September 15, 2023 PAO-09-23-RT-01
There are still many unknowns when it comes to the relationship between host-cell genetics and manufacturing titers of therapeutic proteins, and there are multiple biological processes taking place simultaneously, which complicate matters, like gene transcription and mRNA translation, mRNA degradation, protein folding and degradation, etc. This makes rational, predictive design of strains difficult. Thus, a project for optimizing protein titers may often involve screening large sets of expression vectors to identify those that are the best for each protein, and the results are hampered by screening and data analysis limitations. At Vectron, we are collecting large data sets of real-world gene-expression data that will allow us to build better gene-expression models without necessarily understanding the contribution from all the separate biological processes involved. These models can then assist us in strain development projects by predicting what promoters and expression elements to use with each protein in question to ensure optimal titers.
In the development of detergents, real-world data is everything. One can go to any manufacturer of surfactants, chelators, and other ingredients making up a modern-day detergent. By researching the marketing materials and the very interesting detergent chemistry involved, perhaps even involving AI, one can formulate a product that should work extremely well. But the only way to know whether your “ideal” detergent works is to test it against “real-world” soils, and even that approach is somewhat limited if it is done in purely lab-based studies. As a formulator, you don’t know whether your product works until you get to apply it in actual manufacturing scenarios. We at Alconox Inc. use real-world data gathered in the lab, and data that our customers generate with either current or experimental products and apply them to emerging formulations. This, of course, is not a new approach to formulation. However, detergent formulation seems superficially simple, but is in reality a very complex process that could not be done differently. Going forward, we plan on continuing with this approach and partnering with our customers to create products that work in a wide range of applications while being safe and environmentally friendly.
At PPD, the clinical research business of Thermo Fisher Scientific, we are leveraging real-world data (RWD) and generating real-world evidence (RWE) for use cases across the clinical development and commercialization spectrum. We use RWD to inform strategy for clinical trials, including assessment of protocol feasibility to determine if there are protocol inclusion/exclusion criteria that adversely impact recruitment of the patient population, site selection by assessing access to potential patient populations, diversity alignment, patient recruitment, etc.
Furthermore, we use our own syndicated data as well as primary and secondary data sources that are analyzed to generate RWE to support regulatory decisions in both the clinical development space with solutions like external control arms (ECAs) and the post-approval space with solutions that address post-market regulatory requirements, among others, as well as market access and healthcare decision-making. This evidence helps stakeholders, such as regulatory agencies, pharmaceutical companies, and healthcare providers, make informed decisions about drug safety, effectiveness, and patient outcomes.
As the regulatory acceptability of RWD and RWE increase globally, there will be more opportunities to increase the use cases. RWD could be used to supplement data collection in clinical trials by using connections to RWD to increase data accuracy and decrease site and patient burden. Challenges like data quality, privacy, and standardization continue to be barriers to increasing the use of RWD.
Dynamk Capital currently leverages real-world data in our investment strategies by extensively analyzing market trends, economic indicators, and company-specific data. This data-driven approach has allowed us to make more informed investment decisions, optimize portfolio performance, and manage risk effectively. For example, we use near real-time market data to gauge metrics like the appetite for investment, company valuations, and exit trends to help our portfolio companies make short- and long-term decisions to maximize growth and value.
Our industry leverages real-world data (RWD) and evidence to gain valuable insights for decision-making in clinical development programs and trial designs. This approach involves narrowing down administration configurations, identifying target patient demographics, and minimizing the substantial costs associated with bringing a drug to market. We are encouraged by the FDA's progress in utilizing real-world evidence and data for regulatory decisions.
Real-world evidence (RWE) has now gained acceptance as a reliable source to support various aspects of drug development, including drug approvals, labeling claims, new indications, and meeting post-approval study requirements. This acceptance allows for more accurate forecasting of development and manufacturing volumes for both clinical and commercial production, ultimately reducing the time and costs associated with clinical development programs. Moreover, real-world evidence may enable sponsors to explore new indications for existing drugs based on their real-world usage, as opposed to conducting formal clinical trials. This expansion of potential use cases also aids in supporting reimbursement for these new indications.
