Probing the GnRH receptor agonist binding site identifies methylated triptorelin as a new anti-proliferative agent

D-amino acid substitutions at Glycine postion-6 in GnRH-I decapeptide can possess super-agonist activity and enhanced in vivo pharmacokinetics. Agonists elicit growth-inhibition in tumorigenic cells expressing the GnRH receptor above threshold levels. However, new agonists with modified properties are required to improve the anti-proliferative range. Effects of residue substitutions and methylations on tumourigenic HEK293[SCL60] and WPE-1-NB26-3 prostate cells expressing the rat GnRH receptor were compared. Peptides were ranked according to receptor binding affinity, induction of inositol phosphate production and cell growth-inhibition. Analogues possessing D-Trp6 (including Triptorelin), D-Leu6 (including Leuprolide), D-Ala6, D-Lys6, or D-Arg6 exhibited agonist and anti-proliferative activity. Residues His5 or His5,Trp7,Tyr8, corresponding to residues found in GnRH-II , were tolerated, with retention of sub-nanomolar/low nanomolar binding affinities and EC50s for receptor activation and IC50s for cell growth-inhibition. His5D-Arg6-GnRH-I exhibited reduced binding affinity and potency, effective in the mid-nanomolar range. However, all GnRH-II-like analogues were less potent than Triptorelin. By comparison, three methylated-Trp6 Triptorelin variants showed differential binding, receptor activation and anti-proliferation potency. Significantly, 5-Methyl-DL-Trp6-Triptorelin was equipotent to triptorelin. Subsequent studies should determine whether pharmacologically enhanced derivatives of Triptorelin can be developed by further alkylations, without substitutions or cleavable cytotoxic adducts, to improve the extent of growth-inhibition of tumour cells expressing the GnRH receptor

Prognostic research in psychiatry: towards a clinically-relevant prediction model for first episode psychosis

Background. Prognosis is the determination of risk of future health outcomes in people with a given health condition. The primary aim for my thesis was to conduct prognostic model research into first episode psychosis (FEP). The prognosis of people with FEP is poor in around half of those affected and difficult to predict in individuals. Prognostic prediction models to predict outcome in individuals could facilitate early intervention to change clinical trajectories and improve prognosis. As part of my primary aim, I sought to answer four research questions. 1) Is prediction of individual patient outcome possible in FEP using clinical variables? 2) Does prediction model performance remain robust at external validation? 3) Does prediction model performance improve with the addition of biologically relevant disease markers? 4) Does prediction model performance improve with the application of advanced machine learning classifiers compared to logistic regression? These questions are addressed in studies 1 to 3. The secondary aim for my thesis was to test whether routinely collected electronic healthcare record data could be used for prognostic research in the National Health Service (NHS) in Greater Glasgow and Clyde (GG&C). The coronavirus pandemic delayed collection of routine data in FEP. I took the opportunity to examine this question in a more common area of psychiatric disease, delirium, in the hope that information from this would inform future prospective studies in FEP. Delirium is an important risk factor for subsequent dementia. However, the field lacks large studies with long-term follow-up of delirium in subjects initially free of dementia to clearly establish clinical trajectories. This formed study 4. Study 1. This study aimed to conduct a systematic review of prognostic prediction models developed for predicting poor outcome in FEP. Thirteen studies reporting 31 prediction models across a range of clinical outcomes met criteria for inclusion. Eleven studies used logistic regression with clinical variables. External validation was carried out in four studies. Only one study assessed whether biologically relevant disease markers added value as predictors. Two studies used machine learning but did not provide enough information to allow comparison to logistic regression. Most studies had methodological flaws and the potential for prediction modelling in FEP is yet to be fully realised. Study 3. This study assessed the potential for biologically relevant disease markers as predictor variables and compared advanced machine learning classifiers to logistic regression in 168 patients with FEP. The addition of a biological variable did not improve the performance of a logistic regression model built using clinical variables. It is possible that the usefulness of the biological variables for prediction was curtailed by the lack of a mechanistic link to the pathophysiology of psychosis thereby limiting their effect size. The naïve Bayes machine learning model was better than maximum likelihood estimation (MLE) but not elastic net logistic regression in terms of discrimination. However, for all models except MLE logistic regression there were problems with calibration. Study 4. This study consisted of a retrospective cohort study of all patients over the age of 65 diagnosed with an episode of delirium who were initially dementia free at onset of delirium within NHS GG&C between 1996 and 2020 using routinely collected electronic healthcare record (EHR) data. 12949 patients with an incident episode of delirium were included and followed up for an average of 741 days. The estimated cumulative incidence of dementia was 31% by 5 years. The estimated cumulative incidence of the competing risk of death without dementia was 49.2% by 5 years. The cause-specific hazard of dementia was increased with higher levels of deprivation and also with advancing age from 65, plateauing and decreasing from age 90. Conclusions. Systematic review of the literature showed that there is considerable potential for prognostic prediction modelling in FEP, but that most existing models have methodological flaws. Developing on this literature, my FEP prognostic prediction model can help to identify individual patients at increased risk of nonremission at initial clinical contact and showed robust external validation. However, this approach did not benefit from the addition of biologically relevant disease markers as predictor variables or the application of machine learning methods. Finally, I demonstrated the feasibility of using routinely collected EHR data from NHS GG&C for prognostic research into delirium and the risk of subsequent dementia. This will inform future prospective prognostic modelling studies of routinely collected data in FEP. Altogether, this thesis made several contributions to the growing body of clinical prognostic research in first episode psychosis and delirium. In particular, considerable progress has been made towards the deployment of a useable and informative clinical prediction model which will improve care for people with first episode psychosis

