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Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper's eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community's early reaction to a paper. [ABSTRACT FROM AUTHOR]

Both basic and translational research are continuously evolving, but the principles that underpin research integrity remain constant. These include rational, hypothesis-driven, and adequately planned and controlled science, which is carried out openly, honestly, and ethically. An important component of this should be minimising experimental irreproducibility. Biological systems, in particular, are inherently variable due to the nature of cells and tissues, as well as the complex molecules within them. As a result, it is important to understand and identify sources of variability and to strive to minimise their influence. In many instances, the application of metrology (the science of measurement) can play an important role in ensuring good quality research, even within biological systems that aren’t always amenable to many of the metrological concepts applied in other fields. Here, we introduce the basic concepts of metrology in relation to biological systems and promote the application of these principles to help avoid potentially costly mistakes in both basic and translational research. We also call on funders to encourage the uptake of metrological principles, as well as provide funding and support for later engagement with regulatory bodies. [ABSTRACT FROM AUTHOR]

Rapid point-of-care resistance diagnostics (POC-RD) are a key tool in the fight against antibiotic resistance. By tailoring drug choice to infection genotype, doctors can improve treatment efficacy while limiting costs of inappropriate antibiotic prescription. Here, we combine epidemiological theory and data to assess the potential of resistance diagnostics (RD) innovations in a public health context, as a means to limit or even reverse selection for antibiotic resistance. POC-RD can be used to impose a nonbiological fitness cost on resistant strains by enabling diagnostic-informed treatment and targeted interventions that reduce resistant strains’ opportunities for transmission. We assess this diagnostic-imposed fitness cost in the context of a spectrum of bacterial population biologies and find that POC-RD have a greater potential against obligate pathogens than opportunistic pathogens already subject to selection under “bystander” antibiotic exposure during asymptomatic carriage (e.g., the pneumococcus). We close by generalizing the notion of RD-informed strategies to incorporate carriage surveillance information and illustrate that coupling transmission-control interventions to the discovery of resistant strains in carriage can potentially select against resistance in a broad range of opportunistic pathogens. [ABSTRACT FROM AUTHOR]

We present a selection design that couples S-adenosylmethionine–dependent methylation to growth. We demonstrate its use in improving the enzyme activities of not only N-type and O-type methyltransferases by 2-fold but also an acetyltransferase of another enzyme category when linked to a methylation pathway in Escherichia coli using adaptive laboratory evolution. We also demonstrate its application for drug discovery using a catechol O-methyltransferase and its inhibitors entacapone and tolcapone. Implementation of this design in Saccharomyces cerevisiae is also demonstrated. [ABSTRACT FROM AUTHOR]

Peer-reviewed journal publication is the main means for academic researchers in the life sciences to create a permanent public record of their work. These publications are also the de facto currency for career progress, with a strong link between journal brand recognition and perceived value. The current peer-review process can lead to long delays between submission and publication, with cycles of rejection, revision, and resubmission causing redundant peer review. This situation creates unique challenges for early career researchers (ECRs), who rely heavily on timely publication of their work to gain recognition for their efforts. Today, ECRs face a changing academic landscape, including the increased interdisciplinarity of life sciences research, expansion of the researcher population, and consequent shifts in employer and funding demands. The publication of preprints, publicly available scientific manuscripts posted on dedicated preprint servers prior to journal-managed peer review, can play a key role in addressing these ECR challenges. Preprinting benefits include rapid dissemination of academic work, open access, establishing priority or concurrence, receiving feedback, and facilitating collaborations. Although there is a growing appreciation for and adoption of preprints, a minority of all articles in life sciences and medicine are preprinted. The current low rate of preprint submissions in life sciences and ECR concerns regarding preprinting need to be addressed. We provide a perspective from an interdisciplinary group of ECRs on the value of preprints and advocate their wide adoption to advance knowledge and facilitate career development. [ABSTRACT FROM AUTHOR]

