The development of new drugs is an uncertain and expensive process. While the average research and development (R&D) cost per new drug varies from less than $1 billion to more than $2 billionthe FDA approves a simple 12% of drugs entering clinical trials. Clinical trials – which represent a portion of these costs – remain the benchmark for studying the efficacy and safety of new drugs. However, the experimental conditions required for clinical trials do not necessarily represent real contexts, points out Sonia Araujo, clinical manager at ArisGlobal.
The life science industry’s R&D process can significantly benefit from real-world data (RWD). RWD answers questions that clinical trials cannot answer. It allows drug developers to study how patients use and respond to a drug once it is approved for use and hits the market, generating insights that help drug developers better design and conduct clinical trials .
Real-world data sources
- Electronic Health Records (EHR): A digital version of patient records containing information on medical histories, treatment plans, diagnoses, allergies, etc.
- Claims and billing activities: Information regarding health service utilization, prescribing patterns among patients of different payers, and population coverage.
- Product and disease registers: Organized systems that collect data defined by a particular condition, disease, or exposure.
- Patient Generated Data: Data collected directly from the patient via mobile devices and wearables to inform medical teams of health status in real time.
RWD provides essential information that reflects a broader group of patients and helps inform care decisions. This information allows researchers to develop hypotheses and further investigate clinical research questions. Increasing regulatory success is essential for pharmaceutical companies to reduce the financial risks associated with R&D programs.
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Benefits of real-world data
RWD helps bridge the gap between healthcare research and practice and delivers value throughout the product lifecycle. For example, researchers use RWD to identify potential patients during preliminary study design.
The evidence suggests that Recruitment strategies supported by RWD – such as direct email campaigns to patients identified through complaint data and EHR-supported discussions at the point of care – increase the effectiveness and efficiency of trial recruitment.
Additionally, RWD helps drug developers create appropriate eligibility criteria for clinical trials. Organizations should carefully evaluate the available data against the needs of a particular study. The integration of EHRs helps define patient cohorts more quickly and accurately and sometimes eliminates the need for direct patient screening.
Drug developers also use RWD to overcome the limitations of clinical trials. RWD provides a much larger volume of data, potentially reaching terabytes and petabytes. Life science professionals have access to a broader cross-section of society, giving them a better understanding of how their products work.
To finish, RWD helps drug developers compare drug efficacy. Some diseases have several treatment options available that have not been directly compared in a clinical trial. In these cases, RWD provides a way to assess how drugs measure up outside of routine clinical parameters.
Although RWD offers many advantages, it also presents certain obstacles for the pharmaceutical industry. RWD can give drugmakers and regulators a lower level of confidence in quality than they might receive in the highly controlled setting of randomized clinical trials. Quality issues include data inconsistency and incompleteness.
When evaluating RWD, drug developers may also face challenges when:
- Access the correct datasets.
- Integrate siled data and extract relevant and usable information.
- Exploitation of unstructured data.
- Understand and comply with data privacy regulations.
Patient confidentiality presents another barrier to sharing the RWD. However, despite privacy concerns, studies show that patients generally feel willing to share health data contribute to public health as long as they understand the potential benefits and risks.
Another challenge that hinders the advancement of RWD is the heterogeneity of data formats between different sources and different countries. The FDA recognizes the importance of having a common data model, which has led to an RWD standardization effort over the years, but it’s not 100%.
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Regulator RWD data and training standards
OMOP leverages data normalization to enable network studies and analysis. A coordination center sends study requests remotely to each data owner, who reports their results in the same format. Both parties keep the data confidential and successfully conduct their benchmarking.
OMOP is also making ongoing efforts to bridge the gap between RWD and clinical trial data, benefiting both life sciences and healthcare research. For example, OMOP enables the use of RWD for clinical trial design, to optimize trial planning and recruitment, and to enable the use of clinical data as a source of real-world evidence (RWE).
Such efforts have undoubtedly helped convince regulators of RWD’s value. The FDA already uses RWD and RWE to monitor post-market safety and adverse events and to guide regulatory decisions. The European Medicines Agency (EMA) has been evaluating this area for a long time through its Big Data Taskforce and, more recently, with its DARWIN projectwhich aims to “provide RWEs from across Europe on diseases, populations and drug uses and performance”.
The FDA, EMA, NMPA and other agencies have recently called for closer collaboration among regulators around the world, committing to “encouraging global efforts and enabling greater integration of real-world evidence into regulatory decision-making”.
Regulatory approval requires conclusive evidence from clinical trials. Life science organizations need to anchor this evidence in good study design, appropriate data collection, and thoughtful data analysis. RWD is relevant and essential for innovation and regulatory approval.
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