Obstacles and opportunities in translational research

Self-perception of the acquisition of transferable competencies by the participants in a research congress for undergraduate students: A cross-sectional study

Context

Several curricular initiatives have been developed to improve the acquisition of research competencies by Health Science students.

Objectives

To know how students self-perceived of whether their participation in the XIV National Research Congress for Undergraduate Students of Health Sciences had helped them in the acquisition of 36 research-related transferable competencies (TCs) common to Health Science degrees.

Methods

A survey design (Cronbach's alpha = 0.924), using a self-administered questionnaire, was conducted among undergraduate students who voluntarily participated in the Congress. Data analysis was performed using SPSS 25 and Statgraphics 19. Statistical significance was considered for P < 0.05.

Results

Eighty-one students from 12 Health Science degree programs responded. Key findings are presented in a structured manner, using a Likert-5 scale. Twenty-five of the competencies surveyed obtained an average ≥ 4 highlighting: "Critically evaluate and know how to use sources of clinical and biomedical information to obtain, organize, interpret, and communicate scientific and health information"; "To be able to formulate hypotheses, collect and critically evaluate information for problem solving, following the scientific method", "Critical analysis and research" and "Communicate effectively and clearly, orally and in writing with other professionals". Significance was found in 15 competencies. The development of the competencies "Teamwork", "Critical reasoning" and "Analysis and synthesis abilities" was considered to be of greater "personal utility" by the respondents.

Conclusion

Participation in this event contributed to the development of research-related TCs, critical analysis and information management and communication, especially in relation to learning the sources of clinical and biomedical information, to know, following the scientific method, how to formulate hypotheses that allow students to solve problems in their professional activity. The experience was significantly influenced by the respondents' year, the type of participation in the event and the gender of the students. Limitations and suggestions regarding future research are discussed to encourage further exploration of the topic.

Updating and evaluating a research best practices training course for social and behavioral research professionals

Introduction

The clinical and translational research workforce involved in social and behavioral research (SBR) needs to keep pace with clinical research guidance and regulations. Updated information and a new module on community and stakeholder engagement were added to an existing SBR training course. This article presents evaluation findings of the updated course for the Social and Behavioral Workforce.

Methods and Materials

Participants working across one university were recruited. Course completers were sent an online survey to evaluate the training. Some participants were invited to join in a focus group to discuss the application of the training to their work. We performed descriptive statistics and conducted a qualitative analysis on focus group data.

Results

There were 99 participants from diverse backgrounds who completed the survey. Most reported the training was relevant to their work or that of the study teams they worked with. Almost half (46%) indicated they would work differently after participating. Respondents with community or stakeholder engaged research experience vs. those without were more likely to report that the new module was relevant to study teams they worked with (t = 5.61, p = 0.001), and that they would work differently following the training (t = 2.63, p = 0.01). Open-ended survey responses (n = 99) and focus group (n = 12) data showed how participants felt their work would be affected by the training.

Conclusion

The updated course was rated highly, particularly by those whose work was related to the new course content. This course provides an up-to-date resource for the training and development for the Social and Behavioral Workforce.

Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study

Background

A clinical decision support system (CDSS) is recognized as a technology that enhances clinical efficacy and safety. However, its full potential has not been realized, mainly due to clinical data standards and noninteroperable platforms.

Objective

In this paper, we introduce the common data model–based intelligent algorithm network environment (CANE) platform that supports the implementation and deployment of a CDSS.

Methods

CDSS reasoning engines, usually represented as R or Python objects, are deployed into the CANE platform and converted into C# objects. When a clinician requests CANE-based decision support in the electronic health record (EHR) system, patients’ information is transformed into Health Level 7 Fast Healthcare Interoperability Resources (FHIR) format and transmitted to the CANE server inside the hospital firewall. Upon receiving the necessary data, the CANE system’s modules perform the following tasks: (1) the preprocessing module converts the FHIRs into the input data required by the specific reasoning engine, (2) the reasoning engine module operates the target algorithms, (3) the integration module communicates with the other institutions’ CANE systems to request and transmit a summary report to aid in decision support, and (4) creates a user interface by integrating the summary report and the results calculated by the reasoning engine.

Results

We developed a CANE system such that any algorithm implemented in the system can be directly called through the RESTful application programming interface when it is integrated with an EHR system. Eight algorithms were developed and deployed in the CANE system. Using a knowledge-based algorithm, physicians can screen patients who are prone to sepsis and obtain treatment guides for patients with sepsis with the CANE system. Further, using a nonknowledge-based algorithm, the CANE system supports emergency physicians’ clinical decisions about optimum resource allocation by predicting a patient’s acuity and prognosis during triage.

