Pharmacogenetic studies offer the prospect of the identification of immutable biological predictors of drug response, and may also provide insight into the key molecular mediators of drug efficacy. These studies by Perlis and colleagues and Licinio and colleagues highlight the growth of this field, as well as potential opportunities and challenges associated with this line of inquiry.

Perlis and colleagues have utilized the GWAS approach to dissect the heterogeneity of drug response in the large STEP-BD cohort to identify a number of potential loci associated with response to lithium, and then examined these loci in a separate lithium-treated cohort derived from the U.K. The use of GWAS provides a hypothesis-free comprehensive examination of the genome, and the inclusion of a second sample adds a degree of confidence in the nominally significant GWAS results. The key strength of the study is the large sample size (n = 1,177) and the prospective design of the STEP-BD sample. As the authors note, however, the second sample does not truly represent “replication” as the...  Read more

Pharmacogenetic studies offer the prospect of the identification of immutable biological predictors of drug response, and may also provide insight into the key molecular mediators of drug efficacy. These studies by Perlis and colleagues and Licinio and colleagues highlight the growth of this field, as well as potential opportunities and challenges associated with this line of inquiry.

Perlis and colleagues have utilized the GWAS approach to dissect the heterogeneity of drug response in the large STEP-BD cohort to identify a number of potential loci associated with response to lithium, and then examined these loci in a separate lithium-treated cohort derived from the U.K. The use of GWAS provides a hypothesis-free comprehensive examination of the genome, and the inclusion of a second sample adds a degree of confidence in the nominally significant GWAS results. The key strength of the study is the large sample size (n = 1,177) and the prospective design of the STEP-BD sample. As the authors note, however, the second sample does not truly represent “replication” as the phenotypes assessed differ between cohorts, with the second cohort restricted to a retrospective global assessment of response. However, it should be realized that the often requested “replication” sample may simply not be feasible with pharmacogenetic designs given the complexities of clinical trial design, and the inherent difficulties in designing and conducting the exact same clinical trial in two similar populations.

This study highlights some of the challenges inherent in pharmacogenetics. In order to adequately power the GWAS analysis, a large sample such as STEP-BD was required, as well as a “replication” cohort because the initial GWAS results would not be likely to meet genomewide significance. The use of this sample does introduce considerable heterogeneity into the dataset (multiple sites, different rating teams, several classes and doses of drugs, differential levels of illness chronicity, variance in prior drug exposure, etc.). Perhaps most notable is that only 23 percent of the subjects actually received lithium as a sole mood stabilizer in the trial, and therefore the signal being detected may reflect additional treatment factors not directly related to lithium. However, this limitation is mitigated by the fact that this mirrors the state of clinical care of bipolar disorder, and the result may therefore be more applicable to the clinical aim of generalized treatment response predictor identification, as opposed to assessing specific genotype-drug interactions.

Licinio and colleagues utilize an alternative strategy to GWAS—the candidate gene approach—to detect a modest relationship between a variant in the BDNF gene and antidepressant response. The candidate gene strategy provides a hypothesis-driven approach that may limit the amount of tests conducted, and thus a smaller, more homogenous sample can be utilized. The lack of comprehensive assessment of the entire genome, as well as concerns about how the “candidacy” of each gene is assessed merit caution, but candidate gene strategies remain a particularly powerful approach in pharmacogenetic studies, where it can be more reasonably argued that we have prior knowledge of some aspects of the mechanism of drug action. Replication and extension of these results will be informative, but, as noted above, it will be critical to examine the phenotypes of putative “replication” samples. The publication of these results do provide an opportunity for maximally informative replication studies to be designed going forward.

In conclusion, these two studies suggest that pharmacogenetic studies offer intriguing insights into psychotropic drug efficacy. Although neither result may be “ready for the clinic” at this point, continued progress at both the phenotypic and genotypic levels, as well as the increased attention to pharmacogenetics at the industry level, are encouraging signs. Studies such as these are important milestones along the road to the development of personalized medicine in psychiatry.

