Genetically modified animals as models of neurodevelopmental conditions: A review of systematic review reporting quality.

Using genetically modified animals to model neurodevelopmental conditions helps better our understanding of biology underlying these conditions. Animal research has unique characteristics not shared with clinical research, meaning systematic review methods must be adapted to this context. We aim to evaluate the quantity, characteristics, and reporting quality of systematic reviews which synthesise research using genetically modified animals to model neurodevelopmental conditions. On 23 January 2023, we searched PubMed, Embase, and the Web of Science Core Collection to identify systematic reviews of genetic neurodevelopmental condition animal research where the modified gene was one in a list of 102 genes associated with neurodevelopmental conditions identified through large-scale exome sequencing or Fmr1, Mecp2, or Ube3a. Two independent reviewers screened studies based on full text and assessed the reporting quality of relevant reviews using an adapted version of the PRISMA checklist (PRISMA-Pre). Twelve review publications met our criteria. We found mixed levels of reporting: items such as identifying the publication as a systematic review in the title, search strategies, and funding sources being well reported, and others such as protocol registration and data sharing less well reported. We also identified 19 review registrations via PROSPERO, most of which remain unpublished after their anticipated end dates. Systematic reviews are limited by lack of reporting. Increased awareness of reporting guidelines may help authors increase the transparency and reproducibility, and therefore the reliability, of their systematic reviews.

Safety and efficacy of memantine and trazodone versus placebo for motor neuron disease (MND SMART): stage two interim analysis from the first cycle of a phase 3, multiarm, multistage, randomised, adaptive platform trial.

BACKGROUND: Motor neuron disease represents a group of progressive and incurable diseases that are characterised by selective loss of motor neurons, resulting in an urgent need for rapid identification of effective disease-modifying therapies. The MND SMART trial aims to test the safety and efficacy of promising interventions efficiently and definitively against a single contemporaneous placebo control group. We now report results of the stage two interim analysis for memantine and trazodone. METHODS: MND SMART is an investigator-led, phase 3, double-blind, placebo-controlled, multiarm, multistage, randomised, adaptive platform trial recruiting at 20 hospital centres in the UK. Individuals older than 18 years with a confirmed diagnosis of either amyotrophic lateral sclerosis classified by the revised El Escorial criteria, primary lateral sclerosis, progressive muscular atrophy, or progressive bulbar palsy, regardless of disease duration, were eligible for screening. Participants were randomised (1:1:1) to receive oral trazodone 200 mg once a day, oral memantine 20 mg once a day, or matched placebo using a computer-generated minimisation algorithm delivered via a secure web-based system. Co-primary outcome measures were clinical functioning, measured by rate of change in the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS-R), and survival. Comparisons were conducted in four stages, with predefined criteria for stopping at the end of stages one and two. We report interim analysis from the stage two results, which was done when 100 participants per group (excluding long survivors, defined as >8 years since diagnosis at baseline) completed a minimum of 12 months of follow-up for the candidate investigational medicinal products. The trial is registered on the European Clinical Trials Registry, 2019-000099-41, and ClinicalTrials.gov, NCT04302870, and is ongoing. FINDINGS: Between Feb 27, 2020, and July 24, 2023 (database lock for interim analysis two), 554 people with a motor neuron disease were randomly allocated to memantine (183 [33%]), trazodone (185 [33%]), or placebo (186 [34%]). The primary interim analysis population comprised 530 participants, of whom 175 (33%) had been allocated memantine, 175 (33%) had been allocated trazodone, and 180 (34%) had been allocated placebo. Over 12 months of follow-up, the mean rate of change per month in ALSFRS-R was -0·650 for memantine, -0·625 for trazodone, and -0·655 for placebo (memantine versus placebo estimated mean difference 0·033, one-sided 90% CI lower level -0·085; one-sided p=0·36; trazodone vs placebo: 0·065, -0·051; one-sided p=0·24). The one-sided p values were both above the significance threshold of 10%, indicating that neither memantine nor trazodone groups met the criteria for continuation. There were 483 participants with at least one adverse event (145 [77%] on placebo, 170 [91%] on memantine, and 168 [90%] on trazodone). There were 88 participants with at least one serious adverse event (37 [20%] on memantine, 27 [14%] on trazodone, and 24 [13%] on placebo). A total of 11 serious adverse event led to treatment discontinuation. There was no survival difference between comparisons, with 49 deaths in the memantine group, 52 deaths in the trazodone group, and 48 deaths in the placebo group. INTERPRETATION: Neither memantine nor trazodone improved efficacy outcomes compared with placebo. This result is sufficiently powered to warrant no further testing of trazodone or memantine in motor neuron disease at the doses evaluated in this study. The multiarm multistage design shows important benefits in reducing the time, cost, and participant numbers to reach a definitive result. FUNDING: The Euan MacDonald Centre, MND Scotland, My Name'5 Doddie Foundation, and Baillie Gifford.

