Biomedical Discovery Research Articles

University College London - Academic Careers Office

Help progress science and biomedical research discoveries along the development pathway to patient benefit. Offer academics assistance with: defining development and regulatory strategy; building and managing links to available resources; funder liaising and reporting requirements; managing risks and issues, follow-on funding and on-going project management and support. Bridge the gap between academia and the commercial healthcare sector by de-risking UCL's novel science a team of 24 highly experienced industrial translational scientists tasked with establishing the organisational culture, capability and processes necessary to deliver the UCL and BRC's mission of "Accelerating translation for health and wealth". The TRO supports the development of early stage research from across UCL and its partner hospitals, into projects attractive for translational funding or further development with partners and nurture access to complementary capacity, expertise and skills in translational science. The TRO achieves this through the integrated activities of three specialist groups a Drug Discovery Group (DDG) provides expert advice and practical support for small molecule drug discovery projects across UCL; a Translational Research Group who provide experienced project leaders to guide projects (gene, cell and regenerative medicine therapies, devices, diagnostics, small molecules) through their development; and an Industrial Partnerships Group who initiate and manage relationships with industry. The team have a proven track record of therapeutic delivery and work in partnership with academics and industry partners, enhancing projects to optimise the chances of long-term success and benefit to patients.

BBN-driven urinary bladder cancer mouse model.

Around 3% of new cancer diagnoses and 2% of all cancer deaths every year are caused by urinary bladder cancer (BC). This indicates a great need for intensive studying of BC by using different approaches including indispensable mice models. The most common preclinical mouse model of bladder carcinogenesis relies on the use of a nitrosamine compound, N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN) which causes high-grade, invasive tumors in the urinary bladder. BBN-induced bladder cancer in mice recapitulates the histology and manifests genetic alterations similar to human muscle-invasive bladder cancer. Here we present a detailed protocol for the induction of BC in mice which is based on the administration of 0.05%-0.1% BBN in drinking water. Six-to-eight-week-old mice are treated orally with BBN for 12weeks and tumors are expected 8weeks after the termination of BBN regimen. Histopathologic examination of the lesions should be routinely assessed after hematoxylin and eosin staining by an experienced pathologist and it can vary from urothelial dysplasia to invasive bladder cancer with glandular and squamous divergent differentiation, the incidence of which might depend on the mouse strain, gender, BBN concentration and the timeline of the protocol. Utilizing half of the urinary bladder tissue for the isolation and analysis of RNA, DNA and proteins provides a comprehensive insight into the biology of BC and reduces the number of mice per study. Finally, the successful use of the BC model can facilitate fundamental biomedical discoveries leading to novel diagnostic and therapeutic approaches with clinical benefits.

The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions.

The BioGRID (Biological General Repository for Interaction Datasets, thebiogrid.org) is an open‐access database resource that houses manually curated protein and genetic interactions from multiple species including yeast, worm, fly, mouse, and human. The ~1.93 million curated interactions in BioGRID can be used to build complex networks to facilitate biomedical discoveries, particularly as related to human health and disease. All BioGRID content is curated from primary experimental evidence in the biomedical literature, and includes both focused low‐throughput studies and large high‐throughput datasets. BioGRID also captures protein post‐translational modifications and protein or gene interactions with bioactive small molecules including many known drugs. A built‐in network visualization tool combines all annotations and allows users to generate network graphs of protein, genetic and chemical interactions. In addition to general curation across species, BioGRID undertakes themed curation projects in specific aspects of cellular regulation, for example the ubiquitin‐proteasome system, as well as specific disease areas, such as for the SARS‐CoV‐2 virus that causes COVID‐19 severe acute respiratory syndrome. A recent extension of BioGRID, named the Open Repository of CRISPR Screens (ORCS, orcs.thebiogrid.org), captures single mutant phenotypes and genetic interactions from published high throughput genome‐wide CRISPR/Cas9‐based genetic screens. BioGRID‐ORCS contains datasets for over 1,042 CRISPR screens carried out to date in human, mouse and fly cell lines. The biomedical research community can freely access all BioGRID data through the web interface, standardized file downloads, or via model organism databases and partner meta‐databases.

