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87Personalized medicine -the adaptation of therapies based on an individual's genetic and molecular profile -is one of the most promising aspects of modern medicine. The identification of the relation between genotype and drug response, including both the therapeutic effect and side effect profile, is expected to deeply affect medical practice. In this paper, we review the current knowledge about the genes related to antidepressant treatment response and provide methodologic proposals for future studies. We have mainly focused on genes associated with pharmacodynamics, for which a list of promising genes has been identified despite some inconsistency across studies. We have also synthesized the main results for pharmacokinetic genes, although so far they seem less relevant than those for pharmaco dynamic genes. We discuss possible reasons for these inconsistent findings and propose new study designs.

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Objectives Clinical variables were investigated in the ‘treatment resistant depression ( TRD )‐ III ’ sample to replicate earlier findings by the European research consortium ‘Group for the Study of Resistant Depression’ ( GSRD ) and enable cross‐sample prediction of treatment outcome in TRD . Experimental procedures TRD was defined by a Montgomery and Åsberg Depression Rating Scale ( MADRS ) score ≥22 after at least two antidepressive trials. Response was defined by a decline in MADRS score by ≥50% and below a threshold of 22. Logistic regression was applied to replicate predictors for TRD among 16 clinical variables in 916 patients. Elastic net regression was applied for prediction of treatment outcome. Results Symptom severity (odds ratio ( OR ) = 3.31), psychotic symptoms ( OR = 2.52), suicidal risk ( OR = 1.74), generalized anxiety disorder ( OR = 1.68), inpatient status ( OR = 1.65), higher number of antidepressants administered previously ( OR = 1.23), and lifetime depressive episodes ( OR = 1.15) as well as longer duration of the current episode ( OR = 1.022) increased the risk of TRD . Prediction of TRD reached an accuracy of 0.86 in the independent validation set, TRD ‐I. Conclusion Symptom severity, suicidal risk, higher number of lifetime depressive episodes, and comorbid anxiety disorder were replicated as the most prominent risk factors for TRD . Significant predictors in TRD ‐ III enabled robust prediction of treatment outcome in TRD ‐I.

Up to 60% of depressed patients do not respond completely to antidepressants (ADs) and up to 30% do not respond at all. Genetic factors contribute for about 50% of the AD response. During the recent years the possible influence of a set of candidate genes as genetic predictors of AD response efficacy was investigated by us and others. They include the cytochrome P450 superfamily, the P-glycoprotein (ABCB1), the tryptophan hydroxylase, the catechol-Omethyltransferase, the monoamine oxidase A, the serotonin transporter (5-HTTLPR), the norepinephrine transporter, the dopamine transporter, variants in the 5-hydroxytryptamine receptors (5-HT1A, 5-HT2A, 5-HT3A, 5-HT3B, and 5-HT6), adrenoreceptor beta-1 and alpha-2, the dopamine receptors (D2), the G protein beta 3 subunit, the corticotropin releasing hormone receptors (CRHR1 and CRHR2), the glucocorticoid receptors, the c-AMP response-element binding, and the brain-derived neurotrophic factor. Marginal associations were reported for angiotensin I converting enzyme, circadian locomotor output cycles kaput protein, glutamatergic system, nitric oxide synthase, and interleukin 1-beta gene. In conclusion, gene variants seem to influence human behavior, liability to disorders and treatment response. Nonetheless, gene × environment interactions have been hypothesized to modulate several of these effects.

Treatment-resistant depression (TRD) is a major contributor to the disability caused by major depressive disorder (MDD). Primary care electronic health records provide an easily accessible approach to investigate TRD clinical and genetic characteristics. MDD defined from primary care records in UK Biobank (UKB) and EXCEED studies was compared with other measures of depression and tested for association with MDD polygenic risk score (PRS). Using prescribing records, TRD was defined from at least two switches between antidepressant drugs, each prescribed for at least 6 weeks. Clinical-demographic characteristics, SNP-based heritability (h2SNP) and genetic overlap with psychiatric and non-psychiatric traits were compared in TRD and non-TRD MDD cases. In 230,096 and 8926 UKB and EXCEED participants with primary care data, respectively, the prevalence of MDD was 8.7% and 14.2%, of which 13.2% and 13.5% was TRD, respectively. In both cohorts, MDD defined from primary care records was strongly associated with MDD PRS, and in UKB it showed overlap of 71–88% with other MDD definitions. In UKB, TRD vs healthy controls and non-TRD vs healthy controls h2SNP was comparable (0.25 [SE = 0.04] and 0.19 [SE = 0.02], respectively). TRD vs non-TRD was positively associated with the PRS of attention deficit hyperactivity disorder, with lower socio-economic status, obesity, higher neuroticism and other unfavourable clinical characteristics. This study demonstrated that MDD and TRD can be reliably defined using primary care records and provides the first large scale population assessment of the genetic, clinical and demographic characteristics of TRD.

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