Trace amine-associated receptor 1 (TAAR1) agonism for psychosis: a living systematic review and meta-analysis of human and non-human data.

Background: Trace amine-associated receptor 1 (TAAR1) agonism shows promise for treating psychosis, prompting us to synthesise data from human and non-human studies. Methods: We co-produced a living systematic review of controlled studies examining TAAR1 agonists in individuals (with or without psychosis/schizophrenia) and relevant animal models. Two independent reviewers identified studies in multiple electronic databases (until 17.11.2023), extracted data, and assessed risk of bias. Primary outcomes were standardised mean differences (SMD) for overall symptoms in human studies and hyperlocomotion in animal models. We also examined adverse events and neurotransmitter signalling. We synthesised data with random-effects meta-analyses. Results: Nine randomised trials provided data for two TAAR1 agonists (ulotaront and ralmitaront), and 15 animal studies for 10 TAAR1 agonists. Ulotaront and ralmitaront demonstrated few differences compared to placebo in improving overall symptoms in adults with acute schizophrenia (N=4 studies, n=1291 participants; SMD=0.15, 95%CI: -0.05, 0.34), and ralmitaront was less efficacious than risperidone (N=1, n=156, SMD=-0.53, 95%CI: -0.86, -0.20). Large placebo response was observed in ulotaront phase-III trials. Limited evidence suggested a relatively benign side-effect profile for TAAR1 agonists, although nausea and sedation were common after a single dose of ulotaront. In animal studies, TAAR1 agonists improved hyperlocomotion compared to control (N=13 studies, k=41 experiments, SMD=1.01, 95%CI: 0.74, 1.27), but seemed less efficacious compared to dopamine D 2 receptor antagonists (N=4, k=7, SMD=-0.62, 95%CI: -1.32, 0.08). Limited human and animal data indicated that TAAR1 agonists may regulate presynaptic dopaminergic signalling. Conclusions: TAAR1 agonists may be less efficacious than dopamine D 2 receptor antagonists already licensed for schizophrenia. The results are preliminary due to the limited number of drugs examined, lack of longer-term data, publication bias, and assay sensitivity concerns in trials associated with large placebo response. Considering their unique mechanism of action, relatively benign side-effect profile and ongoing drug development, further research is warranted. Registration: PROSPERO-ID: CRD42023451628.

There is a need for more effective treatments for psychosis, including schizophrenia. Psychosis is a collection of mental health symptoms, such as hearing voices, that can cause distress and impair functioning. These symptoms are thought to be caused by changes in a chemical messenger system in the brain called dopamine. Currently used antipsychotic medications target brain receptors that respond to dopamine. They are not effective in some people and can cause uncomfortable adverse events, such as weight gain and movement disorders, especially with long-term use. A new type of drug is the trace amine-associated receptor 1 (TAAR1) agonists. These drugs act on different brain receptors that can affect the activity of the dopamine system, but do not directly bind to dopamine receptors. We aimed to understand if TAAR1 agonists can reduce symptoms of psychosis, what adverse events they might have, and how they work. We did this by reviewing and collating all available evidence until November 2023. This is a "living" systematic review, so it will be regularly updated in the future. We looked at both human and animal studies investigating TAAR1 agonists. Human studies suggested that two TAAR1 agonists (namely, ulotaront or ralmitaront) might have little to no effect on reducing symptoms of psychosis compared to placebo in people with schizophrenia. They seemed to cause fewer adverse events than current antipsychotics. Data from animal studies suggested that TAAR1 agonists had some positive effects but potentially smaller than other antipsychotics. There were little to no data from both human and animal studies about how TAAR1 agonists actually work. From the current evidence we are uncertain about these results. With the ongoing development of new TAAR1 agonists, more evidence is needed to understand their potential role in the treatment of psychosis.

A Systematic Online Living Evidence Summary of experimental Alzheimer's disease research.