Prognostic models in first-episode psychosis – Authors' reply

No abstract available

Probing the GnRH receptor agonist binding site identifies methylated triptorelin as a new anti-proliferative agent

D-amino acid substitutions at glycine postion 6 in GnRH-I decapeptide can possess super-agonist activity and enhanced in vivo pharmacokinetics. Agonists elicit growth-inhibition in tumorigenic cells expressing the GnRH receptor above threshold levels. However, new agonists with modified properties are required to improve the anti-proliferative range. Effects of residue substitutions and methylations on tumourigenic HEK293[SCL60] and WPE-1-NB26-3 prostate cells expressing the rat GnRH receptor were compared. Peptides were ranked according to receptor binding affinity, induction of inositol phosphate production and cell growth-inhibition. Analogues possessing D-Trp6 (including triptorelin), D-Leu6 (including leuprolide), D-Ala6 , D-Lys6 , or D-Arg6 exhibited agonist and antiproliferative activity. Residues His5 or His5 ,Trp7 ,Tyr8 , corresponding to residues found in GnRH-II, were tolerated, with retention of sub-nanomolar/low nanomolar binding affinities and EC50s for receptor activation and IC50s for cell growth-inhibition. His5D-Arg6 - GnRH-I exhibited reduced binding affinity and potency, effective in the mid-nanomolar range. However, all GnRH-II-like analogues were less potent than triptorelin. By comparison, three methylated-Trp6 triptorelin variants showed differential binding, receptor activation and anti-proliferation potency. Significantly, 5- Methyl-DL-Trp6 -Triptorelin was equipotent to triptorelin. Subsequent studies should determine whether pharmacologically enhanced derivatives of triptorelin can be developed by further alkylations, without substitutions or cleavable cytotoxic adducts, to improve the extent of growth-inhibition of tumour cells expressing the GnRH receptor

The associations between self-reported depression, self-reported chronic inflammatory conditions and cognitive abilities in UK Biobank