OCEAN LAW IN TIMES OF HEALTH EMERGENCY: DEEP SEABED MINING CONTRIBUTIONS AND ITS FEAR OF OVEREXPLOITATION

The deep seabed has one of the most enriched biodiverse places on Earth. Scientists have found some essential biomedical breakthroughs derived from hydrothermal vents involved in treating disease outbreaks among seabed minerals. Futuristic as it may sound, new pharmaceutical discoveries pressure the International Seabed Authority (ISA) into strengthening its global rules on mining exploitation beyond areas of national jurisdiction (ABNJ). This paper presents a general evaluation of the existing legal system of deep seabed mining. It highlights that, increasingly, pharmaceutical companies are shifting to ABNJ seabed areas for exploitation, pressuring the international order for a more coherent and effective mining exploitation system for the next decade. The analysis of international legal frameworks for the Law of the Sea is notable. However, there are still substantial gaps in deep seabed mining’s global governance, expected to commence soon, as ISA rushes to approve a new international mining code. The result supports a transparent mining exploitation process in ABNJ, facilitating cooperation between sectors and between countries, fostering equitable sharing, and preserving the fragile ecosystem.

Anion Receptors for the Discrimination of ATP and ADP in Biological Media.

The design and synthesis of molecular receptors for the selective binding of nucleoside phosphate anions (e. g. ATP, ADP, GTP, GDP, UDP) in aqueous media at physiological pH is a valuable research endeavour, which could lead to new sensing tools for biomedical and drug discovery research. However, this target is very challenging due to similarities in anion size, structure and charge. This Minireview provides an account of the development of receptors capable of discriminating between ATP and ADP, and their utilisation in biological sensing applications. Particular focus is given to the application of receptors for the determination of ATP or ADP concentrations in biological media, tracking ATP levels (or the ATP/ADP ratio) in cells using fluorescence microscopy, or real-time monitoring of enzyme reactions involving ATP and ADP in vitro.

Bisociative Literature-Based Discovery: Lessons Learned and New Word Embedding Approach

The field of bisociative literature-based discovery aims at mining scientific literature to reveal yet uncovered connections between different fields of specialization. This paper outlines several outlier-based literature mining approaches to bridging term detection and the lessons learned from selected biomedical literature-based discovery applications. The paper addresses also new prospects in bisociative literature-based discovery, proposing an advanced embeddings-based technology for cross-domain literature mining.

Linking mechanisms in data repositories: A case study of BioSample database

AbstractThis poster reports upon a case study of the linking mechanisms in the NCBI BioSample database and its immediate connections, examining the types of links, their functions, and implications according to the FAIR data principles. The study allowed the authors to conduct a more detailed analysis of one area of data management by focusing on one database's linking mechanisms, providing a glimpse into the role of linking mechanisms to support biomedical research and discovery and serving as a model for future studies.

Biomedical knowledge discovery based on Sentence‐BERT

AbstractIn the advent of the big data era, the volume of biomedical information grows exponentially, which means that the researchers have to spend a large amount of time in biomedical information mining. Effective mining of biomedical information and building the knowledge graph in the biomedical field are of high importance for biomedical knowledge discovery. In this study, datasets were constructed by using the text similarity corpus of CDD. The average Pearson's correlation coefficient was calculated by using the Sentence‐BERT model with sentence embeddings. Then a comparison was performed among the wordnet‐based similarity method (WBSM), UMLS‐based similarity method (UBSM), and the combination of these two methods. The results showed that the average Pearson correlation r calculated by the Sentence‐BERT model was the highest, the value of which is 74.37%. Triplets consisting of diseases, genes and proteins were extracted by using the Sentence‐BERT model. On this basis, the biomedical knowledge graph was constructed. It was found that this knowledge graph had a good effect on the testing dataset.

