Launch Keynote Speakers

Director of Clinical Research
Kennedy Institute of Rheumatology
University of Oxford

Director, Birmingham NIHR Clinical Research Facility
Institute of Inflammation and Ageing
University of Birmingham

Christopher Buckley


Career summary: I obtained a degree in Biochemistry from the University of Oxford (1985) with subsequent undergraduate training in Medicine (MBBS) at the Royal Free Hospital, London (1990). My postgraduate medical training was in General Medicine and Rheumatology at the Hammersmith Hospital, London (Mark Walport, Dorian Haskard), and John Radcliffe Hospital, Oxford. I obtained a DPhil arising from a Wellcome Trust Clinical Training Fellowship with John Bell and David Simmons at the Institute Molecular Medicine, Oxford in 1996. Funded by a Wellcome Trust Clinician Scientist Fellowship, I joined the Department of Rheumatology in Birmingham later that year. In 2001 I was awarded an MRC Senior Clinical Fellowship and in 2002 became Arthritis Research UK Professor of Rheumatology. In 2012 I was appointed Director of the Birmingham NIHR Clinical Research Facility. In May 2017 I took up a new joint academic post between the Universities of Birmingham and Oxford as Director of Clinical Research at the Kennedy Institute of Rheumatology Oxford and Director of NIHR Infrastructure in Birmingham for Birmingham Health Partners to Direct the Arthritis Therapy Acceleration Programme (A-TAP). Research: The synovium is a thin mesenchymal membrane encapsulating the joint space and is the major site of pathology in rheumatoid arthritis. Synovial fibroblasts comprise a key cell type in the hyperplastic pannus that invades and destroys cartilage and bone but are also major contributors to inflammation by providing an amplificatory loop that drives the production of cytokines such as IL6. Despite their biological importance, remarkably little is known about how fibroblast numbers and subsets change during inflammation in human arthritis.  Difficulties in accessing patients with very early disease, sampling the tissue involved and the lack of good fibroblast markers have all proved obstacles to such work. Our work aims to address how synovial fibroblast subsets, residing either in the lining or sub-lining layer, differentially mediate synovial inflammation and tissue damage. Understanding how fibroblast subsets lead to persistent inflammation compared to  tissue damage would be a major step in their selective therapeutic targeting and would complement current gold standard biologic therapy that target leucocytes and their cytokine products. Until now, functional subclasses of fibroblasts have proven difficult to define, characterize and study in health and disease. Consequently, there are no approved drugs that specifically target fibroblasts in human diseases. In contrast the identification of leucocyte subsets with non-overlapping effector functions provided a molecular framework for the development of targeted therapies that have demonstrated spectacular success in immune-mediated inflammatory diseases (IMIDs). However little is known about how fibroblast numbers change during inflammation and tissue damage in human disease. Furthermore, it remains unknown whether the processes of inflammation and tissue damage, mediated by fibroblasts are always coupled, reflecting cellular plasticity residing within a single fibroblast population or instead, are uncoupled and mediated by different subsets of fibroblasts. In this lecture I explain the interrelationships between synovial fibroblast subsets in the lining and sub-lining layers of the synovium and observe how selective deletion of these subsets or changes in their biology alter the balance between persistent inflammation and tissue damage during the development of arthritis. Next I will describe the functional relationships between alterations in fibroblast subsets and disease outcome during the development of human rheumatoid arthritis. Finally I will speculate on how clinical trials targeting fibroblasts in patients with IMIDs might be delivered using current experimental medicine infrastructures within the new Arthritis Therapy Acceleration Programme, a joint venture between the Universities of Oxford and Birmingham.

Igor Jurisica


Biography: Dr. Igor Jurisica, PhD, DrSc is a Senior Scientist at the Schroeder Arthritis Institute and the Krembil Research Institute, Professor at the University of Toronto and Visiting Scientist at IBM CAS. He is also an Adjunct Professor at the Department of Pathology and Molecular Medicine at Queen’s U, an adjunct scientist at the Institute of Neuroimmunology, Slovak Academy of Sciences and an Honorary Professor at Shanghai Jiao Tong University. Since 2015, he has served as Chief Scientist at the Creative Destruction Lab, Rotman School of Management. He has published extensively on data mining, visualization and cancer informatics, including multiple papers in Science, Nature, Nature Medicine, Nature Methods, J Clinical Investigations, J Clinical Oncology. Dr. Jurisica has won numerous awards, including a Tier I Canada Research Chair in Integrative Cancer Informatics, the IBM Faculty Partnership Award (3-time recipient), and IBM Shared University Research Award (4-time recipient). He has been included in Thomson Reuters 2016, 2015 & 2014 list of Highly Cited Researchers, and The World’s Most Influential Scientific Minds: 2015 & 2014 Reports. In 2019 he was included in the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare list. Research: Research focus on integrative informatics and the representation, analysis and visualization of high-dimensional data to identify prognostic/predictive signatures, determine clinically relevant combination therapies, and develop accurate models of drug mechanism of action and disease-altered signaling cascades. In addition to artificial intelligence, machine learning, data mining, knowledge representation and graph theory are essential approaches for complex data analysis. It has been established that despite inherent noise present in protein-protein interaction data sets, systematically analysing resulting networks uncovers biologically relevant information, such as lethality, functional organization, hierarchical structure and network-building motifs. These results support strong structure-function relationships in these networks. We are developing novel graph theory-based algorithms to gain insights from multiple networks – including protein interaction, transcription regulatory and microRNA-target networks. We use this information to build predictive models and to integrate them with gene/protein expression profiles to identify relevant patient phenotypes and subgroups. While these approaches help us to improve our understanding of healthy and disease tissue, enable biologically meaningful modeling of altered signaling cascades, and use these explainable models to improve patient treatment and outcomes, molecular profiling alone is not sufficient to achieve intelligent molecular medicine. Comprehensive, integrative data analysis and computational decision-support systems enables identifying high risk arthritis patients. In addition to clinical parameters and molecular data, wearable devices provide significant additional data to assess risk, provide feedback to the patient, and vital information to the clinician. Using the proposed tools and methodology, physicians will have more relevant information available at the time of diagnosis and treatment planning, and the patient will have a better explanation of the disease, its origin, progression path and treatment alternatives.

Professor, Department of Computer Science, University of Toronto

Professor, Department of Medical Biophysics, University of Toronto

Visiting Scientist, IBM Centre for Advance Studies, IBM Toronto Lab

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