Personalized gene-modified cell therapies are expensive and complex treatments, and numerous factors contribute to their success or failure for individual patients. A means for predicting outcomes for each patient is needed to ensure that clinicians — together with their patients — are able to select optimal treatments. Such data-driven decision-making will help lower the costs of cell therapies and provide confidence to insurers, ultimately increasing access. The Evidence Engine being developed by IQVIA with support from a consortium of nonprofit organizations including Be The Match BioTherapies is designed to provide the global data sets and analytical tools needed to support this approach.
The Need to Predict Outcomes
Personalized gene-modified cell therapies, initially developed for the treatment of cancer, are currently under investigation for an expanding array of diseases, including both autoimmune and genetic disorders and other conditions. There are many patient-related variables that impact the safety and efficacy of these complex treatments. Success of these therapies is also contingent on the performance of an extremely complicated and expensive manufacturing process and supply chain.
One of the biggest issues with achieving successful gene-modified cell therapy outcomes is matching the right patients with the right treatments. Clinicians are at the heart of this process and are at the center of the gene-modified cell therapy ecosystem. They need more data to make informed decisions regarding who should receive these therapies and when they should be administered. That data should address the variables that must be taken into account to anticipate what the outcome might be for each patient.
Providing this information is becoming increasingly important as gene-modified cell therapies move out of current centers of excellence and into community hospitals. Such a move to the outpatient setting must occur to expand patient access, as most cancer patients do not live near these centers of excellence. Physicians working in community oncology centers that have no experience with cell therapy may find it very difficult to determine which cell therapies are optimal for which patients.
Payers also seek data to support the treatment of patients with very expensive gene-modified cell therapies. They want evidence that a patient who received a cell therapy treatment was truly a good candidate. Similarly, policy makers are interested in increasing access while also reducing the overall cost of healthcare, including gene-modified cell therapies. Here again, this goal can only be achieved if it is possible to determine which patients are most likely to benefit from a given therapy.
A Wealth of Data
Fortunately, there is a great deal of data that can be used — in combination with artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and other advanced digital technologies — to realistically predict patient outcomes.
There are extensive medical records for patients who have received cell therapies in the past, including their histories before treatment and their performance following treatment. Therefore, electronic medical records are a primary data source. Records on apheresis, patient cell packaging and shipment, the manufacturing process, and packaging and shipment of gene-modified cell therapy products are also valuable. All of these data directly or indirectly contribute to patient outcomes.
The Evidence Engine from IQVIA
The Evidence Engine being developed by IQVIA with the support of a consortium of nonprofit groups including Be The Match BioTherapies provides a mechanism for compiling these disparate data and tying them to patient outcomes for the betterment of the medical community and patients in particular.
The goal is to help clinicians determine whether patients diagnosed with different cancers are good candidates for a particular gene-modified cell therapy and why, as well as what to expect post-administration. That will lead to patients realizing the best outcomes with the lowest toxicities. It is also intended to help broaden access to therapies through data-driven decision-making.
It should also help clinical trial investigators decide which cell therapy clinical trials should be conducted at their hospitals. Currently, it is very difficult for physicians to make this assessment, because there is a lack of a means for doing so. Often, these decisions are based on instinct or scientific intuition rather than data.
The Evidence Engine data platform is designed to hold data regarding all of the variables that might impact the safety and efficacy of gene-modified cell therapies in one location. It also includes AI and ML capabilities for continual learning as more data are collated, including global data sets in particular situations
Funding for development of the Evidence Engine comes from the California Institute of Regenerative Medicine. The effort is also supported by a consortium largely comprising nonprofit organizations, including Be The Match BioTherapies and others. The fundamental vision is to combine IQVIA’s data structure and data analysis capabilities with the clinical data provided by the other consortium members. Input from manufacturers will be critical as well.