Qualtrics was designed to capture signals and sentiments that customers and employees generate as they engage with and experience companies, products, brands, and services. In the case of life sciences, we gather VoC (voice of the customer) from patients and providers in the moments surrounding live, digital, and contact center interactions.
The breadth and depth of real-world data being generated comes through responses, behaviors, and voice data that is analyzed through natural language processing. The power in these insights is a scaled, deeper, and more immediate understanding of customer sentiments and challenges integrated into systems of resolution — all at the speed of need.
Qualtrics is helping healthcare measure and enhance several critical metrics, including but not limited to, sentiment, trust, CES (customer effort score), OSAT and CSAT (satisfaction measures), NPS (net promoter score), and, in pharma, LTA (likelihood to adhere). We recognize industry challenges in PROs (patient reported outcomes) and concerns about missed AEs (adverse events) and have several approaches to help mitigate and manage these.
The industry is in very early stages of customer experience compared with retail, hospitality, and even the public sector. Hospital systems have CAHPS and insurers have Star Ratings, both mandated by CMS. These two critical partners in health are starting to expect for themselves from pharma what is expected of them by CMS. The appetite and readiness are high, but execution is behind. The headroom for innovation and real customer centricity is therefore expansive.
At Astrea Bioseparations we recognize the significance of real-world data and evidence in enhancing our chromatography products and services. Currently, we utilize real-world data in several ways:
Market insights: Analyzing real-world data provides us with valuable insights into market trends and customer preferences. This information helps us to make informed decisions about product development and marketing strategies, ensuring that we remain competitive in a rapidly evolving industry.
Product development and improvement: Real-world data helps us to understand how our chromatography products perform in a diverse range of laboratory settings. We gather data from customer feedback and beta test new products to identify areas for product improvement. This data-driven approach ensures that we continuously enhance the quality and efficiency of our products.
New application note development: Voice of the customer and analysis of market trends helps us understand how customers would like to use our products. We can then invest in the development of specific application notes to support these uses.
Quality assurance: Real-world data are essential for maintaining the highest quality standards. We collect and analyze data to monitor product performance, identify any issues or anomalies, and take proactive steps to address them. This ensures that our customers can rely on our chromatography products for accurate and consistent results.
Looking ahead, we anticipate even greater utilization of real-world data.
Collaborative research: We foresee collaborating with research institutions and customers to leverage real-world data for joint research projects; for example, we are funding a EngD in separation and purification of exosomes at UCL. This will foster innovation and further advance the capabilities of our chromatography products.
We will also continue to beta test our products and collaborate with customers to develop new products, allowing us to deliver products to market that best meets customer needs.
In summary, at Astrea Bioseparations, we consider real-world data a cornerstone of our commitment to quality, innovation, and customer satisfaction. We are dedicated to continuously harnessing its potential to improve our chromatography products and services while staying at the forefront of industry developments.
As a leading medical app for healthcare professionals, epocrates has long championed the importance of real-world evidence. A prime example is our mobile antimicrobial susceptibility tool, Bugs + Drugs, which equips clinicians with real-time, geolocated data on superbugs and resistance patterns. This functionality uniquely empowers prescribers to make precise decisions in treating bacterial infections and mitigating antimicrobial resistance.
Our commitment to continuing to enhance our user experience is evident in our new dynamic home screen. By tailoring content based on real usage patterns, we’ve witnessed a 63–77% increase in content consumption across our audience segments since introducing personalization. This underscores the value of relevant, personalized displays of content and tools for our users.
Looking ahead, epocrates envisions numerous opportunities to leverage real-world data. Our data science and search teams are already exploring these possibilities. With the evolution of AI and machine learning, we can foresee predictive analytics utilizing anonymized data to anticipate adverse events or treatment outcomes. This could not only empower healthcare providers with proactive decision-making but also contribute to advancing the medical community’s understanding of therapeutic approaches.
In the early stages of oncology clinical development, a company starts by treating patients who have exhausted other treatment options for their specific cancer types (i.e., their cancer has progressed) or who cannot tolerate other therapies due to their toxicities. We treat these patients in well-controlled small-scale clinical trials (i.e., under an FDA- and IRB-approved and strictly adhered to clinical trial protocol) and at highly specialized, mostly academic trial centers. Although this kind of trial would not be considered ‘real-world’ data gathering, the data gathered in these so-called phase I/II clinical trials does provide the evidence needed to progress a product into registration trials that could ultimately lead to FDA approval and commercialization.
