By better defining addressable patient populations and more precisely predicting likely individual responses, companies are able to reduce a great deal of risk in their clinical trials.

BY Catherine Howe

Patient Selection: The Right Drug for the Right Patient

June 06, 2018

IMAGE: Courtesy of Batavia Biosciences

Healthcare cost reduction is an on-going conversation and challenge within the life sciences industry. While a great deal of attention has focused on lowering drug prices, relatively little has been placed on reducing avoidable treatments through a better understanding of treatment pathways and more effective diagnosis. According to PhRMA, better use of medicines could eliminate US$213 billion in U.S. health care costs annually, amounting to 8% of the nation's health care costs. Furthermore, the Health Industry Distributors Association estimates that lab tests represent only 2% of healthcare spending while influencing 70% of medical decisions.

Beyond the cost benefit, more effective diagnostic tools also tie in to an increasing emphasis on patient-centricity. Nowhere is this more pertinent than in the infectious disease space. “The current paradigm centers on the hypothesis-based testing of the clinician,” commented Mickey Keresz, founder and CEO at Karius, a diagnostics company that has developed a test enabling clinicians to rapidly diagnose infectious diseases by detecting the DNA of over 1,250 pathogens from a standard blood draw. “This is a long and tedious investigation process, which results in empirical treatment with broad-spectrum antimicrobials, a lot of lost time and money, and confusion and suffering. The introduction of genomics into infectious disease diagnostics allows us to capture all microbes within a single test. The way this is done is by looking at the genetic material that these microbes shed into the bloodstream as they infect the patient. Every bacteria, fungus and virus has a genetic code, and that genetic material is shed into the bloodstream as it replicates, which is what we pick up. With sequencing, digitizing and applying machine learning and analytics, we can produce a report that tells the clinician what that patient was infected with.”

Treating patients with drugs empirically leads to a lot of unnecessary care and, in the case of antibacterial drugs, can also lead to resistance. “In the absence of strong diagnostic information, clinicians will use sophisticated formulas and start on one drug and then move on to the next if it has not taken effect in a 12 hour period,” highlighted John McDonough, CEO at T2 Biosystems, a diagnostics company focused on sepsis. “80% of the time, this guesswork has led to the right drug by the time blood culture results comes back which is often days later. Well over 50% of the patients treated will not have needed the drug and the use of drugs when not needed can lead to drug resistance and a costly overuse of medication. The drug resistance can mean that if that patient needs the drug in the future, it might not work for them.”

In other disease areas, current diagnosis options can be extremely invasive and often unnecessary, highlighting a need for alternative options. In cancer, for example, surgical pathology has always served as the gold standard for diagnosis. “In the United States alone, over half-a-million people undergo a work up on a nodule to determine if they have thyroid cancer,” noted Bonnie H. Anderson, CEO and Chairman of the Board at Veracyte, a genomic diagnostics company based in San Francisco. “Roughly 100,000 patients every year do not get a diagnosis from the first test when that nodule is worked up using methods we have available today. This ambiguous point is where we developed our first genomic test, Afirma, to fit into the clinical pathway of care. All of our classifiers have been built using machine learning, which we started using 10 years ago. This has allowed us to create highly accurate, very informative tests. So, when the clinical pathway hits a point of ambiguity, our tests inform the physician on what to do next, helping many patients. To date, we’ve performed over 100,000 Afirma tests and estimate we’ve helped more than 40,000 patients avoid unnecessary surgery and removed over US$800 million in surgery costs from the healthcare system. Because we are collecting genomic information, we can also often inform on those cancers that do need to go to surgery and help physicians decide the extent of surgery. We are transforming the way diseases are diagnosed.”

Veracyte has two further products: Percepta, a lung cancer test, and Envisia, a first-of-its-kind test in interstitial pulmonary fibrosis diagnosis. Veracyte’s Percepta test is based on a signature of genomic changes that occur in the airway when a patient has or is at risk for the development of lung cancer or other diseases, rather than being based on a signature of the tissue from the actual nodule. Referring to this area as the “field of injury”, Anderson explained: “With lung cancer, everything that is breathed in exposes the airway to toxins and carcinogens that disrupt the genomic pathway. That, combined with the patient’s immune status, could determine whether the patient gets lung cancer or whether they are able to fight it off. There has also been published evidence suggesting that field of injury can be used to predict COPD or other lung conditions. With Percepta, we have been able to measure the level of change in the airway and develop a signature that highly correlates with cancer.”

Percepta enables diagnosis through the use of a brushing of the main lung airway, rather than an invasive procedure. “Also, early detection of disease generally implies that the patient already has the cancer,” continued Anderson. “There is a very exciting movement now around the idea of intercepting patients before they actually develop the disease. Cancer is a big element of that. When considering what technologies could be used to get early detection of a patient at risk of lung cancer, the field of injury seems to be the perfect tool. We therefore have early work underway and collaborations with Boston University to examine if we can use a nasal swab test to detect early changes and predict a patient’s risk of developing cancer, even before they get it.”

Pending a coverage decision for Envisia, Veracyte hopes to have all three tests commercialized and covered by Medicare by the end of 2018.


Maximizing success in clinical trials

In conjunction with rising drug development costs, regulatory burdens have also increased, resulting in more complex clinical trials. According to PhRMA, after an average development process of 10 to 15 years, only 12% of investigative medicines entering clinical trials are ultimately approved by the FDA.

As well as determining optimal treatment pathways for patients, diagnostic companies are supporting more targeted patient selection for clinical trials. Alongside the clear benefit of finding the right drug for the right patient, finding the right patient for the trial – in other words, the patients likely to have the highest response rates – could vastly improve the trial success rates, leading to a higher number of approvals.

By better defining addressable patient populations and more precisely predicting likely individual responses, companies are able to reduce a great deal of risk in their clinical trials.


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