BACKGROUND: Despite extensive investment, the development of effective treatments for Alzheimer's disease (AD) has been largely unsuccessful. To improve translation, it is crucial to ensure the quality and reproducibility of foundational evidence generated from laboratory models. Systematic reviews play a key role in providing an unbiased overview of the evidence, assessing rigour and reporting, and identifying factors that influence reproducibility. However, the sheer pace of evidence generation is prohibitive to evidence synthesis and assessment. NEW METHOD: To address these challenges, we have developed AD-SOLES, an integrated workflow of automated tools that collect, curate, and visualise the totality of evidence from in vivo experiments. RESULTS: AD-SOLES is a publicly accessible interactive dashboard aiming to surface and expose data from in vivo experiments. It summarises the latest evidence, tracks reporting quality and transparency, and allows research users to easily locate evidence relevant to their specific research question. COMPARISON WITH EXISTING METHODS: Using automated screening methodologies within AD-SOLES, systematic reviews can begin at an accelerated starting point compared to traditional approaches. Furthermore, through text-mining approaches within the full-text of publications, users can identify research of interest using specific models, outcomes, or interventions without relying on details in the title and/or abstract. CONCLUSIONS: By automating the collection, curation, and visualisation of evidence from in vivo experiments, AD-SOLES addresses the challenges posed by the rapid pace of evidence generation. AD-SOLES aims to offer guidance for research improvement, reduce research waste, highlight knowledge gaps, and support informed decision making for researchers, funders, patients, and the public.

Analysis of animal-to-human translation shows that only 5% of animal-tested therapeutic interventions obtain regulatory approval for human applications.

There is an ongoing debate about the value of animal experiments to inform medical practice, yet there are limited data on how well therapies developed in animal studies translate to humans. We aimed to assess 2 measures of translation across various biomedical fields: (1) The proportion of therapies which transition from animal studies to human application, including involved timeframes; and (2) the consistency between animal and human study results. Thus, we conducted an umbrella review, including English systematic reviews that evaluated the translation of therapies from animals to humans. Medline, Embase, and Web of Science Core Collection were searched from inception until August 1, 2023. We assessed the proportion of therapeutic interventions advancing to any human study, a randomized controlled trial (RCT), and regulatory approval. We meta-analyzed the concordance between animal and human studies. The risk of bias was probed using a 10-item checklist for systematic reviews. We included 122 articles, describing 54 distinct human diseases and 367 therapeutic interventions. Neurological diseases were the focus of 32% of reviews. The overall proportion of therapies progressing from animal studies was 50% to human studies, 40% to RCTs, and 5% to regulatory approval. Notably, our meta-analysis showed an 86% concordance between positive results in animal and clinical studies. The median transition times from animal studies were 5, 7, and 10 years to reach any human study, an RCT, and regulatory approval, respectively. We conclude that, contrary to widespread assertions, the rate of successful animal-to-human translation may be higher than previously reported. Nonetheless, the low rate of final approval indicates potential deficiencies in the design of both animal studies and early clinical trials. To ameliorate the efficacy of translating therapies from bench to bedside, we advocate for enhanced study design robustness and the reinforcement of generalizability.

Evaluating artificial intelligence software for delineating hemorrhage extent on CT brain imaging in stroke: AI delineation of ICH on CT.

BACKGROUND: The extent and distribution of intracranial hemorrhage (ICH) directly affects clinical management. Artificial intelligence (AI) software can detect and may delineate ICH extent on brain CT. We evaluated e-ASPECTS software (Brainomix Ltd.) performance for ICH delineation. METHODS: We qualitatively assessed software delineation of ICH on CT using patients from six stroke trials. We assessed hemorrhage delineation in five compartments: lobar, deep, posterior fossa, intraventricular, extra-axial. We categorized delineation as excellent, good, moderate, or poor. We assessed quality of software delineation with number of affected compartments in univariate analysis (Kruskall-Wallis test) and ICH location using logistic regression (dependent variable: dichotomous delineation categories 'excellent-good' versus 'moderate-poor'), and report odds ratios (OR) and 95 % confidence intervals (95 %CI). RESULTS: From 651 patients with ICH (median age 75 years, 53 % male), we included 628 with assessable CTs. Software delineation of ICH extent was 'excellent' in 189/628 (30 %), 'good' in 255/628 (41 %), 'moderate' in 127/628 (20 %), and 'poor' in 57/628 cases (9 %). The quality of software delineation of ICH was better when fewer compartments were affected (Z = 3.61-6.27; p = 0.0063). Software delineation of ICH extent was more likely to be 'excellent-good' quality when lobar alone (OR = 1.56, 95 %CI = 0.97-2.53) but 'moderate-poor' with any intraventricular (OR = 0.56, 95 %CI = 0.39-0.81, p = 0.002) or any extra-axial (OR = 0.41, 95 %CI = 0.27-0.62, p<0.001) extension. CONCLUSIONS: Delineation of ICH extent on stroke CT scans by AI software was excellent or good in 71 % of cases but was more likely to over- or under-estimate extent when ICH was either more extensive, intraventricular, or extra-axial.