A semantic similarity based methodology for predicting protein-protein interactions: Evaluation with P53-interacting kinases.

Biomedical literature contains unstructured, rich information regarding proteins, ligands, diseases as well as biological pathways in which they are involved. Systematically analyzing such textual corpus has the potential for biomedical discovery of new protein-protein interactions and hidden drug indications. For this purpose, we have investigated a methodology that is based on a well-established text mining tool, Word2Vec, for the analysis of PubMed full text articles to derive word embeddings, and the use of a simple semantic similarity comparison either by itself or in conjunction with k-Nearest Neighbor (kNN) technique for the prediction of new relationships. To test this methodology, three lines of retrospective analyses of a dataset with known P53-interacting proteins have been conducted. First, we demonstrated that Word2Vec semantic similarity can infer functional relatedness among all kinases known to interact with P53. Second, in a series of time-split experiments, we demonstrated that both a simple similarity comparison and kNN models built with papers published up to a certain year were able to discover P53 interactors described in later publications. Third, in a different scenario of time-split experiments, we examined the predictions of P53-interacting proteins based on the kNN models built on data prior to a certain split year for different time ranges past that year, and found that the cumulative number of correct predictions was indeed increasing with time. We conclude that text mining of research papers in the PubMed literature based on Word2Vec analysis followed by a simple similarity comparison or kNN modeling affords excellent predictions of protein-protein interactions between P53 and kinases, and should have wide applications in translational biomedical studies such as repurposing of existing drugs, drug-drug interaction, and elucidation of mechanisms of action for drugs.

Transforming the practice of medicine through team science

BackgroundThe translation of biomedical research discoveries into clinical practice is marked by extended timelines (averaging 17 years) and multiple sequential process steps. However, even after a drug, device, diagnostic tool or unique therapeutic procedure successfully navigates through clinical testing to approval, real barriers remain in applying and scaling the innovation in practice.MethodsMayo Clinic initiated the Transform the Practice programme to facilitate multidisciplinary team and convergence science to continuously reinvent solutions to address unmet patient needs and accelerate the application of next-generation healthcare solutions. During a 5-year period, 24 programme teams received financial resources, barrier-removing engagement from clinical and research leadership, and enhanced administrative support, including dedicated project managers.ResultsThe approach created value in facilitating consistent progress toward project objectives and resulted in multiple publications, new extramural funding sources, and implementation of new tests and services into the clinical practice. This report describes the concentrated institutional effort to accelerate the discovery–translation–application continuum in an academic medical centre and highlights successful applications and persistent obstacles.ConclusionsThe Transform the Practice approach is effective in moving high-potential research discoveries closer to implementation in the clinical practice. Its concepts, including the application of structured project management methodology, may be quickly integrated to shorten an organisation’s time to implementing its most important discoveries.

Illustrated Medical History of the Zurich University Hospital and the Medical Policlinic (Including Cultural and Urban Side Effects)

Illustrated Medical History of the Zurich University Hospital and the Medical Policlinic (Including Cultural and Urban Side Effects) Abstract. Zurich has always played a significant role in the history of medicine, with technical developments and biomedical discoveries (including numerous Nobel Prize laureates) or with Zurich physicians who are now commonplace in the world of medicine as eponyms. The story of the Kantonsspital of the University of Zurich, founded in 1833, is perfect to illustrate and locate Zurich's medical history - but also to show intriguing and unexpected cultural or urban references.

Cell-Penetrating Peptides Escape the Endosome by Inducing Vesicle Budding and Collapse.

Cell-penetrating peptides (CPPs) are capable of delivering membrane-impermeable cargoes (including small molecules, peptides, proteins, nucleic acids, and nanoparticles) into the cytosol of mammalian cells and have the potential to revolutionize biomedical research and drug discovery. However, the mechanism of action of CPPs has remained poorly understood, especially how they escape from the endosome into the cytosol following endocytic uptake. We show herein that CPPs exit the endosome by inducing budding and collapse of CPP-enriched vesicles from the endosomal membrane. This mechanism provides a theoretical basis for designing CPPs and other delivery vehicles of improved efficiencies.