Conclusions

We successfully developed a common data model–based platform that adheres to medical informatics standards and could aid artificial intelligence model deployment using R or Python.

Reproducibility, relevance and reliability as barriers to efficient and credible biomedical technology translation

Biomedical research accuracy and relevance for improving healthcare are increasingly identified as costly problems. Basic research data quality, reporting and methodology, and reproducibility are common factors implicated in this challenge. Preclinical models of disease and therapy, largely conducted in rodents, have known deficiencies in replicating most human conditions. Their translation to human results is acknowledged to be poor for decades. Clinical data quality and quantity is also recognized as deficient; gold standard randomized clinical trials are expensive. Few solid conclusions from clinical studies are replicable and many remain unpublished. The translational pathway from fundamental biomedical research through to innovative solutions handed to clinical practitioners is therefore highly inefficient and costly in terms of wasted resources, early claims from fundamental discoveries never witnessed in humans, and few new, improved solutions available clinically for myriad diseases. Improving this biomedical research strategy and resourcing for reliability, translational relevance, reproducibility and clinical impact requires careful analysis and consistent enforcement at both funding and peer review levels.

Evaluation framework to guide implementation of AI systems into healthcare settings

Objectives

To date, many artificial intelligence (AI) systems have been developed in healthcare, but adoption has been limited. This may be due to inappropriate or incomplete evaluation and a lack of internationally recognised AI standards on evaluation. To have confidence in the generalisability of AI systems in healthcare and to enable their integration into workflows, there is a need for a practical yet comprehensive instrument to assess the translational aspects of the available AI systems. Currently available evaluation frameworks for AI in healthcare focus on the reporting and regulatory aspects but have little guidance regarding assessment of the translational aspects of the AI systems like the functional, utility and ethical components.

Methods

To address this gap and create a framework that assesses real-world systems, an international team has developed a translationally focused evaluation framework termed ‘Translational Evaluation of Healthcare AI (TEHAI)’. A critical review of literature assessed existing evaluation and reporting frameworks and gaps. Next, using health technology evaluation and translational principles, reporting components were identified for consideration. These were independently reviewed for consensus inclusion in a final framework by an international panel of eight expert.

Results

TEHAI includes three main components: capability, utility and adoption. The emphasis on translational and ethical features of the model development and deployment distinguishes TEHAI from other evaluation instruments. In specific, the evaluation components can be applied at any stage of the development and deployment of the AI system.

Discussion

One major limitation of existing reporting or evaluation frameworks is their narrow focus. TEHAI, because of its strong foundation in translation research models and an emphasis on safety, translational value and generalisability, not only has a theoretical basis but also practical application to assessing real-world systems.

Conclusion

The translational research theoretic approach used to develop TEHAI should see it having application not just for evaluation of clinical AI in research settings, but more broadly to guide evaluation of working clinical systems.

Collaborative relationships in translational medical research among Chinese clinicians: an internet-based cross-sectional survey

BACKGROUND

This study aimed to explore the collaborative relationship in translational medical research from the perspective of clinicians in China. The findings are expected to help practitioners optimize and experience the greatest advantages of collaboration.

METHODS

We conducted a national internet-based survey from July 29 to October 12, 2020. Of the 806 responses, 804 were completed with valid responses (valid response rate = 99.8%). The collected data were presented as descriptive statistics and analyzed using nonparametric tests (including the Wilcoxon rank test and Kruskal-Wallis H test) and stepwise logistic regression.

RESULTS

Of the 804 participants, 733 were either willing or very willing to collaborate in translational medical research. Clinicians' willingness was influenced by their current research type, role in current translational medical research, burdens of their present research, preferred partners for collaboration at the institutional or individual level, and preferences for independent or dependent relationships.

CONCLUSIONS

Clinicians should evaluate their time, role, burdens, personal preferences for research relationships, and appropriate partners based on their current translational medical research and its goals, before deciding to collaborate.