As a technology and biometrics services company connecting life sciences companies with their data, our organization has seen the industry’s data ecosystem rapidly transform and evolve in recent years. Along with breakthrough scientific innovations, tech advancement, and strategies to provide analytics to patients and sites, a key factor influencing this ecosystem is the high volume and variability of electronic medical record (EMR) and RWD data across sources. As clinical trials advance and drug development organizations look to leverage patient-centric strategies, such as decentralized trials, patient-engagement applications, and real-world evidence data streams, the increase in non-CRF (case report form) data will continue. Our clients want their data infrastructure and analytics technologies to enable a single source of truth for data, integrating complete data portfolios. We have seen this demand grow as biopharma seeks data ingestion and storage that is scalable to accommodate RWD/RWE, x-omics, and other data use cases.
Ingesting and bringing real-world data and evidence into companies’ platform architectures is a capability we see advancing in the future. For example, historical trial data and RWD can be made available for searching historical data of interest to assist in planning future trials, in combination with operational data and analytics. With availability and access to sufficient historical trial data and robust RWD, these sources could be used to explore various subject subsets to evaluate the potential use as a “synthetic” control arm. Data connectivity and system agnostic interoperability are imperative to reach the full potential of bringing these data streams together, especially for use cases involving unstructured data.
Data are not only by-products of bioprocess development and manufacturing processes, but also strategic assets that can help us create value for our clients and ultimately for patients affected by serious and rare diseases. As a leading global CDMO in the biopharmaceutical industry, we are always looking for ways to enhance our services and capabilities to meet the expectations of our clients.
One of the areas that we are focusing on is data evaluation and analysis, which we believe is essential to extract value from our manufacturing processes and to deliver high-quality therapeutics. RWD/RWE can provide valuable information on the safety, effectiveness, and value of biopharmaceutical products in real-world settings, as well as support the development and approval of new indications, formulations, and delivery methods. We could well imagine offering RWD/RWE services to our clients as soon as these evaluations are routinely outsourced to CDMOs.
Catalent’s Applied Drug Delivery Institute, a not-for-profit, pre-competitive collaboration effort, which is focused on enhancing the application and use of advanced formulation and delivery to achieve improved patient outcomes, has conducted a variety of research to improve patient usability and clinical outcomes. These have been carried out to better understand the patient, caregiver, provider, and payer perspectives on formulation, as well as dose form and drug product design, as well as how these factors could be improved in specific disease states, healthcare settings, or patient populations. This often involves real-world data and patient registries held by third parties, via research collaborations with university health systems, patient advocacy organizations, healthcare providers, and direct research with patients and caregivers.
Through this research, we have learned how patients use medications in real-world settings, including many areas exist where drug substance and drug product design could be improved to enhance dose form usability, decrease polypharmacy regimen complexity, and reduce medication errors, such as dosing, while yielding improved outcomes as a result. The results showed that impact can be driven by disease-specific factors, patient age and genetic profiles, the role of caregivers in drug administration, and a variety of other cultural and behavioral issues. Significant opportunities exist to extend these insights, and we would always welcome further partners to drive this mission forward.
LGM Pharma harnesses the power of real-world data to elevate the quality of our tailored services for clients. By leveraging real-world evidence, we fortify our capacity to provide unwavering support across the diverse stages of the drug product life cycle. This proactive approach not only amplifies operational efficiency but also expedites the journey towards pioneering treatments for patients in need.
The integration of real-world data into our strategies empowers us to cultivate a comprehensive understanding of the pharmaceutical landscape. Furthermore, this understanding forms the bedrock upon which we build our client-centric services, allowing us to anticipate and meet evolving needs with both precision and insight. As we navigate through the intricate phases of drug development, from initial conception to market commercialization, the discerning analysis of real-world data fuels our ability to tailor solutions that align seamlessly with each stage’s distinct challenges and opportunities.