Immunoengineering has arrived.

Immunoengineering is a new discipline that creates and applies engineering tools and principles to investigate and modulate the immune system. It spans from the molecular scale to the scale of populations and is critically important in both health and disease. This perspective discusses the rapid development of immunoengineering as a field, including advances to research and education. On the research side, immunoengineering is poised to revolutionize technologies for tissue engineering, drug delivery, and medical devices, among others. Immunoengineering is shown to unlock new tools for biomedical discovery and innovation and has the potential to safely and effectively treat myriad diseases, from cancer to infectious diseases to type 1 diabetes and autoimmune diseases in novel ways. On the educational side, it is described how immunoengineering centers and educational focus areas are being created at leading universities. Furthermore, data are presented to show how grant agencies are making major investments into the field and high-impact research and translational biotechnologies are being developed.

The Microbiome Revolution Turns to Cholesterol

The story of twentieth century biomedical breakthroughs could be told through cholesterol. Revolutions in genetics, molecular biology, genomics, and antibody-based therapies defined cholesterol's impact on human health and cholesterol-lowering strategies. In this issue, Kenny etal. (2020) bring a key twenty-first century biomedical development-the microbiome revolution-to cholesterol.

Dissecting the Tumor-Immune Landscape in Chimeric Antigen Receptor T-cell Therapy: Key Challenges and Opportunities for a Systems Immunology Approach.

The adoptive transfer of genetically engineered chimeric antigen receptor (CAR) T cells has opened a new frontier in cancer therapy. Unlike the paradigm of targeted therapies, the efficacy of CAR T-cell therapy depends not only on the choice of target but also on a complex interplay of tumor, immune, and stromal cell communication. This presents both challenges and opportunities from a discovery standpoint. Whereas cancer consortia have traditionally focused on the genomic, transcriptomic, epigenomic, and proteomic landscape of cancer cells, there is an increasing need to expand studies to analyze the interactions between tumor, immune, and stromal cell populations in their relevant anatomical and functional compartments. Here, we focus on the promising application of systems biology to address key challenges in CAR T-cell therapy, from understanding the mechanisms of therapeutic resistance in hematologic and solid tumors to addressing important clinical challenges in biomarker discovery and therapeutic toxicity. We propose a systems biology view of key clinical objectives in CAR T-cell therapy and suggest a path forward for a biomedical discovery process that leverages modern technological approaches in systems biology.

Are in vitro and in silico approaches used appropriately for animal-based major depressive disorder research?

The current paradigm for biomedical research and drug testing postulates that in vitro and in silico data inform animal studies that will subsequently inform human studies. Recent evidence points out that animal studies have made a poor contribution to current knowledge of Major Depressive Disorder, whereas the contribution of in vitro and in silico studies to animal studies- within this research area- is yet to be properly quantified. This quantification is important since biomedical research and drug discovery and development includes two steps of knowledge transferability and we need to evaluate the effectiveness of both in order to properly implement 3R principles (Replacement, Reduction and Refinement). Here, we used the citation tracking facility within Web of Science to locate citations of original research papers on in vitro and in silico related to MDD published identified in PubMed by relevant search terms. 67 publications describing target papers were located. Both in vitro and in silico papers are more cited by human medical papers than by animal papers. The results suggest that, at least concerning MDD research, the current two steps of knowledge transferability are not being followed, indicating a poor compliance with the 3R principles.

COVID-19: opportunities for professional development and disruptive innovation.

