How Real-World Data Supports Pharmaceutical Drug Development

Developing new drugs is an uncertain and expensive process. While the average research and development (R&D) cost per new drug ranges from less than $1 billion to over $2 billionOpens a new window , the FDA approves a mere 12% of drugs entering clinical trials. Clinical trials – which comprise part of those costs – remain the gold standard for studying new medicine’s efficacy and safety. However, the experimental conditions required for clinical trials don’t necessarily represent real-world settings, emphasizes Sonia Araujo, head of clinical at ArisGlobal.

The life sciences industry’s R&D process can benefit significantly from real-world data (RWD). RWD answers questions that clinical trials can’t answer. It allows drug developers to study how patients use and respond to a drug once it’s approved for use and reaches the market,  generating insights enabling drug developers to better design and conduct clinical trials.

Sources of Real-world Data

Gartner finds that about 80% of top life science companiesOpens a new window use RWD to support R&D activities and ensure patient safety. Drug developers source RWDOpens a new window from:

• Electronic health records (EHRs): A digital version of patient charts with information about medical history, treatment plans, diagnoses, allergies, and more. 
• Claims and billing activities: Information regarding healthcare services utilization, prescribing patterns between patients of different payors, and population coverage. 
• Product and disease registries: Organized systems that collect data defined by a particular condition, disease, or exposure. 
• Patient-generated data: Data gathered directly from the patient via mobile devices and wearables to update medical teams on real-time health statuses. 

RWD provides essential information that reflects a broader group of patients and helps inform decisions for care. These insights enable researchers to develop hypotheses and further investigate clinical research questions. Increasing regulatory success is essential for pharmaceutical companies to decrease the financial risks associated with R&D programs. 

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Benefits of Real-world Data

RWD helps bridge the gap between research and practice in healthcare and provides value across the entire product lifecycle. For example, researchers use RWD to identify potential patients during pre-trial study design.

Evidence suggests that RWD-supported recruitment strategiesOpens a new window – such as direct email campaigns to patients identified through claims data and EHR-supported discussions at the point of care – increase trial recruitment effectiveness and efficiency.

Additionally, RWD helps drug developers create appropriate eligibility criteria for clinical trials. Organizations must carefully evaluate 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 far greater volume of data, potentially reaching terabytes and petabytes in size. Life science professionals gain access to a broader cross-section of society, increasing their understanding of how their products work.

Finally, RWD helps drug developers compare medicines’ efficacyOpens a new window . Some diseases have several available treatment options that haven’t been directly compared in a clinical trial. In these cases, RWD provides a way to evaluate how medicines measure up outside routine clinical settings.

Potential Hurdles 

While RWD offers many benefits, it also presents some roadblocks for the pharmaceutical industry. RWD may give drug makers and regulators a lower level of confidence regarding quality than they might receive from the highly controlled setting of randomized clinical trials. Quality issues include lack of consistency and incompleteness of data.

When evaluating RWD, drug developers may also face challenges while:

• Accessing correct datasets. 
• Integrating siloed data and deriving relevant, usable insights.
• Leveraging unstructured data.
• Understanding and abiding by data privacy regulations.

Patient confidentiality presents another barrier to sharing RWD. However, despite privacy concerns, studies show that patients generally feel willing to share healthcare dataOpens a new window to contribute to public health as long as they understand the potential benefits and risks. 

Another challenge hindering RWD advancement is the heterogeneity of data formats between different sources and 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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Data Standards and Regulators’ RWD Drive

The Observational Health Data Sciences and Informatics (OHDSIOpens a new window ) community introduced the Observational Medical Outcomes Partnership (OMOPOpens a new window ) common data model to transform RWD into a common format. 

OMOP leverages data standardization to enable network studies and analytics. A coordinating center sends the study queries to each data owner remotely, who reports their results back using the same format. Both sides maintain data privacy and successfully conduct their comparative analysis. 

OMOP also has ongoing efforts to bridge the gap between RWD and clinical trial data, benefitting both life sciences and healthcare research. For example, OMOP allows using RWD for clinical trial design, optimizing trial planning and recruitment, and permitting the use of clinical data as a source for real-world evidence (RWE).

Such efforts undoubtedly helped convince regulators about 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 long evaluated this area as part of its Big Data Taskforce and, more recently, with its DARWIN projectOpens a new window , which aims to “deliver RWE from across Europe on diseases, populations and the uses and performance of medicines.”

The FDA, EMA, NMPA, and other agencies have recently calledOpens a new window for closer collaboration between regulators worldwide, pledging “to foster global efforts and further enable the integration of real-world evidence into regulatory decision-making.”

Regulatory approval requires conclusive evidence from clinical trials. Life science organizations must root this evidence in good study design, appropriate data collection, and thoughtful data analysis. RWD is relevant and essential for innovation and regulatory approval. 

Do you think real-world data is more important can clinical trial data? How can we get the best of both? Share with us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window .

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