Medicamentos e pesquisa translacional: etapas, atores e políticas de saúde no contexto brasileiro

RESUMO A pesquisa translacional surgiu com o objetivo de reduzir o tempo entre a pesquisa básica e a sua aplicação clínica. Para os medicamentos, esse tempo pode chegar a décadas, o que denota a necessidade de se avaliarem possíveis barreiras por meio da pesquisa translacional. Objetivou-se revisar a literatura para identificar as etapas da pesquisa translacional, bem como os atos normativos, as políticas públicas de saúde e os principais atores no contexto brasileiro. Para a identificação de modelos da pesquisa translacional, realizou-se revisão com busca sistemática nas bases PubMed, Embase e Lilacs, sendo selecionadas 23 publicações. Sítios eletrônicos oficiais foram consultados para o levantamento das políticas e dos atores. Como resultados, a literatura inicialmente apontava uma etapa (da bancada ao leito), incorporando recentemente a síntese de pesquisas e a avaliação de impacto na saúde pública como etapas adicionais. Diversos atores são transversais na pesquisa translacional, como universidades, instituições de pesquisa e agências de fomento. Observa-se que o Brasil instituiu políticas importantes nas áreas de assistência farmacêutica, pesquisa, ciência, tecnologia e inovação em saúde, o que pode potencialmente integrar recursos, atores e esforços visando à aplicação prática de resultados para melhorar as condições de saúde e de vida da população.

KETOS: Clinical decision support and machine learning as a service - A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services

Background and objective

To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in this work implements a tool for researchers allowing them to perform statistical analyses and deploy resulting models in a secure environment.

Methods

The proposed system uses Docker virtualization to provide researchers with reproducible data analysis and development environments, accessible via Jupyter Notebook, to perform statistical analysis and develop, train and deploy models based on standardized input data. The platform is built in a modular fashion and interfaces with web services using the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard to access patient data. In our prototypical implementation we use an OMOP common data model (OMOP-CDM) database. The architecture supports the entire research lifecycle from creating a data analysis environment, retrieving data, and training to final deployment in a hospital setting.

Results

We evaluated the platform by establishing and deploying an analysis and end user application for hemoglobin reference intervals within the University Hospital Erlangen. To demonstrate the potential of the system to deploy arbitrary models, we loaded a colorectal cancer dataset into an OMOP database and built machine learning models to predict patient outcomes and made them available via a web service. We demonstrated both the integration with FHIR as well as an example end user application. Finally, we integrated the platform with the open source DataSHIELD architecture to allow for distributed privacy preserving data analysis and training across networks of hospitals.

Conclusion

The KETOS platform takes a novel approach to data analysis, training and deploying decision support models in a hospital or healthcare setting. It does so in a secure and privacy-preserving manner, combining the flexibility of Docker virtualization with the advantages of standardized vocabularies, a widely applied database schema (OMOP-CDM), and a standardized way to exchange medical data (FHIR).

Synthetic Biology and the Translational Imperative

Advances at the interface between the biological sciences and engineering are giving rise to emerging research fields such as synthetic biology. Harnessing the potential of synthetic biology requires timely and adequate translation into clinical practice. However, the translational research enterprise is currently facing fundamental obstacles that slow down the transition of scientific discoveries from the laboratory to the patient bedside. These obstacles including scarce financial resources and deficiency of organizational and logistic settings are widely discussed as primary impediments to translational research. In addition, a number of socio-ethical considerations inherent in translational research need to be addressed. As the translational capacity of synthetic biology is tightly linked to its social acceptance and ethical approval, ethical limitations may-together with financial and organizational problems-be co-determinants of suboptimal translation. Therefore, an early assessment of such limitations will contribute to proactively favor successful translation and prevent the promising potential of synthetic biology from remaining under-expressed. Through the discussion of two case-specific inventions in synthetic biology and their associated ethical implications, we illustrate the socio-ethical challenges ahead in the process of implementing synthetic biology into clinical practice. Since reducing the translational lag is essential for delivering the benefits of basic biomedical research to society at large and promoting global health, we advocate a moral obligation to accelerating translational research: the "translational imperative."

Translating Behavior Analysis: a Spectrum Rather than a Road Map

Much has been written about the potential benefits of translational research in behavior analysis, but a lack of consensus about what constitutes "translational" creates a barrier to effective knowledge-practice innovation within the discipline and across other sciences. We propose a tiered system, adapted from a biomedical translational pathway, for classifying behavior analysis research on a basic-applied spectrum. Tier 0 is blue sky basic science in which the subjects, behaviors, stimuli, and settings are selected for convenience. Tier 1 is use-inspired basic science with a socially important end game and research subject. Tier 2 is solution-oriented research that attempts to solve a specific problem in a socially important subject, but 1 or more aspects of the research are selected for purposes of experimental control rather than social importance. Tier 3 is applied behavior analysis research that studies a problem of social significance for the subject and involves behaviors, stimuli, and settings that are socially important. Tier 4 is impact assessment in which behavioral technology is applied with a direct benefit to society. We provide examples of behavior-analytic research in each tier and evaluate the potential benefits of organizing behavior analysis in this way.