The Evidence Engine also has the potential to help address issues, such as the underrepresentation of some ethnic groups in cell therapy clinical trials. While there are societal problems that underlie this issue, such as deep-seated distrust of the medical system in certain communities, the hope is that, with more data, more people will become comfortable with participating in trials. In addition, by reducing the prices of gene-modified cell therapies through the use of data and real-world evidence, access should be expanded — not just in the United States and within Europe but around the world.
Reducing Cost
The cost of chimeric antigen receptor (CAR)-T cell therapy in the United States is approximately $350,000–$500,000. That price is out of reach for most people in the rest of the world. In order to make these life-changing treatments available to people across the globe, those prices must be reduced.
One way to reduce the cost is to eliminate poorly manufactured therapies, including those based on patient cells that are not suitable for use in the production process, which would lower the cost of goods. Another is to exert pricing pressure through the use of excellent real-world data (RWD) that show when a treatment will work very well, so that all treatments result in successful patient outcomes.
Years in the Making
Ultimately, if data-driven decision-making can be made possible, and gene-modified cell therapies can be administered in community hospitals in an outpatient setting, costs will come down, and widespread access should be possible. Those goals will take time to realize, however. It will be many years before the quantity of global data needed to enable the successful prediction of patient outcomes are available and collated into the Evidence Engine.
One issue is the fractured nature of the healthcare system in the United States. In countries with single-payer healthcare systems, in which electronic health records are maintained in state-run databases, integration of data into the Evidence Engine will be easier. In the United States, however, there are multiple proprietary platforms used to maintain electronic medical records in different file types and formats that are very difficult to integrate. Another concern that must be addressed is how drug companies can confidently share their proprietary data without losing any competitive advantage. The data will need to be anonymized and pooled.
IQVIA is making great progress. The team of experts working on the Evidence Engine are getting better at figuring out how to integrate disparate data sources and are beginning to feel comfortable with the inferences that they are able to make drawing on these disparate health records.
Pilot programs are currently underway in which data from contained studies (e.g., a multiple myeloma study using a BCMA CAR-T run at City of Hope and the University of California, San Francisco) is deposited into the Evidence Engine. This approach is enabling the consortium to build a proof of concept on the basis of discrete studies focused on single indications. Indeed, academic-run clinical studies are more predisposed to sharing data, and a huge proportion of global clinical studies in cell therapy are academic-sponsored studies, so they are a good place to start. Ultimately, data from corporate studies will be needed as well.
Changing Paradigms
There are many shifts occurring in cell therapy, but these shifts do not remove the need for data-driven decision-making to ensure better patient outcomes. The initial centralized manufacturing approach for autologous cell therapies may be shifting to one that is more decentralized. There is also growing interest in allogeneic, off-the-shelf (versus autologous, patient-specific) therapies derived from healthy donor immune cells. Nearly every company that has an autologous therapy in their pipeline now also has an allogeneic therapy as a follow-on in recognition of the challenges of the supply chain. Both shifts eliminate the need to ship patient cells and cell therapy products rapidly by air, thereby lowering costs and eliminating some of the variables that contribute to reduced efficacy.
These shifts do not preclude the need for the Evidence Engine. Whether a cell therapy is autologous or allogeneic, it still involves essential pieces of data that will have a direct impact on the success of that therapy for each patient. For allogeneic therapies, the questions will relate to the donor cells and their characteristics, as well as the distribution of these products. A global database that collects information on all of the different variables can provide invaluable support to clinicians looking to choose the right cell therapy for a specific patient and to investigators determining which clinical studies best fit their site and patient populations.
Benefiting Many Stakeholders
The most immediate beneficiaries of data-driven decision-making will be patients and the clinicians who treat them. The Evidence Engine will help decide both whether cell therapy is a reasonable next treatment and which cell therapy is best. For a patient with a blood cancer, there are several approved gene-modified cell therapies on the market, and hundreds of clinical trials underway. Choosing the one that will give the patient the best chance requires an incredibly complicated calculus. The Evidence Engine will make it possible for that choice to be based on RWD.