Background: Depression and chronic inflammatory medical conditions have been linked to impaired cognitive ability. However despite frequent comorbidity, their combined association with cognitive ability has rarely been examined. Methods: This study examined associations between self-reported depression and chronic inflammatory diseases and their interaction with cognitive performance in 456,748 participants of the UK Biobank, adjusting for sociodemographic and lifestyle factors. Numbers with available data ranged from 94,899 to 453,208 depending on the cognitive test. Results: Self-reported depression was associated with poorer performance compared to controls in several cognitive tests (fully adjusted models, reaction time: B = 6.08, 95% CI = 5.09, 7.07; pairs matching: incidence rate ratio = 1.02, 95% CI = 1.02, 1.03; Trail Making Test B: B = 1.37, 95% CI = 0.88, 1.87; Digit Symbol Substitution Test (DSST): B = −0.35, 95% CI = −0.44, −0.27). Self-reported chronic inflammatory conditions were associated with slower reaction time (B = 3.79, 95% CI = 2.81, 4.78) and lower DSST scores (B = −0.21, 95% CI = −0.30, −0.13). No interaction effects were observed. Discussion: In this large, population-based study we provide evidence of lower cognitive performance in both depression and a comprehensive category of chronic inflammatory conditions. Results are consistent with additive effects of both types of disorder on cognitive ability. Clinicians should be aware of such effects, particularly as cognitive impairment is linked to poorer disease outcomes and quality of life

Delirium and the risk of developing dementia: a cohort study of 12 949 patients

Background: Delirium is an important risk factor for subsequent dementia. However, the field lacks large studies with long-term follow-up of delirium in subjects initially free of dementia to clearly establish clinical trajectories. Methods: We undertook a retrospective cohort study of all patients over the age of 65 diagnosed with an episode of delirium who were initially dementia free at onset of delirium within National Health Service Greater Glasgow & Clyde between 1996 and 2020 using the Safe Haven database. We estimated the cumulative incidence of dementia accounting for the competing risk of death without a dementia diagnosis. We modelled the effects of age at delirium diagnosis, sex and socioeconomic deprivation on the cause-specific hazard of dementia via cox regression. Results: 12 949 patients with an incident episode of delirium were included and followed up for an average of 741 days. The estimated cumulative incidence of dementia was 31% by 5 years. The estimated cumulative incidence of the competing risk of death without dementia was 49.2% by 5 years. The cause-specific hazard of dementia was increased with higher levels of deprivation and also with advancing age from 65, plateauing and decreasing from age 90. There did not appear to be a relationship with sex. Conclusions: Our study reinforces the link between delirium and future dementia in a large cohort of patients. It highlights the importance of early recognition of delirium and prevention where possible

Structure and stability of symptoms in first episode psychosis: a longitudinal network approach.

Early psychosis is characterised by heterogeneity in illness trajectories, where outcomes remain poor for many. Understanding psychosis symptoms and their relation to illness outcomes, from a novel network perspective, may help to delineate psychopathology within early psychosis and identify pivotal targets for intervention. Using network modelling in first episode psychosis (FEP), this study aimed to identify: (a) key central and bridge symptoms most influential in symptom networks, and (b) examine the structure and stability of the networks at baseline and 12-month follow-up. Data on 1027 participants with FEP were taken from the National EDEN longitudinal study and used to create regularised partial correlation networks using the 'EBICglasso' algorithm for positive, negative, and depressive symptoms at baseline and at 12-months. Centrality and bridge estimations were computed using a permutation-based network comparison test. Depression featured as a central symptom in both the baseline and 12-month networks. Conceptual disorganisation, stereotyped thinking, along with hallucinations and suspiciousness featured as key bridge symptoms across the networks. The network comparison test revealed that the strength and bridge centralities did not differ significantly between the two networks (C = 0.096153; p = 0.22297). However, the network structure and connectedness differed significantly from baseline to follow-up (M = 0.16405, p = <0.0001; S = 0.74536, p = 0.02), with several associations between psychosis and depressive items differing significantly by 12 months. Depressive symptoms, in addition to symptoms of thought disturbance (e.g. conceptual disorganisation and stereotyped thinking), may be examples of important, under-recognized treatment targets in early psychosis, which may have the potential to lead to global symptom improvements and better recovery