As a group of medical students from the United Kingdom (UK), we currently find ourselves in a period of uncertainty as the continuing coronavirus disease 2019 (COVID-19) pandemic has caused widespread disruption to medical education. Our medical schools, like many others, have cancelled clinical placements, formal teaching, examinations and international elective programmes. Despite these challenges, we believe that the unprecedented COVID-19 pandemic is generating opportunities for both professional development and innovation in medical education. Social distancing is the most effective preventative strategy for COVID-19, but for many medical students this creates a gap where previously there were clinical attachments, a seemingly infinite syllabus and looming examinations. This unplanned interruption offers a unique opportunity for professional development, which may otherwise be missed, with the chance to consolidate learning, reflect on personal career plans and engage in academic research. Reflection helps us to process experiences and is a powerful way to facilitate learning. With over 60 specialties, and often limited exposure during medical school, choosing a career path in medicine can be daunting. There are many advantages in having a specialty interest when at medical school, and during this period it may be useful for those still unsure to reflect on past experiences and to consider their academic interests and what factors they value most in their careers. This unplanned interruption offers a unique opportunity for professional development, which may otherwise be missed, … Learning to critically evaluate research to inform future evidence-based practice is an essential skill that medical students must develop and, unsurprisingly, time constraints are often the greatest barrier to involvement in research.1 The General Medical Council recommends keeping up to date with current research in an individual's area of interest through independent study and by conducting literature searches and reviews. During the COVID-19 pandemic there will be barriers to engaging in laboratory- and hospital-based research; however, there is great value in performing systematic reviews of the literature and in taking part in qualitative research projects that may be conducted remotely, all of which will improve skills and strengthen applications to future training posts. We already know many fellow medical students engaging in reviews, video teaching, app development and even online language courses during self-isolation. Medical schools are rapidly trying to adapt their methods of teaching and assessment to accommodate the nationwide lockdown. During the severe acute respiratory syndrome (SARS) epidemic of 2003, Chinese medical schools cancelled clinical teaching and examinations. This led to the implementation of novel online problem-based learning that was so successful it was subsequently integrated into the curriculum.2 Similar to then, online teaching will play a key role in the continuation of the curriculum during the COVID-19 pandemic, with highly reproducible content accessible to students at home. During this rapid restructuring there are opportunities to strengthen engagement by involving students in the planning and execution of learning resources. Evidence shows that involving students as stakeholders in their education adds value and fosters intrinsic motivation, which strongly correlates with self-efficacy and academic performance.3 We encourage medical schools to actively engage with students to call on their ingenuity and to develop these resources, which may benefit medical education in the long term and motivate future educators. … there are opportunities to strengthen engagement by involving students in the planning and execution of learning resources The COVID-19 pandemic is catalysing an enduring transformation in medicine, with advances in telemedicine, disease surveillance and artificial intelligence, which have already been used to triage infected patients. We are experiencing an unparalleled boom in cross-industry cooperation to combat this virus, bringing together pharmaceutical, biomedical, data analytic and computational drug discovery companies with a singular goal. In the UK, we have seen airplane manufacturers, carmakers and Formula One teams repurpose their production lines to build ventilators to cope with rising demand. Johns Hopkins University has developed an interactive web-based dashboard to track COVID-19 in real time, and researchers at King's College London have created a COVID-19 symptom tracker app with more than 1.5 million downloads.4 Disruptive events, such as economic recessions and pandemics, can advance innovation, and there are opportunities here for resourceful initiatives in medical education. Students at Harvard Medical School have already created a rapidly updating COVID-19 curriculum, with the latest research and developments, that is benefiting many clinicians.5 In summary, as medical students affected by this global disruption, we see a rare opportunity for professional development in an otherwise heavily structured degree. In addition, innovation driven by medical students during this viral pandemic may accelerate the continuing transformation away from traditional teaching methods in medical education. Analysing the effects of this change, and professional development during the COVID-19 pandemic will have a lasting benefit for medical and scientific communities, and a long-lasting impact on future clinicians’ practice.

