September 2021 - MDisrupt

How Precision Medicine Can Transform Healthcare

by MDisrupt Guest Author | Sep 17, 2021

Meet Bernard Esquivel, MD, PhD,
a leader in precision medicine.

Dr. Esquivel is a clinical immunologist-allergist and international business leader with expertise in developing new markets in genomics and precision medicine. He’s the founder and president of the Latin American Association of Personalized Medicine, ALAMP.

A passion for precision medicine

MDisrupt: Tell us how you turned your passion for precision medicine into a career.

Bernard Esquivel: During my medical training, I learned about the role our genetic information can play in influencing disease development. Once I started practicing medicine, I tried to start testing my patients, and understanding their genes, and then find a way to implement that into my workflow.

That’s when I noticed that, number one, it was very hard to find [genetic] tests. Number two, it was very hard to access the information needed to understand and clinically implement decisions based on that information. And number three, my colleagues thought that I was talking about Star Wars or some dark science.

So, facing those barriers, back in 2014 a colleague of mine and I founded the Latin American Association of Personalized Medicine (ALAMP). The aim was to share knowledge to foster the implementation of precision medicine.

I interacted with a lot of key opinion leaders (KOLs) globally from different fields of precision medicine. And I learned how they were implementing precision medicine, pharmacogenomics, cancer molecular testing, health wearables, and so on. Long story short, for the last 12 years I’ve been 100% into precision medicine, to find a way to bring these fantastic new tools closer to the patients.

Precision vs. personalized medicine

MDisrupt: How do you define precision medicine, and how do you see it as different from personalized medicine?

Bernard Esquivel: If we use the definition that cancer.gov has for precision medicine, it’s a form of medicine that uses information about a person’s own genes or proteins to prevent, diagnose, or treat diseases. But I think there are missing parts to this definition. One of them is “predict.” That’s where I believe precision medicine is heading: to predict, by using data from patients, subpopulations, larger groups, and N-of-1s, and using new technology such as machine learning, to predict how a patient will respond.

Also, precision medicine is not only about genetic information anymore. For example, there are different “omics”—metabolomics, epigenomics, nutrigenomics, proteomics, and also the social determinants of health that are crucial as well. Personalized medicine is specific to the patient.

Getting precision medicine adopted

MDisrupt: What are some obstacles to a widespread adoption of precision medicine?

Bernard Esquivel: The first barrier is the way we run clinical trials. We need to continue following an evidence-based approach, meaning we need to show clinical validity, clinical utility, clinical actionability, and so on. But precision medicine is unique because you may be talking about a single individual with a lot of data points.

The second barrier is about implementation and clinical actionability. For example, in pharmacogenomics, some genetic variants of CYP450 enzymes may impact how the patient will respond to certain medications. You need to take that to the next level: “What can I do next? Is there any other option for that patient? Are there clinical guidelines to help me to customize the dose for that specific patient?” That’s clinical actionability.

The third barrier is, precision medicine needs to be user-friendly for the provider as part of our day-to-day tools. If we don’t implement precision medicine data sets into the clinical workflow, it’s going to be a hard stop.
And last but not least is cost-effectiveness. We need to show that it makes sense to invest in the molecular testing and technological platforms that we need.

MDisrupt: When do you see us being able to bring all that information together to give an individualized view of the patient?

Bernard Esquivel: Within the next five years. I think we are getting there in terms of connectivity and data management. The milestone for the next five years is going to be about ethics—how those corporations are going to be managing, handling, and protecting your information as a patient.

Pharmacogenomics (PGx) in precision medicine

MDisrupt: What’s the ideal implementation of PGx in the health care delivery model?

Bernard Esquivel: Pharmacogenomics is a fantastic example of how precision medicine has evolved. One of the barriers has been the lack of standardization. We need to be sure that our [variant] coverage is as similar as possible in order to compare apples to apples.

The other one is about how you interpret that data, the phenotyping calls. How are you calling [a particular] genetic variant and what are the clinical implications? Several organizations are doing outstanding work trying to tackle those problems. And I strongly believe that champions of pharmacogenetics are and will continue to be the pharmacists.

