Alessandro Serretti

A quantitative approach to neuropsychiatry: The why and the how

The current nosology of neuropsychiatric disorders allows for a pragmatic approach to treatment choice, regulation and clinical research. However, without a biological rationale for these disorders, drug development has stagnated. The recently EU-funded PRISM project aims to develop a quantitative biological approach to the understanding and classification of neuropsychiatric diseases to accelerate the discovery and development of better treatments. By combining clinical data sets from major worldwide disease cohorts and by applying innovative technologies to deeply phenotype stratified patient groups, we will define a set of quantifiable biological parameters for social withdrawal and cognitive deficits common to Schizophrenia (SZ), Major Depression (MD), and Alzheimers Disease (AD). These studies aim to provide new classification and assessment tools for social and cognitive performance across neuropsychiatric disorders, clinically relevant substrates for treatment development, and predictive, preclinical animal systems. With patients and regulatory agencies, we seek to provide clear routes for the future translation and regulatory approval for new treatments and provide solutions to the growing public health challenges of psychiatry and neurology.

The Present and Future of Precision Medicine in Psychiatry: Focus on Clinical Psychopharmacology of Antidepressants

Precision medicine is a concept which is recently gaining momentum in all branches of medicine. In particular in psychiatry it is greatly needed given the huge societal costs of psychiatric disorders and given the long time needed to observe benefit from treatments and the response variability. The future will be based on biological determinants, however until such an interesting but still futuristic aim will be reached, at present we may only rely on clinical features to guide our individualized prescription which is currently still frequently based on personal opinion and subjective previous experiences. The aim of this review is to offer an overview of the main aspects to take into consideration when prescribing an antidepressant treatment to reach the best precision medicine using clinical information. More than 40 compounds are available for treating depression and a similar amount of compounds for other psychiatric disorders. The process of matching the profile of the patient with all different profiles of available compounds is therefore quite complex. Our everyday prescribing procedure should take into consideration a number of factors such as the knowledge of the profile of available compounds versus the symptomatology profile of the subject, previous efficacy, medical comorbidities, tolerability profile, individual preferences, and family history. While we are waiting more complex algorithms including biological or genetic measures, it is possible to optimize our current prescription practice by using all available information in order to obtain as much as possible an evidence based precision medicine prescription.

The influence of the serotonin transporter gene 5-HTTLPR polymorphism on suicidal behaviors: a meta-analysis

Abstract Suicidal Behavior (SB) is the second leading cause of death among youths worldwide and the tenth among all age groups. Inherited genetic differences have a role in suicidality with heritability ranging from 30 to 55%. The SLC6A4 5‐HTTLPR gene variant has been largely investigated for association with SB, with controversial results. In this work, we sought to determine whether the results of previous meta‐analyses were confirmed or modified subsequent to the inclusion of more recent literature data. An electronic literature search was performed to identify relevant studies published until July 2018. Data were analysed through RevMan v5.3. Subgroup and sensitivity meta‐analyses were performed considering different SB sub‐phenotypes, ethnicity, gender and psychiatric diagnostic categories. Our literature search yielded 1186 articles; among these, we identified 45 pertinent case‐control studies (15,341 subjects). No association was found between low‐expressing alleles or genotypes (S + LG alleles or S′ carrier genotypes) and SB in the primary analyses. However, low‐expressing alleles (S + LG) were associated with an increased risk of Violent Suicide Attempt (OR = 1.44, C.I. 1.17–1.78, p = .0007). An effect of the same alleles on SB was found in a subpopulation of substance abusers, but this result was not confirmed after the exclusion of healthy subjects from the control group. The other sensitivity meta‐analyses did not show any significant effect. Our findings contribute to clarify the conflicting previous evidence by suggesting an association between the 5‐HTTLPR and Violent SB. Nonetheless, many other modulators, including environmental factors and epigenetic mechanisms may act to further increase the level of complexity. HighlightsNo association was found between the low‐expressing alleles or genotypes and Suicidal Behavior in the primary analyses.The low‐expressing alleles and genotypes were associated with an increased risk of Violent Suicide Attempt.

Pharmacogenetics in Psychiatry

Mental illness represents a major health issue both at the individual and at the socioeconomical level. This is partly due to the current suboptimal treatment options: existing psychotropic medications, including antidepressants, antipsychotics, and mood stabilizers, are effective only in a subset of patients or produce partial response and they are often associated with debilitating side effects that discourage adherence. Pharmacogenetics is the study of how genetic information impacts on drug response/side effects with the goal to provide tailored treatments, thereby maximizing efficacy and tolerability. The first pharmacogenetic studies focused on candidate genes, previously known to be relevant to the pharmacokinetics and pharmacodynamics of psychotropic drugs. Results were mainly inconclusive, but some replicated candidates were identified and included as pharmacogenetic biomarkers in drug labeling and in some commercial kits. With the advent of the genomic revolution, it became possible to study the genetic variation on an unprecedented scale, throughout the whole genome with no need of a priori hypothesis. This may lead to the personalized prescription of existing medications and potentially to the development of innovative ones, thanks to new insights into the genetics of mental illness. Promising findings were obtained, but methods for the generation and analysis of genome-wide and sequencing data are still in evolution. Future pharmacogenetic tests may consist of hundreds/thousands of polymorphisms throughout the genome or selected pathways in order to take into account the complex interactions across variants in a number of genes.

