A snapshot of translational research funded by the National Institutes of Health (NIH): A case study using behavioral and social science research awards and Clinical and Translational Science Awards funded publications

Analysis of cognitive framework and biomedical translation of tissue engineering in otolaryngology

Tissue engineering is a relatively recent research area aimed at developing artificial tissues that can restore, maintain, or even improve the anatomical and/or functional integrity of injured tissues. Otolaryngology, as a leading surgical specialty in head and neck surgery, is a candidate for the use of these advanced therapies and medicinal products developed. Nevertheless, a knowledge-based analysis of both areas together is still needed. The dataset was retrieved from the Web of Science database from 1900 to 2020. SciMAT software was used to perform the science mapping analysis and the data for the biomedical translation identification was obtained from the iCite platform. Regarding the analysis of the cognitive structure, we find consolidated research lines, such as the generation of cartilage for use as a graft in reconstructive surgery, reconstruction of microtia, or the closure of perforations of the tympanic membrane. This last research area occupies the most relevant clinical translation with the rest of the areas presenting a lower translational level. In conclusion, Tissue engineering is still in an early translational stage in otolaryngology, otology being the field where most advances have been achieved. Therefore, although otolaryngologists should play an active role in translational research in tissue engineering, greater multidisciplinary efforts are required to promote and encourage the translation of potential clinical applications of tissue engineering for routine clinical use.

Exploring the clinical translation intensity of papers published by the world's top scientists in basic medicine

The extent to which basic medical research is translated into clinical practice is a topic of interest to all stakeholders. In this study, we assessed the clinical translation intensity of papers published by scientists who have made outstanding contributions to the field of basic medicine (Lasker Prize winners for Basic Medical Research). Approximate Potential for Translation (APT), Translational science scores (TS), and Citations by clinical research (Cited by Clin.) were analyzed as dependent variables. A traditional citation indicator was used as a reference (relative citation ratio, RCR). In order to examine the correlation between these different indicators and the characteristics of the paper, the author, and the institution. we used nonparametric tests, Spearman correlations, ordinal least squares regressions (OLS), quantile regressions, and zero-inflated negative binomial regression methods. We found that among the basic medical research papers published by Lasker Basic Medicine Award winners, (1) 20% are cited by clinical research; 11.6% of the papers were more valuable for clinical research than basic research; 12.8% have a probability of more than 50% to be cited in future clinical studies; (2) Spearman correlations were conducted among APT, TS, Cited by Clin., RCR, and all of the other continuous variables. There is a significant, positive, low to moderate correlation between APT, TS, and Cited by Clin (APT and TS: r = 0.549, p < 0.01; APT and Cited by Clin: r = 0.530, p < 0.01; TS and Cited by Clin: r = 0.383, p < 0.01). However, the relationship between RCR and the three indicators of clinical translation intensity was not consistent. APT was positively correlated with RCR (r = 0.553, p < 0.01). Cited by Clin. is weakly positively correlated with RCR (r = 0.381, p < 0.01). There is almost no correlation between TS and RCR (r = 0.184, p < 0.01). (3) Publication age, primary research paper, multidisciplinary science, number of disciplines, authors, institutions, funded projects, references, length of the title, length of paper, physical age, gender, nationality, institutional type, Nobel Prize have a significant relationship with 1 to 3 types of clinical translation intensity measures. In a sample of basic medical research papers published by the world's top scientists in basic medicine, we came to the following conclusions: the three indicators, APT, TS and Cited by Clin., measured the clinical translation intensity of the papers from different perspectives. They are both related to each other and have their own characteristics. In a sample of basic medical research papers published by the world's top scientists in basic medicine, characteristics at the paper, winner, and institution level significantly correlated with the measures of clinical translation intensity. Gender effect on the clinical translation intensity of papers was confirmed. Traditional citation-based indicators and translational-focused indicators measure academic impact and clinical impact respectively. There is a certain degree of disconnect between them. Two types of indicators should be used in combination in future assessments of basic medical research.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11192-023-04634-4.

The rocky road to translational science: An analysis of Clinical and Translational Science Awards

Studies point out that the productivity decline in biomedicine is in significant part due to difficulties in translating basic science into clinical application. To promote translational research, the US NIH launched the Clinical and Translational Science Awards (CTSA) program in 2006. Prior evaluations of the CTSA program often assumed that the key to translation is inter-organizational collaboration or multidisciplinarity; hence, changes in either of these were measured as evidence of translational science. However, using novel measures of translational science, this study examines the relationship between CTSAs and translational science per se. We define ‘translational science’ as basic science that has translational features, and we employ two distinct, complementary measures of translational science based on publication data. Using 115 Carnegie R1 universities and their translational science publications, we find that receiving a CTSA does not obviously cause receiving institutions to conduct more translational science. Furthermore, our Principal Investigator-level analysis shows that those who received direct support from the CTSA program had already generated more translational science than others and that their research behavior did not change significantly after receiving a CTSA. Future evaluation research can adopt our novel measures of translational science and evaluation research design in the assessment of translational research activities. Finally, we conclude with a discussion of the implications of our findings for science governance.

