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.

Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research.

The replicability of research results has been a cause of increasing concern to the scientific community. The long-held belief that experimental standardization begets replicability has also been recently challenged, with the observation that the reduction of variability within studies can lead to idiosyncratic, lab-specific results that cannot be replicated. An alternative approach is to, instead, deliberately introduce heterogeneity, known as "heterogenization" of experimental design. Here, we explore a novel perspective in the heterogenization program in a meta-analysis of variability in observed phenotypic outcomes in both control and experimental animal models of ischemic stroke. First, by quantifying interindividual variability across control groups, we illustrate that the amount of heterogeneity in disease state (infarct volume) differs according to methodological approach, for example, in disease induction methods and disease models. We argue that such methods may improve replicability by creating diverse and representative distribution of baseline disease state in the reference group, against which treatment efficacy is assessed. Second, we illustrate how meta-analysis can be used to simultaneously assess efficacy and stability (i.e., mean effect and among-individual variability). We identify treatments that have efficacy and are generalizable to the population level (i.e., low interindividual variability), as well as those where there is high interindividual variability in response; for these, latter treatments translation to a clinical setting may require nuance. We argue that by embracing rather than seeking to minimize variability in phenotypic outcomes, we can motivate the shift toward heterogenization and improve both the replicability and generalizability of preclinical research.

Epidemiology and reporting characteristics of preclinical systematic reviews.

In an effort to better utilize published evidence obtained from animal experiments, systematic reviews of preclinical studies are increasingly more common-along with the methods and tools to appraise them (e.g., SYstematic Review Center for Laboratory animal Experimentation [SYRCLE's] risk of bias tool). We performed a cross-sectional study of a sample of recent preclinical systematic reviews (2015-2018) and examined a range of epidemiological characteristics and used a 46-item checklist to assess reporting details. We identified 442 reviews published across 43 countries in 23 different disease domains that used 26 animal species. Reporting of key details to ensure transparency and reproducibility was inconsistent across reviews and within article sections. Items were most completely reported in the title, introduction, and results sections of the reviews, while least reported in the methods and discussion sections. Less than half of reviews reported that a risk of bias assessment for internal and external validity was undertaken, and none reported methods for evaluating construct validity. Our results demonstrate that a considerable number of preclinical systematic reviews investigating diverse topics have been conducted; however, their quality of reporting is inconsistent. Our study provides the justification and evidence to inform the development of guidelines for conducting and reporting preclinical systematic reviews.

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.

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.

External Validation of e-ASPECTS Software for Interpreting Brain CT in Stroke.

OBJECTIVE: The purpose of this study was to test e-ASPECTS software in patients with stroke. Marketed as a decision-support tool, e-ASPECTS may detect features of ischemia or hemorrhage on computed tomography (CT) imaging and quantify ischemic extent using Alberta Stroke Program Early CT Score (ASPECTS). METHODS: Using CT from 9 stroke studies, we compared software with masked experts. As per indications for software use, we assessed e-ASPECTS results for patients with/without middle cerebral artery (MCA) ischemia but no other cause of stroke. In an analysis outside the intended use of the software, we enriched our dataset with non-MCA ischemia, hemorrhage, and mimics to simulate a representative "front door" hospital population. With final diagnosis as the reference standard, we tested the diagnostic accuracy of e-ASPECTS for identifying stroke features (ischemia, hyperattenuated arteries, and hemorrhage) in the representative population. RESULTS: We included 4,100 patients (51% women, median age = 78 years, National Institutes of Health Stroke Scale [NIHSS] = 10, onset to scan = 2.5 hours). Final diagnosis was ischemia (78%), hemorrhage (14%), or mimic (8%). From 3,035 CTs with expert-rated ASPECTS, most (2084/3035, 69%) e-ASPECTS results were within one point of experts. In the representative population, the diagnostic accuracy of e-ASPECTS was 71% (95% confidence interval [CI] = 70-72%) for detecting ischemic features, 85% (83-86%) for hemorrhage. Software identified more false positive ischemia (12% vs 2%) and hemorrhage (14% vs <1%) than experts. INTERPRETATION: On independent testing, e-ASPECTS provided moderate agreement with experts and overcalled stroke features. Therefore, future prospective trials testing impacts of artificial intelligence (AI) software on patient care and outcome are required before widespread implementation of stroke decision-support software. ANN NEUROL 2022;92:943-957.

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.

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.

The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research.

