The BRITISH ASSOCIATION for PSYCHOPHARMACOLOGY

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The BRITISH ASSOCIATION for PSYCHOPHARMACOLOGY JOURNAL OF PSYCHOPHARMACOLOGY SUPPLEMENT TO ISSUE 31, NUMBER 8, AUGUST 2017 These papers were presented at the Summer Meeting of the BRITISH ASSOCIATION FOR PSYCHOPHARMACOLOGY 23 – 26 July, Harrogate, UK Indemnity The scientific material presented at this meeting reflects the opinions of the contributing authors and speakers. The British Association for Psychopharmacology accepts no responsibility for the contents of the verbal or any published proceedings of this meeting. All contributors completed a Declaration of Interests form when submitting their abstract BAP Office 36 Cambridge Place Hills Road Cambridge CB2 1NS www.bap.org.uk Aii CONTENTS Abstract Book 2017 Abstracts begin on page: SYMPOSIUM 1 Personalized pharmacological treatments for depression: Will big data achieve what clinicians can’t? (S01–S04) A1 SYMPOSIUM 2 The psychopharmacology of emerging calcium channel targets (S05–S08) A2 SYMPOSIUM 3 Behavioural and substance addictions: Similarities and differences (S09–S12) A4 SYMPOSIUM 4 Adverse and beneficial effects of cannabinoids – new insights from genetics and clinical trials (S13–S16) A7 SYMPOSIUM 5 The role of brain connectivity in brain disorders and their treatment (S17–S20) A9 SYMPOSIUM 6 Psychopharmacology of the older and, almost certainly, degenerating brain (S21–S24) A11 SYMPOSIUM 7 Treatment of dysfunction in hot and cold cognition in mood disorders (S25–S28) A13 SYMPOSIUM 8 The brain-gut-adipose axis: breaking the dichotomy between physical and mental health (S29–S32) A16 SYMPOSIUM 9 Biomarkers of treatment response for schizophrenia and bipolar affective disorder: Latest updates (S33–S36) A18 CONTENTS Aiii POSTERS Group A: Anxiety (A01–A35) A21 Group B: Developmental Disorders (B01–B31) A33 Group C: Affective Disorders (C01–C52) A41 Group D: Cognition (D01–D11) A75 Group E: Schizophrenia/Psychosis (E01–E09) A87 Group F: Recreational Drugs and Drug Dependence (F01–F07) A119 POSTDOCTORAL SYMPOSIUM Treatments in depression – new insights for an ongoing problem (PD01–PD04) A135 SHORT ORAL PRESENTATIONS Short Orals 1: Mechanisms of anxiety and its treatment See abstracts A10, A15, A12, C18 Short Orals 2: Circuits in schizophrenia See abstracts E36, E43, E34, E02 Short Orals 3: Regulation and relevance of inflammation in affective disorders See abstracts A18, C12, C32, C09 2017 PSYCHOPHARMACOLOGY AWARDS (PW01–PW03) A138 ABSTRACTS A1 S01 WILL A GENETIC TEST HELP SELECT TREATMENT FOR INDIVIDUALS WITH DEPRESSION? Uher R, Dept of Psychiatry, Dalhousie University, Abbie J. Lane Bldg, room 4082, 5909 Veterans’ Memorial Lane, Halifax, Nova Scotia, Canada, B3H 2E2 [email protected] Outcomes of major depressive disorder (MDD) could be improved if treatment choice is informed by genetic data. Genetic test would be valuable since it only needs to be carried out once in individual’s life-time and provides high accuracy measurement at moderate cost. Genetic tests could help decide between different types of antidepressant medication or help personalize indications for different treatment modalities, including psychological treatment and neurostimulation. Several large-scale studies have tested the hypothesis that a single common genetic variant exists that could predict response to antidepressants in a clinically meaningful way, but have identified no such variant. It is estimated that between a third and a half of variance in whether individuals respond to common antidepressant treatments could be explained by a large number of common genetic variants of individually small effects. Current efforts are directed towards the integration of multiple genetic and clinical predictors to obtain a reproducible and clinically meaningful prediction of who is likely to respond to which treatment. Recent data collection includes a broad array of clinical variables and brain measurement in addition to genetic and transcriptomic data in individuals treated with antidepressants with and without augmentation, cognitive-behavioral therapy and transcranial magnetic stimulation. The presenter will critically discuss the promise and challenges of such multimodal approaches. The work leading to this presentation has been completed thanks to funding from the Canada Research Chairs Program, the Canadian Institutes of Health Research (Grant reference numbers 124976, 142738 and 148394), European Commission, Nova Scotia Health Research Foundation, Ontario Brain Institute and the Dalhousie Medical Research Foundation. S02 INFLAMMATION AND THE BRAIN IN DEPRESSION: CAN INFLAMMATION INFORM TREATMENT CHOICES? Harrison NA, Brighton and Sussex Medical School, University of Sussex, Clinical Imaging Sciences Centre University of Sussex Brighton, BN1 9RR [email protected] Inflammation