Systems approaches to drug repositioning

PhD ThesisDrug discovery has overall become less fruitful and more costly, despite vastly increased biomedical knowledge and evolving approaches to Research and Development (R&D). One complementary approach to drug discovery is that of drug repositioning which focusses on identifying novel uses for existing drugs. By focussing on existing drugs that have already reached the market, drug repositioning has the potential to both reduce the timeframe and cost of getting a disease treatment to those that need it. Many marketed examples of repositioned drugs have been found via serendipitous or rational observations, highlighting the need for more systematic methodologies. Systems approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but require an integrative approach to biological data. Integrated networks can facilitate systems-level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person can identify portions of the graph that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated procedures are required to mine integrated networks systematically for these subgraphs and bring them to the attention of the user. The aim of this project was the development of novel computational methods to identify new therapeutic uses for existing drugs (with particular focus on active small molecules) using data integration. A framework for integrating disparate data relevant to drug repositioning, Drug Repositioning Network Integration Framework (DReNInF) was developed as part of this work. This framework includes a high-level ontology, Drug Repositioning Network Integration Ontology (DReNInO), to aid integration and subsequent mining; a suite of parsers; and a generic semantic graph integration platform. This framework enables the production of integrated networks maintaining strict semantics that are important in, but not exclusive to, drug repositioning. The DReNInF is then used to create Drug Repositioning Network Integration (DReNIn), a semantically-rich Resource Description Framework (RDF) dataset. A Web-based front end was developed, which includes a SPARQL Protocol and RDF Query Language (SPARQL) endpoint for querying this dataset. To automate the mining of drug repositioning datasets, a formal framework for the definition of semantic subgraphs was established and a method for Drug Repositioning Semantic Mining (DReSMin) was developed. DReSMin is an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. The ability of DReSMin to identify novel Drug-Target (D-T) associations was investigated. 9,643,061 putative D-T interactions were identified and ranked, with a strong correlation between highly scored associations and those supported by literature observed. The 20 top ranked associations were analysed in more detail with 14 found to be novel and six found to be supported by the literature. It was also shown that this approach better prioritises known D-T interactions, than other state-of-the-art methodologies. The ability of DReSMin to identify novel Drug-Disease (Dr-D) indications was also investigated. As target-based approaches are utilised heavily in the field of drug discovery, it is necessary to have a systematic method to rank Gene-Disease (G-D) associations. Although methods already exist to collect, integrate and score these associations, these scores are often not a reliable re flection of expert knowledge. Therefore, an integrated data-driven approach to drug repositioning was developed using a Bayesian statistics approach and applied to rank 309,885 G-D associations using existing knowledge. Ranked associations were then integrated with other biological data to produce a semantically-rich drug discovery network. Using this network it was shown that diseases of the central nervous system (CNS) provide an area of interest. The network was then systematically mined for semantic subgraphs that capture novel Dr-D relations. 275,934 Dr-D associations were identified and ranked, with those more likely to be side-effects filtered. Work presented here includes novel tools and algorithms to enable research within the field of drug repositioning. DReNIn, for example, includes data that previous comparable datasets relevant to drug repositioning have neglected, such as clinical trial data and drug indications. Furthermore, the dataset may be easily extended using DReNInF to include future data as and when it becomes available, such as G-D association directionality (i.e. is the mutation a loss-of-function or gain-of-function). Unlike other algorithms and approaches developed for drug repositioning, DReSMin can be used to infer any types of associations captured in the target semantic network. Moreover, the approaches presented here should be more generically applicable to other fields that require algorithms for the integration and mining of semantically rich networks.European and Physical Sciences Research Council (EPSRC) and GS

Structural Equation Modelling: Guidelines for Determining Model Fit

The following paper presents current thinking and research on fit indices for structural equation modelling. The paper presents a selection of fit indices that are widely regarded as the most informative indices available to researchers. As well as outlining each of these indices, guidelines are presented on their use. The paper also provides reporting strategies of these indices and concludes with a discussion on the future of fit indices

Delay in Hospital Presentation Is the Main Reason Large Vessel Occlusion Stroke Patients Do Not Receive Intravenous Thrombolysis

OBJECTIVES: Intravenous thrombolysis (IVT) and endovascular therapy (EVT) are the mainstays of treatment for large vessel occlusion stroke (LVOS). Prior studies have examined why patients have not received IVT, the most cited reasons being last-known-well (LKW) to hospital arrival of \u3e4.5 hours and minor/resolving stroke symptoms. Given that LVOS patients typically present moderate-to-severe neurologic deficits, these patients should be easier to identify and treat than patients with minor strokes. This investigation explores why IVT was not administered to a cohort of LVOS patients who underwent EVT. METHODS: This is an analysis of the Optimizing the Use of Prehospital Stroke Systems of Care (OPUS-REACH) registry, which contains patients from 9 endovascular centers who underwent EVT between 2015 and 2020. The exposure of interest was the receipt of intravenous thrombolysis. Descriptive summary statistics are presented as means and SDs for continuous variables and as frequencies with percentages for categorical variables. Two-sample RESULTS: Two thousand forty-three patients were included and 60% did not receive IVT. The most common reason for withholding IVT was LKW to arrival of \u3e4.5 (57.2%). The second most common contraindication was oral anticoagulation (15.5%). On multivariable analysis, 2 factors were associated with not receiving IVT: increasing age (odds ratio [OR] 0.86; 95% confidence interval [CI] 0.78-0.93) and increasing time from LKW-to hospital arrival (OR 0.45 95% CI 0.46-0.49). CONCLUSION: Like prior studies, the most frequent reason for exclusion from IVT was a LKW to hospital presentation of \u3e4.5 hours; the second reason was anticoagulation. Efforts must be made to increase awareness of the time-sensitive nature of IVT and evaluate the safety of IVT in patients on oral anticoagulants

A Shorter Door-In-Door-Out Time Is Associated with Improved Outcome in Large Vessel Occlusion Stroke.

