How Real-World Data Is Transforming Cell and Gene Therapy Commercialization

When Kymriah® was introduced to the market in 2017, it was lauded as a revolution in immunotherapy. As the first CAR-T therapy for cancer, the drug ushered in a new era of hope for pediatric and young adult patients with acute lymphoblastic leukemia (ALL), and set off a wave of new research into cell and gene therapies that would change the way the world thinks about cancer treatment. Chimeric antigen receptor T cell therapy, more commonly known by the shorthand CAR-T, is a breakthrough form of immunotherapy that genetically alters the body’s own immune cells in a lab to help them locate and destroy cancer more effectively.

Then, the realities of CAR-T’s complex manufacturing process and its sometimes inconsistent results began to set in, slowing mainstream adoption of cell and gene therapies. For all of the potential this approach to immunotherapy represents, even today, it is still not widely used. 

That’s partially due to its complexity. Existing cell therapies, such as CAR-T, require that the patient’s cells are collected, the cells are then separated from the plasma and re-engineered in an external setting, and re-infused into the patient. While there will be “off-the-shelf” options that come to market over time, the manufacturing issues (e.g., turnaround time, temperature control during shipping) in creating and transporting these therapies are significant hurdles to adoption. Additionally, even with a case-by-case approval policy, many payers won’t cover the high price tag — which could range anywhere between $500K and $1M for a full cycle of therapy.

Additionally, the therapy does not always provide long-term results. When CAR-T therapy works, it works spectacularly, but cancer still returns for many patients. In lymphoma, for example, just over half of treated patients do not experience lasting remissions. Add the fact that there are only a  limited number of treatment centers that could provide this life-saving treatment, and it starts to become clear why gene and cell therapies have not seen the massive surge in adoption the world was expecting five years ago.

Fortunately, one area of Life Sciences that has made a quantum leap forward over that same time period is the use of real-world data (RWD) to speed the clinical development and evidence-based research processes. By tapping into longitudinal datasets that track the real-world patient journeys of hundreds of thousands of patients, along with powerful analytics that spot gaps in care and opportunities to introduce new therapies in specific populations, it is possible to remove many of the systemic obstacles that have kept cell and gene therapies from reaching their fullest potential.

In fact, by using Komodo’s Healthcare Map™ of real-world patient encounters, we’ve been able to chart the evolution of cell and gene therapy at a highly granular level to better understand current trends in prescribing patterns among providers. Specifically, we conducted an analysis to answer two key questions:

1. Has increased experience with cell and gene therapy among healthcare providers resulted in a change in the average age of patients receiving CAR-T relative to the previous standard treatment protocol, autologous stem cell transplants (ASCT)?
2. Has there been a significant evolution in the sequencing of other types of immunotherapies, such as monoclonal antibodies (e.g, Polivy, Monjuvi, Zynlonta) versus CAR-T for the treatment of diffuse large B-cell lymphoma (DLBCL)?

We started our analysis by looking back to January 2018, shortly after the first CAR-T treatments were approved by the FDA for DLBCL. We first identified nearly 3,500 patients who received a CAR-T agent (i.e. Yescarta, Kymriah, or Breyanzi), and we grouped patents into six-month time intervals based on the date of CAR-T receipt. In order to make a comparison, we conducted the same analysis for a previous cell therapy treatment protocol, autologous stem cell transplants (ASCT) in DLBCL. Our analysis found that the average age for CAR-T patients has increased ~6 years (from 57 to 63 years), while the average age for ASCT patients remained relatively stagnant (around 57 years). While incremental, this could point to healthcare providers gradually becoming more comfortable treating patients, monitoring patient response, and managing side effects associated with CAR-T as they gain more real-world experience. 

Digging one layer deeper, we also used the Healthcare Map to track trends in the sequencing of other types of immunotherapies, such as monoclonal antibodies (mAbs) like Monjuvi, Polivy, and Zynlonta. These mAbs target CD19, a surface cell protein that is a common target for immunotherapies in DLBCL.  HCPs have mixed opinions on whether to continue to target the same antigen for a given tumor. Some providers prefer to treat patients with a mAb first, then move to CAR-T treatment, and others prefer starting with CAR-T, then using mAbs. By understanding these considerations at the healthcare provider level, commercial teams are able to better understand physician prescribing patterns and preferences for different types of immunotherapies. In addition, they are able to improve product positioning strategies (e.g., sales messaging) to potentially influence those decisions. 

In order to conduct this analysis, we selected all patients who received only CAR-T after the FDA approval of mAbs in DLBCL and checked to see what percent of patients had already been exposed to a mAb prior to CAR-T receipt. The data clearly shows a trend that, within three years of mAb approval, nearly ~20% of DLBCL CAR-T patients have already been exposed to a CD-19 targeting mAbs. By understanding a provider’s sequencing decisions, commercial teams can create product positioning strategies and targeted messaging and specific field force education to help increase CAR-T access to eligible patients. 

Previously, analyses like this were labor intensive and required considerable market research to glean only the most surface-level insights about CAR-T adoption and sequencing. These two examples are only scratching the surface of how RWD can be used to unlock the systemic, structural, and behavioral factors influencing cell and gene therapy adoption among healthcare providers. By leveraging RWD coupled with advanced analytics and powerful software tools, like Komodo’s Prism and Sentinel applications as used here, we are getting a deeper understanding of how therapies are prescribed and adopted, and the ability to track associated outcomes. Armed with these insights, Life Sciences teams can begin to map referral patterns amongst providers, identify key opinion leaders, and measure market penetration and adoption to ensure timely intervention and improve patient outcomes. Most importantly, they can approach providers with concrete data to illustrate the potential in a new treatment and, ultimately, help speed the adoption of promising therapies.