Methods for identifying biomedical translation: a systematic review

Translational medicine is an important area of biomedicine, and has significantly facilitated the development of biomedical research. Despite its relevance, there is no consensus on how to evaluate its progress and impact. A systematic review was carried out to identify all the methods to evaluate translational research. Seven methods were found according to the established criteria to analyze their characteristics, advantages, and limitations. They allow us to perform this type of evaluation in different ways. No relevant advantages were found between them; each one presented its specific limitations that need to be considered. Nevertheless, the Triangle of Biomedicine could be considered the most relevant method, concerning the time since its publication and usefulness. In conclusion, there is still a lack of a gold-standard method for evaluating biomedical translational research.

Improving Translational Paradigms in Drug Discovery and Development

Despite improved knowledge regarding disease causality, new drug targets, and enabling technologies, the attrition rate for compounds entering clinical trials has remained consistently high for several decades, with an average 90% failure rate. These failures are manifested in an inability to reproduce efficacy findings from animal models in humans and/or the occurrence of unexpected safety issues, and reflect failures in T1 translation. Similarly, an inability to sequentially demonstrate compound efficacy and safety in Phase IIa, IIb, and III clinical trials represents failures in T2 translation. Accordingly, T1 and T2 translation are colloquially termed 'valleys of death'. Since T2 translation dealt almost exclusively with clinical trials, T3 and T4 translational steps were added, with the former focused on facilitating interactions between laboratory- and population-based research and the latter on 'real world' health outcomes. Factors that potentially lead to T1/T2 compound attrition include: the absence of biomarkers to allow compound effects to be consistently tracked through development; a lack of integration/'de-siloing' of the diverse discipline-based and technical skill sets involved in drug discovery; the industrialization of drug discovery, which via volume-based goals often results in quantity being prioritized over quality; inadequate project governance and strategic oversight; and flawed decision making based on unreliable/irreproducible or incomplete data. A variety of initiatives have addressed this problem, including the NIH National Center for Advancing Translational Sciences (NCATS), which has focused on bringing an unbiased academic perspective to translation, to potentially revitalize the process. This commentary provides an overview of the basic concepts involved in translation, along with suggested changes in the conduct of biomedical research to avoid valleys of death, including the use of Translational Scoring as a tool to avoid translational attrition and the impact of the FDA Accelerated Approval Pathway in lowering the hurdle for drug approval. © 2021 Wiley Periodicals LLC.

Participatory needs assessment and action planning for a clinical and translational research network

The goal of this study was to assess the utility of participatory needs assessment processes for continuous improvement of developing clinical and translational research (CTR) networks. Our approach expanded on evaluation strategies for CTR networks, centers, and institutes, which often survey stakeholders to identify infrastructure or resource needs, using the case example of the Great Plains IDeA-CTR Network. Our 4-stage approach (i.e., pre-assessment, data collection, implementation of needs assessment derived actions, monitoring of action plan) included a member survey (n = 357) and five subsequent small group sessions (n = 75 participants) to better characterize needs identified in the survey and to provide actionable recommendations. This participatory, mixed-methods needs assessment and strategic action planning process yielded 11 inter-related recommendations. These recommendations were presented to the CTR steering committee as inputs to develop detailed, prioritized action plans. Preliminary evaluation shows progress towards improved program capacity and effectiveness of the network to respond to member needs. The participatory, mixed-methods needs assessment and strategic planning process allowed a wide range of stakeholders to contribute to the development of actionable recommendations for network improvement, in line with the principles of team science.

A Bibliometric Measure of Translational Science

Science funders are increasingly requiring evidence of the broader impacts of even basic research. Initiatives such as NIH's CTSA program are designed to shift the research focus toward more translational research. However, tracking the effectiveness of such programs depends on developing indicators that can track the degree to which basic research is influencing clinical research. We propose a new bibliometric indicator, the TS score, that is relatively simple to calculate, can be implemented at scale, is easy to replicate, and has good reliability and validity properties. This indicator is broadly applicable in settings where the goal is to estimate the degree to which basic research is used in more applied downstream research, relative to use in basic research. The TS score should be of use for a variety of policy analysis and research evaluation purposes.

Scope, Influence, and Interdisciplinary Collaboration: The Publication Portfolio of the NIH Clinical and Translational Science Awards (CTSA) Program From 2006 Through 2017

The Clinical and Translational Science Awards (CTSA) program sponsors an array of innovative, collaborative research. This study uses complementary bibliometric approaches to assess the scope, influence, and interdisciplinary collaboration of publications supported by single CTSA hubs and those supported by multiple hubs. Authors identified articles acknowledging CTSA support and assessed the disciplinary scope of research areas represented in that publication portfolio, their citation influence, interdisciplinary overlap among research categories, and characteristics of publications supported by multihub collaborations. Since 2006, CTSA hubs supported 69,436 articles published in 4,927 journals and 189 research areas. The portfolio is well distributed across diverse research areas with above-average citation influence. Most supported publications involved clinical/health sciences, for example, neurology and pediatrics; life sciences, for example, neuroscience and immunology; or a combination of the two. Publications supported by multihub collaborations had distinct content emphasis, stronger citation influence, and greater interdisciplinary overlap. This study characterizes the CTSA consortium's contributions to clinical and translational science, identifies content areas of strength, and provides evidence for the success of multihub collaborations. These methods lay the foundation for future investigation of the best policies and priorities for fostering translational science and allow hubs to understand their progress benchmarked against the larger consortium.