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Clinical Trials Q&A with Dave Messina

 In Advanced Applications, Cofactor Genomics, Molecular Diagnostics, Q&A

Major Improvements in Clinical Trials are Near

Cofactor Genomics’ COO, Dave Messina, is featured in this week’s Q&A. Dave talks about the weaknesses in current clinical trial processes and shares his thoughts on the next significant improvements that can be made to create faster, more successful trials.

Q: Why do many clinical trials deliver less-than-promising results? Where can improvements be made?

A: Clinical trials are one of the key parts of bringing a new drug to market and really understanding who that drug is going to be best suited for. One of the challenges that drug developers have is recruiting the right patients and making sure that we actually get people into the trial. Something like 45% of clinical trials don’t meet their enrollment targets and something like 80% of them are delayed due to challenges in enrollment. As a result anything that we can do to improve patient recruitment will make an impact on the ability of clinical trials to be successfully run and get new drugs to market for patients.

One of areas where enrollment can be improved is with a change to the way patients are chosen for a trial. The traditional way of marketing a drug is to go for a broad labeling where the drug is intended to be prescribed for as many people as possible. The drug maker is choosing to go for more people who may have a potentially weak response to that medication. The flip side of that is the cost for each patient would be lower. In areas like cancer where we have made a lot of progress in recent years, drug developers are adopting a new model that is more targeted towards which patients the drug is going to be intended to be prescribed to. They use more sophisticated ways of selecting those patients for the clinical trials and that’s partly possible because we now better understand things like the mechanism of action and how those diseases are developing. It’s possible to use biomarkers to select the patients that are more likely to respond to a medication so they can recruit the right patients for that drug earlier in the process.

Q: What do you think will be the biggest change we see in clinical trials over the next 1-3 years?

A: There has been some really interesting developments in how clinical trials are run and even how they’re designed. That traditional approach for clinical trials in many ways hasn’t really changed in 80 years or so. This approach has a control arm with people who aren’t receiving the treatment and an experimental arm where they are. This traditional model is very simple and easy to understand, but it’s actually really inefficient. We are starting to see much more sophisticated statistical designs that take advantage of ways that we mathematically understand the significance of the treatment or how effective the treatment is for a population, which leads to a better efficiency in the number of patients that are needed. One approach is called a synthetic control arm which allows control patients to be used for multiple trials for the same type of disease instead of having to recruit new ones for every trial. Another area is in patient recruitment or in the sites where where clinical trials are being run. In the traditional model, clinical trials are very expensive and require a lot of infrastructure to run, so the number of places that are capable of absorbing those costs are few. Typically they happen in large research hospitals or centers. There are lots of doctors who would love to enroll patients in clinical trials who do not work at major medical centers and are spread throughout the medical community in hospitals and even small practices across the US. We are beginning to see companies arise that do a lot of the heavy lifting of managing and running a clinical trial for the clinician and his office, so it’s no longer necessary for them to hire on people specifically to to manage a trial. Today it’s possible for a physician who has even one patient to enroll them in a trial and use a service like this. I think we’re going to see over the next few years democratization of the actual running of clinical trials into fewer sites and many more patients should have access to clinical trials than ever before.

Q: How do you believe multidimensional biomarkers will help improve clinical trials?

A: Multidimensional biomarkers should make a huge impact on our ability to run a clinical trial. I’ll step back and explain how this all works. A traditional biomarker is simply a measurement of some molecule in a patient that allows us to make some prediction around how that patient will respond to a treatment, what their likely prognosis is, and ultimately, whether they are right for the trial or not. We know there is a lot of complex biology going on inside every patient and specifically in cancer, we’ve learned a lot over the last few years about the rich complexity inside a tumor. It’s no surprise that having a more sophisticated biomarker would allow us to do a better job of capturing and understanding that biological complexity inside a tumor. Today, we mostly use what are called single analyte biomarkers that look at a single molecule or element inside a patient and use that to understand what’s going on inside that patient. The biology is complex, so it’s not surprising that it would be difficult for a single analyte biomarker to capture all of that complexity. People have begun to use multi-analyte biomarkers to better capture that complexity, but even then, it’s still looking at independent elements and it’s not an accurate reflection of what we know to be true about the mechanism of action and a patient.

I believe that as we adopt multi-dimensional biomarkers, we’re going to do a much better job of capturing that complexity of a patient’s disease. We’re taking in lots of data from the patient and feeding that into a sophisticated machine learning approach that allows us to be maximally predictive for a response to a particular treatment. If we take multi-dimensional biomarkers, which are going to better reflect what’s going on in the patient and be much more predictive, then we can do a much better job of enrolling the right patients in a clinical trial. I think this multidimensional approach will make a huge impact on the efficiency of clinical trials and the ability to run trials faster to get drugs to patients more quickly than ever before.

Q: Is Cofactor’s Predictive Immune Modeling platform suitable for clinical trials?

A: Yes, absolutely. The Predictive Immune Modeling platform is designed precisely for that. ImmunoPrism is our first assay that leverages the platform and spans the whole range of clinical use. It starts from early discovery work in humans and goes all the way through clinical trials and even into into the clinic where physicians can use it to make treatment decisions about their patients. We’ve validated the assay under CAP guidelines, so it’s pre-approved for clinical use and clinical decision making. We are offering it both as a service in our lab and also as a kit, so it’s possible for reference labs or centers that have their own clinical sequencing facilities to to use the kit and validate it for their own use.

Questions about Cofactor or our product offerings?  Reach out to schedule a time to speak with one of our Project Scientists today.


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