Oncology is a complex therapeutic area owing to the heterogeneity of cancer types, diversity of patient responses, and diverse treatment options available. Drug development trials in oncology are numerous, but because of incomplete data and small sample sizes, they sometimes fail to perform in real-world settings. Clinical trials prove impractical for certain disease subsets related to cancer. The associated costs and the long lag time to obtain results in clinical trials add to the existing challenges. All this reflects the gaps in existing research methods.
To meet these challenges, there is an increased need for real-world evidence (RWE) for oncology drug research and development (R&D). RWE provides useful information through the extrapolation of data from real-world sources such as electronic health records, claims, and patient registries. Regulatory bodies, such as the FDA and EMA, have also acknowledged the importance of RWE in supporting the drug development process.
RWE is important in directing the process of drug development by offering greater depth and granularity in knowledge. This helps in identifying the right cancer and its subtypes and treatment regimen for the same. It guarantees that oncology medications not only show favorable outcomes in clinical trials but also provide long-term value when used in real-world scenarios.
RWD is the raw data related to a patient’s health that is collected from sources outside of randomized clinical trials (RCTs). These sources include electronic health records, claims databases, pathology, and imaging data. This data can include, among other information, patient demographics, diagnoses, treatments, lab results, and patient outcomes.
Real-world evidence (RWE) comes from analyzing real-world data (RWD). In simple terms, RWE is what we learn from looking at and interpreting RWD, giving insights about the efficacy of drugs in real-world situations.
Now, RWE is generated from a number of sources coming from different health systems. RWD is often fragmented, meaning that patient data is stored in various systems that may not work well for oncology. This fragmentation makes it hard for different systems to communicate with each other and to maintain data flow.
Linking the different types of Real-World Data (RWD) overcomes the challenge of fragmented health data. This is done by connecting a variety of data sources, including electronic health records, claims data, genomic data, and imaging data, into one cohesive framework. These linked datasets deliver a complete picture of disease, transcending the siloed framework of traditional healthcare data systems. Through connecting RWD across several touchpoints, it provides a more precise insight into disease progression, efficacy of treatments, which is critical in oncology. Through the comprehensive approach, decision-making in drug R&D is enhanced and optimized, resulting in the development of useful treatments.
1. Identifying unmet needs in oncology
RWE is essential to identify major unmet oncology needs by analyzing data on available therapies, patient outcomes, and limitations in current treatment options. By combining real-world evidence from various data sources, it uncovers regions where an oncology drug may be able to make a bigger impact. By evaluating the actual real-world performance of treatments and patient responses for an already approved drug, RWE helps determine potential drug unmet needs. With this knowledge, the research and development of drug pipelines becomes more targeted, allowing us to create drugs that effectively address unmet needs.
2. Developing Target Product Profiles (TPPs)
RWE gathered from disparate data sources provides essential information to understand disease prevalence, incidence, and trends. This information is an essential element in the creation of Target Product Profiles (TPPs). Through data-driven observations, pharmaceutical firms can better understand unmet oncology needs that inform internal decision-making throughout the process of creating drugs. By integrating real-world data, drug developers are able to match their product profiles closer to the real-world needs of patients, enhancing the possibility of successful therapy.
3. Molecular profiling and discovery of therapeutic targets
Combining molecular profiling and RWE increases the identification of new therapeutic targets. RWE is capable of giving insights into genetic mutations, protein expressions, and other molecular markers that affect disease progression in oncology. Such information will enable researchers to determine new targets and mechanisms of response or resistance to treatments. This will further help create precision treatments based on unique patient profiles, which will increase the probability of success of treatment and minimize side effects.
4. Understanding disease natural history
Understanding the natural history of disease is essential to drug development, and RWE is essential to that effort. By connecting real-world datasets, researchers better understand how different types of cancer grow and progress over time. That would include identifying key molecular drivers of disease growth, such as specific mutations or proteins that play a role in tumor growth and metastasis. This data becomes helpful in developing targeted drugs, such as EGFR inhibitors for lung cancer or HER2-targeting drugs in breast cancer, ultimately yielding more effective and customized treatment.
Integration of Real-World Data (RWD) from various healthcare systems is progressing at a fast pace but is challenged by a number of crucial issues. There are several critical hindrances, such as interoperability, lack of standardization of data, data protection issues, and governance issues. These impediments make the integration of RWD from different sources of electronic health records (EHRs), claims, and newer data sources like real-world imaging data challenging. And without linking different real-world data sources, the full potential of these datasets is difficult to achieve. These barriers will stall the drug development and clinical research processes.
To overcome these issues, the deployment of solutions such as tokenization and standardized data frameworks is crucial. Tokenization is a method where individual information is substituted by automated, safe tokens, safeguarding privacy as well as allowing various systems to connect data securely. Moreover, the utilization of standardized data gives improved efficiency in sharing and utilizing data. Through the implementation of these methods, the integration of various RWD sources becomes possible and efficient.
As the healthcare industry increasingly recognizes and accepts real-world evidence, it's clear that real-world data adds value to the drug development process. And when the data from different sources is combined, it gives better insights into the disease and further guides the development of treatment. RWD linked from various sources enables understanding of the natural history of disease, helping identify treatment targets and unmet needs in oncology. And with the utilization of tokenization and standardized RWD data, the linking of RWD data sources becomes possible.
At Segmed, we provide fit-for-purpose, regulation-grade imaging datasets for pharmaceutical research and development. These datasets are integrated with various other datasets (EHR, claims & pathology), offering a comprehensive and holistic view of cancer and its subtypes.
Connect with us to find out how our high-quality, diverse, tokenized imaging datasets can support a myriad of research and development for oncology R&D.