How Computational Life Sciences Will Help Us Address and Advance Health Equity

There is long-standing knowledge that the US spends significantly more on healthcare than other developed nations but still sees poorer health outcomes for that expenditure (1).  In fact, we have the worst life expectancy at birth compared to other similar nations and our life expectancy has fallen for two years in a row (2).  And any health benefits we do have are not available broadly across geographies or socio-economic status (3). 

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For one of the richest nations, that is unacceptable but a complicated problem to solve.  This post will not delve into the fact that the US subsidizes innovation in healthcare or the pluses and minuses of single payor healthcare.  Instead I’ll share my thoughts on how technology can start to take some of the things that work well in our system and strive to make them cheaper and more accessible to all Americans. 

The COVID-19 pandemic upended our healthcare system and drove adoption of health IT almost overnight.  An industry that had resisted adopting technology solutions had to quickly pivot to telemedicine in order to provide many types of patient care and generate revenue.  A window has perhaps opened to encourage this traditionally tech-averse crowd to look to this industry for solutions to solvable problems.  Now that the window is open, we should think about other ways to bring technology into healthcare and drug discovery beyond just telemedicine.

Another macro event of the last decade or so has been a true unlocking of the potential of molecular biology. One of the most widely adopted technologies of this revolution has been the use of mRNA vaccines for COVID-19.  Developed and tested in less than a year, these vaccines took advantage of years of work into understanding how proteins are generated in the body from DNA through RNA and delivering those instructions safely into a person to train their immune system to recognize the spike protein of the virus that causes COVID-19, SARS-CoV.  The unparalleled speed and safety means we now have new abilities to generate vaccines for many infectious diseases as well as quickly pivot when new variants of concern develop.

But in addition to that technology, other technologies have been developed and approved by the FDA that take advantage of our expanding knowledge of molecular biology.  These technologies are often one time treatments that fundamentally correct a genetic defect or precisely target a therapy, minimizing side effects and maximizing efficacy.  

My personal favorite as an immunologist is CAR-T cells.  These therapies take a person’s T cells and make them specific for their cancer.  Some patients with no remaining treatment options left have had their cancers eradicated using these therapies (4,5,6,7).  Other examples include Spinraza, a gene therapy that corrects the uniformly lethal gene in childhood disease Spinal Muscular Atrophy (8).  It does this by providing a functional copy of the gene that is missing in these patients).  And we have just started seeing results from early trials of drugs using CRISPR gene editing – a tool that actually goes into a patient’s DNA and does a small edit to correct a genetic mutation (9).

The barrier to wide adoption for all of these technologies is cost and access.  For many of these next generation precision medicine therapies, one can only access them at the leading academic medical centers like Duke, Johns Hopkins, Sloan-Kettering or our local gem, UT Southwestern.  Advanced technologies like machine learning and artificial intelligence have a role to play in identifying efficiencies in these processes and reducing variability and improving safety.  These advances will allow us to deliver this level of sophisticated care to rural and other underrepresented groups regardless of their access to academic medical centers. 

If we can use these technologies to improve commerce, cybersecurity, and investing, it’s time healthcare catches up!

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