Why We Are Still Not Acting to Save the World: the Upward Challenge of a Post-Skinnerian Behavior Science

Basic research on derived stimulus relations reveals many effects that may be useful in understanding and resolving significant and complex societal problems. Applied research on derived stimulus relations has done little to fulfill this promise, focusing instead mainly on simple demonstrations of well-known phenomena. We trace the research tradition of derived stimulus relations from laboratory to wide-scale implementation, and put forward several suggestions for how to progress effective and impactful research on derived relational responding to issues of immense social importance. To advance a science of behavior from relative social obscurity to the developing world-saving technologies, we must evaluate our own behavior as scientists in the grander social context.

Five Dimensions of Research Ethics: A Stakeholder Framework for Creating a Climate of Research Integrity

The authors explore five dimensions of research ethics: (1) normative ethics, which includes meta-ethical questions; (2) compliance with regulations, statutes, and institutional policies; (3) the rigor and reproducibility of science; (4) social value; and (5) workplace relationships. Each of the five dimensions is important not only because it addresses an aspect of good research done in a good manner but also because it addresses the concerns of key stakeholders in the research enterprise. The five-dimension framework can guide institutions as they answer three questions central to any research ethics program: (1) Who should champion research ethics? (2) What should interventions look like? and (3) Who should participate in the interventions? The framework is valuable because the answers to these three questions are radically different depending on the dimension under consideration. An expanded vision of research ethics does not entail that institutions should require additional online training or approvals from institutional review boards. However, without acknowledging all five dimensions, programs risk missing an important aspect of research ethics or ignoring the interests of important stakeholders.

Emerging Trends in Micro- and Nanoscale Technologies in Medicine: From Basic Discoveries to Translation

We discuss the state of the art and innovative micro- and nanoscale technologies that are finding niches and opening up new opportunities in medicine, particularly in diagnostic and therapeutic applications. We take the design of point-of-care applications and the capture of circulating tumor cells as illustrative examples of the integration of micro- and nanotechnologies into solutions of diagnostic challenges. We describe several novel nanotechnologies that enable imaging cellular structures and molecular events. In therapeutics, we describe the utilization of micro- and nanotechnologies in applications including drug delivery, tissue engineering, and pharmaceutical development/testing. In addition, we discuss relevant challenges that micro- and nanotechnologies face in achieving cost-effective and widespread clinical implementation as well as forecasted applications of micro- and nanotechnologies in medicine.

Stakeholder views on participant selection for first-in-human trials in cancer nanomedicine

BACKGROUND

Participant selection for first-in-human (fih) trials involves complex decisions. The trial design makes it unlikely that participants will receive clinically relevant therapeutic benefit, but they are likely to experience risks of various magnitudes and types. The aim of the present paper was to describe and discuss the views of investigators and ethics committee members about the choice of trial participants for fih trials in cancer nanomedicine.

METHODS

We drew insights from an exploratory qualitative study involving thematic analysis of 46 in-depth interviews with key stakeholders in Europe and North America involved in fih nanomedicine trials. The present work draws on subset of 21 interviews with investigators and ethics committee members who have either conducted or reviewed a fih cancer nanomedicine trial or are planning one.

RESULTS

Investigators and ethics committee members are aware of the ethics standards for recruiting patients with end-stage cancer into fih trials, but they nonetheless question the practice and provide reasons against it.

CONCLUSIONS

Although it is a standard and ethically accepted practice to enrol patients with end-stage cancer and no treatment options into fih trials of investigational chemotherapeutic molecules, doing so can threaten the validity and generalizability of the trials, thereby weakening translational research. Another possibility is to stratify and include patients with less advanced disease who demonstrate certain biomarkers or cancer genotypes and who have a disease profile similar to that tested in preclinical studies. The latter approach could be a step toward personalized medical research and targeted drug development. Such a patient selection approach requires multi-stakeholder discussion to reach scientific and ethics consensus.

Optimising Translational Research Opportunities: A Systematic Review and Narrative Synthesis of Basic and Clinician Scientists' Perspectives of Factors Which Enable or Hinder Translational Research

INTRODUCTION

Translational research is central to international health policy, research and funding initiatives. Despite increasing use of the term, the translation of basic science discoveries into clinical practice is not straightforward. This systematic search and narrative synthesis aimed to examine factors enabling or hindering translational research from the perspective of basic and clinician scientists, a key stakeholder group in translational research, and to draw policy-relevant implications for organisations seeking to optimise translational research opportunities.

