Detection and characterization of translational research in cancer and cardiovascular medicine

The Under-representation and Stagnation of Female, Black, and Hispanic Authorship in the Journal of the American Medical Association and the New England Journal of Medicine

Publication in leading medical journals is critical to knowledge dissemination and academic advancement alike. Leveraging a novel dataset comprised of nearly all articles published in JAMA and NEJM from 1990 to 2020, along with established reference works for name identification, we explore changing authorship demographics in two of the world's leading medical journals. Our main outcomes are the annual proportion of male and female authors and the proportion of racial/ethnic identities in junior and senior authorship positions for articles published in JAMA and NEJM since 1990. We found that women remain under-represented in research authorship in both JAMA (at its peak, 38.1% of articles had a female first author in 2011) and NEJM (peaking at 28.2% in 2002). The rate of increase is so slow that it will take more than a century for both journals to reach gender parity. Black and Hispanic researchers have likewise remained under-represented as first and last authors in both journals, even using the best-case scenario. Their appearance as authors has remained stagnant for three decades, despite attention to structural inequalities in medical academia. Thus, analysis of authorship demographics in JAMA and NEJM over the past three decades reveals the existence of inequalities in high-impact medical journal authorship. Gender and racial/ethnic disparities in authorship may both reflect and further contribute to disparities in academic advancement.

Tracking biomedical articles along the translational continuum: a measure based on biomedical knowledge representation

Keeping track of translational research is essential to evaluating the performance of programs on translational medicine. Despite several indicators in previous studies, a consensus measure is still needed to represent the translational features of biomedical research at the article level. In this study, we first trained semantic representations of biomedical entities and documents (i.e., bio-entity2vec and bio-doc2vec) based on over 30 million PubMed articles. With these vectors, we then developed a new measure called Translational Progression (TP) for tracking biomedical articles along the translational continuum. We validated the effectiveness of TP from two perspectives (Clinical trial phase identification and ACH classification), which showed excellent consistency between TP and other indicators. Meanwhile, TP has several advantages. First, it can track the degree of translation of biomedical research dynamically and in real-time. Second, it is straightforward to interpret and operationalize. Third, it doesn't require labor-intensive MeSH labeling and it is suitable for big scholarly data as well as papers that are not indexed in PubMed. In addition, we examined the translational progressions of biomedical research from three dimensions (including overall distribution, time, and research topic), which revealed three significant findings. The proposed measure in this study could be used by policymakers to monitor biomedical research with high translational potential in real-time and make better decisions. It can also be adopted and improved for other domains, such as physics or computer science, to assess the application value of scientific discoveries.

Precision Medicine: Historiography of Life Sciences and the Geneticization of the Clinics

In 2013, Hans Jörg Rheinberger proposed that Mendelian genetics and molecular biology were "scientific ideologies," that is, for him they are systems of thought whose objects are hyperbolic; they are not, or not yet, in the realm of and not, or not yet, under the control of that system. This article proposes that precision medicine today is a scientific ideology and analyses the implications of this statement for historians of biology, genetics, and medicine.

Insights from full-text analyses of the Journal of the American Medical Association and the New England Journal of Medicine

Analysis of the content of medical journals enables us to frame the shifting scientific, material, ethical, and epistemic underpinnings of medicine over time, including today. Leveraging a dataset comprised of nearly half-a-million articles published in the Journal of the American Medical Association (JAMA) and the New England Journal of Medicine (NEJM) over the past 200 years, we (a) highlight the evolution of medical language, and its manifestations in shifts of usage and meaning, (b) examine traces of the medical profession's changing self-identity over time, reflected in its shifting ethical and epistemic underpinnings, (c) analyze medicine's material underpinnings and how we describe where medicine is practiced, (d) demonstrate how the occurrence of specific disease terms within the journals reflects the changing burden of disease itself over time and the interests and perspectives of authors and editors, and (e) showcase how this dataset can allow us to explore the evolution of modern medical ideas and further our understanding of how modern disease concepts came to be, and of the retained legacies of prior embedded values.