Transparency lies at the heart of the open lab notebook movement. Open notebook scientists publish laboratory experiments and findings in the public domain in real time, without restrictions or omissions. Research on rare diseases is especially amenable to the open notebook model because it can both increase scientific impact and serve as a mechanism to engage patient groups in the scientific process. Here, I outline and describe my own success with my open notebook project, LabScribbles, as well as other efforts included in the openlabnotebooks.org initiative. [ABSTRACT FROM AUTHOR]

Horseshoe crabs have been integral to the safe production of vaccines and injectable medications for the past 40 years. The bleeding of live horseshoe crabs, a process that leaves thousands dead annually, is an ecologically unsustainable practice for all four species of horseshoe crab and the shorebirds that rely on their eggs as a primary food source during spring migration. Populations of both horseshoe crabs and shorebirds are in decline. This study confirms the efficacy of recombinant Factor C (rFC), a synthetic alternative that eliminates the need for animal products in endotoxin detection. Furthermore, our findings confirm that the biomedical industry can achieve a 90% reduction in the use of reagents derived from horseshoe crabs by using the synthetic alternative for the testing of water and other common materials used in the manufacturing process. This represents an extraordinary opportunity for the biomedical and pharmaceutical industries to significantly contribute to the conservation of horseshoe crabs and the birds that depend on them. [ABSTRACT FROM AUTHOR]

Determining the functions of human genes is a key objective for understanding disease and enabling development of new therapeutic approaches. A number of recent studies have shown that the amount of attention the research community gives to each of the more than 20,000 human genes is dramatically skewed toward specific, well-known genes. In this issue, Stoeger and colleagues uncover the factors that explain this bias and offer a way ahead to move more genes into the research limelight. [ABSTRACT FROM AUTHOR]

Millions of people worldwide currently suffer from serious neurological diseases and injuries for which there are few, and often no, effective treatments. The paucity of effective interventions is, no doubt, due in large part to the complexity of the disorders, as well as our currently limited understanding of their pathophysiology. The bleak picture for patients, however, is also attributable to avoidable impediments stemming from quality concerns in preclinical research that often escape detection by research regulation efforts. In our essay, we connect the dots between these concerns about the quality of preclinical research and their potential ethical impact on the patients who volunteer for early trials of interventions informed by it. We do so in hopes that a greater appreciation among preclinical researchers of these serious ethical consequences can lead to a greater commitment within the research community to adopt widely available tools and measures that can help to improve the quality of research. [ABSTRACT FROM AUTHOR]

The current push for rigor and reproducibility is driven by a desire for confidence in research results. Here, we suggest a framework for a systematic process, based on consensus principles of measurement science, to guide researchers and reviewers in assessing, documenting, and mitigating the sources of uncertainty in a study. All study results have associated ambiguities that are not always clarified by simply establishing reproducibility. By explicitly considering sources of uncertainty, noting aspects of the experimental system that are difficult to characterize quantitatively, and proposing alternative interpretations, the researcher provides information that enhances comparability and reproducibility. [ABSTRACT FROM AUTHOR]

Human protection policies require favorable risk–benefit judgments prior to launch of clinical trials. For phase I and II trials, evidence for such judgment often stems from preclinical efficacy studies (PCESs). We undertook a systematic investigation of application materials (investigator brochures [IBs]) presented for ethics review for phase I and II trials to assess the content and properties of PCESs contained in them. Using a sample of 109 IBs most recently approved at 3 institutional review boards based at German Medical Faculties between the years 2010–2016, we identified 708 unique PCESs. We then rated all identified PCESs for their reporting on study elements that help to address validity threats, whether they referenced published reports, and the direction of their results. Altogether, the 109 IBs reported on 708 PCESs. Less than 5% of all PCESs described elements essential for reducing validity threats such as randomization, sample size calculation, and blinded outcome assessment. For most PCESs (89%), no reference to a published report was provided. Only 6% of all PCESs reported an outcome demonstrating no effect. For the majority of IBs (82%), all PCESs were described as reporting positive findings. Our results show that most IBs for phase I/II studies did not allow evaluators to systematically appraise the strength of the supporting preclinical findings. The very rare reporting of PCESs that demonstrated no effect raises concerns about potential design or reporting biases. Poor PCES design and reporting thwart risk–benefit evaluation during ethical review of phase I/II studies. [ABSTRACT FROM AUTHOR]

