Dr. Dahari on the advances and challenges of the HCV research presented:
Q: Why did new theoretical advances (e.g. mathematical modeling) for hepatitis c
virus (HCV) translate slowly into patient care?
A: The simplest and most direct explanation begins with understanding the complicated ecosystem of stakeholders that something big, like treatments and a cure for viral hepatitis c (HCV) requires. This intersection of business, science, academia, and healthcare providers can be an incredibly effective set of partnerships. It also takes the right balance of goals, agendas and timing, to which there is no preset formula. In the case of HCV, for many years it was simply enough that a treatment was discovered in the form of interferon-based therapies (INF). Once a milestone like this is achieved, it can ironically work against the overall mission to find a better treatment protocol or cure. Interest and urgency can wane, funding can be diverted to causes that have yet to discover a treatment or cure. Public policy as well as societal prejudice and pressure can impact progress as well. It is a complicated ecosystem we operate in.
But Science is really, at its heart, the story of individuals whose commitment and passion makes a real difference. In the case of Dahari Lab, a partnership with Israeli researcher Dr. Yoav Lurie in 2013, explored the use of a non-INF treatment on one patient. The research used mathematical modeling in real-time to predict how long the patient would need treatment to be cured. Two milestones were achieved. This was the first use of modeling in real-time and the patient was cured with a shortened treatment schedule. Around the same time, direct-acting antiviral agents (DAAs) emerged as the preferred therapy for curing HCV. The good news with DAAs was a dramatic reduction in time-to-cure and far less serious side-effects than the INF-based treatment from before. The bad news was the extremely high cost of the treatment, making it unrealistic to globally scale. Their emergence also meant the recent success in real-time modeling would wait several more years before contributing to another milestone, this time in combination with DAAs.
In 2018, our partners in Israel saw an opportunity and pursued a very non-traditional path to achieve another milestone. Leveraging the successful real-time modeling from 2013, Drs. Ohad Etzion and Amir Shlomai convinced the Israeli national health insurance agency, Clalit, to fund a trial for the predictive model using DAA-based therapy. What makes all of this unique is not just the research itself, but the backing of Clalit. Typically, clinical trials are sponsored by pharmaceutical companies, not insurers. It was this intersection of research, resources and people that allowed the application of previous modeling work in new research. Without this moment, we would not be discussing breakthroughs today like real-time or personalized treatment for HCV, the potential of huge cost-savings for over-burdened healthcare systems around the globe, or a more effective and compassionate treatment for patients.
There are many ways to achieve research milestones and breakthroughs. On the outside, these can look quite slow and labored or very sudden and quickly evolving. I hope the example above illustrates what a path to success can look like and all the variables that had to align. This year, in 2020, we are scaling this research to a much larger group of patients. We are very excited to see the data and results of this clinical trial. It is important to note these solutions can only be tackled at scale with institutional support like the National Institutes of Health (NIH) and Clalit. We are fortunate to have them as partners, and grateful for the support they have provided for the research at Dahari Lab.
Q: What are the advantages of mathematical modeling in research for hepatitis C virus (HCV) treatments and advances in patient care?
A: First, it is helpful to understand that in the hard sciences such as physics, it is taken for granted that there are formulas (or models) to follow. These are considered the building blocks of research. For the life sciences, this is not necessarily the case. Putting complicated organic systems into a model is difficult and time-consuming. However, this time invested up front is incredibly effective and efficient once the models are developed.
Creating these models has two key advantages that directly impact the areas of treatment research and advances in patient care. In the case of research for treatments, once these models are established, the research is more efficient and effective than if proceeding without models. A by-product of creating these models is the contribution to the broader scientific community. This new knowledge can be used in any area of research referred to as basic science. It can be applied to areas of research well beyond hepatitis treatments or a cure.
Models play a critical role in the advancement of patient care. Without them, personalized treatment would not be possible, as well as the associated benefits of cost-savings and shortened treatment schedules. With respect to the World Health Organization (WHO) goal of eliminating HCV globally, the models are critical in scaling up treatments to understand their effectiveness. This is especially critical in populations like people who inject drugs (PWID) where infection and reinfection rates can undermine this goal. Models are extremely effective and flexible at macro and micro levels. They can be used to map the virus itself, the person (host) who has the virus, or rates of infection and modes of transmission within groups.
Q: What is the biggest change today for patients receiving HCV treatment?
A: There has been a dramatic change for patients receiving HCV treatment. Our research presented here is like a timeline of improvement for patient care. Originally the treatment for HCV was long, averaging 48 weeks, was expensive, complicated to administer, requiring a medical office visit and administering thru the blood stream and the side-effects were severe and unpleasant. Today, we have a pill-based treatment with few-to-no side effects and the treatment schedule varies by patient but is nowhere near the previous 48 weeks. In some cases, it is 6 or 8 weeks of treatment. We are looking at a future where the treatment cycle and administering will be in the patient’s hands, of course with the proper medical oversight to ensure safety and efficacy.
Q: What is the next big challenge in Hepatitis research?
A: I see the immediate challenge divided into two areas. One is the continued effort to eliminate HCV. In the course of tackling a challenge this big, it is important to remember the process one needs to go through. It is very long and complicated and there is no “straight-line” that is the nature of research on the edge. But we have finally reached a moment where we started with treatment, have made great progress in prevention, and now the so-called third leg of the stool is possible, which is elimination. This is a very real possibility and a goal that will take hard work and focus. Central to this goal are three components: making diagnosis easy and stigma-free, making treatment more accessible and affordable, and ultimately, empowering patients to self-administer and monitor treatment to cure.
The second immediate challenge is Hepatitis B and Hepatitis D (Delta). These areas were not as well funded or researched previously because of the extent and awareness of HCV, but that has changed. We are seeing a lot of interest, funding and research happening at the moment. Our lab is also involved in this expanded scope of Hepatitis research. It is really an exciting time in the field. As you asked before about why the research remained flat for so long, we have now entered an era of greater strides forward, global cooperation in real-time in most cases and the ability to collect and analyze massive amounts of data quite quickly.
Ms. Echevarria talking about her experience with Dahari Lab
Q: When did you start working in Dahari Lab and how did you get involved?
A: When I started working with Dr. Dahari during my Senior Year of High School, the lab was called PETM. It was initially a mentorship program that appealed to my interest in math and science. Post-graduation I continued to work with the lab because I learned the skills necessary for data analysis of a research paper in progress.