Blanca Rodriguez: Computational simulation of the human heart
The University of Oxford professor believes computer modeling and simulation are poised to trigger major breakthroughs in the diagnosis, treatment, and care of cardiology patients.
When Blanca Rodriguez began her exploration of the computational simulation of the human heart more than 20 years ago, the idea that an individual heart could be digitally recreated and analyzed using AI and machine learning to simulate which therapies would most effectively treat heart diseases was little more than a promising concept.
Today, having devoted her career to the nascent field of computational cardiology, Rodriguez, a professor of computational medicine at the University of Oxford and a Wellcome Trust Senior Research Fellow, is convinced that computer modeling and simulation of the heart are poised to trigger major breakthroughs in the diagnosis, treatment, and care of cardiology patients.
Computer simulation is hardly a new technology. In 1960, an Oxford biologist named Denis Noble began experimenting with mathematical models of the heart. Engineers in the automotive and aerospace industries have long embraced such simulation techniques, Rodriguez points out. All new vehicles and aircraft are designed with AI-based computer simulation as a key tool to virtually model each function, design element, and potential outcome. This concept, known as a digital twin, is now being embraced in the world of cardiology, and Rodriguez is a leading proponent.
“We’re doing the same thing with the heart, which is very challenging,” Rodriguez said. “We are gathering the clinical data of a patient and trying to build a virtual tool with those data. We want to simulate how that particular heart works, and simulate whether certain therapies or devices would work better than others so that we can understand how the diseases are affecting a particular patient in a particular way.”
Using AI and machine learning to crunch the massive amounts of clinical data, along with the ability to personalize that data for each individual patient, represents a significant breakthrough in computational cardiovascular science, Rodriguez said. As the technology is refined and improved, the ability to accommodate each patient’s unique physiology will inevitably lead to better and less invasive outcomes.
“We’re trying to understand and predict whether some therapies work better for certain patients and to understand disease conditions in a more personalized way,” Rodriguez explained.
In silico method
This “in silico” or computational methodology — as opposed to in vitro or in vivo — is likely to become the de facto method for drug development, and potentially for clinical treatment of heart patients in the future. Rather than having a generic, one-size-fits-all model, the digital twin will reveal the intricacies and disease conditions that impact each human being in a distinctive way.
A 2018 recipient of an AWS Machine Learning Research Award, Rodriguez’s immersion at the intersection of cardiology and computer science was hardly the career path she anticipated. A native of the Mediterranean port city of Valencia in Spain, Rodriguez received a degree in electrical engineering from the Universidad Politecnica de Valencia in 1997.
“I knew nothing about medicine or cardiology and nobody in my family was doing anything like this,” she said. But when she attended a talk about research in cardiology by Jose Jalife, a renowned University of Michigan arrhythmia specialist, she became “absolutely fascinated by the topic.” She immediately decided to pursue a PhD in computational medicine. She joined the Oxford faculty as a senior post-doctoral fellow in 2004 and has devoted her career to breakthrough research in the field.
Her work has attracted both academic and industry attention. Computer simulation is already having an impact in the medical and pharmaceutical communities. Until recently, drug companies have relied solely on animal testing for the most accurate and reliable way to test new drugs for effectiveness and side effects. According to research, animal testing yields a 75 to 85 percent accuracy rate and sometimes leads to drugs being withdrawn from the market due to safety issues.
The promise of computational models
Computational models of human heart cells are already providing much higher accuracy levels, with the added benefit of reducing the controversial use of animal testing, improving drug safety, and having greater likelihood of predicting adverse drug reactions in humans.
“For the prediction of cardio drug toxicity or side effects on the heart, we have already reached 90 percent accuracy with our computer models, and that’s what has made industry very interested,” Rodriguez said. “We can replace some of the animal experiments and lower the costs. Plus, it’s fast.”
For the prediction of cardio drug toxicity or side effects on the heart, we have already reached 90 percent accuracy with our computer models.
To that end, Rodriguez’s lab at Oxford has been collaborating not only with clinicians but also with the pharmaceutical industry, which is intrigued by the promise of computer models to test drug therapies prior to clinical trials. She is working with such giants at GSK, AstraZeneca, Sanofi, UCB, and Merck.
Gaining this kind of industry credibility is one of the most significant outcomes, according to Rodriguez, because several years ago these companies “were very skeptical. They had little knowledge of these computational methods so we had to collaborate with them and make the software really easy to use,” she explained. “We worked not only on the computational aspects, but also the human aspects to build credibility for these methods. That was always a challenge.”
Using these techniques, drug makers can determine early on whether a certain drug has side effects. “Our knowledge of the human heart is such that we can build mathematical equations on the data we have and embed those equations in software programs that we can use to simulate what a drug is doing to the human heart,” she explained.
In addition, the work has attracted the attention and cooperation of important regulatory agencies such as the US Food and Drug Administration and various European regulators. Rodriguez’s Oxford lab is already jointly publishing white papers with such agencies.
The AWS Machine Learning Research Award has been a significant addition to the available resources for her group, Rodriguez said. “The MLRA has been instrumental for our work in generating methodological advances and demonstrating the potential of in silico clinical trials,” Rodriguez said. “We have published important papers describing the development of mathematical models of the human heart. These are being used for drug testing in academia, industry and regulatory agencies such as the FDA.”
Faster (and better) data analysis
In recent years, breakthroughs in AI and ML techniques have enabled much greater effectiveness using computational simulation by dramatically accelerating the speed of large dataset analysis. Images of thousands of human hearts can be analyzed in nanoseconds and, simultaneously, new biomarkers emerge that more accurately predict patient outcomes and preferred therapies.
These AI programs also enable the identification of subgroups of patients who share similar features but might have different conditions. People who have had a heart attack, for example, tend to be lumped together in one massive group. “But actually, the manifestation of the heart attack is very different in individual patients. AI and machine learning can help in identifying subgroups of patients who share the same features and could potentially benefit from a particular therapy,” Rodriguez said.
Among the challenges for AI and machine learning researchers is gaining access to huge databases of clinical data in order to test these models and train the algorithms. At Oxford, Rodriguez’s team has access to the massive UK Biobank, a large-scale biomedical database and research resource, and some hospitals are already sharing digitized clinical data. But due to privacy issues and cost constraints, vital data sets like these remain elusive.
“Our work depends on access to good datasets,” Rodriguez pointed out. “Not all hospitals are gathering data and there are a lot of ethical issues involved. Another challenge is finding candidates to do the research, particularly computer scientists who are able to understand medicine. People need to be both technically talented but also aware and knowledgeable about the clinical challenges.”
Already seeing the impact of her work, Rodriguez said the technology can accelerate the development and implementation of important cardiovascular therapies, making those therapies more effective and safer for patients. The next decade promises to hold dramatic advances. “I don’t think it’s a dream. It’s happening,” she declared. “It’s just going to take time.”