Prevention of infections and fever to improve outcome in older patients with acute stroke (PRECIOUS): a randomised, open, phase III, multifactorial, clinical trial with blinded outcome assessment.

Background: Infections and fever after stroke are associated with poor functional outcome or death. We assessed whether prophylactic treatment with anti-emetic, antibiotic, or antipyretic medication would improve functional outcome in older patients with acute stroke. Methods: We conducted an international, 2∗2∗2-factorial, randomised, controlled, open-label trial with blinded outcome assessment in patients aged 66 years or older with acute ischaemic stroke or intracerebral haemorrhage and a score on the National Institutes of Health Stroke Scale ≥ 6. Patients were randomly allocated (1:1) to metoclopramide (oral, rectal, or intravenous; 10 mg thrice daily) vs. no metoclopramide, ceftriaxone (intravenous; 2000 mg once daily) vs. no ceftriaxone, and paracetamol (oral, rectal, or intravenous; 1000 mg four times daily) vs. no paracetamol, started within 24 h after symptom onset and continued for four days. All participants received standard of care. The target sample size was 3800 patients. The primary outcome was the score on the modified Rankin Scale (mRS) at 90 days analysed with ordinal logistic regression and reported as an adjusted common odds ratio (an acOR < 1 suggests benefit and an acOR > 1 harm). This trial is registered (ISRCTN82217627). Findings: From April 2016 through June 2022, 1493 patients from 67 European sites were randomised to metoclopramide (n = 704) or no metoclopramide (n = 709), ceftriaxone (n = 594) or no ceftriaxone (n = 482), and paracetamol (n = 706) or no paracetamol (n = 739), of whom 1471 were included in the intention-to-treat analysis. Prophylactic use of study medication did not significantly alter the primary outcome at 90 days: metoclopramide vs. no metoclopramide (adjusted common odds ratio [acOR], 1.01; 95% CI 0.81-1.25), ceftriaxone vs. no ceftriaxone (acOR 0.99; 95% CI 0.77-1.27), paracetamol vs. no paracetamol (acOR 1.19; 95% CI 0.96-1.47). The study drugs were safe and not associated with an increased incidence of serious adverse events. Interpretation: We observed no sign of benefit of prophylactic use of metoclopramide, ceftriaxone, or paracetamol during four days in older patients with a moderately severe to severe acute stroke. Funding: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No: 634809.

The Automated Systematic Search Deduplicator (ASySD): a rapid, open-source, interoperable tool to remove duplicate citations in biomedical systematic reviews.

BACKGROUND: Researchers performing high-quality systematic reviews search across multiple databases to identify relevant evidence. However, the same publication is often retrieved from several databases. Identifying and removing such duplicates ("deduplication") can be extremely time-consuming, but failure to remove these citations can lead to the wrongful inclusion of duplicate data. Many existing tools are not sensitive enough, lack interoperability with other tools, are not freely accessible, or are difficult to use without programming knowledge. Here, we report the performance of our Automated Systematic Search Deduplicator (ASySD), a novel tool to perform automated deduplication of systematic searches for biomedical reviews. METHODS: We evaluated ASySD's performance on 5 unseen biomedical systematic search datasets of various sizes (1845-79,880 citations). We compared the performance of ASySD with EndNote's automated deduplication option and with the Systematic Review Assistant Deduplication Module (SRA-DM). RESULTS: ASySD identified more duplicates than either SRA-DM or EndNote, with a sensitivity in different datasets of 0.95 to 0.99. The false-positive rate was comparable to human performance, with a specificity of > 0.99. The tool took less than 1 h to identify and remove duplicates within each dataset. CONCLUSIONS: For duplicate removal in biomedical systematic reviews, ASySD is a highly sensitive, reliable, and time-saving tool. It is open source and freely available online as both an R package and a user-friendly web application.