Abstract: Meta-research is research about research. Meta-research may not be as click-worthy as a meta-pug—a pug dog dressed up in a pug costume—but it is crucial to understanding research. A particularly valuable contribution of meta-research is to identify biases in a body of evidence. Bias can occur in the design, conduct, or publication of research and is a systematic deviation from the truth in results or inferences. The findings of meta-research can tell us which evidence to trust and what must be done to improve future research. We should be using meta-research to provide the evidence base for implementing systemic changes to improve research, not for discrediting it. [ABSTRACT FROM AUTHOR]

In the scientific literature, spin refers to reporting practices that distort the interpretation of results and mislead readers so that results are viewed in a more favourable light. The presence of spin in biomedical research can negatively impact the development of further studies, clinical practice, and health policies. This systematic review aims to explore the nature and prevalence of spin in the biomedical literature. We searched MEDLINE, PreMEDLINE, Embase, Scopus, and hand searched reference lists for all reports that included the measurement of spin in the biomedical literature for at least 1 outcome. Two independent coders extracted data on the characteristics of reports and their included studies and all spin-related outcomes. Results were grouped inductively into themes by spin-related outcome and are presented as a narrative synthesis. We used meta-analyses to analyse the association of spin with industry sponsorship of research. We included 35 reports, which investigated spin in clinical trials, observational studies, diagnostic accuracy studies, systematic reviews, and meta-analyses. The nature of spin varied according to study design. The highest (but also greatest) variability in the prevalence of spin was present in trials. Some of the common practices used to spin results included detracting from statistically nonsignificant results and inappropriately using causal language. Source of funding was hypothesised by a few authors to be a factor associated with spin; however, results were inconclusive, possibly due to the heterogeneity of the included papers. Further research is needed to assess the impact of spin on readers’ decision-making. Editors and peer reviewers should be familiar with the prevalence and manifestations of spin in their area of research in order to ensure accurate interpretation and dissemination of research. [ABSTRACT FROM AUTHOR]

Antibiotic regimens often include the sequential changing of drugs to limit the development and evolution of resistance of bacterial pathogens. It remains unclear how history of adaptation to one antibiotic can influence the resistance profiles when bacteria subsequently adapt to a different antibiotic. Here, we experimentally evolved Pseudomonas aeruginosa to six 2-drug sequences. We observed drug order–specific effects, whereby adaptation to the first drug can limit the rate of subsequent adaptation to the second drug, adaptation to the second drug can restore susceptibility to the first drug, or final resistance levels depend on the order of the 2-drug sequence. These findings demonstrate how resistance not only depends on the current drug regimen but also the history of past regimens. These order-specific effects may allow for rational forecasting of the evolutionary dynamics of bacteria given knowledge of past adaptations and provide support for the need to consider the history of past drug exposure when designing strategies to mitigate resistance and combat bacterial infections. [ABSTRACT FROM AUTHOR]

The Open Science Prize was established with the following objectives: first, to encourage the crowdsourcing of open data to make breakthroughs that are of biomedical significance; second, to illustrate that funders can indeed work together when scientific interests are aligned; and finally, to encourage international collaboration between investigators with the intent of achieving important innovations that would not be possible otherwise. The process for running the competition and the successes and challenges that arose are presented. [ABSTRACT FROM AUTHOR]

Ever since the discovery of neural stem cells in the mammalian brain, the possibility of brain tissue regeneration has captured the minds of scientists, clinicians, and the public. Neural stem cells have been envisioned as a source of donor cells for transplantation and vectors for the delivery of gene therapy. Over the past decade, many researchers have contributed to characterizing these cells and their lineages, providing the foundation for their utilization as therapeutic devices. In a new study, Azim and colleagues took a different approach: using pharmacogenomics to focus on neural stem cell lineage, they identified specific compounds that can direct neural stem cell fate toward a specific lineage in vivo, both in physiological and pathological conditions. Their work opens new avenues for treatment of neurodegenerative and demyelinating disorders. [ABSTRACT FROM AUTHOR]