One of the most compelling outcomes of harnessing real-world data is the acceleration of the drug development process. As we glean insights form real-world evidence, we can streamline decision-making and enhance the trajectory of novel treatments. This acceleration not only expedites the availability of innovative therapies but could also have the ability to impact patient outcomes in a more timely and positive manner. An underlying thread of our commitment to utilizing real-world data is the pivotal role in driving industry-wide innovation within the pharmaceutical space. Ultimately, the integration of real-world data serves as a catalyst, propelling the entire pharmaceutical industry into a new era of innovation and advancement.
In the realm of Industry 4.0, Smart Quality is enabling pharmaceutical companies to harness the power of AI/ML technologies for automating quality processes. This transition empowers organizations to expedite and enhance decision-making precision, all while minimizing the likelihood of quality complications and regulatory non-compliance. Nevertheless, the scarcity of pertinent historical data often hampers the formulation of substantial business cases for customized AI/ML solutions tailored to digitization and automation needs. Recognizing this gap, LabVantage is embracing a 'hybridized' approach to development and model training, ensuring the utmost accuracy and adaptability of AI/ML models for Smart Quality.
LabVantage's primary Smart Quality objective revolves around automating batch evaluation procedures. This strategy capitalizes on pivotal real-world data to construct LIMS batch release models. Drawing data from laboratory and production sources, encompassing batch and sample specifics, quality outcomes, equipment information, and historical decision data, LabVantage crafts input features and labeling decisions. These elements facilitate the AI/ML model in scrutinizing, learning, and extrapolating pertinent patterns.
Once implemented at the customer's premises, their own historical data play a pivotal role in refining the AI/ML model's setup and continuous learning. Post-deployment, the model remains in a perpetual state of learning from real-time data, adapting to fluctuations and enhancing precision. Customers are capitalizing on these functionalities to effectuate real-time quality process monitoring. These data aid in anomaly detection and prompt interventions during batch release decisions, automate risk evaluation, and guide informed determinations related to root cause analysis and continual enhancements.
Within the neurodegenerative disease space, there are few areas where we can utilize real-world data to gain data-driven insights into disease progression, treatment response, and side effects. At Rune Labs, our goal is to better understand disease through data generated from mobile devices and wearable technologies. Part of our mission at Rune Labs is to make clinical brain data useful.
Our StrivePD software ecosystem for Parkinson's disease (PD) has been granted 510(k) clearance by the FDA to passively monitor Parkinson’s symptoms, including tremor and dyskinesia, through measurements made by Apple Watch. What makes our software unique is the ability to continuously collect and analyze data directly from patients in an automated manner. From self-reported data, historical clinical data, brain imaging, genetic history, and passive collection of brain signals from a deep brain stimulation implant, we can help physicians and patients monitor patterns in behavior, such as medication and therapy compliance, and help communicate with care teams.
In the future, we believe the real-world data we gather from PD patients will set a precedent in how we monitor disease progression and treatment strategy. Many patients suffering from neurological disorders may only visit their clinicians once a year, which may prevent physicians from fully understanding a patient’s disease. By gaining a comprehensive view of the patient’s day-to-day activities over a long period, we can take a precision approach to care for PD and other neurodegenerative diseases.
By understanding the need for utilizing real-world evidence to tie data from our single-cell technology with patient outcomes, Mission Bio is enabling the customization of precision medicine with more granularity, helping target treatment to individual groups of cells. Through a multi-omics platform, we are connecting genotype to phenotype so that life science companies can improve patient stratification, as well as predict resistance and response to new therapies.
One transformative use of our platform is the single-cell detection of measurable residual disease (MRD) in leukemia, which allows for early detection of potential disease relapse. Our assay can identify clonality and architecture, as well as the co-occurrence of new driver or resistance mutations, structural variants, and changing surface expression of therapeutic targets. The single-cell multi-analyte nature of Mission Bio’s technology goes beyond mere detection and offers prognostic and treatment guidance, giving clinicians a head start on intervention strategies.
Today, our partners are conducting clinical utility studies with our technology in patients, confirming the prognostic impact of our platform. Tomorrow, real-world data from these studies will help drug developers create and test new medicines targeted for the right patients at the right time. It is part of our commitment to making a real, measurable impact on the lives of patients through precision medicine.