Blood biomarker changes following therapeutic hypothermia in ischemic stroke.

INTRODUCTION: Therapeutic hypothermia is a promising candidate for stroke treatment although its efficacy has not yet been demonstrated in patients. Changes in blood molecules could act as surrogate markers to evaluate the efficacy and safety of therapeutic cooling. METHODS: Blood samples from 54 patients included in the EuroHYP-1 study (27 treated with hypothermia, and 27 controls) were obtained at baseline, 24 ± 2 h, and 72 ± 4 h. The levels of a panel of 27 biomarkers, including matrix metalloproteinases and cardiac and inflammatory markers, were measured. RESULTS: Metalloproteinase-3 (MMP-3), fatty-acid-binding protein (FABP), and interleukin-8 (IL-8) increased over time in relation to the hypothermia treatment. Statistically significant correlations between the minimum temperature achieved by each patient in the hypothermia group and the MMP-3 level measured at 72 h, FABP level measured at 24 h, and IL-8 levels measured at 24 and 72 h were found. No differential biomarker levels were observed in patients with poor or favorable outcomes according to modified Rankin Scale scores. CONCLUSION: Although the exact roles of MMP3, FABP, and IL-8 in hypothermia-treated stroke patients are not known, further exploration is needed to confirm their roles in brain ischemia.

The UK Reproducibility Network: A progress report.

There is growing awareness that the ways in which academic research is conducted could be improved. A number of exciting innovations are emerging, alongside a broader agenda that includes a growing emphasis on open research. This short article outlines the rationale, progress and plans of the UK Reproducibility Network, which is one of a growing number of similar initiatives internationally that promote more rigorous and transparent research.

New living evidence resource of human and non-human studies for early intervention and research prioritisation in anxiety, depression and psychosis.

In anxiety, depression and psychosis, there has been frustratingly slow progress in developing novel therapies that make a substantial difference in practice, as well as in predicting which treatments will work for whom and in what contexts. To intervene early in the process and deliver optimal care to patients, we need to understand the underlying mechanisms of mental health conditions, develop safe and effective interventions that target these mechanisms, and improve our capabilities in timely diagnosis and reliable prediction of symptom trajectories. Better synthesis of existing evidence is one way to reduce waste and improve efficiency in research towards these ends. Living systematic reviews produce rigorous, up-to-date and informative evidence summaries that are particularly important where research is emerging rapidly, current evidence is uncertain and new findings might change policy or practice. Global Alliance for Living Evidence on aNxiety, depressiOn and pSychosis (GALENOS) aims to tackle the challenges of mental health science research by cataloguing and evaluating the full spectrum of relevant scientific research including both human and preclinical studies. GALENOS will also allow the mental health community-including patients, carers, clinicians, researchers and funders-to better identify the research questions that most urgently need to be answered. By creating open-access datasets and outputs in a state-of-the-art online resource, GALENOS will help identify promising signals early in the research process. This will accelerate translation from discovery science into effective new interventions for anxiety, depression and psychosis, ready to be translated in clinical practice across the world.

Systematic online living evidence summaries: emerging tools to accelerate evidence synthesis.

Systematic reviews and meta-analysis are the cornerstones of evidence-based decision making and priority setting. However, traditional systematic reviews are time and labour intensive, limiting their feasibility to comprehensively evaluate the latest evidence in research-intensive areas. Recent developments in automation, machine learning and systematic review technologies have enabled efficiency gains. Building upon these advances, we developed Systematic Online Living Evidence Summaries (SOLES) to accelerate evidence synthesis. In this approach, we integrate automated processes to continuously gather, synthesise and summarise all existing evidence from a research domain, and report the resulting current curated content as interrogatable databases via interactive web applications. SOLES can benefit various stakeholders by (i) providing a systematic overview of current evidence to identify knowledge gaps, (ii) providing an accelerated starting point for a more detailed systematic review, and (iii) facilitating collaboration and coordination in evidence synthesis.

Accuracy of artificial intelligence software for CT angiography in stroke.

