(FoRCE): Powering clinical trials research through a secure and integrated data management platform
Critical care units are one of the most data-rich environments in clinical settings, with data being generated by advanced patient monitoring, frequent laboratory and radiologic tests, and around-the-clock evaluation. There are substantial opportunities in linking data that are collected as a part of such clinical practice with data collected in a research setting, such as genome wide studies or comprehensive imaging protocols. However, security and privacy issues have historically been a significant barrier to the storage, analysis, and linkage of such biomedical data. Further, disparate technologies hinder collaboration across teams, most of which lack the secure systems required to enable federation and sharing of these data. This is particularly true when clinical practice or research designs require close to real time analysis and timely feedback, such as when dealing with streamed medical data or output from clinical laboratories. Current commercial and research solutions often fail to integrate different data types, are incapable of handling streaming data, and rely solely on the security measures put in place by the organizations that deploy them.
This proposal seeks to build FoRCE (Focus on Research and Clinical Evaluation), a scalable and adaptable add-on module to the existing Indoc Informatics platform that will address the critical gaps in cybersecurity and privacy infrastructure within shared clinical and research settings, while fulfilling important unmet needs for both the clinical and research communities. FoRCE will provide the secure architecture and processes to support the collection, federation and sharing of data from distributed clinical settings, including critical care units, clinical laboratories, and imaging facilities. The proposed platform will address several key issues including security considerations, infrastructure and software requirements for linkage, and solutions for handling streaming real time medical data, and ensuring regulatory and ethics compliance when linking diverse medical data modalities in a clinical setting.
FoRCE will be designed and developed with broad applicability in mind, and will therefore allow the different data types from numerous technologies and across multiple disease states to utilize the platform. The long term impact of FoRCE on improving the health of Ontarians is of course dependent on its utilization within research and clinical settings. An initial project which will utilize the platform as part of the testing and validation of FoRCE includes Dr. Maslove’s integrated approach to merging genomic and physiologic data streams from the ICU in the context of clinical research. FoRCE will enable Dr. Maslove’s team of critical care researchers to move beyond predictors of survival to focus on predictors of response to therapy, so that clinical trials in the ICU can be optimized to produce actionable evidence and personalized results. This will lead to better allocation of ICU resources, which in Canada cost nearly $3,000 per patient per day – $3.72 billion per year.
A cloud‐based, multi‐modal, cognitive ophthalmic imaging platform for enhanced clinical trial design and personalized medicine in blinding eye disease
Age Related Macular Degeneration is the leading cause of irreversible blindness in Canada and the industrialized world, yet there are no treatments for the vast majority of patients. Led by Tracery Ophthalmics inc, and working with Translatum Medicus inc (TMi) and academic partners at the Robarts Research Institute, Western University, and the “High Risk Dry AMD Clinic” of St Michael’s Hospital, we will engage SOSCIP’s Cloud Analytics platform, including servers, software and human resources, to accelerate the search for new treatments.
Specifically, Tracery has developed a novel functional imaging method, “AMD Imaging” (AMDI) that has already generated unprecedented pictures of the retina (the film of the eye) that include both known and unknown “flavours” of disease (the phenotype). These complex images will be compared against an individual’s genetic makeup (their genotype) and their concurrent illnesses, medications, and lifestyle history (their epigenetics). Further, Tracery’s imaging will help identify particular patients that will benefit from TMi’s drug development program, and ultimately help doctors choose which treatment will work best. Over the course of two years, we will involve increasing numbers of medical experts and their patients to generate and amass AMDI images, evaluating them over time and against other modalities.
Ultimately, through the “I3” program, we will work with IBM to train Watson and the Medical Sieve to recognize and co‐analyse complex disease patterns in the context of the ever‐expanding scientific literature. In short, we will leverage cloud‐based computing, to integrate image‐based and structured data, genomics and large data analytic to unite global users. We anticipate that this approach will significantly accelerate drug development, providing personalized treatment for the right patient at the right time.