is increasingly implicated in the aetiology of depression. Patients with depression have elevated pro-inflammatory markers, particularly IL-6 and CRP and large-scale transcriptomics implicate NF-κB and type-I Interferon signalling pathways. Furthermore, recent studies have linked raised inflammatory markers to impaired response to classical anti-depressants while small-scale trials of ‘anti-inflammatory’ agents have suggested anti-depressant efficacy in patients withaised r inflammatory markers. In this talk I shall review evidence for a ‘pro-inflammatory’ profile in patients with depression and discuss current evidence for how this may help inform anti-depressant treatment choice. No sponsorship was received for this study. S03 MRI AND COGNITIVE BIOMARKERS IN PREDICTION OF ANTIDEPRESSANT RESPONSE: BIG DATA STUDIES Godlewska B, Dept of Psychiatry, Univ of Oxford, Warneford Lane, Oxford, OX3 7EQ beata.godlewska@ psych.ox.ac.uk Predicting treatment response in major depressive disorder would mean a major breakthrough in treatment delivery. Current treatment attempts are still ‘trial and error’, with less than 50% of patients responding to their first treatment, and weeks or months passing until response is achieved. Being able to make a choice of an effective treatment before it is started would mean a tremendous decrease in the burden at both individual and societal level. Of the areas explored in this search, cognitive and MRI biomarkers have provided some promising findings. Over the past two decades MRI related techniques have been increasingly employed in the search for biological markers of treatment response. This is linked to a relatively quick development of technologies, which provide more accurate and detailed information A2 ABSTRACTS about the structure and function of specific regions of the brain. This allows more precision in exploring the links between processes in the brain and treatment outcomes. Extensive research has led to an identification of regions of the brain which activity differentiated between responders and non responders to various treatments, such as the pregenual or subgenual ACC or the amygdala. The development of other techniques, such as MRS at high field strengths, allows a better understanding of baseline biological processes, at the level of neurotransmitters, and an exploration of their relationship with treatment response. Cognitive biomarkers may be simpler to assess and hence more useful at the practical level than MRI related ones. However, despite promising and consistently replicated findings at group levels, at this point no single cognitive or MRI marker has shown sufficient power to allow reliable matching of an individual to an effective treatment and none can be used in standard clinical practice. The next important step is big data approach, involving an exploration of multiple markers, their combinations, and testing hypotheses on large numbers of patients in large collaborative studies. In this talk I will review the current state of knowledge regarding MRI and cognitive biomarkers of differential response to various forms of treatment, such as pharmacotherapy, tDCS, and psychotherapy. I will talk about studies conducted by our group, and big collaborative efforts such as ENIGMA and EMBARC projects. S04 USING MACHINE LEARNING AND CLINICAL VARIABLES TO PREDICT ANTIDEPRESSANT RESPONSE Browning M, Psychiatry, University of Oxford and P1vital Ltd, Neuroscience Building, Warneford Hospital, Oxford, OX3 7JX [email protected] Background Antidepressants have a slow clinical onset of action and patients will often not respond to the initial medications prescribed. No test exists to guide clinicians as to whether their patient is responding or not. This often results in delays of many months before patients are initiated on effective treatment. The PReDicT (Predicting Response to Depression Treatment) test is a computer-based task which measures antidepressant induced change in the processing of emotional information as well as symptoms of depression. It has been designed to predict, early in the course of antidepressant treatment, whether a patient will respond to that treatment. In this talk I will describe initial studies during which the test performance of the PReDicT test was established as well as an ongoing multi-site RCT which is assessing the impact of using the PReDicT test to guide antidepressant treatment in primary care. Methods A pilot study of 57 primary care patients with depression from the UK was completed to establish predictive test performance. The PReDicT test was completed at baseline and after 1 week of treatment. All patients were treated with citalopram and response was defined as a greater than 50% reduction of baseline QIDS score by week 4-6. A multi-centre randomised controlled trial
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