OBJECTIVE: Software developed using artificial intelligence may automatically identify arterial occlusion and provide collateral vessel scoring on CT angiography (CTA) performed acutely for ischemic stroke. We aimed to assess the diagnostic accuracy of e-CTA by Brainomix™ Ltd by large-scale independent testing using expert reading as the reference standard. METHODS: We identified a large clinically representative sample of baseline CTA from 6 studies that recruited patients with acute stroke symptoms involving any arterial territory. We compared e-CTA results with masked expert interpretation of the same scans for the presence and location of laterality-matched arterial occlusion and/or abnormal collateral score combined into a single measure of arterial abnormality. We tested the diagnostic accuracy of e-CTA for identifying any arterial abnormality (and in a sensitivity analysis compliant with the manufacturer's guidance that software only be used to assess the anterior circulation). RESULTS: We include CTA from 668 patients (50% female; median: age 71 years, NIHSS 9, 2.3 h from stroke onset). Experts identified arterial occlusion in 365 patients (55%); most (343, 94%) involved the anterior circulation. Software successfully processed 545/668 (82%) CTAs. The sensitivity, specificity and diagnostic accuracy of e-CTA for detecting arterial abnormality were each 72% (95% CI = 66-77%). Diagnostic accuracy was non-significantly improved in a sensitivity analysis excluding occlusions from outside the anterior circulation (76%, 95% CI = 72-80%). INTERPRETATION: Compared to experts, the diagnostic accuracy of e-CTA for identifying acute arterial abnormality was 72-76%. Users of e-CTA should be competent in CTA interpretation to ensure all potential thrombectomy candidates are identified.

Designing, conducting, and reporting reproducible animal experiments.

In biomedicine and many other fields, there are growing concerns around the reproducibility of research findings, with many researchers being unable to replicate their own or others' results. This raises important questions as to the validity and usefulness of much published research. In this review, we aim to engage researchers in the issue of research reproducibility and equip them with the necessary tools to increase the reproducibility of their research. We first highlight the causes and potential impact of non-reproducible research and emphasise the benefits of working reproducibly for the researcher and broader research community. We address specific targets for improvement and steps that individual researchers can take to increase the reproducibility of their work. We next provide recommendations for improving the design and conduct of experiments, focusing on in vivo animal experiments. We describe common sources of poor internal validity of experiments and offer practical guidance for limiting these potential sources of bias at different experimental stages, as well as discussing other important considerations during experimental design. We provide a list of key resources available to researchers to improve experimental design, conduct, and reporting. We then discuss the importance of open research practices such as study preregistration and the use of preprints and describe recommendations around data management and sharing. Our review emphasises the importance of reproducible work and aims to empower every individual researcher to contribute to the reproducibility of research in their field.

Systematic, comprehensive, evidence-based approach to identify neuroprotective interventions for motor neuron disease: using systematic reviews to inform expert consensus.

OBJECTIVES: Motor neuron disease (MND) is an incurable progressive neurodegenerative disease with limited treatment options. There is a pressing need for innovation in identifying therapies to take to clinical trial. Here, we detail a systematic and structured evidence-based approach to inform consensus decision making to select the first two drugs for evaluation in Motor Neuron Disease-Systematic Multi-arm Adaptive Randomised Trial (MND-SMART: NCT04302870), an adaptive platform trial. We aim to identify and prioritise candidate drugs which have the best available evidence for efficacy, acceptable safety profiles and are feasible for evaluation within the trial protocol. METHODS: We conducted a two-stage systematic review to identify potential neuroprotective interventions. First, we reviewed clinical studies in MND, Alzheimer's disease, Huntington's disease, Parkinson's disease and multiple sclerosis, identifying drugs described in at least one MND publication or publications in two or more other diseases. We scored and ranked drugs using a metric evaluating safety, efficacy, study size and study quality. In stage two, we reviewed efficacy of drugs in MND animal models, multicellular eukaryotic models and human induced pluripotent stem cell (iPSC) studies. An expert panel reviewed candidate drugs over two shortlisting rounds and a final selection round, considering the systematic review findings, late breaking evidence, mechanistic plausibility, safety, tolerability and feasibility of evaluation in MND-SMART. RESULTS: From the clinical review, we identified 595 interventions. 66 drugs met our drug/disease logic. Of these, 22 drugs with supportive clinical and preclinical evidence were shortlisted at round 1. Seven drugs proceeded to round 2. The panel reached a consensus to evaluate memantine and trazodone as the first two arms of MND-SMART. DISCUSSION: For future drug selection, we will incorporate automation tools, text-mining and machine learning techniques to the systematic reviews and consider data generated from other domains, including high-throughput phenotypic screening of human iPSCs.