Reproducible science requires transparent reporting. The ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) were originally developed in 2010 to improve the reporting of animal research. They consist of a checklist of information to include in publications describing in vivo experiments to enable others to scrutinise the work adequately, evaluate its methodological rigour, and reproduce the methods and results. Despite considerable levels of endorsement by funders and journals over the years, adherence to the guidelines has been inconsistent, and the anticipated improvements in the quality of reporting in animal research publications have not been achieved. Here, we introduce ARRIVE 2.0. The guidelines have been updated and information reorganised to facilitate their use in practice. We used a Delphi exercise to prioritise and divide the items of the guidelines into 2 sets, the "ARRIVE Essential 10," which constitutes the minimum requirement, and the "Recommended Set," which describes the research context. This division facilitates improved reporting of animal research by supporting a stepwise approach to implementation. This helps journal editors and reviewers verify that the most important items are being reported in manuscripts. We have also developed the accompanying Explanation and Elaboration (E&E) document, which serves (1) to explain the rationale behind each item in the guidelines, (2) to clarify key concepts, and (3) to provide illustrative examples. We aim, through these changes, to help ensure that researchers, reviewers, and journal editors are better equipped to improve the rigour and transparency of the scientific process and thus reproducibility.

Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0.

Improving the reproducibility of biomedical research is a major challenge. Transparent and accurate reporting is vital to this process; it allows readers to assess the reliability of the findings and repeat or build upon the work of other researchers. The ARRIVE guidelines (Animal Research: Reporting In Vivo Experiments) were developed in 2010 to help authors and journals identify the minimum information necessary to report in publications describing in vivo experiments. Despite widespread endorsement by the scientific community, the impact of ARRIVE on the transparency of reporting in animal research publications has been limited. We have revised the ARRIVE guidelines to update them and facilitate their use in practice. The revised guidelines are published alongside this paper. This explanation and elaboration document was developed as part of the revision. It provides further information about each of the 21 items in ARRIVE 2.0, including the rationale and supporting evidence for their inclusion in the guidelines, elaboration of details to report, and examples of good reporting from the published literature. This document also covers advice and best practice in the design and conduct of animal studies to support researchers in improving standards from the start of the experimental design process through to publication.

Animal models of chemotherapy-induced peripheral neuropathy: A machine-assisted systematic review and meta-analysis.

We report a systematic review and meta-analysis of research using animal models of chemotherapy-induced peripheral neuropathy (CIPN). We systematically searched 5 online databases in September 2012 and updated the search in November 2015 using machine learning and text mining to reduce the screening for inclusion workload and improve accuracy. For each comparison, we calculated a standardised mean difference (SMD) effect size, and then combined effects in a random-effects meta-analysis. We assessed the impact of study design factors and reporting of measures to reduce risks of bias. We present power analyses for the most frequently reported behavioural tests; 337 publications were included. Most studies (84%) used male animals only. The most frequently reported outcome measure was evoked limb withdrawal in response to mechanical monofilaments. There was modest reporting of measures to reduce risks of bias. The number of animals required to obtain 80% power with a significance level of 0.05 varied substantially across behavioural tests. In this comprehensive summary of the use of animal models of CIPN, we have identified areas in which the value of preclinical CIPN studies might be increased. Using both sexes of animals in the modelling of CIPN, ensuring that outcome measures align with those most relevant in the clinic, and the animal's pain contextualised ethology will likely improve external validity. Measures to reduce risk of bias should be employed to increase the internal validity of studies. Different outcome measures have different statistical power, and this can refine our approaches in the modelling of CIPN.

Effectiveness of biomaterial-based combination strategies for spinal cord repair - a systematic review and meta-analysis of preclinical literature.

STUDY DESIGN: Systematic review and meta-analysis of preclinical literature. OBJECTIVES: To assess the effects of biomaterial-based combination (BMC) strategies for the treatment of Spinal Cord Injury (SCI), the effects of individual biomaterials in the context of BMC strategies, and the factors influencing their efficacy. To assess the effects of different preclinical testing paradigms in BMC strategies. METHODS: We performed a systematic literature search of Embase, Web of Science and PubMed. All controlled preclinical studies describing an in vivo or in vitro model of SCI that tested a biomaterial in combination with at least one other regenerative strategy (cells, drugs, or both) were included. Two review authors conducted the study selection independently, extracted study characteristics independently and assessed study quality using a modified CAMARADES checklist. Effect size measures were combined using random-effects models and heterogeneity was explored using meta-regression with tau2, I2 and R2 statistics. We tested for small-study effects using funnel plot-based methods. RESULTS: 134 publications were included, testing over 100 different BMC strategies. Overall, treatment with BMC therapies improved locomotor recovery by 25.3% (95% CI, 20.3-30.3; n = 102) and in vivo axonal regeneration by 1.6 SD (95% CI 1.2-2 SD; n = 117) in comparison with injury only controls. CONCLUSION: BMC strategies improve locomotor outcomes after experimental SCI. Our comprehensive study highlights gaps in current knowledge and provides a foundation for the design of future experiments.

Introducing our series: research synthesis and meta-research in biology.

Research synthesis is the process of bringing together findings and attributes from different publications, for example, to give a more complete description of phenomena than is usually possible in a single work. We bring the Research Synthesis Series to BMC Biology to promote meta-analyses, other research syntheses including meta-research studies, and research synthesis methodologies in biology, facilitating their dissemination to broader communities.