The issue of nonreplicable evidence has attracted considerable attention across biomedical and other sciences. This concern is accompanied by an increasing interest in reforming research incentives and practices. How to optimally perform these reforms is a scientific problem in itself, and economics has several scientific methods that can help evaluate research reforms. Here, we review these methods and show their potential. Prominent among them are mathematical modeling and laboratory experiments that constitute affordable ways to approximate the effects of policies with wide-ranging implications. [ABSTRACT FROM AUTHOR]

The iconic image of the DNA double helix embodies the central role that three-dimensional structures play in understanding biological processes, which, in turn, impact health and well-being. Here, that role is explored through the eyes of one scientist, who has been lucky enough to have over 150 talented people pass through his laboratory. Each contributed to that understanding. What follows is a small fraction of their story, with an emphasis on basic research outcomes of importance to society at large. [ABSTRACT FROM AUTHOR]

Despite the potential benefits of sequential designs, studies evaluating treatments or experimental manipulations in preclinical experimental biomedicine almost exclusively use classical block designs. Our aim with this article is to bring the existing methodology of group sequential designs to the attention of researchers in the preclinical field and to clearly illustrate its potential utility. Group sequential designs can offer higher efficiency than traditional methods and are increasingly used in clinical trials. Using simulation of data, we demonstrate that group sequential designs have the potential to improve the efficiency of experimental studies, even when sample sizes are very small, as is currently prevalent in preclinical experimental biomedicine. When simulating data with a large effect size of d = 1 and a sample size of n = 18 per group, sequential frequentist analysis consumes in the long run only around 80% of the planned number of experimental units. In larger trials (n = 36 per group), additional stopping rules for futility lead to the saving of resources of up to 30% compared to block designs. We argue that these savings should be invested to increase sample sizes and hence power, since the currently underpowered experiments in preclinical biomedicine are a major threat to the value and predictiveness in this research domain. [ABSTRACT FROM AUTHOR]

While it is well known that the homogeneity of clinical trial participants often threatens the goal of attaining generalizable knowledge, researchers often cite issues with recruitment, including a lack of interest from participants, shortages of resources, or difficulty accessing particular populations, to explain the lack of diversity within sampling. It is proposed that social media might provide an opportunity to overcome these obstacles through affordable, targeted recruitment advertisements or messages. Recruiters are warned, however, to be cautious using these means, since risks related to privacy and transparency can take on a new hue. [ABSTRACT FROM AUTHOR]

The article discusses studies that described placebo responses, mostly in neurological diseases such as Parkinson's, depression, chronic pain and epilepsy. Topics discussed include research that provides evidence that placebo treatment can produce significant pain relief and flags a biomarker for the response, and a trial wherein patients underwent brain scans before being randomly assigned to receive a placebo or duloxetine drug.

Discovering new medicines is difficult and increasingly expensive. The pharmaceutical industry has responded to this challenge by embracing open innovation to access external ideas. Historically, partnerships were usually bilateral, and the drug discovery process was shrouded in secrecy. This model is rapidly changing. With the advent of the Internet, drug discovery has become more decentralised, bottom-up, and scalable than ever before. The term open innovation is now accepted as just one of many terms that capture different but overlapping levels of openness in the drug discovery process. Many pharmaceutical companies recognise the advantages of revealing some proprietary information in the form of results, chemical tools, or unsolved problems in return for valuable insights and ideas. For example, such selective revealing can take the form of openly shared chemical tools to explore new biological mechanisms or by publicly admitting what is not known in the form of an open call. The essential ingredient for addressing these problems is access to the wider scientific crowd. The business of crowdsourcing, a form of outsourcing in which individuals or organisations solicit contributions from Internet users to obtain ideas or desired services, has grown significantly to fill this need and takes many forms today. Here, we posit that open-innovation approaches are more successful when they establish a reliable framework for converting creative ideas of the scientific crowd into practice with actionable plans. [ABSTRACT FROM AUTHOR]

