MEI Lab Advances Work on Cardiovascular Bio Digital Twin in Conference Paper and Tests Proof of Concept with Japan’s National Cerebral and Cardiovascular Center (NCVC)
SUNNYVALE, Calif.--(BUSINESS WIRE)--#TechforGood--NTT Research, Inc., a division of NTT (TYO:9432), today announced that its Medical & Health Informatics (MEI) Lab has delivered a paper proposing a system design for optimizing drug infusions for acute heart failure and has begun testing out the design at Japan’s National Cerebral and Cardiovascular Center (NCVC). Lead author and MEI Lab Research Scientist Yasuyuki Kataoka presented the paper at the IEEE Engineering in Medicine and Biology (EMB) 2022 conference, held July 11–15 in Glasgow. Titled “System Design for Optimizing Drug Infusions Using Cardiovascular Space Mapping for Acute Heart Failure,” the paper proposes a system that controls cardiovascular (CV) performance metrics within desired ranges as dictated by CV parameters. Setting parameters to realistic values in 5,600 simulated patients, this study showed that the optimized drug combinations and dosages kept CV metrics within desired ranges, corresponded to the recommended clinical use guidelines and were able to predict the limitations of a tailored drug therapy, thus supporting a clinical decision to pivot quickly to alternative treatment strategies. With the support of NCVC collaborators well trained in animal research, the MEI Lab team subsequently tested the control algorithm for a small library of drugs and obtained data demonstrating the algorithm’s proof-of-concept, setting the stage for future autonomous closed-loop treatment experiments. Drug therapy optimization is a major focus for the MEI Lab’s CV bio digital twin initiative, which is aimed at developing the infrastructure for a digital replica of an individual’s heart.
Acute heart failure, a serious condition that can lead to mortality, is caused by various factors and requires multiple drug therapies to remedy underlying causes. Due to pharmacological complexities, few studies have theoretically addressed the multidrug optimization problem. The key to the system proposed in this paper, co-authored by five MEI Lab scientists and a key external advisor, is both modeling and controlling CV parameters (systemic vascular resistance, cardiac contractility, heart rate and stressed blood volume) to yield desired CV metrics (cardiac output, left atrial pressure and mean arterial pressure). The proposal involves two key techniques: tailored drug infusion, which optimizes the drug combination and dosage from the initial state of heart failure into the desired state of stable hemodynamics within the CV parameters space; and CV space mapping, which identifies the desired CV parameters from the desired CV metrics by deriving the analytical solution of the metrics as functions of the parameters.
“Our goal in the context of the CV bio digital twin project is to develop a system for using autonomous closed-loop control to deliver optimal therapies based on a large library of many drugs, applied using various modalities, rates of infusion and so forth,” said Dr. Joe Alexander (M.D., Ph.D.), one of the paper’s co-authors and director of the MEI Lab. “This will be an ongoing process over many years, and ultimately, a similar process will be undertaken to build therapeutic libraries of devices, including devices for neuromodulation.”
In addition to Dr. Alexander and Mr. Kataoka, who holds an M.S. and B.S. in mechanical and control system engineering from Tokyo Institute of Technology where he graduated valedictorian of the department, the co-authors of the paper include MEI Lab Research Scientist Dr. Yukiko Fukuda (M.D.), MEI Lab Distinguished Scientist Dr. Jon Peterson (Ph.D.), MEI Lab Research Scientist Iris Shelly (M.S.) and Dr. Kenji Sunagawa (M.D., Ph.D.), founder and director of the Circulatory System Research Foundation in Tokyo. Dr. Sunagawa is also Professor Emeritus at the Department of Cardiovascular Medicine, Kyushu University, Fukuoka, Japan and a fellow of the American Heart Association, European Society of Cardiology and IEEE. He has provided regular input to MEI Lab initiatives. The IEEE EMB conference, where this paper was delivered, is one of the world’s leading biomedical engineering events, drawing engineers, clinicians, scientists, and entrepreneurs, as well as students and other professionals.
“The realization of hemodynamic control in real animals requires physiology, systems control engineering and machine learning for accurate circulatory system modeling and control. I am extremely excited about updating the technology level and showing the scientific evidence and the clinical value with my diverse and talented coworkers as well as researchers across the globe," Mr. Kataoka said. “We are pleased to have research partnerships with Dr. Sunagawa, whose advice has always accelerated my research, and with NCVC, which would also be the ideal place to transition our research from theory to practice in animal experiments.”
The MEI Lab’s ongoing work with the NCVC is grounded in a joint research agreement struck in February 2021. In addition, the MEI Lab has undertaken joint research with the Neuroelectrical Group within the Munich School of Bioengineering, part of the Technical University of Munich (TUM), and will be announcing an additional collaboration with another university in the coming months.
About NTT Research
NTT Research opened its offices in July 2019 as a new Silicon Valley startup to conduct basic research and advance technologies that promote positive change for humankind. Currently, three labs are housed at NTT Research facilities in Sunnyvale: the Physics and Informatics (PHI) Lab, the Cryptography and Information Security (CIS) Lab, and the Medical and Health Informatics (MEI) Lab. The organization aims to upgrade reality in three areas: 1) quantum information, neuroscience and photonics; 2) cryptographic and information security; and 3) medical and health informatics. NTT Research is part of NTT, a global technology and business solutions provider with an annual R&D budget of $3.6 billion.
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