PGx success

MDisrupt: Is there an example of a health system that has been successful in implementing a PGx program?

Bernard Esquivel: Yes, several. St Jude’s Hospital has been a pioneer in implementing PGx into the electronic medical record and having expert pharmacists help other providers implement it. Also Mayo Clinic with its center for individualized medicine.

The Netherlands is a fascinating example of a countrywide PGx implementation. They use a single electronic medical record for the entire country. They already have a specific PGx piece that will follow the patient wherever they go. They’re publishing data on how they are saving money countrywide by using pharmacogenomics.

How digital health innovators can improve precision medicine

MDisrupt: When you think about precision medicine, what could digital health innovators do more of and what are they not doing enough of?

Bernard Esquivel: Number one will be having a smooth workflow in terms of integration. Then, once you’ve got all those data sets, how are you going to start organizing that information? You need to allow new technologies such as machine learning to start making predictive models, then [integrate] that information with genomics, microbiome, exposure, behaviors, clinical tests, even patient contributed data. And then find ways to connect all that to clinical information and deliver it to the final user. I know it sounds hard, but many people are working on this right now.

MDisrupt: What advice would you give a founder interested in precision medicine solutions?

Bernard Esquivel: Have the right experts working with you. If you don’t make that investment at the beginning, it’s going to be way more expensive “learning during the flight.”

With the right team on board, I recommend three pillars: Number one, the regulatory landscape—look into the regulatory requirements, talk about your idea with the regulatory agency.

Then, invest in developing the right evidence behind your product. And number three is clinical actionability: You can go-to-market with the minimal viable product, but you need to always be thinking of how this information will trigger action from the clinical standpoint.

Healthcare’s future

MDisrupt: What do you think the health system is going to look like in 10 years?

Bernard Esquivel: We know that the way we are spending money in healthcare is not working. So everything will change into value-based care and precision medicine will play a critical role there.

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Overly White Genetic Databases = Decades More Health Disparities

by Ruby Gadelrab | Sep 14, 2021

MDisrupt CEO and founder Ruby Gadelrab on how more inclusive genetic databases can reduce health disparities and bring precision medicine to everyone.

The precision medicine problem no one talks about

Precision medicine has long been a promise of the The Human Genome Project (HGP). Humans are 99.9% genetically similar, but it is the 0.1% difference that holds the key to the causes and potential cures of our diseases. The goal of the HGP was that by building genetic databases large enough to allow scientists to see the patterns and variations in the 0.1% difference in our genetics, we could give healthcare providers “immense new powers to treat, prevent, and cure disease” through precision medicine (pharmacogenetics, cancer screening and diagnostics, and much more). However, nearly 20 years later, this promise of better diagnostics and personalized therapeutics is only a reality for those of European descent. That’s because most of the world’s genetic databases consist primarily of genomes from people of European descent – and yet we know that individuals of other ancestries suffer from certain genetic diseases at a much higher prevalence.

So, how did we get here?

3 reasons genetic databases are biased

First, the genome studies conducted that led to building the first genetic databases, the genome-wide association studies (GWAS) were done in the United States and, to a lesser extent, in Europe. A 2009 analysis of the GWAS studies showed that 96% of participants were of European descent.

Second, recruitment of participants in scientific research is notoriously difficult. The faster researchers recruit, enroll, and consent participants, the faster they get the data and can publish. Participants in the GWAS studies were mostly volunteers who lived near well-funded academic institutions, and who had the motivation and the means to travel to those institutions. This resulted in the study populations not being representative of the diversity of the US population. Very few institutions tried or were able to build trust with underserved and underrepresented populations in order to successfully enroll them in the studies. The majority of the studies were conducted by scientists who identified as white. In fact, according to the National Institutes of Health (NIH), only 7% of all NIH R01 Grants were awarded to Black American and Latinx scientists.

