Throughout the past year, substantial investment continued to pour into digital health and personalized medicine. Personalized medicine involves the discovery of particular genetic variations that contribute to human illness, and those that contribute to patients’ responses to treatments. This enables targeting the molecular cause of certain diseases and enables development of molecular diagnostic tests to better predict patient outcome, understandably driving investment. This approach is particularly powerful when applied with digital health systems such as electronic medical records and laboratory information systems.
Roche’s recent acquisitions are indicative of the recent surge in digital health and personalized medicine, beginning with its acquisition of Viewics, Inc. in late 2017. Viewics developed a cloud-based tool for storing and analyzing patient data. In principle, deployment of this system could provide comprehensive treatment information for each patient, ranging from genetic sequence information to the cost of particular treatment options. Increasing access and utility of this information provides significant new opportunities to both generate and deliver value, in part by facilitating the personalized medicine approach and by providing larger data sets for analysis of genetic variation.
Roche also acquired Ignyta, Inc. in late 2017. Ignyta focuses on precisely targeted therapeutics guided by diagnostics for patients with rare cancers, and is seeking tissue-agnostic FDA approval for entrectinib, which targets mutant forms of TRK, ALK and ROS1 kinases. Ignyta’s approach, representative of personalized medicine, would permit use of entrectinib to treat any tumor with a particular genetic signature, irrespective of the tumor location in the body. The first such approval was granted in 2017 to Merck’s blockbuster drug Keytruda.
Earlier this month, Roche announced yet another acquisition. This time, it is Flatiron Health, Inc., a technology company focusing on electronically compiling fragmented patient information from disparate sources. For example, by aggregating genetic, histological, and radiological information to best evaluate and select treatment options. Thus, this platform may facilitate current molecular targeted therapeutics as well as enable discovery of new clinically relevant molecular variants. Similarly, if the database is large enough, and provides consistent data for standard of care treatment, it could in time supplement (or supplant) the active control arm of Phase 3 studies.
While the push for digitization and personalized medicine abounds in the pharmaceutical and biotechnology industries, significant hurdles remain. For example, there are still major organizational and legislative boundaries, such as privacy and security concerns, as well as incompatibility of different virtual tools with different electronic health records systems.
Part II will address the patentability requirements and recent U.S. Patent and Trademark Office guidance on personalized medicine.