Failure in cancer drug development exacts heavy burdens on patients and research systems. To investigate inefficiencies and burdens in targeted drug development in cancer, we conducted a systematic review of all prelicensure trials for the anticancer drug, sorafenib (Bayer/Onyx Pharmaceuticals). We searched Embase and MEDLINE databases on October 14, 2014, for prelicensure clinical trials testing sorafenib against cancers. We measured risk by serious adverse event rates, benefit by objective response rates and survival, and trial success by prespecified primary endpoint attainment with acceptable toxicity. The first two clinically useful applications of sorafenib were discovered in the first 2 efficacy trials, after five drug-related deaths (4.6% of 108 total) and 93 total patient-years of involvement (2.4% of 3,928 total). Thereafter, sorafenib was tested in 26 indications and 67 drug combinations, leading to one additional licensure. Drug developers tested 5 indications in over 5 trials each, comprising 56 drug-related deaths (51.8% of 108 total) and 1,155 patient-years (29.4% of 3,928 total) of burden in unsuccessful attempts to discover utility against these malignancies. Overall, 32 Phase II trials (26% of Phase II activity) were duplicative, lacked appropriate follow-up, or were uninformative because of accrual failure, constituting 1,773 patients (15.6% of 11,355 total) participating in prelicensure sorafenib trials. The clinical utility of sorafenib was established early in development, with low burden on patients and resources. However, these early successes were followed by rapid and exhaustive testing against various malignancies and combination regimens, leading to excess patient burden. Our evaluation of sorafenib development suggests many opportunities for reducing costs and unnecessary patient burden in cancer drug development. [ABSTRACT FROM AUTHOR]

Translational research is often afflicted by a fundamental problem: a limited understanding of disease mechanisms prevents effective targeting of new treatments. Seeking to accelerate research advances and reimagine its role in the community, the Montreal Neurological Institute (Neuro) announced in the spring of 2016 that it is launching a five-year experiment during which it will adopt Open Science—open data, open materials, and no patenting—across the institution. The experiment seeks to examine two hypotheses. The first is whether the Neuro’s Open Science initiative will attract new private partners. The second hypothesis is that the Neuro’s institution-based approach will draw companies to the Montreal region, where the Neuro is based, leading to the creation of a local knowledge hub. This article explores why these hypotheses are likely to be true and describes the Neuro’s approach to exploring them. [ABSTRACT FROM AUTHOR]

Publication bias in animal research, its extent, its predictors, and its potential countermeasures are increasingly discussed. Recent reports and conferences highlight the potential strengths of animal study registries (ASRs) in this regard. Others have warned that prospective registration of animal studies could diminish creativity, add administrative burdens, and complicate intellectual property issues in translational research. A literature review and 21 international key-informant interviews were conducted and thematically analyzed to develop a comprehensive matrix of main- and subcategories for potential ASR-related strengths, weaknesses, facilitators, and barriers (SWFBs). We identified 130 potential SWFBs. All stakeholder groups agreed that ASRs could in various ways improve the quality and refinement of animal studies while allowing their number to be reduced, as well as supporting meta-research on animal studies. However, all stakeholder groups also highlighted the potential for theft of ideas, higher administrative burdens, and reduced creativity and serendipity in animal studies. Much more detailed reasoning was captured in the interviews than is currently found in the literature, providing a comprehensive account of the issues and arguments around ASRs. All stakeholder groups highlighted compelling potential strengths of ASRs. Although substantial weaknesses and implementation barriers were highlighted as well, different governance measures might help to minimize or even eliminate their impact. Such measures might include confidentiality time frames for accessing prospectively registered protocols, harmonized reporting requirements across ASRs, ethics reviews, lab notebooks, and journal submissions. The comprehensive information gathered in this study could help to guide a more evidence-based debate and to design pilot tests for ASRs. [ABSTRACT FROM AUTHOR]