Prediction models in first-episode psychosis: systematic review and critical appraisal

Background: People presenting with first-episode psychosis (FEP) have heterogenous outcomes. More than 40% fail to achieve symptomatic remission. Accurate prediction of individual outcome in FEP could facilitate early intervention to change the clinical trajectory and improve prognosis. Aims: We aim to systematically review evidence for prediction models developed for predicting poor outcome in FEP. Method: A protocol for this study was published on the International Prospective Register of Systematic Reviews, registration number CRD42019156897. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance, we systematically searched six databases from inception to 28 January 2021. We used the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Prediction Model Risk of Bias Assessment Tool to extract and appraise the outcome prediction models. We considered study characteristics, methodology and model performance. Results: Thirteen studies reporting 31 prediction models across a range of clinical outcomes met criteria for inclusion. Eleven studies used logistic regression with clinical and sociodemographic predictor variables. Just two studies were found to be at low risk of bias. Methodological limitations identified included a lack of appropriate validation, small sample sizes, poor handling of missing data and inadequate reporting of calibration and discrimination measures. To date, no model has been applied to clinical practice. Conclusions: Future prediction studies in psychosis should prioritise methodological rigour and external validation in larger samples. The potential for prediction modelling in FEP is yet to be realised

Re-establishing the ‘outsiders’: English press coverage of the 2015 FIFA Women’s World Cup

In 2015, the England Women’s national football team finished third at the Women’s World Cup in Canada. Alongside the establishment of the Women’s Super League in 2011, the success of the women’s team posed a striking contrast to the recent failures of the England men’s team and in doing so presented a timely opportunity to examine the negotiation of hegemonic discourses on gender, sport and football. Drawing upon an ‘established-outsider’ approach, this article examines how, in newspaper coverage of the England women’s team, gendered constructions revealed processes of alteration, assimilation and resistance. Rather than suggesting that ‘established’ discourses assume a normative connection between masculinity and football, the findings reveal how gendered ‘boundaries’ were both challenged and protected in newspaper coverage. Despite their success, the discursive positioning of the women’s team as ‘outsiders’, served to (re)establish men’s football as superior, culturally salient and ‘better’ than the women’s team/game. Accordingly, we contend that attempts to build and, in many instances, rediscover the history of women’s football, can be used to challenge established cultural representations that draw exclusively from the history of the men’s game. In such instances, the 2015 Women’s World Cup provides a historical moment from which the women’s game can be relocated in a context of popular culture

Development and validation of a non-remission risk prediction model in first episode psychosis:An Analysis of 2 Longitudinal Studies

Psychosis is a major mental illness with first onset in young adults. The prognosis is poor in around half of the people affected, and difficult to predict. The few tools available to predict prognosis have major weaknesses which limit their use in clinical practice. We aimed to develop and validate a risk prediction model of symptom nonremission in first-episode psychosis. Our development cohort consisted of 1027 patients with first-episode psychosis recruited between 2005 and 2010 from 14 early intervention services across the National Health Service in England. Our validation cohort consisted of 399 patients with first-episode psychosis recruited between 2006 and 2009 from a further 11 English early intervention services. The one-year nonremission rate was 52% and 54% in the development and validation cohorts, respectively. Multivariable logistic regression was used to develop a risk prediction model for nonremission, which was externally validated. The prediction model showed good discrimination C-statistic of 0.73 (0.71, 0.75) and adequate calibration with intercept alpha of 0.12 (0.02, 0.22) and slope beta of 0.98 (0.85, 1.11). Our model improved the net-benefit by 15% at a risk threshold of 50% compared to the strategy of treating all, equivalent to 15 more detected nonremitted first-episode psychosis individuals per 100 without incorrectly classifying remitted cases. Once prospectively validated, our first episode psychosis prediction model could help identify patients at increased risk of nonremission at initial clinical contact.</p