Advancing microarray analysis with GPU-based image analysis
Multiplexed tests detect multiple analytes from a single biological specimen in an automated fashion using microarrays. These can be used to determine patient immune response with minimal invasiveness and to better quantify biomarkers for advanced biological tests. To do this, microarrays require the printing of biological and chemical materials onto an optically transparent substrate (this require dispensing, curing, putting down a protective coating that is then dried to create a plate). Each well contains multiple spots (sensors) to detect different proteins. The proposed work improves analytical tools with a focus on accuracy, significantly decreased time to results and advanced image analysis using capability provided by SOSCIP. The SOSCIP platforms allow testing new approaches using cloud-based analysis as well as develop tools necessary for in-house analysis. The work will enhance the performance of current assay designs and inform the next generation of assays to support the partner’s technology leadership position. The results will be implemented immediately by the industry partner – as has been done in the previous work. The impact will be to place SQI in a unique market position in the diagnostics market. Ultimately, this work aligns with the partner’s goal of “cheaper, better, faster”.
Agile computing for rapid DNA sequencing in mobile platforms
DNA can now be measured in terms of electronic signals with pocket-sized semiconductor devices instead of 100-pound machines. This has started to transform DNA analysis into a mobile activity with the possibility to track and analyze the health of organisms at unprecedented levels of detail, time, and population. But the remote cloud-based computer services currently needed to process the electronic signals generated by these miniature DNA-meters cost over $100 to complete an initial analysis on one human genome and consume over 1000 Watts of power. Also, the cost of wirelessly transmitting measured data to these cloud-based analyzers can exceed $1000 per human genome. Further, reliance on external high-performance compute services poses a greater risk for compromising the security of the DNA data collected. This project proposes the construction of a specialized high-performance miniature computer – the agile base caller (ABC) – built from re-configurable silicon chips that can connect directly to the DNA-meter and analyze the DNA it measures in real-time.
The ABC, by virtue of its size, will preserve the mobility of emerging DNA measurement machines, and will enable them to analyze data for less than $1 while consuming less than 10 Watts. These cost/power performance improvements will significantly drop the barriers to the application of genomic analysis to non-laboratory settings. For example, they will allow continuous monitoring of Canada’s food supply for the presence of harmful biological agents with the possibility of cutting analysis delays from weeks to hours.
Application of advanced machine learning and structure-based approaches for repurposing & discovering therapeutics for COVID-19
The novel COVID-19 pandemic caused by SARS-CoV-2 is a global health emergency of international concern with an estimated death toll in millions worldwide. The development, testing and approval of the COVID-19 vaccine may take at least 12-18 months. By then, the virus could mutate and reduce the vaccine’s efficacy. This project aims to leverage the latest advances in AI to repurpose existing drugs and identify novel ones that could be further developed as COVID-19 therapies in an extremely condensed manner. We will utilize Apollo 1060, 99andBeyond’s AI-augmented decision-making platform that can rapidly search a chemical space that is orders of magnitude larger than competitors. We will collaborate with experimentalists, and aim to have a set of compounds confirmed in a set of in vitro COVID-19 assays within the next six months. The proposed set of compounds and their corresponding biological activity will be openly published to help the community build powerful predictive models for COVID-19 targets. Their further testing and development will require the engagement of collaborating physicians in the hospitals and may attract partnerships with leading pharma and biotech in the US and Canada.