METHODS AND RESULTS

We searched SCOPUS and Web of Science from inception until April 2015 for papers reporting scientists' views of the factors they perceive as enabling or hindering the conduct of translational research. We screened 8,295 papers from electronic database searches and 20 papers from hand searches and citation tracking, identifying 26 studies of qualitative, quantitative or mixed method designs. We used a narrative synthesis approach and identified the following themes: 1) differing concepts of translational research 2) research processes as a barrier to translational research; 3) perceived cultural divide between research and clinical care; 4) interdisciplinary collaboration as enabling translation research, but dependent on the quality of prior and current social relationships; 5) translational research as entrepreneurial science. Across all five themes, factors enabling or hindering translational research were largely shaped by wider social, organisational, and structural factors.

CONCLUSION

To optimise translational research, policy could consider refining translational research models to better reflect scientists' experiences, fostering greater collaboration and buy in from all types of scientists. Organisations could foster cultural change, ensuring that organisational practices and systems keep pace with the change in knowledge production brought about by the translational research agenda.

Thomas Willis, a pioneer in translational research in anatomy (on the 350th anniversary of Cerebri anatome)

The year 2014 marked the 350th anniversary of the publication in London of Cerebri anatome, a ground-breaking work of neuroscience heavily influenced by the political and cultural context of Baroque Europe and mid-17th century England. This article aims to review the work of the English physician and anatomist Thomas Willis, specifically with regard to the contents of his Cerebri anatome. Willis's academic and professional career was influenced by the turbulent period of the English Civil War during which he studied medicine. Willis went from chemistry to dissection arguably because of his need to justify the body-brain-soul relationship. As a result, he became a fellow of a select club of eminent experimentalists, and afterward was a Fellow of the Royal Society. Later on, he went to London, leaving the academic life to dedicate himself fully to the profession of medicine. As a physician, Willis did not base his practice on aphorisms but on a 'bench to bedside' approach to medicine, while studying neuroanatomy--covering embryology, comparative anatomy and pathological anatomy--as a basis for the comprehension of neurological pathology. He developed innovative anatomical methods for the preservation and dissection of the brain, injection of coloured substances and illustration of his findings. In Cerebri anatome, Willis recognized the cerebral cortex as the substrate of cognition. He also claimed that the painful stimuli came from the meninges, but not from the brain itself. He explained for the first time the pathological and functional meaning of the brain's circular arterial anastomosis, which is named after him. He also specified some features of the cranial origin of the sympathetic nerves and coined the term 'neurologie'. Cerebri anatome marked the transition between the mediaeval and modern notions of brain function, and thus it is considered a cornerstone of clinical and comparative anatomy of the nervous system. The new contributions and methods employed by Willis justify his place as a father of neurology and a pioneer of translational research.

Rethinking biobanking and translational medicine in the Netherlands: how the research process stands to matter for patient care

Biobanking has been identified as one of the key components of translational medicine, and while current models for translation tend to focus their attention on how the products of research projects are fed back into health-care practices, we suggest that in addition to that the research process itself can have beneficial effects on the delivery of high-quality health care by streamlining diagnostic and follow-up protocols, reduced patient waiting times, and facilitating data comparison across patients. This Viewpoint is based on experiences with, and observations of, the neurodegenerative component of a clinical biobanking initiative in the Netherlands called the Parelsnoer Institute (PSI), which links all eight of the University Medical Centers for harmonized and standardized collection and storage processes for multiple disease conditions.

Challenges and opportunities: what we are learning from the clinical natalizumab experience

The approval of natalizumab for relapsing forms of multiple sclerosis, and the subsequent voluntary suspension of its use due to an unexpected viral infection, is a cautionary tale of how much we have to learn about how to prioritize and perform the necessary research and development of novel therapeutics for human diseases, the ethics of placebo-controlled trials and the relationships between researchers, regulatory authorities and the pharmaceutical industry.

Translational research policies: disruptions and continuities in biomedical innovation systems in Austria, Finland and Germany

Increasing the rate of biomedical research that is relevant to clinical innovation has been an intensifying concern of the research community and of policy-makers. In response, some of these actors have recently promoted varied approaches they label as translational research (TR) and translational medicine. This movement started in the USA in the early 1990s, and has since evolved to encompass large and ambitious initiatives. Its advocates contend that the productivity of biomedical innovation systems can be bolstered by: (1) the extension of large-scale development collaborations; (2) the strengthening of clinical experimental platforms; (3) training and supporting dedicated human capital; (4) achieving higher collective coordination of research teams than was previously common practice. In this paper, we examine to which extent these objectives have been put into practice by communities of biomedical actors and policymakers, by characterizing current translational initiatives in three European countries—Austria, Finland and Germany. This research draws on an analysis of policy documents and 26 semi-structured interviews conducted with policy-makers and TR advocates from these countries. Traditions of science and technology policy-making in each country have made them differentially receptive to the TR movement. German biomedical actors have most fully put into practice TR propositions, while Finland has seen policy-level debate of the notions but little in the way of concrete implementation and Austria appears to be a middle case.

