Chiara Fabbri

Bio: Chiara Fabbri is an academic researcher from King's College London. The author has contributed to research in topics: Major depressive disorder & Medicine. The author has an hindex of 28, co-authored 150 publications receiving 2528 citations. Previous affiliations of Chiara Fabbri include University of Bologna & Kansai Medical University.

Pharmacogenetics in major depression: A comprehensive meta-analysis

Abstract: A number of candidate gene studies focused on major depression (MD) and antidepressant (AD) efficacy have been carried out, but results mainly remain inconclusive. We performed a comprehensive meta-analysis of published candidate gene studies focused on AD efficacy in MD to evaluate the cumulative evidence. A random-effect model was applied to study the polymorphisms with genotypic counts available from at least three independent studies. On the base of previous evidence, the analysis was stratified by ethnicity (Caucasian, Asian, and other/mixed), and AD class (SSRIs and mixed/other ADs). Genotypic data were available for 16 polymorphisms in 11 genes. After the exclusion of 5-HTTLPR in SLC6A4 included in another recent meta-analysis, 15 polymorphisms in 11 genes were included in the present meta-analysis (BDNF rs6265, SLC6A4 STin2, HTR1A rs6295, HTR2A rs6311, rs6313 and rs7997012, HTR6 rs1805054, TPH1 rs1800532, SLC6A2 rs5569, COMT rs4680, GNB3 rs5443, FKBP5 rs1360780 and rs3800373, and ABCB1 rs1045642 and rs2032582). Our results suggested that BDNF rs6265 (Val66Met) heterozygous genotype was associated with better SSRIs response compared to the homozygous genotypes, particularly in Asians (OR=1.53, 95%CI 1.12-2.07, p=0.007). SLC6A4 STin2, HTR2A rs6311 and rs7997012, GNB3 rs5443, FKBP5 rs1360780 and rs3800373, and ABCB1 rs2032582 showed associations with AD efficacy, but these results were highly dependent on one or two single studies. In conclusion, our findings suggested the BDNF Val66Met as the best single candidate involved in AD response, with a selective effect on SSRI treatment. Our overall results supported no major effect of any single gene variant on AD efficacy.

Pharmacogenetics of antidepressant response

Abstract: Nowadays, the efficacy of antidepressant treatment and the profile of side effects are not predictable: sociodemographic and clinical variables have not provided clinicians with consistent support. Emerging evidence reports promising results from the genetic approach: pharmacogenetics identifies which genetic variations are associated with drug treatment outcome. Both pharmacodynamic and pharmacokinetic key genes have been investigated, and even though consistent conclusions are yet to come, several lines of evidence have led researchers to assume a promising future of effective pharmacogenetic-based pretreatment assessment. The most important antidepressant pharmacogenetic results in this field are reviewed in this chapter and future research lines are proposed.

Pharmacogenetics of antidepressant drugs: An update after almost 20 years of research

Abstract: Major depressive disorder (MDD) is an emergent cause of personal and socio-economic burden, both for the high prevalence of the disorder and the unsatisfying response rate of the available antidepressant treatments. No reliable predictor of treatment efficacy and tolerance in the single patient is available, thus drug choice is based on a trial and error principle with poor clinical efficiency. Among modulators of treatment outcome, genetic polymorphisms are thought to explain a significant share of the inter-individual variability. The present review collected the main pharmacogenetic findings primarily about antidepressant response and secondly about antidepressant induced side effects, and discussed the main strengths and limits of both candidate and genome-wide association studies and the most promising methodological opportunities and challenges of the field. Despite clinical applications of antidepressant pharmacogenetics are not available yet, previous findings suggest that genotyping may be applied in the clinical practice. In order to reach this objective, further rigorous pharmacogenetic studies (adequate sample size, study of better defined clinical subtypes of MDD, adequate covering of the genetic variability), their combination with the results obtained through complementary methodologies (e.g., pathway analysis, epigenetics, transcriptomics, and proteomics), and finally cost-effectiveness trials are required.

Pharmacogenetics of antidepressants

Abstract: Up to 60% of depressed patients do not respond completely to antidepressants (AD) 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 (CYP) superfamily, the P-glycoprotein (ABCB1), the tryptophan hydroxylase (TPH), the catechol-O-methyltransferase (COMT), the monoamine oxidase A (MAOA), the serotonin transporter (5-HTTLPR), the norepinephrine transporter (NET), the dopamine transporter (DAT), variants in the 5HT1A, 2A , 3A, 3B and 6 receptors, adrenoreceptor beta-1 (ADRB1) and alpha-2 (ADRA2A), the dopamine receptors (D2), the G-protein beta3-subunit (GNB3), the corticotropin releasing hormone (CRH) receptors (CRHR1 and CRHR2), the glucocorticoid receptors (GR), the c-AMP response-element binding (CREB) and the brain-derived neurotrophic factor (BDNF). Marginal associations were reported for angiotensin I converting enzyme (ACE), circadian locomoter output cycles kaput protein (CLOCK), glutamatergic system, nitric oxide synthase (NOS) and interleukin 1-beta (IL-1beta) gene. In conclusion, gene variants seem to influence human behavior, liability to disorders and treatment response. Nonetheless, gene x enviroment interactions have been hypothesized to modulate several of these effects.

A rating scale for drug-induced akathisia

Abstract: A rating scale for drug-induced akathisia has been derived that incorporates diagnostic criteria for pseudoakathisia, and mild, moderate, and severe akathisia. It comprises items for rating the observable, restless movements which characterise the condition, the subjective awareness of restlessness, and any distress associated with the akathisia. In addition, there is an item for rating global severity. A standard examination procedure is recommended. The inter-rater reliability for the scale items (Cohen's kappa) ranged from 0.738 to 0.955. Akathisia was found in eight of 42 schizophrenic in-patients, and nine had pseudoakathisia, where the typical sense of inner restlessness was not reported.

Consensus Guidelines for Therapeutic Drug Monitoring in Neuropsychopharmacology: Update 2017.

Abstract: Therapeutic drug monitoring (TDM) is the quantification and interpretation of drug concentrations in blood to optimize pharmacotherapy. It considers the interindividual variability of pharmacokinetics and thus enables personalized pharmacotherapy. In psychiatry and neurology, patient populations that may particularly benefit from TDM are children and adolescents, pregnant women, elderly patients, individuals with intellectual disabilities, patients with substance abuse disorders, forensic psychiatric patients or patients with known or suspected pharmacokinetic abnormalities. Non-response at therapeutic doses, uncertain drug adherence, suboptimal tolerability, or pharmacokinetic drug-drug interactions are typical indications for TDM. However, the potential benefits of TDM to optimize pharmacotherapy can only be obtained if the method is adequately integrated in the clinical treatment process. To supply treating physicians and laboratories with valid information on TDM, the TDM task force of the Arbeitsgemeinschaft fur Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP) issued their first guidelines for TDM in psychiatry in 2004. After an update in 2011, it was time for the next update. Following the new guidelines holds the potential to improve neuropsychopharmacotherapy, accelerate the recovery of many patients, and reduce health care costs.