The ARRIVE guidelines 2.0: updated guidelines for reporting animal research.

Reproducible science requires transparent reporting. The ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) were originally developed in 2010 to improve the reporting of animal research. They consist of a checklist of information to include in publications describing in vivo experiments to enable others to scrutinise the work adequately, evaluate its methodological rigour, and reproduce the methods and results. Despite considerable levels of endorsement by funders and journals over the years, adherence to the guidelines has been inconsistent, and the anticipated improvements in the quality of reporting in animal research publications have not been achieved. Here, we introduce ARRIVE 2.0. The guidelines have been updated and information reorganised to facilitate their use in practice. We used a Delphi exercise to prioritise and divide the items of the guidelines into 2 sets, the 'ARRIVE Essential 10,' which constitutes the minimum requirement, and the 'Recommended Set,' which describes the research context. This division facilitates improved reporting of animal research by supporting a stepwise approach to implementation. This helps journal editors and reviewers verify that the most important items are being reported in manuscripts. We have also developed the accompanying Explanation and Elaboration document, which serves (1) to explain the rationale behind each item in the guidelines, (2) to clarify key concepts, and (3) to provide illustrative examples. We aim, through these changes, to help ensure that researchers, reviewers, and journal editors are better equipped to improve the rigour and transparency of the scientific process and thus reproducibility.

Can harmonisation of outcomes bridge the translation gap for pre-clinical research? A systematic review of outcomes measured in mouse models of type 2 diabetes.

BACKGROUND: In pre-clinical research, systematic reviews have the potential to mitigate translational challenges by facilitating understanding of how pre-clinical studies can inform future clinical research. Yet their conduct is encumbered by heterogeneity in the outcomes measured and reported, and those outcomes may not always relate to the most clinically important outcomes. We aimed to systematically review outcomes measured and reported in pre-clinical in vivo studies of pharmacological interventions to treat high blood glucose in mouse models of type 2 diabetes. METHODS: A systematic review of pre-clinical in vivo studies of pharmacological interventions aimed at addressing elevated blood glucose in mouse models of type 2 diabetes was completed. Studies were screened for eligibility and outcomes extracted from the included studies. The outcomes were recorded verbatim and classified into outcome domains using an existing outcome taxonomy. Outcomes were also compared to those identified in a systematic review of registered phase 3/4 clinical trials for glucose lowering interventions in people with type 2 diabetes. RESULTS: Review of 280 included studies identified 532 unique outcomes across 19 domains. No single outcome, or domain, was measured in all studies and only 132 (21%) had also been measured in registered phase 3/4 clinical trials. A core outcome set, representing the minimum that should be measured and reported, developed for type 2 diabetes effectiveness clinical trials includes 18 core outcomes, of these 12 (71%) outcomes were measured and reported in one or more of the included pre-clinical studies. CONCLUSIONS: There is heterogeneity of outcomes reported in pre-clinical research. Harmonisation of outcomes across the research pathway using a core outcome set may facilitate interpretation, evidence synthesis and translational success, and may contribute to the refinement of the use of animals in research. Systematic review registration: The study was prospectively registered on the PROSPERO Database, registration number CRD42018106831.

The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research.

Reproducible science requires transparent reporting. The ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) were originally developed in 2010 to improve the reporting of animal research. They consist of a checklist of information to include in publications describing in vivo experiments to enable others to scrutinise the work adequately, evaluate its methodological rigour, and reproduce the methods and results. Despite considerable levels of endorsement by funders and journals over the years, adherence to the guidelines has been inconsistent, and the anticipated improvements in the quality of reporting in animal research publications have not been achieved. Here, we introduce ARRIVE 2.0. The guidelines have been updated and information reorganised to facilitate their use in practice. We used a Delphi exercise to prioritise and divide the items of the guidelines into 2 sets, the "ARRIVE Essential 10," which constitutes the minimum requirement, and the "Recommended Set," which describes the research context. This division facilitates improved reporting of animal research by supporting a stepwise approach to implementation. This helps journal editors and reviewers verify that the most important items are being reported in manuscripts. We have also developed the accompanying Explanation and Elaboration document, which serves (1) to explain the rationale behind each item in the guidelines, (2) to clarify key concepts, and (3) to provide illustrative examples. We aim, through these changes, to help ensure that researchers, reviewers, and journal editors are better equipped to improve the rigour and transparency of the scientific process and thus reproducibility.

The Experimental Design Assistant.

Addressing the common problems that researchers encounter when designing and analysing animal experiments will improve the reliability of in vivo research. In this article, the Experimental Design Assistant (EDA) is introduced. The EDA is a web-based tool that guides the in vivo researcher through the experimental design and analysis process, providing automated feedback on the proposed design and generating a graphical summary that aids communication with colleagues, funders, regulatory authorities, and the wider scientific community. It will have an important role in addressing causes of irreproducibility.