A road map to Translational Medicine in Qatar and a model for the world

Translational Medicine (TM) in Qatar is part of a concerted effort of the Qatari medical and scientific leadership supported by a strong political will by Qatari authorities to deliver world-class health care to Qatari residents while participating in the worldwide quest to bridge the gap between bench-to-bedside-to-community. TM programs should embrace the Qatar National vision for research to become an international hub of excellence in research and development, based on intellectual merit, contributing to global knowledge and adhering to international standards, to innovate by translating new and original ideas into useful applications, to be inclusive at the national and international level, to build and maintain a competitive and diversified economy and ultimately improve the health and well-being of the Qatar’s population. Although this writing focuses on Qatar, we hope that the thoughts expressed here may be of broader use for the development of any TM program particularly in regions where an established academic community surrounded by a rich research infrastructure and/or a vibrant biotechnology enterprise is not already present.

A decade plus of translation: what do we understand?

It has been more than a decade that the term "Translational" (medicine, Research, Science) has trickled through the minds of academics, clinicians, business persons, regulators, policy makers, patients and their families, patient advocates, politicians and the public. Although the term means different things for different stake holders, it reflect and eagerness to see a fruitful outcome of the resources invested in biotechnology to benefit primarily the patients but also provide financial return for those who invested. Skeptics remain who feel the concept if abused by those attempting to deviate funds for basic or clinical research to a new domain performing similar tasks under a different egida. In reality, translational sciences are not different in scope any from previous efforts to focus the goals of research toward the relevant object of helping the disabled. The difference is that, in recent decades, awareness has risen about the difficulties of reaching this goal. In particular, it has become clear that the difficulties are not limited to scientific challenges, but to a myriad of hurdles that make testing and licensing of novel concepts unnecessarily burdensome. Moreover, it was recognized that the infrastructure to support clinical research is frequently outdated and inappropriate. The biggest hurdle, however, remains the cost and the length of clinical testing that could prolong of decades the application of even the most successful treatments. As for any expanding field, a plethora of journals has appeared with "Translational" in their title. This is a positive sign of the growth in interest for the field and the need to respond to a need for editorial boards competent in the challenges of judging clinical testing. In this editorial, we will discuss the meaning of translational medicine, its goals and needs; we will summarize the remaining challenges and will provide a personal overview of the strategies that remain to be implemented.

Unraveling the autoimmune translational research process layer by layer

Autoimmune diseases have a complex etiology and despite great progress having been made in our comprehension of these disorders, there has been limited success in the development of approved medications based on these insights. Development of drugs and strategies for application in translational research and medicine are hampered by an inadequate molecular definition of the human autoimmune phenotype and the organizational models that are necessary to clarify this definition.

Prospects for translational regenerative medicine

Translational medicine is an evolutional concept that encompasses the rapid translation of basic research for use in clinical disease diagnosis, prevention and treatment. It follows the idea "from bench to bedside and back", and hence relies on cooperation between laboratory research and clinical care. In the past decade, translational medicine has received unprecedented attention from scientists and clinicians and its fundamental principles have penetrated throughout biomedicine, offering a sign post that guides modern medical research toward a patient-centered focus. Translational regenerative medicine is still in its infancy, and significant basic research investment has not yet achieved satisfactory clinical outcomes for patients. In particular, there are many challenges associated with the use of cell- and tissue-based products for clinical therapies. This review summarizes the transformation and global progress in translational medicine over the past decade. The current obstacles and opportunities in translational regenerative medicine are outlined in the context of stem cell therapy and tissue engineering for the safe and effective regeneration of functional tissue. This review highlights the requirement for multi-disciplinary and inter-disciplinary cooperation to ensure the development of the best possible regenerative therapies within the shortest timeframe possible for the greatest patient benefit.