Automated cytogenetic dosimetry as a public health emergency medical countermeasure
Biodosimetry is a useful tool for assessing the radiation dose received by an individual when no reliable physical dosimetry is available. Our Automated Dicentric Chromosome Identifier software (ADCI) has been developed to automate dose estimation of gamma and X-ray radiation exposures. Biodosimetry laboratories currently process these data manually, and the capacity to handle more than a few samples at the same time would quickly overwhelm the laboratories. The software has been developed to handle radiation exposure estimation in mass casualty or moderate scale radiation events. Federal biodosimetry and clinical cytogenetic laboratories have automated systems to collect digital chromosome images of cells with and without chromosomes exposed to radiation. We have developed advanced image segmentation and artificial intelligence methods to analyze these images. ADCI identifies dicentric chromosomes (DCC), a widely recognized, gold standard hallmark of radiation damage. The number and distribution of DCCs are determined and compared with a calibration curve of known radiation dose. Our ADCI software can also generate these calibration curves. Our software computes the dose received of one or more test samples and generates a report for the user. The desktop version of ADCI contains an easy-to-use graphical user interface to perform all of these functions. The supercomputer version of this software proposed here will be optimized to determine the dose for many samples simultaneously, which would be essential in the event of a mass casualty.
Big cardiac data
We propose to develop a cloud based image centered informatics system powered by newly developed big data analytics from our group for automatic diagnosis and prognosis of heart failure (HF). HF is a leading cause of morbidity and mortality in Ontario and around the world. There is no cure. Therefore early diagnosis and accurate prediction is very critical before the symptoms appears. However, previously lack of efficient image analytics tool has been the major pitfall to have an accurate prognosis and early diagnosis system. The system will greatly improve the clinical accuracy and enables accurate early diagnosis and prediction by intelligently analyzing all associated historical images and clinical reports. The final system will not only greatly reduce the sudden death and irreversible cardiac conditions, but also offers a great optimization of decision system in current healthcare. This project is based on strength built upon one successful SOSCIP project and two OCE projects.
Cloud‐based data analytic platform for real‐world evidence generation
Randomized controlled trials (RCTs) are considered the gold standard for supportive clinical evidence, however RCTs rarely describe the effectiveness of an intervention in real life practice. As such, regulators, payers, and health‐care providers are turning towards real world data (RWD) to understand how well an intervention performs in clinical practice.
The best source of RWD is source data – that is, data that are collected at the interface of the patient and the health care system, as well patient monitoring and self‐reported data captured directly within the patients’ home environment. Unfortunately, these data are stored in different and distinct systems that are not well integrated and in formats that do not allow for rapid analytic capabilities. The University of Waterloo in partnership with Roche Canada, are therefore proposing to develop the “CARE” (Clinical Analytics for Real‐World Evidence) platform.
The “CARE” platform will be a holistic cloud‐based data analytic solution featuring a large central repository for consolidating data obtained from disparate data systems (including data from electronic medical records, lab data, physician transcriptions, and patient monitoring devices and self‐reported surveys). The “CARE” platform will serve as a central hub for researchers that includes integrated and sophisticated data analytics, access control, security and study management tools in order to curate data for research and clinical purposes. This project begins the initial steps to address relevant research objectives in lung cancer by providing the tool and infrastructure required to process and analyze the enormous amount of scattered oncology data within an institution and across multiple institutions. Ultimately, this work will make it possible to mine currently siloed and/or unstructured data across the system and produce data‐driven insights in order to deliver the right care to the right patient at the right time through scientific innovation and research excellence.
Continuous vital sign monitoring using intelligent bed sheet
Studio 1 labs developed wireless intelligent bed sheet patient monitoring system that continuously captures client vital signs. In collaboration with Dr. Laura Nicholson and York University’s Faculty of Health, Studio 1 labs will work with SOSCIP infrastructure to match vital signs with gold standards approved medical decides for the highest level of accuracy through clinical validation and scientific evidence. With millions of data points collected from each device to output clinical grade quality information continuously, AI solutions allow modeling to predict health emergencies and diseases. This contributes to efficient health monitoring solutions that are simple and effective for use by older adults and healthcare providers.
Development of cardiac specific machine learning infrastructure
Analytics for Life, Inc. (A4L) is an early stage medical device company that specializes in the development of technologies to analyze patient physiological signals in order to evaluate cardiac performance, status and risk. A4L’s core competencies include identifying and developing mathematical features from physiological signals and assembling these features into clinically informative formulae using machine learning techniques. A4L has used third party machine learning tools (open source and licensed products) for the formula generation aspect of the product development cycle.
Specifically, A4L has used these tools to demonstrate the feasibility of computing left ventricular ejection fraction, cardiac ischemic burden and other cardiac performance/status parameters for simple to collect, non-invasive physiological signals (surface voltage gradients, SPO2, Impedance etc.). As a result of this experience, A4L have learned the benefits and insufficiencies of these tools for A4L’s specific purposes. A4L plans to file with the U.S. Food and Drug Administration (FDA) an application for approval of a physiological signal collection device and will soon afterwards be seeking market clearance for products assessing cardiac health emanating from the machine learning process. A4L believes it can build a machine learning tool specifically tailored for cardiac evaluation based on experience with the tools used to date.
This A4L-specific machine learning paradigm will search only relevant mathematical spaces, cutting down on time and CPU power needed to iterate to solutions and will allow for an assessment of a much wider array of potential solutions. Furthermore, this A4L-specific machine learning paradigm will provide a controlled and validated system that can be audited and evaluated by regulatory bodies, something that is not possible with the current machine learning tool(s). A4L proposes a hybridization of paradigms within a set mathematical space. This will create efficiency in the search, and therefore more searches can be performed in the same period of time. This will lead to more solutions being available for evaluation, resulting in more accurate and efficiently produced end solutions. If successful, this new paradigm will allow for simple, non-invasive, rapid and relatively inexpensive cardiac diagnostic capabilities, bringing tertiary care diagnostics to primary care settings and disrupting the current infrastructure and capital cost-centric model of diagnostic delivery.
Genetic variation and structure-based drug polypharmacology: multiscale structural pharmacogenomics
Over the past 25 years, drug discovery efforts have been aimed at identifying small organic molecules that exert a strong effect on a single protein that corresponds to the biological target of a disease. Although designed to fulfill a single biological function, small molecule drugs can incidentally interact with hundreds of the ~20,000 known human gene products. These off-target interactions may lead to side effects, new therapeutic effects, or otherwise affect drug response. Mapping a drug to all of its human protein interaction partners is known as polypharmacology. Thus, it is advantageous to understand the polypharmacology of each drug in development prior to clinical testing in order to better anticipate off-target effects that could potentially cause toxicity. However, to address drug polypharmacology experimentally is prohibitively expensive in time and monetary cost. Predicting a drug’s polypharmacology is Cyclica’s central mandate. Our drug discovery platform is based on proteome-wide screening, and centered around PROBEx, a cloud-based software solution that simulates a drug’s interaction with 100,000’s of proteins on the basis of their molecular structures. PROBE x maps out the possible off-target protein partners and consequently pharmacology for any pharmaceutical or nutraceutical compounds, to service academic research institutes, hospitals, and pharmaceutical companies developing new disease therapies. Pharmacogenomics is the study of how genes affect a person’s response to drugs by combining pharmacology (the science of drugs), and genomics (the study of genes and their functions) to develop effective, safe medications that are tailored to a person’s genetic makeup.
This field, commonly termed “personalized medicine” has recently emerged and gained significant attention from many companies, including IBM Watson Life Sciences. To our knowledge however, information pertaining to the structural proteome and its relation to drug binding, has not yet been systematically leveraged to improve pharmacogenetic analyses. This technology gap represents a unique opportunity for Cyclica to contribute to personalized medicine, by working with SOSCIP and the IBM cloud analytics platform. Cyclica can integrate its drug polypharmacology predictions with clinical and genomic data to build models that can explain and predict variation in drug response between different individuals. Through Industrial Partner: Cyclica Inc. this program, we will create new tools that match patients to the pharmaceuticals best suited for their distinct genetic features. Ultimately, the effort will improve outcomes and limit the loss of valuable time associated with trial-and-error medicine.
Harnessing the diversity of phage displace libraries to capitalize on single domain antibodies with high target affinity and improved protease stability
AbCelex Technologies is developing a line of novel antibody-based products delivered as feed additives to poultry for the prevention of foodborne illnesses caused by Campylobacter and Salmonella. AbCelex’s single domain antibody (sdAb) platform technology is based on camelid antibodies, which have significantly higher thermal and protease stability, accessibility to target due to their small size (1/10 of conventional antibody) while providing affinities that exceed that of conventional antibodies. The current platform utilizes bioinformatics approaches that take into account genetic and protein diversity of the pathogens and in silico antibody engineering predictions to inform optimal design of sdAbs with competitive affinity, effectivity and cost as feed additives. In this project, we aim to combine the benefits of the natural scaffold of the camelid antibody and the expertise we have developed on understanding the interactions of these sdAbs with their target to develop computational libraries that we can then validate in vitro, ex vivo and ultimately in vivo.
For this purpose, we propose to collaborate with Dr. Mauricio Terebiznik, with whom we have been developing a detailed database of the target-antibody interaction mechanisms. He is an expert in cellular biology mechanisms altered by the pathogens such as Salmonella. The major barrier to achieve this aim is access to high performance computing and the SOSCIP platform is ideal to overcome this barrier. Furthermore, the postdoctoral funding will allow the candidate to build on the backbone of camelid antibodies rationally selected peptides that would provide target specificity and cross-reactivity across different strains of the same bacterial pathogen through this proposed collaboration.
High-throughput transcriptomic and eQTL analyses of silicon-induced resistance against Fusarium head blight on wheat
Wheat is an essential part of the agriculture and agri-food industry in Ontario with an average annual production of 2.5 Million tons. High yield and quality of wheat is seriously threatened by biotic and abiotic factors, among which Fusarium head blight (FHB, caused by the fungus Fusarium graminearum) has historically been most damaging. The mycotoxin produced by this fungus is harmful for human health and livestock feed and productivity. Conventional chemical fungicides are commonly used as an important mean to control FHB; however, they also pose a serious risk to the health of humankind and the environment. One of the most promising non-hazardous, eco-friendly methods to control different plant pathogens is the use of silicon, which induces resistance.
Backed by SOSCIP high-performance computing resources, and using novel bioinformatic approaches, this study aims at conducting high-throughput genome-wide transcriptomic analysis of silicon-induced resistance against FHB, and possibly other important diseases, in wheat. Such study will lead us to:
1. understanding the complex underlying induced defense mechanisms
2. identifying gene modules and regulatory elements that control such mechanisms, and finally and more importantly
3. providing valuable building blocks and framework for future breeding programs which will be focused on the development of novel disease-resistant wheat cultivars in Ontario.
HPC cloud analytics / machine learning support for Watson Pepper clinical study
Skin cancer is the most common type of cancer. 80,000 cases of cancer are diagnosed in Canada every year. 5000 of these cases are melanoma, the deadliest form of cancer (Canadian Skin Cancer Foundation). Current prevention efforts to reduce skin cancer focus on educating individuals on preventative actions that they can take to reduce the risk of this cancer. However, research has shown that both communication failure and information overload are significant problems affecting the quality of patient centered care. Social robotics and artificial intelligence have been used effectively to communicate and positively influence behavior, thus this research proposes to develop and test these combined technologies as an intervention for skin cancer prevention education.