Licinio and colleagues undertook a thorough analysis of the BDNF gene, in a medium-large sample of 264 controls and 272 majorly depressed patients. The study finds its rationale on the assumption that the identification of novel rare variants located in candidate genes may provide some molecular and disease-related breakthroughs. Licinio and colleagues report convincing evidence that rs12273539 and rs61888800 may be modulators of the risk for major depression disorder and of the response to antidepressant treatment, respectively. Moreover, two haplotypes in different blocks were found to be associated with the risk of being diagnosed with a major depressive disorder. The robustness of the applied statistics and the high-ranked technical methodologies which characterize the paper make it worth being taking into consideration for further analysis and independent confirmatory investigations.

Nonetheless, it is common opinion that even the most intensive methodological analysis of a single gene may not sort out the key disruptions that likely lead to major depressive...  Read more

Licinio and colleagues undertook a thorough analysis of the BDNF gene, in a medium-large sample of 264 controls and 272 majorly depressed patients. The study finds its rationale on the assumption that the identification of novel rare variants located in candidate genes may provide some molecular and disease-related breakthroughs. Licinio and colleagues report convincing evidence that rs12273539 and rs61888800 may be modulators of the risk for major depression disorder and of the response to antidepressant treatment, respectively. Moreover, two haplotypes in different blocks were found to be associated with the risk of being diagnosed with a major depressive disorder. The robustness of the applied statistics and the high-ranked technical methodologies which characterize the paper make it worth being taking into consideration for further analysis and independent confirmatory investigations.

Nonetheless, it is common opinion that even the most intensive methodological analysis of a single gene may not sort out the key disruptions that likely lead to major depressive disorder, in that there is good reason to think that a multigenic background characterizes psychiatric disorders. It is rational to think that a set of variations with functional roles, associated with coding or regulatory events, promotes a shift from homeostasis to allostasis, which may or may not result in a frank disease until the allostatic load is maintained. In other words, controls may bear some variations in the BDNF gene which are shared by cases and which influence risk of a depressive disorder only in the case of specific combinations with other variations not necessarily located in the gene under investigation. Moreover, gene X environment analysis may grant some new insight in this sample of patients. These elements, epistasis and gene X environment interaction, provoke a noise effect which seemed, nevertheless, to be overridden by the strength of association, Licinio and colleagues report, suggesting that rs12273539 and rs61888800 should be taken into serious consideration when designing new genetic association studies on major depressive disorder.

Perlis and colleagues report on a GWAS focused on the pharmacogenomics of the outcome of lithium treatment in a large sample of bipolar I and bipolar II patients. Unfortunately, none of the 1.4 million SNPs analyzed reached the genomewide significant threshold (10^-8), and the authors could only report a suggestive association with a borderline significance (10^-7) for rs10795189. This is not conceptually surprising as such a high threshold would imply a large effect of the gene, which is unlikely, and more probably true associations are hidden within results with much lower significance, but located in key mechanisms. In fact, the authors report that associations were detected between regions coding for a subunit of the ligand-gated ionotropic glutamate receptor, GluR2/GLURB, Syndecan-2 (SDC2) which codes for a cell-surface proteoglycan, synaptic vesicle, glycoprotein- 2B which plays a role in the hippocampus, and the human homologue of Drosophila odd Oz.

Two relevant points arise from this result: 1) GWAS still do not yield the expected results in the field of psychiatry, continuing to provide starting points more than solutions to the open questions in psychiatry; 2) lithium response is under the control of multiple genes, as the authors conclude, as well as under the influence of a set of environmental and psychological variables which should be taken in a more detailed account in the next investigations. Interestingly, the most common proteins which have been thought to be associated with lithium’s efficacy did not show themselves in this paper: inositol monophosphatase (IMPase [isoenzymes 1 and 2]); inositol polyphosphate 1-phosphatase (IPPase); bisphosphate nucleotidase (BPNase); fructose 1,6-bisphosphastase (FBPase); phosphoglucomutase (PGM) and glycogen synthase kinase-3 (GSK-3β) are inhibited by lithium at therapeutic concentration but their variation was not labeled as significant in this research. This stresses the prime relevance of GWAS in identifying new molecular paths associated with drug dynamics which could be quite far away from the theories that have been so far been built in vitro and in animal models: this kind of falsification strategy is of major help in stimulating new and hopefully more informative directions of investigation.