PLENARY SPEAKERS
Professor Hutchinson is the Director of the ARC Centre of Excellence for Nanoscale BioPhotonics (CNBP) and a Professor within the Adelaide Medical School at the University of Adelaide.
Professor Hutchinson’s research explores the “other brain” or the other 90% of cells in the brain and spinal cord. These immune-like cells are termed glia. Mark’s research has implicated the brain immune-like cells in the action of drugs of dependence and the negative side effects of pain treatments. He has pioneered research which has led to the discovery of novel drug activity at innate immune receptors. His work has enabled the translation of compounds at the lab bench to clinical agents used at the bedside.
He has now added Director of the CNBP to his roles. The CNBP is an ARC Centre of Excellence with $50M of funding committed for 7 years, headquartered at The University of Adelaide, with nodes at Macquarie University, Sydney and the RMIT, Melbourne. We are partnered with universities and companies in Europe, the US and China, as well as other Australian institutions. Prof Hutchinson’s work with the CNBP is to "Discover new approaches to measure nano-scale dynamic phenomena in living systems” and allow the first minimally invasive realtime visualisations of the “other brain”.
Title:
How quantifying how you know you are sick can change how we understand pain.
Abstract:
The aetiology of persistent pain in humans is comprised of a complex, twisted and multi factorial journey that culminates in a "cancer of the soul". Recent advances in the basic science underpinning our mechanistic understanding of persistent pain have embraced "the other brain" as an integrator of multiple life stimuli. This complex integration of life experiences, which are translated into neurokine signals cause the neuroimmune cells of the central nervous system to adapt and change the environment in which the neuronal system operates. If these adaptations present in the somatosensory neuroanatomical locations then this can present as hypernociception and eventual persistent pain. Our appreciation for this neuroimmune signalling and its contributions to the health and disease of the brain has its origins in the study of the illness response. It is now apparent that these specialised brain-immune processes are engaged in a range of other disparate responses, including the rewarding properties of drugs of abuse. However, no one has yet visualised the working neuroimmune synapse in a behaving clinical or preclinical model. This also means that the molecular origins of pain have yet to be quantified. This presentation will summarise recent studies conducted within the Australian Research Council Centre of Excellence for Nanoscale BioPhotonics in this field and equip the attendees with further insights of the complexity and power that visualising and sensing the “other brain” with next generation light science and related technologies can brings to understanding persistent pain and drug responses.
The focus of Dr. Connie Wong's research is investigating the pathophysiology of stroke and the subsequent host inflammatory response. After completing her PhD at Monash University in 2008, Connie was trained in the Snyder Institute for Chronic Diseases at the University of Calgary in Canada (2008-2012) and returned to Monash University in 2013, before heading her own lab in 2015. Connie has published >45 journal articles, including first/senior author in Science, Nature Immunology and Nature Medicine.
Connie was awarded "The Centenary Institute Lawrence Creative Prize" in 2013 and Victorian Tall Poppy award in 2017. Her research is funded by NHMRC and National Heart Foundation. She is a current recipient of the CSL Centenary Fellowship.
Title:
Intravital microscopy of leukocytes.
Abstract:
The capacity of leukocytes to move between blood and tissues or organs, interact with diverse types of other cells within the body, and adjust behaviour and morphology upon environmental changes, are all crucial features for securing the survival of an organism as a whole. As such, an ability to image motile leukocytes in 3 dimensions in situ in tissues and organs over time (the 4th dimension) in a living organism presents a unique and powerful tool that allows for real-time investigation of their function and behaviour. This approach of “seeing” cells in a live animal is called intravital imaging or in vivo microcopy. Although intravital imaging is considered a new tool in biomedical research, in reality, its roots originated in the 19th century. By the end of the 20th century, in an era of fluorescent and confocal microscopy, the technique was substantially enhanced embracing not only new microscopic technologies but also the development of new surgical approaches which expanded the repertoire of tissues and organs that can be imaged. In this talk, I will present the latest advances in research on leukocytes that were made possible with the application of intravital microscopy.
Eva Bezak is a Professor in Medical Radiations and Centre Director for Translational Cancer Research at the University of South Australia. Previously she was Chief Physicist at the Department of Medical Physics, Royal Adelaide Hospital, providing services to radiation oncology in South Australia. She has authored and co-authored over 140 papers, 250 conference presentations and co-authored books on medical physics and supervised over 30 HDR students.
Bezak and her group are national leaders in radiation biology modelling using Monte Carlo algorithms. These were either developed in-house or using the existing Monte Carlo packages: SRIM, EGSnrc, GEANT4, MCNP. Other research interests include targeted alpha therapy, microdosimetry and artificial intelligence in health care. At UniSA, Prof Bezak and her group have established themselves as national and international leaders in radiation biology modelling using Monte Carlo computational algorithms. Some of the models developed are world class, first of its kind, attracting praise from international referees, e.g. their work was considered to be "light years ahead of everyone else in the field".
They have developed the most advanced and sophisticated 4D (temporal and spatial) in-silico tumour model presently available in the world that has true biological and radiobiological properties of specific cancers (e.g. a head and neck cancer), including tumour growth, cellular hierarchy (including cancer stem cells and differentiated cells), spatial distribution and chaotic tumour vasculature (and therefore can predict oxygenation of cells as a function of distance from blood vessels). Following irradiation, DNA cluster damage is calculated and cell death predicted based on radiation damage to DNA. Tumour growth restarts post “virtual irradiation”. Treatment regimens can be thus be simulated and treatment outcomes predicted.
Title:
Radiation biology and the quest for personalized cancer therapies: in silico approaches.
Abstract:
At present, quite correctly, radiation therapy for cancer is delivered based on departmental protocols derived from published clinical trials. However, reported data show that radiotherapy response varies from patient-to-patient, despite using uniform treatment protocols. This is due to a number of patient specific factors, like comorbidities and lifestyle as well as due to other factors (interaction of radiation, uncertainties in organ motion, etc.). But perhaps the most dominant factor (up to 80%) in radiation response of individual patients, is their own genetic predisposition, dictating how radiosensitive an individual is. This in turn means that using uniform solutions/protocols does not benefit all patients.
As such, more personalized radiation oncology approaches are needed based on understanding and utilization of broad data currently available.
How can we resolve all these challenges? We can have a) more clinical trials (but these are time consuming and costly, and sometimes not beneficial to patients), b) additional R&D (in vitro, animal models) or c) computational (or in silico) modelling.
Computational modeling of various treatment regimens and their input parameters can offer a comprehensive understanding of the radiobiological interactions and also the treatment outcome, without the involvement of lengthy trials.
Computational models allow us to explain observations, for example compare clinical trial results; they can predict clinical outcomes under conditions not previously measured (e.g. alternative schedules); allow us to optimise radiotherapy and other treatments (e.g. chemotherapy) and identify and evaluate risks; e.g. radiation/chemo side effects.
As such those treatment protocols that can be identified as not beneficial to patients can be eliminated immediately and do not have to progress to clinical trials. Only those treatment regimens that show promise and benefit can be then trialed in a clinical setting.