The trouble with translational medicine

Increasingly more reports in the literature are reflecting on the hurdles of effective translation from promising results of biomedical research into useful therapeutics. A recurrent theme is that good intentions are frustrated by extrinsic factors upon which 'translationalists' have little control. It is possible that the problem resides, at least in part, within the translational community itself, which has failed to prioritize the steps required to approach the problem systematically. Most importantly, there is disproportionate emphasis on bench-to-bedside efforts, rather than confronting a priori the need to increase the understanding of human pathophysiology. Thus, therapeutic concepts based on experimental conditions that may not and indeed often do not represent the nature of human genetics lead to drug development that is not sufficiently applicable to the human condition. The damage is then amplified when these ill-fated concepts are tested in clinical trials at great cost. The use of surrogate biomarkers that could allow early assessment of efficacy currently requires long-term assessment of clinical benefit. This can delay by years or decades essential feedback about clinical efficacy. Moreover, scant effort is applied to learning whether a drug has achieved its biological endpoint, or why it failed its clinical endpoint. Thus, the feedback loop is not only delayed, but is often uninformative. As a consequence, researchers continue to produce novel therapeutic candidates based on experimental models without the essential benefit of lessons learned from previous failures. Biomedical research will succeed when drug development is guided by experience gained through informative clinical trials with the purpose of not only testing the effectiveness of treatment but also providing mechanistic insights into the differences between expected and observed results. This can only be achieved through the courageous effort of the research community to change the way biomedical research is funded, published and rewarded.

Prediction of exposure-response relationships to support first-in-human study design

In drug development, phase 1 first-in-human studies represent a major milestone as the drug moves from preclinical discovery to clinical development activities. The safety of human subjects is paramount to the conduct of these studies and regulatory considerations guide activities. Forces of evolution on the pharmaceutical industry are re-shaping the first-in-human dose selection strategy. Namely, high attrition rates in part due to lack of efficacy have led to the re-organization of research and development organizations around the umbrella of translational research. Translational research strives to bring basic research advances into the clinic and support the reverse transfer of information to enhance compound selection strategies. Pharmacokinetic/pharmacodynamic (PK/PD) modeling holds a unique position in translational research by attempting to integrate diverse sets of information. PK/PD modeling has demonstrated utility in dose selection and trial design for later stages of drug development and is now being employed with greater prevalence in the translational research setting to manage risk (i.e., oncology and inflammation/immunology). Moving from empirical E (max) models to more mechanistic representations of the biological system, a higher fidelity of human predictions is expected. Strategies that have proven useful for PK predictions are being applied to PK/PD predictions. This review article examines examples of the application of PK/PD modeling in establishing target concentrations for supporting first-in-human study design.

AAAS joins the Translational Medicine family

The AAAS has announced the launch of Science Translational Medicine. This is further and critical recognition of this discipline and we are deeply gratified that translational medicine has risen to the level of recognition by one of the world's most prestigious scientific organizations. We believe that Science Translational Medicine will provide another valuable venue for the rapid and broad dissemination of important articles in the field and contribute to enhancing the effectiveness of translational medicine overall.

It has been almost six years since we launched the Journal of Translational Medicine as an open-access journal with Biomed Central [1]. At the beginning, we faced the inevitable skepticism and received several inquires among others also from Science reporters questioning both the significance of translational medicine in today's biomedical world and the need for a new journal dedicated to it.

New take on comparative immunology: relevance to immunotherapy

It is becoming increasingly recognized that experimental animal models, while useful to address monothematic biological questions, bear unpredictable relevance to human disease. Several reasons have been proposed. However, the uncontrollable nature of human genetics and the heterogeneity of disease that can only be replicated with difficulty experimentally play a leading role. Comparative immunology is a term that generally refers to the analysis of shared or diverging facets of immunology among species; these comparisons are carried out according to the principle that evolutionarily conserved themes outline biologic functions universally relevant for survival. We propose that a similar strategy could be applied to searching for themes shared by distinct immune pathologies within our own species. Identification of common patterns may outline pathways necessary for a particular determinism to occur, such as tissue-specific rejection or tolerance. This approach is founded on the unproven but sensible presumption that nature does not require an infinite plethora of redundant mechanisms to reach its purposes. Thus, immune pathologies must follow, at least in part, common means that determine their onset and maintenance. Commonalities among diseases can, in turn, be segregated from disease-specific patterns uncovering essential mechanisms that may represent universal targets for immunotherapy.

Pathways from science findings to health benefits

There have been numerous exhortations for more 'translational research'. A selective review of historical examples of research leading to health benefits is used to consider the various forms of successful interplay between basic science and clinical applications. This is followed by a consideration of key neuroscience findings that might be relevant for translation, and then by a discussion of the challenges and opportunities in relation to mental disorders. The time-frame for the pathway from science findings to health benefits is usually long, and generally requires an interactive interplay among different scientific strategies. There is a false dichotomy between so-called basic and applied research and translation needs to proceed from the bedside to the laboratory as well as in the opposite direction. There is a key need for bridging research of the hypothesis-testing experimental medicine variety. Health benefits may involve either public health considerations or the treatment of individual patients, or both. There are now some opportunities for direct translational research but there is a much greater need for hypothesis-based bridging studies that occupy a crucial mid-phase in the pathway from science findings to health benefits.

