Person‐centered measurement‐based care for depression

tic clinical utility. The recent emergence of low-cost pharmacogenomic techniques has sparked new interests in combinatorial use of allelic variations in drug transporters or metabolic genes as biomarkers that might predict drug response. An initial generation of research identified a number of candidate genes with apparent validity as predictors of treatment efficacy and treatment-related side effects. These candidates include genes implicated in serotonergic function, the ABC family of xenobiotic transporters located in the blood-brain barrier, and the cytochrome P450 detoxification enzymes. However, to date, there are no effective biological methods to objectively assess depression endophenotypes, severity, or treatment response. Previous efforts to achieve better treatment outcomes in psychiatry have led to the introduction of pharmacogenomics based decision-support tools, to help identify which patients are more or less likely to have a favorable outcome with specific pharmacotherapies, based on single nucleotide polymorphisms (SNPs) and gene variants in transporters and metabolizing enzymes. Genome-wide association studies have revealed that common genetic variations are unlikely to explain sufficient variance in treatment response to guide selection of treatment for individual patients. Rare gene variants have greater explanatory power than common variants, but such individual markers would likely apply to relatively few patients. Thus, if neither common nor rare gene variants are likely to have widespread predictive value as “stand alone” predictors of treatment response in typical clinical trials, a new strategy is needed, one that integrates several types of clinical and neurobiological markers to guide clinical decision making for depressive disorders. Since it is unlikely that a single biological alteration will have a one-to-one mapping with a DSM-defined or RDoCspecified mental phenomenon, a viable alternative to the single-biomarker approach is the development of biosignatures that aim to profile a diverse array of peripheral/serum growth factors, cytokines, hormones and metabolic markers. Additionally, integration with neurological, cognitive and psychological assessments will provide coverage of multiple abnormalities that contribute to the heterogeneity of depressive disorders. Such a biosignature will not only improve our ability to identify specific subtypes of depressive disorders, but will also assist with the selection of treatments that are likely to be more clinically useful. Based on this, some of the most promising variables to evaluate include: comprehensive clinical phenotype; magnetic resonance imaging using measures of cortical structure; diffusion tensor imaging to assess cortical white matter tract integrity; functional magnetic resonance imaging assessing brain activation patterns to both emotional conflict and reward-dependent learning tasks; quantitative electroencephalography (EEG) to assess cortical and subcortical brain activation patterns; cortical evoked EEG potentials; behavioral neuropsychological tasks to assess reaction time and motor processing speed; DNA, mRNA, and plasma, urine and saliva protein and metabolomics samples, collected at baseline and throughout the study; socio-economic, demographic and life habits parameters. Using this comprehensive approach, however, requires a large number of participants to be characterized in order to define subgroups in relation to treatment response. It also requires the use of effective computational tools to make integration of the wealth of knowledge generated from the diverse platforms possible. Herein lays our greatest challenge: developing large cohorts of depressed patients that will lead us to the discovery of not only new, meticulously-defined subtypes of depression, but also identification of precise treatments for each individual patient. If we are successful, this will propel the treatment of depression to equal the effectiveness of treatments for cancer and chronic heart disease.