Introduction: Endovascular thrombectomy (EVT) significantly improves outcomes in large vessel occlusion stroke (LVOS). When a patient with a LVOS arrives at a hospital that does not perform EVT, emergent transfer to an endovascular stroke center (ESC) is required. Our objective was to determine the association between door-in-door-out time (DIDO) and 90-day outcomes in patients undergoing EVT. Methods: We conducted an analysis of the Optimizing Prehospital Stroke Systems of Care-Reacting to Changing Paradigms (OPUS-REACH) registry of 2,400 LVOS patients treated at nine ESCs in the United States. We examined the association between DIDO times and 90-day outcomes as measured by the modified Rankin scale. Results: A total of 435 patients were included in the final analysis. The mean DIDO time for patients with good outcomes was 17 minute shorter than patients with poor outcomes (122 minutes [min] vs 139 min, P = 0.04). Absolute DIDO cutoff times of ≤60 min, ≤90 min, or ≤120 min were not associated with improved functional outcomes (46.4 vs 32.3%, P = 0.12; 38.6 vs 30.6%, P = 0.10; and 36.4 vs 28.9%, P = 0.10, respectively). This held true for patients with hyperacute strokes of less than four-hour onset. Lower baseline National Institutes of Health Stroke Scale (NIHSS) score (11.9 vs 18.2, P = \u3c.001) and younger age (62.5 vs 74.9 years (P \u3c .001) were associated with improved outcomes. On multiple regression analysis, age (odds ratio [OR] 1.71, 95% confidence interval [CI] 1.45-2.02) and baseline NIHSS score (OR 1.67, 95% CI 1.42-1.98) were associated with improved outcomes while DIDO time was not associated with better outcome (OR 1.13, 95% CI 0.99-1.30). Conclusion: Although the DIDO time was shorter for patients with a good outcome, this was non-significant in multiple regression analysis. Receipt of intravenous thrombolysis and time to EVT were not associated with better outcomes, while male gender, lower age, arrival by private vehicle, and lower NIHSS score portended better outcomes. No absolute DIDO-time cutoff or modifiable factor was associated with improved outcomes for LVOS. This study underscores the need to streamline DIDO times but not to set an artificial DIDO time benchmark to meet

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

3 years of liraglutide versus placebo for type 2 diabetes risk reduction and weight management in individuals with prediabetes: a randomised, double-blind trial

Background: Liraglutide 3·0 mg was shown to reduce bodyweight and improve glucose metabolism after the 56-week period of this trial, one of four trials in the SCALE programme. In the 3-year assessment of the SCALE Obesity and Prediabetes trial we aimed to evaluate the proportion of individuals with prediabetes who were diagnosed with type 2 diabetes. Methods: In this randomised, double-blind, placebo-controlled trial, adults with prediabetes and a body-mass index of at least 30 kg/m2, or at least 27 kg/m2 with comorbidities, were randomised 2:1, using a telephone or web-based system, to once-daily subcutaneous liraglutide 3·0 mg or matched placebo, as an adjunct to a reduced-calorie diet and increased physical activity. Time to diabetes onset by 160 weeks was the primary outcome, evaluated in all randomised treated individuals with at least one post-baseline assessment. The trial was conducted at 191 clinical research sites in 27 countries and is registered with ClinicalTrials.gov, number NCT01272219. Findings: The study ran between June 1, 2011, and March 2, 2015. We randomly assigned 2254 patients to receive liraglutide (n=1505) or placebo (n=749). 1128 (50%) participants completed the study up to week 160, after withdrawal of 714 (47%) participants in the liraglutide group and 412 (55%) participants in the placebo group. By week 160, 26 (2%) of 1472 individuals in the liraglutide group versus 46 (6%) of 738 in the placebo group were diagnosed with diabetes while on treatment. The mean time from randomisation to diagnosis was 99 (SD 47) weeks for the 26 individuals in the liraglutide group versus 87 (47) weeks for the 46 individuals in the placebo group. Taking the different diagnosis frequencies between the treatment groups into account, the time to onset of diabetes over 160 weeks among all randomised individuals was 2·7 times longer with liraglutide than with placebo (95% CI 1·9 to 3·9, p<0·0001), corresponding with a hazard ratio of 0·21 (95% CI 0·13–0·34). Liraglutide induced greater weight loss than placebo at week 160 (–6·1 [SD 7·3] vs −1·9% [6·3]; estimated treatment difference −4·3%, 95% CI −4·9 to −3·7, p<0·0001). Serious adverse events were reported by 227 (15%) of 1501 randomised treated individuals in the liraglutide group versus 96 (13%) of 747 individuals in the placebo group. Interpretation: In this trial, we provide results for 3 years of treatment, with the limitation that withdrawn individuals were not followed up after discontinuation. Liraglutide 3·0 mg might provide health benefits in terms of reduced risk of diabetes in individuals with obesity and prediabetes. Funding: Novo Nordisk, Denmark

Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.</div

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements