Hello, welcome to the final day of the NTT Research Summit Upgrade 2020. My name is Joe Alexander, and I belong to the Medical and Health Informatics lab, so-called MEI lab, and I lead the development of the bio digital twin. I’d like to give you a high-level overview of what we mean by bio digital twin, what some of our immediate research targets are, and a description of our overall approach. You will note that my title is not simply bio digital twin, but more specifically a cardiovascular bio digital twin, and you’ll soon understand why.

What do we mean by digital twin? For our project, we’re taking the definition on approach used in commercial aviation, mostly for predictive maintenance of jet engines. A digital twin is an up-to-date virtual representation, an electronic replica if you will.

Now, if anything which gives you real-time insight into the status of the real-world asset to enable better management and to inform decision-making. It aims to merge the real and the virtual world. It enables one to design, simulate, and verify products digitally, including mechanics and multi-physics. It allows integration of complex systems. It allows for predictive maintenance through direct real-time monitoring of the health and structure of the plane parts, mitigating danger. It enables monitoring of all machines, anywhere, at all times. This allows feeding back insights to continuously optimize the digital twin of the product, which in turn leads to continuous improvement of the product in the real world. A robust platform is needed for digital twins to live, learn and run. Because we aim to apply these concepts to biological systems for predictive maintenance of health, we use the term bio digital twin.

We’re aiming for a precision medicine and predictive health maintenance. And while, ultimately, we intend to represent multiple organ systems and the diseases affecting them, we will start with the cardiovascular system.

When we revisit concepts from the last slide, there’s the one-to-one mapping as you can see on this slide. A cardiovascular bio digital twin is an up-to-date virtual representation, as well, but of a cardiovascular system, which gives you real-time insight into the status of the cardiovascular system of a real-world patient to enable better care management and to inform clinical decision-making. It does so by merging the real and virtual worlds. It enables one to design, simulate, and verify drug and device treatments digitally, including cardiovascular mechanics and multi-physics. It allows integration of complex organ systems. It allows for predictive maintenance of health care through direct, real-time monitoring of the health and functional integration, or excuse me, functional integrity of body parts, mitigating danger. It enables monitoring of all patients, anywhere, at all times. This allows feedback to continuously optimize the digital twins of subjects, which in turn leads to continuous improvements to the health of subjects in the real world. Also, a robust platform is needed for digital twins to live, learn, and run. One platform under evaluation for us is called embodied bio-sciences. And it is a cloud-based platform leveraging AWS distributed computing database and cuing solutions.

There are many cardiovascular diseases that might be targeted by cardiovascular bio digital twin. We have chosen to focus on the two most common forms of heart failure, and those are ischemic heart failure and hypertensive heart failure.

Ischemic heart failure is usually due to coronary artery disease and hypertensive heart failure usually is secondary to high blood pressure. By targeting heart failure, number one, it forces us to automatically incorporate biological mechanisms, common to many other cardiovascular diseases. And two, heart failure is an area of significant unmet medical need, especially given the world’s aging population. The prevalence of heart failure is estimated to be one to one and a half. I’m sorry, 1 to 5% in the general population. Heart failure is a common cause of hospitalization. The risk of heart failure increases with age. About a third to a half of the total number of patients diagnosed with heart failure have a normal ejection fraction.

Ischemic heart failure occurs in the setting of an insult to the coronary arteries causing atherosclerosis. The key patho-physiologic mechanisms of ischemic heart failure are increased myocardial oxygen demand in the face of a limited myocardial oxygen supply. And hypertensive heart failure is usually characterized by complex myocardial alterations resulting from the response to stress imposed by the left ventricle by a chronic increase in blood pressure.

In order to achieve precision medicine or optimized and individualized therapies for heart failure, we will develop three computational platforms over a five-year period. A neuro-hormonal regulation platform, a mechanical adaptation platform, and an energetics platform.

The neuro-hormonal platform is critical for characterizing a fundamental feature of chronic heart failure, which is neuro-humoral activation and alterations in regulatory control by the autonomic nervous system. We will also develop a mechanical adaptation and remodeling platform. Progressive changes in the mechanical structure of the heart, such as thickening or thinning of its muscular walls in response to changes in workloads are directly related to future deterioration in cardiac performance and heart failure. And we’ll develop an energetics platform, which includes the model of the coronary circulation: that is, the blood vessels that supply the heart organ itself. And will thus provide a mechanism for characterizing the imbalances between the oxygen and metabolic requirements of cardiac tissues and their lack of availability due to neuro-hormonal activation and heart failure progression.

We consider it the landscape of other organizations pursuing innovative solutions that may be considered as cardiovascular bio digital twins, according to a similar definition or conceptualization as ours. Some are companies like the UT Heart, Siemens Healthineers, Computational Life. Some are academic institutions like the Johns Hopkins Institute for Computational Medicine, the Washington University Cardiac Bio Electricity and Arrhythmia Center. And then some are consortia such as ECHOES, which stands for enhanced cardiac care through extensive sensing. And that’s a consortium of academic and industrial partners. These other organizations have different aims, of course, but most are focused on cardiac electrophysiology and disorders of cardiac rhythm.

Most use both physiologically-based and data driven methods, such as artificial intelligence and deep learning. Most are focused on the heart itself without robust representations of the vascular load, and none implement neuro hormonal regulation or mechanical adaptation and remodeling, nor aim for the ultimate realization of closed-loop therapeutics.

By autonomous closed-loop therapeutics, I mean, using the cardiovascular bio digital twin, not only to predict cardiovascular events and determine optimal therapeutic interventions for maintenance of health or for disease management, but also to actually deliver those therapeutic interventions. This means not only the need for smart sensors, but also for smart actuators, smart robotics, and various nanotechnology devices.

Going back to my earlier comparisons to commercial aviation, autonomous closed-loop therapeutics means not only maintenance of the plane and its parts, but also the actual flying of the plane in autopilot. In the beginning, we’ll include the physician pilots in the loop, but the ultimate goal is an autonomous bio digital twin system for the cardiovascular system.

The goal of realizing autonomous closed-loop therapeutics in humans is obviously a more long-term goal. We’re expecting to demonstrate that first in animal models. And our initial thinking was that this demonstration would be possible by the year 2030, that is 10 years. As of this month, we were planning ways of reaching this target even sooner.

Finally, I would also like to add that by setting our aims at such a high ambition target, we drive the quality and accuracy of old milestones along the way. Thank you. This concludes my presentation. I appreciate your interest and attention. Please enjoy the remaining sessions, thank you.