Applied Biomedical Science

The development of new medicines generally begins with breakthroughs in basic science, leading to target identification and lead development, then pre-clinical and clinical trials before translation to patients (clinical efficacy) and eventually into practice (implementation). Biomedical scientists work at any stage along this translational “bench-to-bedside” process. The first half of this module will illustrate the translational research arc by focusing specifically on malaria, covering the natural history of malaria, the life cycle of the malaria parasite, followed by an overview of the discovery and development of early antimalarials, clinical studies and resistance reversal strategies.

In the second half of the module, students will be introduced to great variety of translational research projects carried out by researchers at City St George’s.

The data generated from randomised controlled clinical trials are considered to be the most robust in an era of evidence-based research. Understanding the principles underpinning clinical trials and the complexities involved in set-up and management enables a broad appreciation of the validity and reliability of the trial results and how trials are conducted in real world settings.

This 15-credit module will introduce students to these important principles and concepts. Teaching sessions will be delivered by lecture with accompanying practical sessions, quizzes and online courses to consolidate knowledge.

Early lectures will introduce clinical trial related concepts and theories along with fundamental knowledge, while subsequent lectures, presented by experts in the field (internal and external to SGUL), will focus on specific aspects of trial conduct. Students will be encouraged to read and understand the research literature through assigned pre-lecture reading each week and via seminar style Q&A sessions at the end of each lecture.

Importantly, an online research ethics course and good clinical practice (GCP) course will form part of the ICA with a certificate awarded on successful completion. This is a key requirement for those working within clinical trials. Students will complete practical sessions based on the topic of the preceding lecture. Work from the practical sessions will be documented in a practical session notebook, which will count towards the ICA. The final part of the ICA will involve a written assignment (in the form of a mini protocol) where students will be asked to design a clinical trial to answer a specific research question. This will test the students’ fundamental knowledge and critical assessment of trial design, implementation, ethics, analysis and reporting.

The final sessions of the module will draw together the key themes explored with a presentation on landmark trials from experts in the field.

Understanding of large biomedical datasets and data analysis skills are now required at all stages of biomedical research; training in this area is thus greatly desired by potential employers.

Since the human genome was first sequenced in 2003, the biomedical sciences have experienced an explosion of DNA sequence data, functional genomics collections and epidemiological information. The exponential progress in this area was made possible by the emergence of novel technologies used to sequence DNA, investigate gene function and to store and analyse large amounts of data. New technologies, comprehensive datasets and advances in data storage have ushered in the field of ‘systems biology’, a catchphrase used in reference to growing efforts of using holistic approaches to study complex problems in biology and medicine. Availability of properly trained scientists who can analyse such data is now becoming a limiting factor in industry and academia.

Much of modern biomedical research generates vast amounts of data, including genomics, proteomics, and metabolomics data, as well as electronic health records and imaging data. Efficiently managing, processing, and interpreting these large and complex datasets require robust data analysis skills.

This introductory module runs in term 1 and will provide students with an understanding of fundamental concepts and skills that will lay the foundation for more specialised, data-based modules in term 2 such as Computational Biology and Population Health Research.

The health research spectrum ranges from controlled environments such as laboratory experiments, to interventional and observational population settings. Clinical trials are somewhere in the middle of this wide range of approaches in terms of the extent of control that researchers can exert through carefully planned design and concerted efforts for data collection accuracy.

Observational studies aim at understanding “real world" information collected without researchers’ attempts to alter it or interfere. This module is aiming at structuring a series of challenges that these setting present to researchers and at demonstrating the principles, approaches and solutions commonly applied to overcome them.

Population-based policies and changes in clinical practice rely on both descriptive and inferential data analyses. Inferential studies seek to produce reproducible results or estimates that can be generalized to large populations. Understanding patterns in population health and the interconnected pathways between disease, demographics, lifestyle, socioeconomic factors, environmental exposures, and interventions is crucial for designing efficient, evidence-based public health policies.

This module will provide students with a comprehensive understanding of public health, epidemiology, study designs, measures of association between diseases and potential risk factors, and the statistical methods used to quantify their magnitude and significance. Students will be guided through all phases of the research development lifecycle, from the initial research question and the importance of pilot and feasibility studies to complex observational epidemiological settings, such as cross-sectional, cohort, case-control, and longitudinal studies.

The associated statistical concepts and techniques will include not only simple hypothesis testing but also basic elements of statistical modelling, addressing bias and confounding in observational studies, and methods to minimize them. An introduction to the Bayesian framework for statistical inference may also be included, depending on the students’ level of attainment in statistics.

The R programming language will be used as a tool for exploring, understanding, and analysing population data. Emphasis will be placed on understanding when and why specific statistical techniques are applicable or tailored to certain epidemiological designs and measures, the necessary assumptions for their validity, and how to apply them effectively.

Staying abreast of the relevant scientific literature and orally presenting research results are two of the most important skills of practising scientists.

This module will provide students with repeated practice of reading, analysing and presenting scientific papers in the field of biomedical science. A particular focus will be on the identification of publications of high significance, at the forefront of contemporary research and of technical interest. In addition to critical analysis skills students will develop their ability to operate in a team by working with peers to discuss publications and to competently design and present coherent presentations. Analysis of research papers will also familiarise students with a wide variety of scientific techniques and expose them to professional standards of scientific writing and data documentation.

Students will be given introductory lectures to introduce them to selection and evaluation methods. Subsequently sessions will take the form of whole group discussion based on self-guided analysis that the students have carried out alone and in small groups. Students will be expected to play an active role in selecting suitable papers for discussion and feedback by tutors will be available at all stages of the self-guided and group discussion sessions.

The current module complements the Data Analysis Skills and Hot Topics in Biomedical Science modules by providing students with training in basic laboratory skills commonly required for bench-based biomedical research. The module will run over the first two terms.

The ultimate goal of personalised, or precision, medicine is to create healthcare strategies that are tailored to each individual patient. This will be achieved by integrating molecular information with traditional clinical and pathological signs of disease. Advances in genomic technologies have provided new perspectives across medicine, particularly in screening, diagnosis, disease classification and treatment. This module explores how this research is being translated into clinical practice to make personalised medicine a reality.

Personalised medicine is not limited to pharmacogenetics (which examines the effect of genetic variation on drug targets, metabolism, efficacy and toxicity) but looks more broadly at how molecular profiling can influence health outcomes. Examples include molecular therapies used in oncology, advances in pre-natal screening and management of infection. We also consider ethical and social issues surrounding personalised medicine, including patient responses to genetic testing, the challenge of translating research results into clinical practice, genetic discrimination and regulation.

Examples are drawn from cancer, infection and other clinical specialties.

The supervised research project constitutes a central learning activity by providing immersive, work-based training in translational biomedical science. A research project involves choosing a subject, formulating a specific research question or aim, devising a research strategy to address this question, performing the research and analysing the resulting data. Project background, experimental procedures, results and discussion are written up as a 10,000-word dissertation and presented orally to an audience with the aid of a poster or PowerPoint presentation.

When completing your application, you will be asked to provide contact details of two referees. Please ensure these details are accurate. As soon as you have submitted your application, your referees will be contacted by the university asking them to upload a reference to your online application.

One must be a recent academic reference. The other should be either a second academic reference or a professional/employer reference. They should cover your suitability for the course and your academic ability.

Your referees should know you well enough, in an official capacity, to write about you and your suitability for higher education. We do not accept references from family, friends, partners, ex-partners or yourself.

We will send reminder emails to your referees but it is your responsibility to ensure that contact details are correct and referees are available to submit a reference. References should be uploaded within two weeks of making your application.