Beyond networks: Aligning qualitative and computational science studies

This article examines the thorny issue of the relationship (or lack thereof) between qualitative and quantitative approaches in Science and Technology Studies (STS). Although quantitative methods, broadly understood, played an important role in the beginnings of STS, these two approaches subsequently strongly diverged, leaving an increasing gap that only a few scholars have tried to bridge. After providing a short overview of the origins and development of quantitative analyses of textual corpora, we critically examine the state of the art in this domain. Focusing on the availability of advanced network structure analysis tools and Natural Language Processing workflows, we interrogate the fault lines between the increasing offer of computational tools in search of possible uses and the conceptual specifications of STS scholars wishing to explore the epistemic and ontological dimensions of techno-scientific activities. Finally, we point to possible ways to overcome the tension between ethnographic descriptions and quantitative methods while continuing to avoid the dichotomies (social/cognitive, organizing/experimenting) that STS has managed to discard.

Insights into Doxorubicin-induced Cardiotoxicity: Molecular Mechanisms, Preventive Strategies, and Early Monitoring

Doxorubicin (DOX) is one of the most effective anticancer drugs to treat various forms of cancers; however, its therapeutic utility is severely limited by its associated cardiotoxicity. Despite the enormous amount of research conducted in this area, the exact molecular mechanisms underlying DOX toxic effects on the heart are still an area that warrants further investigations. In this study, we reviewed literature to gather the best-known molecular pathways related to DOX-induced cardiotoxicity (DIC). They include mechanisms dependent on mitochondrial dysfunction such as DOX influence on the mitochondrial electron transport chain, redox cycling, oxidative stress, calcium dysregulation, and apoptosis pathways. Furthermore, we discuss the existing strategies to prevent and/or alleviate DIC along with various techniques available for therapeutic drug monitoring (TDM) in cancer patients treated with DOX. Finally, we propose a stepwise flowchart for TDM of DOX and present our perspective at curtailing this deleterious side effect of DOX.

Team Assembly Mechanisms and the Knowledge Produced in the Mexico's National Institute of Geriatrics: A Network Analysis and Agent-Based Modeling Approach

Mexico's National Institute of Geriatrics (INGER) is the national research center of reference for matters related to human aging. INGER scientists perform basic, clinical, and demographic research which may imply different scientific cultures working together in the same specialized institution. In this paper, by a combination of text mining, coauthorship network analysis, and agent-based modeling, we analyzed and modeled the team assembly practices and the structure of the knowledge produced by scientists from INGER. Our results showed a weak connection between basic and clinical research and the emergence of a highly connected academic leadership. Importantly, basic and clinical-demographic researchers exhibited different team assembly strategies: basic researchers tended to form larger teams mainly with external collaborators, while clinical and demographic researchers formed smaller teams that very often incorporated internal (INGER) collaborators. We showed how these two different ways to form research teams impacted the organization of knowledge produced at INGER. Following these observations, we modeled, via agent-based modeling, the coexistence of different scientific cultures (basic and clinical research) exhibiting different team assembly strategies in the same institution. Three virtual experiments were run in our agent-based model. The three experiments kept similar values to the collaborating dynamics of INGER in terms of average team size and probabilities of choosing incumbents and external collaborators. The only difference among these experiments was the value of homophily defined as the trend to collaborate with research studies from the same field (14% corresponding to the 46% and 79%). The main result of these experiments is that by modulating just one variable (homophily), we could successfully reproduce the current situation of INGER (homophily of 79%) and simulate alternative scenarios in which interdisciplinary (46%) and transdisciplinary (14%) research could be done.

