment with antidepressants and antipsychotics.
Retrospective analysis of genotyping of patients with depression suggests a good
agreement between the poor metabolism (PM) and ultrarapid metabolism (UM)
genotypes, the TDM data and clinical outcome (Sjoqvist et al. 2007 ). TDM com-
bined with genotyping of CYP2D6 is particularly useful in verifying concentration-
dependent adverse drug reactions (ADRs) due to PM and diagnosing pharmacokinetic
reasons, e.g. UM for drug failure. This is because ADRs may mimic the psychiatric
illness itself and therapeutic failure due to UM may be mistaken for poor compli-
ance with the prescription.
Role of Protein sFRP3 in Predicting Response to Antidepressants
A Wnt signaling inhibitor, secreted frizzled-related protein 3 (sFRP3), has been
identifi ed as a molecular target of antidepressant treatments in rodent models, and
revealed the signifi cant association of 3 SNPs in FRZB (the sFRP3 human ortholog)
with early antidepressant responses in a clinical cohort (Jang et al. 2013 ). This pro-
tein is the target of both antidepressant drugs and electroconvulsive therapy (ECT).
Results of the experiments explain how these therapies likely work to relieve
depression by stimulating neural stem cells (NSCs) in the brain to grow and mature.
In addition, these experiments raise the possibility of predicting individual’s
response to antidepressant therapy, and adjusting treatment accordingly. The authors
compared gene activity in the brains of mice that had and had not been treated with
ECT, looking specifi cally at genes with protein products that are known to regulate
NSCs. The comparison turned up differences in the activity of one inhibitor gene for
a chemical chain reaction that had been previously implicated in stimulating NSCs.
Specifi cally, the therapy reduced the amount of protein the inhibitor gene, sFRP3,
produced, which would in turn have given the growth-stimulating chain reaction
freer rein. To learn more about sFRP3’s effects, the team next compared normal
mice with mice that had been engineered to lack the sFRP3 protein. They found that
the modifi ed mice behaved like normal mice on antidepressants. Moreover, giving
antidepressants to the modifi ed mice did not further change their behavior. This
strongly suggested that antidepressants work by blocking sFRP3, and without
sFRP3, the modifi ed mice had nothing to block.
In order to correlate the fi ndings in mice to what happens in the human brain, the
researchers next analyzed genetic information from patients with depression and
Psychopharmacogenetics/Psychopharmacodynamics