Often, the decision to treat a patient with a gene-modified cell therapy is not made just by the clinician and the patient — the finance, legal, and administration departments within centers of excellence also have a say, because these therapies are so expensive. In some cases, hospitals must prepay for the therapies before receiving any reimbursement, which creates an administrative and financial burden, which hopefully can be eliminated by using a data-driven decision-making process. Similarly, payers will have more confidence that the patients receiving these expensive treatments will have positive outcomes, making it possible for them to make good coverage decisions.
Regulators will clearly also benefit.
Be The Match: An Honest Data Broker
Be The Match has historically been a coordinating center. The original Health Resources and Services Administration's (HRSA) grant awarded to the organization was specifically to coordinate the efforts of disparate entities involved in the transplant field so that they could work in unison.
The Cord Blood Alliance is a great example. Cord blood is increasingly becoming an interesting source material for allogeneic cell therapy development. There are numerous public and private cord blood banks located across the United States, and drug developers are going from one bank to another looking for source material. The Cord Blood Alliance, spearheaded by Be The Match BioTherapies, makes that process much simpler by coordinating the activities of many of the public banks and serving as a single resource for drug developers. The Alliance also provides a new and much-needed revenue source for the cord blood banking industry.
Be The Match Biotherapies can search over 300,000 units in the public banks virtually instantaneously, dramatically streamlining the process. We are well positioned to coordinate the activities of the Cord Blood Alliance, because we maintain a software platform through which the public inventory can be viewed and searched for many different biologic and demographic characteristics.
The next phase of the journey on which Be The Match has embarked is moving beyond cord units as a starting material into health donor cells. Be The Match BioTherapies has been in this business for a while already, identifying donors, gaining consent, and harvesting cells for further manufacture. There is considerable industry interest and demand for this type of service, so we are undertaking an initiative to better support the development of allogeneic cell therapies through the provision of consistent, compliant donor starting material.
IQVIA’s Evidence Engine fits with this theme of coordinating activities and working as an honest broker of data very well. In this case, we will help funnel clinical trial, patient, and other relevant data into a single repository that is easily and equably accessible to clinicians seeking to ensure the best outcomes for their patients.
Contributing to and Benefiting from The Evidence Engine
Be The Match has a very long history in cell and gene therapy, having been a leader in cell therapy — bone marrow transplants — for 35 years. The processes, procedures, and supply chain for bone marrow transplant are very similar to that for gene-modified cell therapies. Donor cells are harvested through apheresis or sometimes a bone marrow aspirate and then packaged and shipped on commercial aircraft.
Through Be The Match’s Center for International Blood and Marrow Transplant Research (CIBMTR), the outcomes of all transplants are tracked for patients as long as they survive. To date, approximately 650,000 transplant outcome data sets have been accumulated. Notably, apheresis, supply chain, and other relevant data — all of which is fundamental to the concept of the Evidence Engine — are also included.
Be The Match BioTherapies also plans to compile data on emerging cell therapies: apheresis, supply chain, and outcomes in partnership with industry. The CIBMTR is performing outcomes data gathering for the autologous CAR-T cell therapies currently on the market.
We have the ability to assemble all of those data sets and use IQVIA’s data analytics and power to round out the story. As a result, both Be The Match and Be The Match BioTherapies will continue to play very significant roles in helping to make the Evidence Engine a reality. That we will be supplying all this data in an organized manner to the clinical decision-making community through the Evidence Engine is very exciting to our organization. It is another mechanism for serving patients as an honest broker of data.
Indeed, bone marrow transplant is a cell therapy that does not work for everyone. With our mission to serve patients as effectively as possible, particularly patients that have blood and marrow cancers, we embrace a future in which transplant is not always utilized and in some cases may be replaced. It is a difficult notion for an organization founded specifically to support successful transplants, but it is possible that, at some point in the future, transplant as a treatment will be needed only by a small subset of patients. If there is an emerging cell therapy, CAR-T or otherwise, that has a better safety and efficacy profile, Be The Match will absolutely support it.
The Evidence Engine can also be used to rationalize the transplant data set to identify the really good candidates for transplant or other types of cell therapy.