Oncology is a data-rich field with an enormous amount of information from patient samples that can guide research, yet it remains arguably the most challenging to conquer with curative therapies. While most research focuses on ways to attack cancer cells, we’ve used data from retrospective studies to validate hypotheses and better understand how to optimize the potency of cell therapies using genetic engineering.
Previous studies have elucidated CD5 as an inhibitory signaling pathway of T cell effector function. However, CD5’s multifaceted influence on T cells has obscured its potential value as a therapeutic target. To confirm the clinical relevance of CD5’s inhibitory role in T cell mediated anti-tumor responses, Vittoria utilized retrospective data and performed an in silico analysis that included over 9,000 biopsies showing that low CD5 expression in pre-treatment biopsies strongly correlated with improved overall survival in cancer patients. Accordingly, by bypassing the CD5 pathway, we can produce T cells that have an inherent cancer-fighting advantage. We’ve applied this finding through our proprietary Senza5Ô platform to generate cell therapies with superior anti-tumor efficacy, stemness, potency, and durability. Our data across multiple liquid and solid tumor models suggest that this platform technology can be applied broadly to improve the efficacy of CAR-T therapeutics and unlock the potential of cell therapies for patients.
Beyond our findings, the advent of AI and computing power can deconvolute the wealth of real-world oncology data in different ways to overcome the limitations of preclinical studies and create the opportunity to improve emerging therapeutic modalities.
At Alto, we take a comprehensive approach to data collection to ensure that results from our studies encompass real-world data to reflect the actual experiences, behaviors, and outcomes of patients with mental health conditions. We obtain a large amount of high-quality data across multiple collection modalities — including EEG, behavioral task performance, wearable devices, genetic tests, health records, and other sources — to support drug-response predictions and maximize clinical impact. Almost daily, we’re solidifying and polishing our analysis pipeline in light of new findings as they come in, building confidence in the biomarkers we’ve discovered, and guiding future clinical development. For example, in response to real-world data, we launched decentralized studies to ease the burden of participating in research and increase patient access. Real-world data also help our team identify trends and associations to support new research hypotheses.
Since the patient perspective is often more complex than an objective measure like a biomarker, we obtain several self-reported outcomes to better understand and incorporate this meaningful perspective. In the past two years, we’ve gathered anonymized information from over 20,000 participants who have opted into our in-house database to be considered for Alto’s studies; this provides a wealth of knowledge on patient demographics, medical histories, comorbidities, lifestyle factors, environmental exposures, and socioeconomic factors that may impact mental health outcomes. In the future, we anticipate further harmonizing data from these different sources to improve patient care and advance the field’s scientific knowledge of various mental health conditions.
At Elicio Therapeutics, we view real-world data and evidence (RWE) as a critical component for optimizing our drug development process –– particularly the design and execution of our AMPLIFY studies including patient selection and biomarkers of therapeutic response.
We capitalized on the insights from real-world genomic data sets, which showed us patients with mKRAS-driven pancreatic and colorectal cancers, who have circulating tumor DNA (ctDNA) following standard surgery and chemotherapy and are at a high risk of relapse, and when they do, the prognosis is poor.
This, among other insights, informed our clinical trial protocol including employing an investigational in vitro diagnostic test to detect ctDNA earlier than the traditional radiographic scans, with the goal of pinpointing patient cohorts for our ongoing AMPLIFY studies evaluating two peptide formulations of our investigational cancer vaccine ELI-002. RWE has also helped us define the efficacy of interventions, which in our clinical trial is tumor biomarker reductions following adjuvant treatment with our investigational cancer vaccine.
I believe RWE will continue to play a pivotal role in the development of novel therapies for cancer enabling biopharmaceutical companies to identify a highly responsive patient population, ensure their clinical trials are aligned with real-world care scenarios, curtail clinical trial costs, and enhance the likelihood of technical and regulatory success.
At Karius, we’ve created a liquid biopsy for infectious diseases, harnessing metagenomics, next-generation sequencing, and artificial intelligence (AI) to help enhance the precision and speed of infectious disease diagnosis. We recognize the need for more reliable, rapid, and accurate diagnostics that leverage real-world data to help improve health outcomes. As an example, based on the body of clinical evidence, the Karius Test was recently added to the 2023 Duke-ISCVID Criteria for Infective Endocarditis by the International Society of Cardiovascular Infectious Diseases (ISCVID). The new criteria, published in Clinical Infectious Diseases, show how the Karius Test can rapidly detect pathogens causing endocarditis, demonstrating real-world evidence for using the Karius Test for infective endocarditis. With continued data in more than 100+ peer-reviewed publications showing the usefulness of our test and its implementation as part of recommended diagnostic testing, we hope to continue our mission of delivering better care to patients, whether through prevention of disease transmission, improved triage and prioritization, and more effective treatment strategies.