Translational integrity and continuity: personalized biomedical data integration

Translational research data are generated in multiple research domains from the bedside to experimental laboratories. These data are typically stored in heterogeneous databases, held by segregated research domains, and described with inconsistent terminologies. Such inconsistency and fragmentation of data significantly impedes the efficiency of tracking and analyzing human-centered records. To address this problem, we have developed a data repository and management system named TraM (http://tram.uchicago.edu), based on a domain ontology integrated entity relationship model. The TraM system has the flexibility to recruit dynamically evolving domain concepts and the ability to support data integration for a broad range of translational research. The web-based application interfaces of TraM allow curators to improve data quality and provide robust and user-friendly cross-domain query functions. In its current stage, TraM relies on a semi-automated mechanism to standardize and restructure source data for data integration and thus does not support real-time data application.

The role of quantitative pharmacology in an academic translational research environment

Translational research is generally described as the application of basic science discoveries to the treatment or prevention of disease or injury. Its value is usually determined based on the likelihood that exploratory or developmental research can yield effective therapies. While the pharmaceutical industry has evolved into a highly specialized sector engaged in translational research, the academic medical research community has similarly embraced this paradigm largely through the motivation of the National Institute of Health (NIH) via its Roadmap initiative. The Clinical and Translational Science Award (CTSA) has created opportunities for institutions which can provide the multidisciplinary environment required to engage such research. A key component of the CTSA and an element of both the NIH Roadmap and the FDA Critical Path is the bridging of bench and bedside science via quantitative pharmacologic relationships. The infrastructure of the University of Pennsylvania/Children's Hospital of Philadelphia CTSA is highlighted relative to both research and educational objectives reliant upon quantitative pharmacology. A case study, NIH-sponsored research program exploring NK1r antagonism for the treatment NeuroAIDS is used to illustrate the application of quantitative pharmacology in a translational research paradigm.

The continuum of translation research in genomic medicine: how can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention?

Advances in genomics have led to mounting expectations in regard to their impact on health care and disease prevention. In light of this fact, a comprehensive research agenda is needed to move human genome discoveries into health practice in a way that maximizes health benefits and minimizes harm to individuals and populations. We present a framework for the continuum of multidisciplinary translation research that builds on previous characterization efforts in genomics and other areas in health care and prevention. The continuum includes four phases of translation research that revolve around the development of evidence-based guidelines. Phase 1 translation (T1) research seeks to move a basic genome-based discovery into a candidate health application (e.g., genetic test/intervention). Phase 2 translation (T2) research assesses the value of a genomic application for health practice leading to the development of evidence-based guidelines. Phase 3 translation (T3) research attempts to move evidence-based guidelines into health practice, through delivery, dissemination, and diffusion research. Phase 4 translation (T4) research seeks to evaluate the "real world" health outcomes of a genomic application in practice. Because the development of evidence-based guidelines is a moving target, the types of translation research can overlap and provide feedback loops to allow integration of new knowledge. Although it is difficult to quantify how much of genomics research is T1, we estimate that no more than 3% of published research focuses on T2 and beyond. Indeed, evidence-based guidelines and T3 and T4 research currently are rare. With continued advances in genomic applications, however, the full continuum of translation research needs adequate support to realize the promise of genomics for human health.

What's next in translational medicine?

Translational medicine is the integrated application of innovative pharmacology tools, biomarkers, clinical methods, clinical technologies and study designs to improve disease understanding, confidence in human drug targets and increase confidence in drug candidates, understand the therapeutic index in humans, enhance cost-effective decision making in exploratory development and increase phase II success. Translational research is one of the most important activities of translational medicine as it supports predictions about probable drug activities across species and is especially important when compounds with unprecedented drug targets are brought to humans for the first time. Translational research has the potential to deliver many practical benefits for patients and justify the extensive investments placed by the private and public sector in biomedical research. Translational research encompasses a complexity of scientific, financial, ethical, regulatory, legislative and practical hurdles that need to be addressed at several levels to make the process efficient. Several have resisted the idea of supporting translational research because of its high costs and the fear that it may re-direct funds from other biomedical disciplines. Resistance also comes from those more familiar with traditional clinical research methods. In this review, we argue that translational research should be seen as enabled by ongoing efforts in basic and clinical research and not competing with them. Translational research provides the knowledge necessary to draw important conclusions from clinical testing regarding disease and the viability of novel drug mechanisms. Advancing translational research requires education and new sources of funding. This could be achieved through public and congressional education by a joint coalition of patients' advocacy groups, academia, drug regulatory agencies and industry.