What clinical issue is OncoHost looking to solve?
For many years, researchers have tried to understand why some cancer patients respond to treatment while others do not. The National Cancer Institute named “resistance to therapy” as one of the major challenges in clinical oncology today.
Over the past decade, the oncology industry has been witnessing the revolution of the precision approach to therapy, leading to the realization that ‘one-size-fits-all’ treatment protocols are not the way to conquer cancer. Unfortunately, in many cases of advanced cancer, clinicians struggle to find reliable biomarkers to guide their complex decision-making process. This challenge results in a trial-and-error pattern of guesswork.
In this unsustainable situation, patients are wasting the one thing they can’t afford to lose – time. Not to mention the waste of resources, the frustration due to treatment failure, and unnecessary adverse events.
In a recent article, Bishal Gyawali, medical oncologist, and professor at Queen’s University Department of Oncology, discussed in beautiful detail the clinical issue that OncoHost is trying to solve.
Gyawalk said: “Immunotherapy has been transformational in the treatment of certain cancers such as melanoma and lung cancers. And we can reasonably congratulate ourselves for the transformation we’ve seen in patients’ lives.
These are drugs that are improving the longevity and quality of patients’ lives. However, I think that the paradigms of immunotherapy and precision medicine have become antithetical to each other. We have let our precision medicine principle of ‘right dose for the right patient at the right time’ fly out the window when it comes to immunotherapy.
“To put it simply, immunotherapy feels anything but precise. With immunotherapy, we are treating too many patients too long too often, and at too high a dose. Contrary to the premise of precision medicine, we don’t have good biomarkers to predict a patient’s response to immunotherapy, nor do we have good biomarkers to predict toxicities. Some patients derive long-term benefit from immunotherapy, whereas others don’t benefit at all.”
Our PROphet biomarker model is here to solve this exact issue.
Why is the timing right for innovation in biotech in the precision oncology space?
The timing is right for several reasons. Firstly, we have reached a plateau in the efficacy of the current treatment modalities available to cancer patients. If we look at recent publications, we see marginal improvements in the efficacy of treatment, if any. There has been no improvement in toxicity profiles of drugs. In fact, we’re seeing more because we’re dealing with many more combinations of treatments. The questions many clinicians are asking themselves are: “Am I choosing the best treatment for my patient?” and “Are there any tools or biomarkers that can better direct my treatment planning process?”
Secondly, we are seeing hundreds, if not thousands, of cancer treatment combinations currently being tested in clinical trials around the world. If we look two to five years into the future, clinicians will have a choice between not one, two or three combination options; they will have to choose between 10 to 15 modalities! How can they possibly make a choice without biomarkers or precision medicine tools to guide their decision-making strategy?
Thirdly, we are facing a great cost issue. Across the world, payors are struggling to pay the huge amounts of money required to provide funding for the expensive medications becoming available to patients.
Policymakers say they want to lower health care costs, yet the Centers for Medicare & Medicaid Services (CMS) is once again pursuing policies that will exacerbate consolidation pressures. Oncology has one of the highest rates of consolidation, largely driven by payment disparities across different sites of service. If we want treatments to be covered, we need better biomarkers that prove the treatment will work. Payors simply will not continue paying for treatments that are only efficacious for the minority of patients.
Lastly, innovation in biotech in the precision oncology space is currently very opportunistic. Thanks to advanced AI and machine learning, today we have the required tools to overcome the many clinical challenges we face.
In short, the time is now.