OM1 is a real-world data (RWD) and health technology company focused on ingestion, normalization, and linkage of multiple sources of RWD, including structured and unstructured data. The integration of these various sources (EHR, claims, and patient-generated data, for example) provides a more complete view of the patient journey and the safety and effectiveness of healthcare interventions. OM1’s tech-enabled approach for data acquisition and linkage underpins a real-world evidence (RWE) platform that is flexible and reusable to support various use cases and evolving analytic efforts.
In the future, we expect to see integration and curation of increasingly complex data sources with ease as technologies mature. Additionally, the use of artificial intelligence (AI) will accelerate the pace in which data can be processed, variables created from unstructured data, etc. Importantly, we expect that, as more RWD is used in regulatory submissions, there will be clearer guidelines published.
Our research arm, The Center for International Blood and Marrow Transplant Research, has been collecting outcomes data on transplants since 1972. The sustained improvement in success of this therapy can be attributed, in part, to a stream of research advancements in histocompatibility that have been translated to changes in practice. Since the early 1990d, we have been banking cells from the patient and the donor/product for research. This material has been used over the past 30 years to prove out new innovations in immunogenomics and the bioinformatics and data standards needed to move this innovation into the clinic. Although the human genome was sequenced in 2003, it has not been possible to characterize the most clinically relevant regions (e.g., MHC, immune cell receptor complexes). Our biorepository samples are currently being characterized by new methods that provide diploid sequences of these complex regions we expect will open up new opportunities for improving clinical care.
Recently, we established the Cellular Immunotherapy Data Resource (CIDR) as a program, funded by the National Cancer Institute, to support the Immuno-Oncology Translational Network (IOTN). The same data collection infrastructure and framework for observational research has been repurposed to support new cell and gene therapies. This system collects data on sic approved CAR T therapies with over 6,000 therapies as of 2021 and long-term follow-up for at least 15 years.
Cell therapy is a unique space. Multiple products have reached the market, and real-world data has demonstrated their remarkable efficacy in treating –– even curing — patients with certain challenging lymphomas.
However, many more potential therapies are still in their infancy. A large number of these therapies are being developed by smaller biotech startups, which rely on investor fundraising to reach and progress through clinical trials. Unfortunately, some companies deplete their initial funding before they can reach the clinical milestones that lead to additional funding.
Analysis of the real-world data suggests that unrepeatable manufacturing processes, acquiring or building costly facilities, and the need to reinvent processes on the path to scale-up are often to blame. For this reason, ScaleReady enables developers to reach clinical milestones as fast and cost effectively as possible. Our platforms allow for linear scale-up from research scale to clinical scale and beyond.
Critically, the opposite is also important — the ability to scale-down when early clinical trial data inform the need for further modifications. Running optimization experiments at research scale instead of full-scale results in appreciable savings in time and money. As the field continues to mature, we will continue to monitor the real-world impact that early process development platform decisions have on a company's longevity and therefore the impact on bringing lifesaving therapies to patients.
One of the challenges for using real-world data in the cell and gene therapy industry is that there are fewer data points available than standard pharmaceutical products, making trending and analysis more challenging. This is partly down to the fact that the industry is still relatively young but also due to relatively small patient populations.
Nevertheless, it is an area of interest for many, as the possibilities that additional analysis could offer for forecasting and management within the supply chain alone are very promising. Consequentially, at TrakCel, we are considering ways to aggregate data from our customer base to provide the cell and gene therapy industry with enough data points to facilitate meaningful trending analysis.
The results from trending these data will inform TrakCel’s customer base regarding benchmarking both their performance and potentially that of partner organizations against industry-wide averages and using this to aid development and continuous improvement.