Screening for in vitro systematic reviews: a comparison of screening methods and training of a machine learning classifier.

OBJECTIVE: Existing strategies to identify relevant studies for systematic review may not perform equally well across research domains. We compare four approaches based on either human or automated screening of either title and abstract or full text, and report the training of a machine learning algorithm to identify in vitro studies from bibliographic records. METHODS: We used a systematic review of oxygen-glucose deprivation (OGD) in PC-12 cells to compare approaches. For human screening, two reviewers independently screened studies based on title and abstract or full text, with disagreements reconciled by a third. For automated screening, we applied text mining to either title and abstract or full text. We trained a machine learning algorithm with decisions from 2000 randomly selected PubMed Central records enriched with a dataset of known in vitro studies. RESULTS: Full-text approaches performed best, with human (sensitivity: 0.990, specificity: 1.000 and precision: 0.994) outperforming text mining (sensitivity: 0.972, specificity: 0.980 and precision: 0.764). For title and abstract, text mining (sensitivity: 0.890, specificity: 0.995 and precision: 0.922) outperformed human screening (sensitivity: 0.862, specificity: 0.998 and precision: 0.975). At our target sensitivity of 95% the algorithm performed with specificity of 0.850 and precision of 0.700. CONCLUSION: In this in vitro systematic review, human screening based on title and abstract erroneously excluded 14% of relevant studies, perhaps because title and abstract provide an incomplete description of methods used. Our algorithm might be used as a first selection phase in in vitro systematic reviews to limit the extent of full text screening required.

Advanced methods and implementations for the meta-analyses of animal models: Current practices and future recommendations.

Meta-analytic techniques have been widely used to synthesize data from animal models of human diseases and conditions, but these analyses often face two statistical challenges due to complex nature of animal data (e.g., multiple effect sizes and multiple species): statistical dependency and confounding heterogeneity. These challenges can lead to unreliable and less informative evidence, which hinders the translation of findings from animal to human studies. We present a literature survey of meta-analysis using animal models (animal meta-analysis), showing that these issues are not adequately addressed in current practice. To address these challenges, we propose a meta-analytic framework based on multilevel (linear mixed-effects) models. Through conceptualization, formulations, and worked examples, we illustrate how this framework can appropriately address these issues while allowing for testing new questions. Additionally, we introduce other advanced techniques such as multivariate models, robust variance estimation, and meta-analysis of emergent effect sizes, which can deliver robust inferences and novel biological insights. We also provide a tutorial with annotated R code to demonstrate the implementation of these techniques.

Trace amine-associated receptor 1 (TAAR1) agonists for psychosis: protocol for a living systematic review and meta-analysis of human and non-human studies.

BACKGROUND: There is an urgent need to develop more effective and safer antipsychotics beyond dopamine 2 receptor antagonists. An emerging and promising approach is TAAR1 agonism. Therefore, we will conduct a living systematic review and meta-analysis to synthesize and triangulate the evidence from preclinical animal experiments and clinical studies on the efficacy, safety, and underlying mechanism of action of TAAR1 agonism for psychosis. METHODS: Independent searches will be conducted in multiple electronic databases to identify clinical and animal experimental studies comparing TAAR1 agonists with licensed antipsychotics or other control conditions in individuals with psychosis or animal models for psychosis, respectively. The primary outcomes will be overall psychotic symptoms and their behavioural proxies in animals. Secondary outcomes will include side effects and neurobiological measures. Two independent reviewers will conduct study selection, data extraction using predefined forms, and risk of bias assessment using suitable tools based on the study design. Ontologies will be developed to facilitate study identification and data extraction. Data from clinical and animal studies will be synthesized separately using random-effects meta-analysis if appropriate, or synthesis without meta-analysis. Study characteristics will be investigated as potential sources of heterogeneity. Confidence in the evidence for each outcome and source of evidence will be evaluated, considering the summary of the association, potential concerns regarding internal and external validity, and reporting biases. When multiple sources of evidence are available for an outcome, an overall conclusion will be drawn in a triangulation meeting involving a multidisciplinary team of experts. We plan trimonthly updates of the review, and any modifications in the protocol will be documented. The review will be co-produced by multiple stakeholders aiming to produce impactful and relevant results and bridge the gap between preclinical and clinical research on psychosis. PROTOCOL REGISTRATION: PROSPERO-ID: CRD42023451628.