Hot Genes in Schizophrenia: How Clinical Datasets Could Help to Refine their Role

We investigated the effect of a set of SNPs within 5 genes identified by GWASs as possible risk genes for schizophrenia (SCZ) in two independent samples, comprising 176 SCZ patients and 326 controls of Korean origin and 83 SCZ patients and 194 controls of Italian origin. The PANSS was used to assess psychopathology severity and antipsychotic response (AR). Several clinical features were assessed at recruitment. In the Korean sample, the SP4 gene haplotype rs2282888-rs2237304-rs10272006-rs12673091 (p = 0.02) was associated with SCZ. In the Italian sample, PPP3CC rs11780915 (genotypic: p = 0.006; allelic: p = 0.001) and rs2249098 (genotypic: p = 0.0004; allelic: p = 0.00006) were associated with SCZ, as well as the PPP3CC rs11780915-rs10108011-rs2249098 and the ZNF804A rs7603001-rs1344706 haplotypes (p = 0.03 and p = 0.02). Several RORA variants were associated with AR in both the samples, although only the haplotype rs1020729-rs1871858 in the Korean sample survived to the statistical correction (p = 0.01). Exploratory analyses suggested that: (1) PPP3CC, ST8SIA2, and SP4 genes may modulate psychotic symptoms, and (2) RORA and ZNF804A genes may influence AR. Our results partially support a role for these genes in SCZ and AR. Analyses in well phenotyped samples may help to refine the role of the genes identified by GWASs.

Low comorbid obsessive-compulsive disorder in patients with major depressive disorder – Findings from a European multicenter study

BACKGROUND This cross-sectional European multicenter study examined the association between major depressive disorder (MDD) and comorbid obsessive-compulsive disorder (OCD). METHODS Socio-demographic, clinical, and treatment features of 1346 adult MDD patients were compared between MDD subjects with and without concurrent OCD using descriptive statistics, analyses of covariance (ANCOVA), and binary logistic regression analyses. RESULTS We determined a point prevalence of comorbid OCD in MDD of 1.65%. In comparison to the MDD control group without concurrent OCD, a higher proportion of patients in the MDD + comorbid OCD group displayed concurrent panic disorder (31.81% vs 7.77%, p<.001), suicide risk (52.80% vs 44.81%, p=.04), polypsychopharmacy (95.45% vs 60.21%, p=.001), and augmentation treatment with antipsychotics (50.00% vs 25.46%, p=.01) and benzodiazepines (68.18% vs 33.31%, p=.001). Moreover, they were treated with higher mean doses of their antidepressant drugs (in fluoxetine equivalents: 48.99mg/day ± 18.81 vs 39.68mg/day ± 20.75, p=.04). In the logistic regression analyses, comorbid panic disorder (odds ratio (OR)=4.17, p=.01), suicide risk (OR=2.56, p=.04), simultaneous treatment with more psychiatric drugs (OR=1.51, p=<.05), polypsychopharmacy (OR=14.29, p=.01), higher antidepressant dosing (OR=1.01, p=<.05), and augmentation with antipsychotics (OR=2.94, p=.01) and benzodiazepines (OR=4.35, p=.002) were significantly associated with comorbid OCD. CONCLUSION In summary, our findings suggest that concurrent OCD in MDD (1) has a low prevalence rate compared to the reverse prevalence rates of comorbid MDD in OCD, (2) provokes higher suicide risk, and (3) is associated with a characteristic prescription pattern reflected by a high amount of polypsychopharmaceutical treatment strategies comprising particularly augmentation with antipsychotics and benzodiazepines.

Antidepressant induced weight gain

Lifestyle advice and weight monitoring are sensible responses to this important side effect

Clinical correlates of augmentation/combination treatment strategies in major depressive disorder

This multicenter, multinational, cross‐sectional study aimed to investigate clinical characteristics and treatment outcomes associated with augmentation/combination treatment strategies in major depressive disorder (MDD).

Gene-Environment Interaction Studies in Suicidal Behaviour

Increasing evidence supports the involvement of both heritable and environmental risk factors in suicidal behaviour (SB). Gene-environment interaction (G × E) studies may be useful for elucidating the

Highlights on Pharmacogenetics and Pharmacogenomics in Depression

This chapter summarizes the current knowledge about the pharmacogenetics and pharmacogenomics of antidepressant drugs in major depressive disorder (MDD). Pharmacogenetics is referred to results of candidate gene studies, i.e., studies focused on a limited number of polymorphisms in genes that are known to play a role in antidepressant mechanisms of action (e.g., the serotonin transporter or serotonin receptor) or antidepressant metabolism or transport (e.g., cytochrome P450 genes and MDR1). These studies provided the basis for the first clinical applications that are however of limited clinical use because of the lack of clear evidence of cost/benefits advantages. Importantly, the genetic makeup of antidepressant response is known to be highly polygenic with complex interactions among the variants involved; thus candidate gene studies are per se not enough to provide meaningful findings. For this reason pharmacogenomics has provided a more appropriate methodological approach through genome-wide association studies (GWAS). Analysis approaches derived from GWAS (e.g., pathway analysis and polygenic risk scores) are expected to provide better power to identify the complex polygenic profile modulating antidepressant response. In the last few years, further technological improvements are rapidly leading to a widespread use of whole genome sequencing and the study of genomics in relation to health-related measures collected using medical health records in the general population which will further enhance the knowledge on this field.