The emergence and evolution of the research fronts in HIV/AIDS research

In this paper, we have identified and analyzed the emergence, structure and dynamics of the paradigmatic research fronts that established the fundamentals of the biomedical knowledge on HIV/AIDS. A search of papers with the identifiers "HIV/AIDS", "Human Immunodeficiency Virus", "HIV-1" and "Acquired Immunodeficiency Syndrome" in the Web of Science (Thomson Reuters), was carried out. A citation network of those papers was constructed. Then, a sub-network of the papers with the highest number of inter-citations (with a minimal in-degree of 28) was selected to perform a combination of network clustering and text mining to identify the paradigmatic research fronts and analyze their dynamics. Thirteen research fronts were identified in this sub-network. The biggest and oldest front is related to the clinical knowledge on the disease in the patient. Nine of the fronts are related to the study of specific molecular structures and mechanisms and two of these fronts are related to the development of drugs. The rest of the fronts are related to the study of the disease at the cellular level. Interestingly, the emergence of these fronts occurred in successive "waves" over the time which suggest a transition in the paradigmatic focus. The emergence and evolution of the biomedical fronts in HIV/AIDS research is explained not just by the partition of the problem in elements and interactions leading to increasingly specialized communities, but also by changes in the technological context of this health problem and the dramatic changes in the epidemiological reality of HIV/AIDS that occurred between 1993 and 1995.

"Triple negative breast cancer": Translational research and the (re)assembling of diseases in post-genomic medicine

The paper examines the debate about the nature and status of "Triple-negative breast cancer", a controversial biomedical entity whose existence illustrates a number of features of post-genomic translational research. The emergence of TNBC is intimately linked to the rise of molecular oncology, and, more generally, to the changing configuration of the life sciences at the turn of the new century. An unprecedented degree of integration of biological and clinical practices has led to the proliferation of bio-clinical entities emerging from translational research. These translations take place between platforms rather than between clinical and laboratory settings. The complexity and heterogeneity of TNBC, its epistemic and technical, biological and clinical dualities, result from its multiple instantiations via different platforms, and from the uneven distribution of biological materials, techniques, and objects across clinical research settings. The fact that TNBC comes in multiple forms, some of which seem to be incompatible or, at least, only partially overlapping, appears to be less a threat to the whole endeavor, than an aspect of an ongoing translational research project. Discussions of translational research that rest on a distinction between basic research and its applications fail to capture the dynamics of this new domain of activity, insofar as application is built-in from the very beginning in the bio-clinical entities that emerge from the translational research domain.

[Translation of knowledge on cervical cancer: is there a gap between research on causes and research on patient care?]

This article constructs a map on the translation of knowledge concerning cervical cancer, based on citation networks analysis and the use of Gene Ontology terms and Medical Subject Headings. We identified two areas of research that are poorly interconnected and differ in structure, content, and evolution. One focuses on causes of cancer and the other on patient care. The first research area showed a knowledge translation process where basic research and clinical research are communicated through a set of articles that consolidate human papillomavirus infection as the necessary cause of cervical cancer. The first area aims to prevent HPV infection and the development of cervical cancer, while the second aims to stage and treat the disease.

Mapping knowledge translation and innovation processes in Cancer Drug Development: the case of liposomal doxorubicin

We explored how the knowledge translation and innovation processes are structured when theyresult in innovations, as in the case of liposomal doxorubicin research. In order to map the processes, a literature network analysis was made through Cytoscape and semantic analysis was performed by GOPubmed which is based in the controlled vocabularies MeSH (Medical Subject Headings) and GO (Gene Ontology). We found clusters related to different stages of the technological development (invention, innovation and imitation) and the knowledge translation process (preclinical, translational and clinical research), and we were able to map the historic emergence of Doxil as a paradigmatic nanodrug. This research could be a powerful methodological tool for decision-making and innovation management in drug delivery research.

Liposomes versus metallic nanostructures: differences in the process of knowledge translation in cancer

This research maps the knowledge translation process for two different types of nanotechnologies applied to cancer: liposomes and metallic nanostructures (MNs). We performed a structural analysis of citation networks and text mining supported in controlled vocabularies. In the case of liposomes, our results identify subnetworks (invisible colleges) associated with different therapeutic strategies: nanopharmacology, hyperthermia, and gene therapy. Only in the pharmacological strategy was an organized knowledge translation process identified, which, however, is monopolized by the liposomal doxorubicins. In the case of MNs, subnetworks are not differentiated by the type of therapeutic strategy, and the content of the documents is still basic research. Research on MNs is highly focused on developing a combination of molecular imaging and photothermal therapy.

