Aiming for a Moonshot: A Conversation with Dr. Joe Alexander

In May, NTT Research announced that Joe Alexander, M.D., Ph.D., had joined its Medical and Health Informatics (MEI) Lab as a Distinguished Scientist. Dr. Alexander, who is also a Fellow of the American College of Cardiology, joined NTT Research after more than two decades in the pharmaceutical industry. He had previously spent eight years at Vanderbilt University, where he completed a two-year residency in internal medicine and served as a professor of medicine and biomedical engineering. Dr. Alexander obtained his M.D. and Ph.D. (biomedical engineering) degrees at the Johns Hopkins Medical School. His post graduate training included fellowships at Albert Einstein College of Medicine in the Bronx and Kyushu University in Fukuoka, Japan.

At NTT Research, he will lead the MEI Lab’s bio digital twin initiative. He recently shared the following thoughts about his new role and prolific background in the fields of medicine and science:

Could you tell us a little about why you decided to join NTT Research and what you hope to accomplish?

I worked in Pharma for a total of 20-plus years. I am grateful for all I learned there and for those opportunities to make a global impact. However, the modeling and simulation work I was doing – some of it, drug-agnostic – was not fully embraced as part of my main role. There was a certain tension, at least on my side. I joined NTT Research primarily to help accomplish MEI’s moonshot vision for bio digital twin creation. But I will also add that the opportunity to work with Dr. Tomoike, whom I’ve known for many years, as well as the reputation of NTT as a company were also strong contributing factors.

The idea of a bio digital twin seems a bit fantastic. How do you think it will actually take shape?

Since our overall goal is to build bio digital twins for particular individuals, we must be able to handle individuals with co-morbidities: diseases affecting multiple organ systems. We will add various organ systems over time. First, however, before adding any organ system, we must build a modeling and simulation engine capable of handling the various physiologic system models for the different organ systems as well as information derived from various data types: imaging, catheterization lab data, multi-omics data, sensor data, etc. We will begin with the cardiovascular (CV) system and call this effort, the CV bio digital twin.

You and Dr. Tomoike both have backgrounds in cardiovascular systems. Is that a natural starting place for this kind of work in precision medicine?

Yes. It is very true that cardiovascular dynamics is a very comfortable starting place for the reasons you mention, but it is also important to recognize that according to 2017 data from the Centers for Disease Control and Prevention (CDC), heart disease is still the leading cause of death in the United States. Perhaps related to this fact, there are tremendous amounts of clinical trial and other data (‘big data’) and peer-reviewed literature from bench to bedside on cardiovascular function both in health and disease.

In light the ongoing pandemic, considerable medical attention has turned naturally toward public health. How does the goal of finely tuned individual treatments fit within this broader context?

When we consider the variety of complex presentations of patients infected with COVID-19 – some with mild symptoms but others in critical condition, some with type 1 but others with type 2 ARDS (acute respiratory distress syndrome), some requiring ventilators but others where conditions are only worsened by ventilators, and some but not all with concurrent thromboembolic and immunologic events resulting in ischemic strokes, COVID toes, cytokine storms and most recently pediatric inflammatory multisystem syndrome (PIMS) – we bear sad witness to the profound need for individualized care.

In a future world where all patients have bio digital twins, it will be easier to examine patients virtually – using their twins – to determine what unique factors may influence their course of illness. Moreover, we could run ‘what if’ scenarios to predict their best treatment options. Taking it one step further, it would be possible to recruit and run virtual clinical trials for new drugs and vaccines using patient bio digital twins rather than the actual patients themselves.

What can you share about your previous work in modeling and simulation and how does it bear upon your current research?

My most recent modeling and simulation projects involved the building of two types of virtual lab platforms: one for understanding the underlying pathophysiology of a disease (pulmonary hypertension), the other for combining various data types in order to predict individual patient responses to therapy (for neuropathic pain). My current work will require aspects of both: modeling of underlying disease mechanisms as well as incorporation of a multitude of data types (e.g., multi-omics data), all making sense within a single, bio digital twin platform.

How big of a challenge will it be to acquire useful and properly anonymized or privacy-protected medical and patient data?

It all depends on the strength of collaborations and alliances that can be formed with strategic partners with a shared vision. That, and the sharing of potential IP that may result! Potential partners could be National Institutes of Health (NIH), academic institutions, medical societies, pharmaceutical companies, etc. NTT itself has a medical center in Tokyo. We are working on establishing such partnerships now.

When you look back on your career to date, going back to your undergraduate days at Auburn University, what are some of your top takeaways?

  • Science is a discipline of taking things apart. Engineering is a discipline of putting things together. Medicine is both and can be greater than the sum of the two parts taken separately.
  • A good idea is often preceded by a period of creative silence, and the moment of its first beginnings can only be noted retrospectively.
  • It is fortunate to have experienced a life immense in both the sciences and the liberal arts – especially the latter.
  • It is necessary to use a lot of words when you don’t have any data. No wonder books attempting to explain gravity are large and heavy, thus demonstrating more than explaining!
  • Compare the words of younger artists and younger scientists and you’ll find them remarkably different; however, compare the words of older artists and older scientists and you’ll find them remarkably the same. The true nature of things is revealed over time.
  • Nothing teaches humility like biology; a coronavirus might teach us how to solve the environmental crisis.
  • The heart of humankind is immeasurable. Sometimes immeasurably large, sometimes immeasurably small.
  • Hauntings exist; ghosts do not.
  • Having a good memory is not the same as having intelligence.
  • In Artificial Intelligence, “Deep Learning” should not imply deep understanding, rather the opposite, in fact.
  • Data and information are not the same; information is what one hopes to extract from data.
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