Drugs and diagnostics make a good team, particularly in cancer R&D where pharma companies are increasingly setting their sights on a precision medicine strategy. And the way forward hinges on testing tools to accurately identify patients for clinical trials and treatment.
But when it comes to generating excitement among investors, current screening tools can’t compete.
Global venture funding for diagnostics totaled just $1.7 billion last year. By comparison, AI and machine learning raked in $15.5 billion, according to DealForma.
And only a few pharma giants have made major plays in the diagnostics sector.
Roche has long been the industry’s leader and remains heavily invested in the space. Just last week, the large pharma announced it’s paying $595 million through an affiliate company to snap up SAGA Diagnostics and its tumor-informed molecular residual disease platform. Roche is also upping its production footprint, last year pledging a $550 million expansion of a diagnostics manufacturing hub in Indianapolis.
Johnson & Johnson also has a firm footing in the med tech arena, including diagnostics. In addition to leveraging devices in development for key drugs, the company has continued to support diagnostics R&D through a range of partnerships and collaborations.
But Abbott Laboratories, a pharma and medtech giant, made the most eye-catching move this year when it struck a $21 billion deal to purchase the cancer screening company Exact Sciences.
All of this momentum reflects pharma’s ongoing investment in how diagnostics can go “hand in hand” with precision medicine, said Arnon Chait, chief innovation officer at Cleveland Diagnostics.
“If you have a drug that is highly specific and efficacious for a cohort of patients, and if the receptor it targets leaves a sign in the body’s blood circulation, we can find that mutation in a test,” Chait said. “Pharma does the magic. We can say which patients benefit from that magic.”
Bridging the pharma-diagnostics divide
Cleveland Diagnostics scored its first FDA nod in December for a test designed to improve early detection of prostate cancer. The typical route to diagnosis involves an antigen blood test, and then if the patient shows elevated levels of the PSA biomarker, a tissue biopsy. But up to 75% of those subsequent biopsies are negative for high-grade disease, a situation that means “millions” are subjected to an unnecessary procedure, according to the company.
Why is there a gap between the results of blood tests and biopsies? It all goes back to the fact that standard tests measure the concentration of PSA. But Cleveland Diagnostics’ tool instead analyzes the structure of PSA in the blood, offering a more accurate measurement of disease risk and a more detailed assessment of the need for biopsy.
The new diagnostic was created with the company’s IsoClear platform, which the company said could deliver more cancer tests down the line. In its last round of fundraising, Cleveland Diagnostics won backing from Novo Holdings and is in talks with other pharma companies who’ve shown an interest in the platform’s potential, Chait said.
While early detection is where the company shines, Chait said the tests could be developed to help pharma companies win a label expansion for its therapies.
“Virtually all drugs target a protein,” Chait said. “So we could tell you if indeed someone has a cancer of a certain type and the protein you’re targeting.”
Blood-based biomarker tests also help scientists understand disease progression, potentially helping pharma move into earlier lines of care, and can detect the ongoing presence of a cancer even after a tumor is surgically removed.
While pharma and diagnostics complement each other in many ways, the two sectors still mostly work in silos. Perhaps, with AI on the scene, that could start to change.
“Let’s venture into the future,” Chait offered. “Right now, you’ve got pharma investing in drugs, companies like ours investing in diagnostics and the medical records industry investing in their smart tools. What happens when someone brings that all together and does deep learning on all that?”
In this idealized healthcare system, AI could track patient health and generate predictions for risk, screening or therapy. Essentially, doctors would quickly know when to order tests, when to prescribe drugs and what the patient’s “survival curve” looks like.
“Physicians would have the entire patient journey — from symptoms all the way to the cure and everything in between — integrated under this AI system,” Chait said.
In its own immediate future, Cleveland Diagnostics is focused on developing the next screening tools, particularly in thyroid cancer.
For Chait’s broader vision to pan out, the public sector would likely have to be the ringleader for steering major changes in how the separate industries work together. That might be asking a lot in today’s tumultuous and politicized regulatory environment, but if that shifts, pharma and diagnostics companies could demonstrate how to “take a patient through the entire treatment process,” Chait said.
“That would result in a huge benefit not just for the healthcare system but everyone else too,” he said.