The research team and collaborating research partner IBM will integrate IBM Watson cognitive computing applications with Softbank Robotics advanced robotics platform, the Pepper robot. The Watson Pepper prototype will be used as a controlled variable in a randomized controlled clinical trial (N = 200) to assess the efficacy of socially assistive robotics intervention for behavioural change in skin cancer prevention knowledge and practices among medical patients, the first clinically tested implementation of a Watson Pepper robot for healthcare communication. The research proposes commercialization and business implementation of the integrated IBM Watson robot in an expanded scale and scope of healthcare communication applications. To support the achievement of this innovative technology milestone, SOSCIP will provide the critical cloud data analytics and memory capacity to support the analysis and modeling of the large multivariate data sets associated with this project.
Personalized predictive risk for medical imaging radiation exposure
This project will build the expert team and create the tools required to understand the long-term effects of low dose radiation exposure from medical imaging on populations and facilitate the adoption of best practices to decrease the impact of imaging related radiation exposure. Although this project will focus on low dose radiation exposure from medical imaging, the tools and approaches that will be developed will be transferrable to other specialties in the health care field.
To achieve these goals, we propose to:
(1) Develop a standard-based, extensible data model for a provincial platform to reconcile radiation dose with patient medical information;
(2) Develop generic data mining tools that can be customized to query Big Data repositories for use with major modalities, including Electronic Patient Records and Electronic Medical Records across the province;
(3) Investigate algorithmic approaches to extract and structure data from reports and text in DICOM headers, and from free text in notes and summaries in electronic patient records;
(4) Develop intelligent terminology mapping and quality assurance mechanisms to ensure data quality; and,
(5) Develop a feedback mechanism for decision support at the point of care related to imaging procedures.
Quantitative analysis of Parkinson’s disease symptoms using whole-body kinematic technology for optimizing deep brain stimulation
Parkinson’s disease (PD) is a degenerative and progressive neurological disorder that can severely affect the mobility, particularly in advanced stages. Deep brain stimulation (DBS) is a surgical procedure used to treat the motor symptoms of Parkinson disease patients by implanting electrodes into the brain. Motor symptoms of PD like slowness of movement, tremor, walking and speech difficulty, and dyskinesia, are the key parameters that are taken into consideration by clinicians to program the DBS device. These motor symptoms differ across the body and are unique to each patient. Moreover, patient variability in terms of medication and non-motor symptoms like sleep and mood makes programming DBS electrode setting a significant challenge. On the other hand, the biomechanics of the whole-body are too complex to accurately assess visually due to the multitude of simultaneous movements. Therefore, a detailed, quantitative method of assessment needs to be employed to make the DBS setting optimization as accurate as possible.
Safe learning-based control for high-precision assembly robots in advanced aerospace manufacturing
Ontario’s aerospace industry has a distinguished history of innovation, and it provides a strong contribution to the Canadian economy with its more than 350 firms employing more than 20,000 Ontarians. Our industrial partner, MDA Robotics and Automation, is a leading company in providing advanced engineering solutions for various industries, including aerospace robotics and manufacturing. Having designed and built the famous Canadarm and Canadarm2 for manipulation in space, building versatile, high-precision assembly robots for aerospace applications on earth (such as airframe assembly for an airplane) is at the top of the company’s current priorities. Potential advantages include lowering the assembly cost, increasing the productivity, and reducing the risk of human error. Unlike high-volume manufacturing processes, for which it pays off to go through the lengthy, possibly month-long, process of manually programming and tuning the robot controls to carry out a limited range of repetitive tasks in a well-defined environment, the diversity of tasks and the uncertainty of the work environment in aerospace manufacturing motivate the need for smarter robots that can safely interact with their unpredictable environment, improve their performance through learning, and more importantly, generalize the knowledge from previous tasks and/or other robots to adapt to new tasks not trained on before.
The objective of the project is to combine advanced methods from control theory, machine learning, and optimization to develop computationally efficient, learning-based control algorithms that improve the assembly robots’ performance in uncertain scenarios. Our strategy includes utilizing (i) well developed algorithms in our group for online, safe learning of robots, and (ii) deep learning for transferring the knowledge learned by one robot in a particular task to other similar tasks and/or robots.