In this article, I’m excited to dive into the opportunities and challenges in the field of immunotherapy treatments, and to share several case studies detailing the data-driven approach we’re taking to tackle a major challenge in immunotherapy – cytokine release syndrome.
Immunotherapy is a quickly growing and evolving field that has particularly changed the landscape of cancer treatment with new and effective therapy options (Khan et al., 2021). Immunotherapies that redirect T cells against tumor-specific antigens, such as Chimeric antigen receptor (CAR)-modified T cells and bispecific T cell-engaging antibodies (also sometimes referred to as BiTEs), have demonstrated success, particularly in the treatment of hematologic or blood cancers such as lymphomas, some forms of leukemia, and multiple myeloma. Currently there are seven CAR-T cell therapies (National Cancer Institute) and six T-cell engager therapies approved by the FDA (AACR).
CAR-T cell and T-cell engager immunotherapies have great potential to advance treatment of cancers and other diseases, but one major challenge is the risk of severe, life-threatening toxicities such as cytokine release syndrome and neurotoxicity (Khan et al., 2021).
Cytokine release syndrome (CRS) is a systemic, hyperactive inflammatory response following immunotherapy treatment. CRS is the most common toxicity risk following CAR-T cell therapy treatment, with a reported incidence rate of 42-100%, with 0-46% of patients developing severe CRS (≥ grade 3) after infusion (Xiao et al., 2021). Common CRS onset includes flu-like symptoms like fever and chills, but can develop into more severe symptoms including hypotension, hypoxia, and cardiac events/failure (Adkins, 2019).
Because CRS is common and can be life threatening if not addressed quickly, several days of inpatient hospital treatment is the standard of care following CAR-T cell therapy to ensure patients’ safety. However, this creates several barriers that limit the development of and access to new life-saving immunotherapy cancer treatments. The need for in-hospital care is expensive (Abramson et al., 2021) and can hamper efficient recruitment in clinical trials due to increased participant burden and limited accessibility to qualified treatment centers.
If CRS is monitored for and treated in a timely manner, most symptoms are reversible, and the mortality rate of CRS has been reported as less than 1% across 84 studies (Lei et al., 2021). Digital health technologies offer a new solution that can reduce the duration of inpatient care while preserving participant safety in immunotherapy clinical trials. Recently, a set of core resources for leveraging digital innovations to support the development of CRS de-risking products was shared through the DATAcc by the Digital Medicine Society (DiMe).
Continuous, remote monitoring with digital health technology gives us the ability to capture high-quality data of patient vital signs and functioning. By enrolling patients on a digital health platform and collecting baseline data using wearable devices, the patient can then be
In this way, using cutting-edge digital health tools, we can address current challenges of CRS monitoring to -
Ultimately, this approach can advance the development of CAR-T cell and T-cell engager immunotherapies for people with cancer and immunology diseases by allowing for more rapid development, increased geographic accessibility, and more affordability of these life-saving treatments.
Immunotherapies are an emerging area in oncology treatment, and there is not widespread standardization with respect to how patients are monitored or treated for adverse events such as CRS. Further, very few practitioners have had the opportunity to incorporate digital health technologies to help monitor these populations. Monitoring is further complicated because these patients are often older and can be very ill.
The case studies below demonstrate our experience gathering and analyzing immunotherapy patient data. These experiences have provided us with critical insights into what is required from operational and technical standpoints to collect high-quality data and better standardize the collection of these data, allowing for a deeper understanding of these treatments, more uniform care, and algorithm development that will increase access to these treatments.
Background: We partnered with a biotech company developing a T-cell engager immunotherapy. Our partner provided us with standard of care data from hundreds of patients in completed clinical trials. Our goal was to predict the onset of CRS of certain grades as early as possible following treatment using the vital sign data collected under the standard of care.
Approach: We used these data to develop multivariate AI-powered algorithms for CRS risk prediction. At 12- and 16-hours following infusion, we were able to distinguish between a CRS Grade of 1 or fewer and Grade 2 or more.
Figure 2. Two case studies from real-world data are shown: i. a patient who did not develop CRS with temperature and blood pressure values at pre-infusion levels in the 16 hrs after treatment; ii. another patient who developed CRS Grade ≥2 post-infusion with signs of fever and hypotension.
Key takeaway: This retrospective study validates the feasibility of CRS detection models using objective vital sign data alone.
Background: We partnered with Mayo Clinic to collect continuous digital data, episodic digital data, and expert CRS grading following CAR-T cell therapy treatment in a real-world setting.
Approach: Using wearable sensors, we collected continuous digital measures of vital signs including heart rate, respiratory rate, skin temperature, SpO2, and activity. We also collected episodic data points including heart rate, respiratory rate, oral temperature, SpO2, and blood pressure. Patients were monitored under Mayo’s outpatient standard-of-care treatment for CAR-Ts and CRS events were graded by experts using ASTCT guidelines. Experts were not accessing data from continuous monitoring devices and analyses were undertaken retrospectively.
During this study, we saw the power of continuous data as compared to episodic patient monitoring. There are large intrapersonal variations in physiological signals over time that could be due to pathologies, natural circadian rhythms, and more. Infrequent episodic monitoring does not provide a detailed enough picture to understand what types of physiological changes may be natural and what may be pathological for an individual patient.
Changes in vital sign distributions from data collected continuously over many days provide more powerful evidence of changes that are more likely to indicate an adverse event compared to episodic data that is more susceptible to the influence of external factors such as circadian rhythms or physical activity. Given this, it’s clear that real-world, continuous data collection of multiple vital sign measures will provide a more robust and detailed data set to develop and validate an accurate CRS detection algorithm.
Key takeaway: This study demonstrates the value of multivariate continuous data collected from patients during CAR-T cell therapy treatment to inform robust model development for CRS risk prediction and has provided numerous learnings about the operational factors involved in gathering data for these models.
Background: We partnered with Vanderbilt-Ingram Cancer Center to assess the safety and efficacy of remote monitoring for patients receiving CAR-T cell therapy, axicabtagene ciloleucel (YESCARTA®).
Approach: Patients who received treatment were continuously monitored using medical-grade wearable sensors during their 7-10 days post-infusions hospital stay, giving the care team the ability to monitor vital signs through a digital health platform (in addition to standard of care safety monitoring).
Key takeaway: The collection of high-frequency and high-quality digital data has the potential to advance current standards of care to enhance patient safety and broaden access to effective immunotherapies with remote monitoring solutions.
These case studies demonstrate that digital health technologies (DHTs) provide a viable opportunity to address one of the major challenges and accelerate development of life-saving immunotherapy treatments. The potential of this application is not limited only to CRS monitoring but could also be applied to monitoring other side effects of immunotherapy treatment such as neurotoxicity and sepsis.
For example, using an existing set of hourly patient vital sign data from a benchmark publicly available dataset, we developed a model to effectively predict sepsis before clinical onset (Pettinati et al., 2020).
In addition to lowering patient burden and reducing the cost of immunotherapy trials, continuous, objective data collecting with medical-grade wearable sensors can provide a more detailed and wholistic view of the immune response. This information can provide novel clinical insights important for the future development of more effective and safe immunotherapies.
We’re excited to continue leading the way on the pathway to developing effective and patient-centric treatments for those suffering from cancers and immunological diseases.
For more information about how digital measures can advance the development of cancer treatments, applicable digital endpoints, and relevant regulatory guidance, check out our Oncology Digital Endpoint Guide.