Broad-coverage biomedical relation extraction with SemRep

BackgroundIn the era of information overload, natural language processing (NLP) techniques are increasingly needed to support advanced biomedical information management and discovery applications. In this paper, we present an in-depth description of SemRep, an NLP system that extracts semantic relations from PubMed abstracts using linguistic principles and UMLS domain knowledge. We also evaluate SemRep on two datasets. In one evaluation, we use a manually annotated test collection and perform a comprehensive error analysis. In another evaluation, we assess SemRep’s performance on the CDR dataset, a standard benchmark corpus annotated with causal chemical-disease relationships.ResultsA strict evaluation of SemRep on our manually annotated dataset yields 0.55 precision, 0.34 recall, and 0.42 F 1 score. A relaxed evaluation, which more accurately characterizes SemRep performance, yields 0.69 precision, 0.42 recall, and 0.52 F 1 score. An error analysis reveals named entity recognition/normalization as the largest source of errors (26.9%), followed by argument identification (14%) and trigger detection errors (12.5%). The evaluation on the CDR corpus yields 0.90 precision, 0.24 recall, and 0.38 F 1 score. The recall and the F 1 score increase to 0.35 and 0.50, respectively, when the evaluation on this corpus is limited to sentence-bound relationships, which represents a fairer evaluation, as SemRep operates at the sentence level.ConclusionsSemRep is a broad-coverage, interpretable, strong baseline system for extracting semantic relations from biomedical text. It also underpins SemMedDB, a literature-scale knowledge graph based on semantic relations. Through SemMedDB, SemRep has had significant impact in the scientific community, supporting a variety of clinical and translational applications, including clinical decision making, medical diagnosis, drug repurposing, literature-based discovery and hypothesis generation, and contributing to improved health outcomes. In ongoing development, we are redesigning SemRep to increase its modularity and flexibility, and addressing weaknesses identified in the error analysis.

The Evolution of Stem Cells, Disease Modeling, and Drug Discovery for Neurological Disorders.

Human neurological disorders are among the most challenging areas of translational research. The difficulty of acquiring human neural samples or specific representative animal models has necessitated a multifaceted approach to understanding disease pathology and drug discovery. The dedifferentiation of somatic cells to human induced pluripotent stem cells (hiPSCs) for the generation of neural derivatives has broadened the capability of biomedical research to study human cell types in neurological disorders. The initial zeal for the potential of hiPSCs for immediate biomedical breakthroughs has evolved to more reasonable expectations. Over the past decade, hiPSC technology has demonstrated the capacity to successfully establish "disease in a dish" models of complex neurological disorders and to identify possible novel therapeutics. However, as hiPSCs are used more broadly, an increased understanding of the limitations of hiPSC studies is becoming more evident. In this study, we review the challenges of studying neurological disorders, the current limitations of stem cell-based disease modeling, and the degrees to which hiPSC studies to date have demonstrated the capacity to fill essential gaps in neurological research.

Light‐Activated Protein Conjugation and 89Zr‐Radiolabelling with Water‐Soluble Desferrioxamine Derivatives

Protein-conjugates are vital tools in biomedical research, drug discovery and imaging science. For example, functionalised monoclonal antibodies (mAbs) coupled to the desferrioxamine B (DFO) chelate and radiolabelled with 89 Zr4+ ions are used as radiopharmaceuticals for diagnostic positron emission tomography (PET). In this context, protein functionalisation requires the formation of a covalent bond that must be achieved without compromising the biological properties of the mAb. Photochemistry offers new synthetic routes toward protein conjugates like 89 Zr-mAbs but to harness the potential of light-induced conjugation reactions new photoactivatable reagents are required. Herein, we introduce two photoactivatable DFO-derivatives functionalised with an aryl azide (ArN3 ) for use in light-activated conjugation and radiosynthesis of 89 Zr-mAbs. Incorporation of a tris-polyethylene glycol (PEG)3 linker between DFO and the ArN3 group furnished water-soluble chelates that were used in the one-pot, photoradiosynthesis of different 89 Zr-radiolabelled protein conjugates with radiochemical yields up to 72.9±1.9 %. Notably, the DFO-PEG3 chelates can be readily synthesised in accordance with Good Laboratory Practice (GLP), which will facilitate clinical trials with photoradiolabelled 89 Zr-mAbs.