Testing devices or experimental systems? Cancer clinical trials take the genomic turn

Clinical trials are often described as machine-like systems for generating specific information concerning drug safety and efficacy, and are understood as a component of the industrial drug development processes. This paper argues that contemporary clinical trials in oncology are not reducible to mere drug testing. Drawing on ethnographic fieldwork and interviews with researchers in the field of oncology from 2010 to 2013, we introduce a conceptual contrast between trials as testing machines and trials as clinical experimental systems to draw attention to the ways trials are increasingly being used to ask open-ended scientific questions. When viewed as testing machines, clinical trials are seen as a means to produce answers to straightforward questions and deviations from the protocol are seen as bugs in the system; but practitioners can also treat trials as clinical experimental systems to investigate as yet undefined problems and where heterogeneity becomes a means to produce novel biological or clinical insights. The rise of "biomarker-driven" clinical trials in oncology, which link measurable biological characteristics such as genetic mutations to clinical features such as a patient's response to a particular drug, exemplifies a trend towards more experimental styles of clinical work. These transformations are congruent with changes in the institutional structure of clinical research in oncology, including a movement towards more flexible, networked research arrangements, and towards using individual patients as model systems for asking biological questions.

Therapy's Shadow: A Short History of the Study of Resistance to Cancer Chemotherapy

This article traces the history of research on resistance to drug therapy in oncology using scientometric techniques and qualitative analysis. Using co-citation analysis, we generate maps to visualize subdomains in resistance research in two time periods, 1975–1990 and 1995–2010. These maps reveal two historical trends in resistance research: first, a shift in focus from generic mechanisms of resistance to chemotherapy to a focus on resistance to targeted therapies and molecular mechanisms of oncogenesis; and second, a movement away from an almost exclusive reliance on animal and cell models and toward the generation of knowledge about resistance through clinical trial work. A close reading of highly cited articles within each subdomain cluster reveals specific points of transition from one regime to the other, in particular the failure of several promising theories of resistance to be translated into clinical insights and the emergence of interest in resistance to a new generation of targeted agents such as imatinib and trastuzumab. We argue that the study of resistance in the oncology field has thus become more integrated with research into cancer therapy – rather than constituting it as a separate domain of study, as it has done in the past, contemporary research treats resistance as the flip side to treatment, as therapy’s shadow.

[The emergence and development of gene expression profiling: a key component of the 3B (bench, bedside, bytes) in translational research]

This paper examines the emergence and development of one of the key components of genomics, namely gene expression profiling. It does so by resorting to computer-based methods to analyze and visualize networks of scientific publications. Our results show the central role played by oncology in this domain, insofar as the initial proof-of-principle articles based on a plant model organism have quickly led to the demonstration of the value of these techniques in blood cancers and to applications in the field of solid tumors, and in particular breast cancer. The article also outlines the essential role played by novel bioinformatics and biostatistical tools in the development of the domain. These computational disciplines thus qualify as one of the three corners (in addition to the laboratory and the clinic) of the translational research triangle.

Critical appraisal of translational research models for suitability in performance assessment of cancer centers

BACKGROUND

Translational research is a complex cumulative process that takes time. However, the operating environment for cancer centers engaged in translational research is now financially insecure. Centers are challenged to improve results and reduce time from discovery to practice innovations. Performance assessment can identify improvement areas that will help reduce translational delays. Currently, no standard method exists to identify models for use in performance assessment. This study aimed to critically appraise translational research models for suitability in performance assessment of cancer centers.

METHODS

We conducted a systematic review to identify models and developed a set of criteria based on scientometrics, complex adaptive systems, research and development processes, and strategic evaluation. Models were assessed for linkage between research and care components, new knowledge, systems integration, performance assessment, and review of other models.

RESULTS

Twelve models were identified; six described phases/components for translational research in different blocks (T models) and six described the process of translational research (process models). Both models view translational research as an accumulation of new knowledge. However, process models more clearly address systems integration, link research and care components, and were developed for evaluating and improving the performance of translational research. T models are more likely to review other models.

CONCLUSION

Process models seem to be more suitable for performance assessment of cancer centers than T models. The most suitable process models (the Process Marker Model and Lean and Six Sigma applications) must be thoroughly tested in practice.