Placebo response in the clinical trial setting is poorly understood and alleged to be driven by statistical confounds, and its biological underpinnings are questioned. Here we identified and validated that clinical placebo response is predictable from resting-state functional magnetic-resonance-imaging (fMRI) brain connectivity. This also led to discovering a brain region predicting active drug response and demonstrating the adverse effect of active drug interfering with placebo analgesia. Chronic knee osteoarthritis (OA) pain patients (n = 56) underwent pretreatment brain scans in two clinical trials. Study 1 (n = 17) was a 2-wk single-blinded placebo pill trial. Study 2 (n = 39) was a 3-mo double-blinded randomized trial comparing placebo pill to duloxetine. Study 3, which was conducted in additional knee OA pain patients (n = 42), was observational. fMRI-derived brain connectivity maps in study 1 were contrasted between placebo responders and nonresponders and compared to healthy controls (n = 20). Study 2 validated the primary biomarker and identified a brain region predicting drug response. In both studies, approximately half of the participants exhibited analgesia with placebo treatment. In study 1, right midfrontal gyrus connectivity best identified placebo responders. In study 2, the same measure identified placebo responders (95% correct) and predicted the magnitude of placebo’s effectiveness. By subtracting away linearly modeled placebo analgesia from duloxetine response, we uncovered in 6/19 participants a tendency of duloxetine enhancing predicted placebo response, while in another 6/19, we uncovered a tendency for duloxetine to diminish it. Moreover, the approach led to discovering that right parahippocampus gyrus connectivity predicts drug analgesia after correcting for modeled placebo-related analgesia. Our evidence is consistent with clinical placebo response having biological underpinnings and shows that the method can also reveal that active treatment in some patients diminishes modeled placebo-related analgesia. Trial Registration ClinicalTrials.gov ClinicalTrials.gov [ABSTRACT FROM AUTHOR]

In the aftermath of the Ebola crisis, the global health community has a unique opportunity to reflect on the lessons learned and apply them to prepare the world for the next crisis. Part of that preparation will entail knowing, with greater precision, what the scale and scope of our specific global health challenges are and what resources are needed to address them. However, how can we know the magnitude of the challenge, and what resources are needed without knowing the current status of the world through accurate primary data? Once we know the current status, how can we decide on an intervention today with a predicted impact decades out if we cannot project into that future? Making a case for more investments will require not just better data generation and sharing but a whole new level of sophistication in our analytical capability—a fundamental shift in our thinking to set expectations to match the reality. In this current status of a distributed world, being transparent with our assumptions and specific with the case for investing in global health is a powerful approach to finding solutions to the problems that have plagued us for centuries. [ABSTRACT FROM AUTHOR]

The Global Health 2035 report notes that the “grand convergence”—closure of the infectious, maternal, and child mortality gap between rich and poor countries—is dependent on research and development (R&D) of new drugs, vaccines, diagnostics, and other health tools. However, this convergence (and the R&D underpinning it) will first require an even more fundamental convergence of the different worlds of public health and innovation, where a largely historical gap between global health experts and innovation experts is hindering achievement of the grand convergence in health. The Global Health 2035 report notes that the “grand convergence”—closure of the infectious, maternal, and child mortality gap between rich and poor countries—is dependent on research and development (R&D) of new drugs, vaccines, diagnostics, and other health tools. New tools alone are estimated to deliver a 2% decline each year in the under-5 mortality rate, maternal mortality ratio, and deaths from HIV/AIDS and tuberculosis (TB) []. However, this convergence (and the R&D underpinning it) is unlikely unless we first have an even more fundamental convergence of the parallel worlds of public health and innovation. At the moment, these worlds are often disconnected, with major gaps to be bridged at both the intellectual and practical levels before we can truly reach a grand convergence in health. [ABSTRACT FROM AUTHOR]

: This study identifies an unusual sulfur-based chemical as a novel and specific inhibitor of the tyrosine phosphatase STEP and shows that it can improve the cognitive function of a mouse model of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

: A small molecule inhibitor of the malarial protease Plasmepsin V impairs protein export and cellular remodeling, reducing parasite survival in human erythrocytes. [ABSTRACT FROM AUTHOR]

: Numerous nonsense mutation reporter assays fail to reveal read-through activity for the drug PTC124. [ABSTRACT FROM AUTHOR]

In this Formal Comment, the authors of the recent publication "Large-scale investigation of the reasons why potentially important genes are ignored" maintain that it can be read as an opportunity to explore the unknown. [ABSTRACT FROM AUTHOR]