How does precision medicine prove its value to skeptical payers?
Now that genomic sequencing allows us to see the intricate components of a patient’s cancer, “What’s next?” the skeptical payers are asking. Insurers are not yet convinced. With a few exceptions, private insurance and Medicare have been reluctant to cover genetic testing, even when they are an important part of assessing a cancer treatment plan. Many insurers want more evidence that the prescription for the cancer is correctly based on proven and rational specific genetic abnormalities that are responsible for cancer growth. Some insurers doubt the accuracy of matching the genetic identification with treatment. They want convincing proof of its value.
Frequently, advancement of cancer care is caught in a vicious cycle. Companies focused on introducing products into the health care sector may need to absorb costs due to lack of insurance coverage for their product, thus impacting funding for further research and technological advances. Costs for research and trials that lead to clinical validity, can run into millions of dollars. These hurdles have impacted the adoption of precision medicine and not yet led to lower costs for cancer care. Sadly, while fighting to beat cancer and manage their own care, the average cancer patient is additionally burdened with rising hospital charges, as well as high costs for newly approved drugs.
Making matters worse, many cancer drugs work only in some patients and not in others with the exact same disease. According to a Fortune Magazine article, “With cancer treatments, for example, the no-benefit rate is commonly as high as 85%. This is because one patient’s disease may have arisen from a subtly different genetic variation than other patients’ disease.” 1.
For patients facing cancer, most current treatment options are hit-or-miss. The oncologist looks at mounting piles of data, researches the options, and then selects one or more drugs that may impact the tumor. When one round of therapy does not work, another is tried, but there is no clear way to tell which treatment will work best for that particular patient, at that precise moment.
The promise of “personalized therapy” has left clinicians with information overload, yet rarely helps them to quickly pinpoint the most effective therapy for that patient.
What if predictive analytics could identify the best drug matches drawn from all FDA-approved medications for a given patient, that targets multiple mutations in that patient’s cancer, with a very high degree of accuracy?
The healthcare industry thinks that predictive analytics combined with treatment is little more than a pipe dream.
It is now a reality.
Cellworks brings this tool to the Oncologist’s arsenal, with advanced software and a unique approach to personalized cancer therapy. Cellworks has advanced the practices of simulation and modeling from the semiconductor industry to develop precise, individualized tumor modeling that can optimize cancer therapy. Cellworks analytically selects the unique combination of FDA-approved drugs that target the mutations in each patient’s particular cancer, resulting in death of the tumor cells and prevention of progression., Within 72 hours after receiving the tumor genomic profile, Cellworks provides the oncologist with a comprehensive report that identifies unique drug combinations for the most effective treatments of that patient.
This predictive ‘dress rehearsal’ is a new paradigm for cancer care. The hit-or-miss process happens within the software, sparing the patient wasted time on ineffective therapies. Cellworks bio-simulation has been validated through over 3000 predictions, 50 global collaborations and over 50 peer-reviewed papers. The Cellworks approach is now helping patients with blood related cancer, and trials are ongoing for patients with Multiple Myeloma, Myelodysplasia Syndrome (MDS), Acute Myeloid Leukemia and other diseases.
Recently some insurers are covering comprehensive genomic profiling for solid tumors. However, further reimbursement for personalized medicine is still in its infancy and the criteria for coverage is ill defined. Payers are keeping a close watch on this field, learning and collecting validity on its effectiveness for cancer patients. It would be a significant net benefit for insurers to cover costs of accurate care, early on, when the prognosis is better and likely-to-fail treatment costs can be eliminated. With a predictive <em, they would cover costs with more confidence.
1 Fortune article: http://fortune.com/2016/01/12/sotu-obama-personalized-medicine/