Another hot topic at the moment is that of AI (artificial intelligence), as many industries look at how it could benefit their sector. In a highly regulated industry, harnessing the power of AI might seem challenging, but within personalized medicine, each patient journey is unique, and this can make pattern analysis and recognition complex. In the future, there is an opportunity to use AI to review data to improve advanced therapy supply — for example, to optimize supply chain patterns and slot allocation for autologous therapies. This might be particularly powerful if it can be applied to complex data patterns.
Real-world evidence (RWE) can play a crucial role in the development of therapies — especially for less common diseases, where we see a growing role for RWE to help identify unmet needs and evaluate whether a treatment can address those needs.
Because of our expansive market reach, we are able to utilize several of our platforms to acquire RWE from diverse regions like Asia-Pacific, the United States, and the European Union. These data allow for extrapolation, protocol creation, and study design, providing valuable insights for developing effective treatments.
We have an ongoing collaboration in triple-negative breast cancer, which affects a smaller patient population but has a high mortality rate and a short life expectancy after diagnosis. Our aim is to leverage RWE to improve the ability of our partner’s novel treatment approach to extend patients’ lives.
There is also tremendous opportunity for RWE in novel therapeutic spaces like advanced therapies, where the field is extremely promising, but nothing is truly optimized. Using real-world data collected with from our Spectra Optia platform during therapeutic apheresis — the process used to draw starting material from patients with sickle cell disease — we worked with a leading academic to develop and implement protocols that significantly improved collection efficiency. We expect applications like these will expand access to cutting-edge therapies.
As regulators grow more comfortable accepting RWE, we are exploring new technology like wearables and AI to generate new kinds of insights, which we expect to shape the development and utility of the next generation of therapy.
Cell therapy developers are focused on the active assessment and management of the complexities of starting materials, key variables that have the potential to impact the quality, safety, and efficacy of the final therapy. In the case of autologous therapies, the starting point — a patient’s own immune cells — can vary depending on their disease state and treatment history. For allogeneic therapies, the cells are from donors, who qualify based on a range of criteria that varies depending on developer needs.
Because the field is so new, many drugmakers are sometimes operating on assumptions in the absence of established industry standards. For example, a common requirement is for donors who exclusively test negative for cytomegalovirus (CMV) exposure — which is so common that up to 80% of adults in the United States could be ineligible. But depending on the situation, could the bar be lowered to include people with no active infection, regardless of exposure?
To answer such questions, we’re building an extensive database, including demographic, behavioral, and health history data for 30,000 donors — and growing. In time, the data could correlate donor attributes with downstream characteristics — even patient outcomes.
We also participate in NIH’s All of Us initiative, which collects long-term data from volunteers to help develop personalized healthcare. The data set contains the largest number of fully sequenced genomic data in the world thus far. Our hope is that in time, these real-world data will also be used to inform donor recruitment for cell and gene therapy clinical trials.
One of our platform technologies, arsenic trioxide (ATO), has a long history and has demonstrated therapeutic benefits for the last two decades, and is now approved to treat leukemias like acute promyelocytic leukemia (APL). More recently, we’ve also seen encouraging evidence that it may also be used to treat a range of conditions, including chronic autoimmune diseases.
Real-world data shows that ATO is safe, even as patients with APL take over 100 IV doses. However, to expand into autoimmune indications, real-world evidence tells us that market opportunities and patient adherence –– and therefore outcomes — are suboptimal without an oral option. That's why we're developing an oral form of ATO for our next phase III clinical trial to treat chronic graft-versus-host Disease (cGvHD).
Looking forward, new paradigms are evolving around cutting-edge methods of delivering therapeutic molecules to cells, especially extracellular vesicles (EVs) like exosomes. We're excited about their potential for targeted delivery with EVs and are exploring their capabilities. As we advance our lead programs, we'll be watching EV therapeutics very closely as they enter the new pharmaceutical landscape and demonstrate their ability to impact patients. And we’ll be active participants in pushing this new space forward.
As a process development organization dedicated to designing automated solutions for cell therapy manufacturing, we routinely collect and analyze real-world manufacturing data with the goal of gaining insights into meaningful process parameters that have an impact on clinical manufacturing strategy and process design. These insights help to inform process innovations and optimal strategies for efficient manufacturing of cell therapies.
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