During the GWAS era, I worked as the head of international marketing for Affymetrix (now Thermo Fisher Scientific). Affymetrix was a leading manufacturer of microarrays, the technology used to conduct GWAS research. Even back then, we were concerned about the European bias in genetic studies, so my team and I spent 2009 to 2012 traveling around the world meeting with ministers of health and major research institutions, encouraging them to fund and build genetic databases representative of their own populations. Some of these initiatives did eventually take off—notable examples include The Saudi Genome Program, H3Africa and the China Genome Project.

Third, over the last five years, new types of genetic databases emerged from the private sector. More than 26 million people purchased direct-to-consumer genetic tests. While these products have done wonders for accessibility of genetic information, they are also cost-prohibitive for underserved populations and sold by companies that are primarily US-based. This has resulted in new genetic mega-databases that, once again, are biased to people of European descent.

Private sector solutions

Individuals of African descent are highly underrepresented in genetic databases and yet genetic diversity in Africa is higher than any other region in the world. What’s more, African populations have the highest burden of disease due to Africa’s complex population history; large variations in diet; climate; and elevated exposure to infectious disease.

To their credit, some private sector genetic testing companies have tried to address this gaping disparity in genetic databases.

23andMe had the right idea and tried to address the problem through The African Genetics Project, which sought to recruit and provide detailed ancestry results to 23andMe customers of African descent.

The Nigeria-based company 54Gene is also seeking to equalize precision medicine by creating the world’s largest biobank of African genomes, which will be used to build the next generation of diagnostics and therapeutics.

Even so, we have made only marginal improvements in the diversity of our genetic databases. In 2020, a study conducted under the H3Africa Consortium showed that sequencing 426 individuals from 13 African countries resulted in the discovery of over three million novel genetic variants. This implies that we haven’t even scratched the surface of discovering the clinically important variants from those of African and other non-European descent.

5 ways to address health disparities in precision medicine

So what can be done to address the critical issue of underrepresentation in genetic databases? Here are the key areas that I believe will lead to change:

1. Proactive recruiting.

Genetic researchers should be proactively recruiting underrepresented populations for future studies. This will require some non-traditional methods of recruitment into the studies,including engaging key community stakeholders and building trust in historically mistreated and underrepresented minority populations. Initiatives must also include community outreach and education (e.g., the creation of multilingual recruitment materials). Without this, there is no way we can make precision medicine equitable.

2. Do the right studies.

A continued and concerted effort is needed to conduct studies that address specific underrepresented populations, similar to the methodology in the H3Africa study mentioned above. We must take a systematic approach to ensure that the entire global population’s genetics are appropriately and proportionately represented in genetic databases.

3. Create incentives.

Government funding agencies must build incentives for those who are recruiting and researching diverse cohorts. This includes the rebalancing of research funding for minority scientists.

4. Increase private sector investment in minority founders.

Important efforts to build non-Eurocentric genetic databases may actually come from the private sector, similar to the approaches that 23andMe and 54Gene are taking. However, implementing this on a larger scale would require a significantly increased level of investment into Black American and Latinx company founders, who received only 2.6% of all VC investments in 2020.

5. Build diverse leadership.

Both academia and the private sector must actively recruit diverse leadership teams—not just as entry-level and mid-level managers, but also in leadership roles, in the C-suite, and on boards of directors. Diverse teams are better at decision making, better at brainstorming, and better at creating products that represent a bigger proportion of the population.

We must do better

In 2020 and 2021, the murders of George Floyd, Breonna Taylor, Ahmaud Arbery, and several other Black Americans, along with hate crimes against Black and Asian communities—and in conjunction with the ongoing COVID-19 pandemic—have shone a spotlight on systemic racial disparities and inequities, which are also inherent in our healthcare system. European bias in genetic databases has huge implications for the health of individuals of non-European descent. It has the potential to contribute to decades of health disparities if we continue down this path. Without the changes outlined above, the genetic data we use to create the next generation of diagnostics, disease risk assessments, and therapeutic interventions will continue to make precision medicine available only to those of European descent. If we don’t address this now, history will hold us accountable.

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