Exploring the Role of Real-World Data in Drug Development

Real-world data (RWD) is no longer a supplementary resource in drug development; it is now a central pillar in regulatory submissions and post-marketing surveillance. According to a 2023 Nature study, RWD was used in 85% of FDA-approved new drug and biologics license applications, accelerating timelines and improving safety assessments. Unlike data from controlled randomized clinical trials (RCTs), RWD reflects how medicines perform in real-life settings, providing insights that are critical for both regulators and healthcare providers.

What is Real-World Data & Real-World Evidence?

From clinical trials to applications for new drugs and biologics, pharmaceutical companies have now started to rely greatly on real-world data, rather than data from Randomized Clinical Trials (RCT), to enhance the drug development process and to achieve greater results. A study conducted by ‘The Nature’ states that the Real-World Data (RWD) is involved in 85% of FDA-approved new drug and biologics license applications, which increases the rate of drug development.

Real-world data (RWD) refers to health information collected outside traditional clinical trials such as from electronic health records, insurance claims, or wearable devices. When this data is analyzed to generate clinical insights, it becomes real-world evidence (RWE). For instance, wearable device data showing long-term heart rate trends can be analyzed to assess the real-world effectiveness of a cardiovascular drug.

Sources of Real-World Data

Real World Data or RWD can come from a variety of sites such as:

EHRs or electronic health records offer thorough patient histories.
Health care utilization trends in Insurance claim data.
Databases for patients that compile information on diseases or ailments.
Wearable devices based on digital health technology, that continuously track health metrics like heart rate and physical activity.
Mobile health app or other gadget to record patient information.

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Role of Real-World Data in Drug-Development

Real- World Data has a significant impact in the drug development process which is described below:

Drug Identification: Detects various disease progression patterns, based on its data analysis that helps to discover novel therapeutic targets Based on actual patient response, it can validate specific drug targets for their application in healthcare.

Clinical Trial Design Optimization: Facilitates adaptive trial designs that define specific inclusion/exclusion criteria, for better execution. It also selects appropriate clinical endpoints and creates artificial control groups.

Analysing Drug Safety: Monitors the safety and efficacy of drugs, as well as assist in early detection of adverse effects in large number of people. Can also identify specific Drug interactions, for risk minimization strategies.

Detecting Patient Response: By identifying, off-label therapeutic uses (new therapeutic applications) for current medications, it can increase the range of treatments available for different patients.

Technologies Powering Real-World Data in Drug Development

The main strategies involved in the proper functioning of RWD in real life scenarios is:

Big Data Analytics: To derive valuable insights from the massive volumes of data produced in healthcare systems, sophisticated analytical tools such as machine learning and artificial intelligence are needed.

Natural language processing (NLP): NLP methods are used to extract important information related to drug development, based on analysis of unstructured data such as patient reports and doctor’s notes.

Blockchain and Cloud Computing: These two technologies are involved in the managing and protection of massive datasets, produced by RWD research.

What are the Applications of Real-World Data?

Some major advances have been made in the pharmaceutical sector due to increase in use of RWD:

Better drug discovery: Faster drug target validation and invention of new therapeutic compounds, by analysis of large and diverse datasets. For example, drug approval of palbociclib for breast cancer.

Better Understanding of treatment effects: RWD offers a better understanding on how the treatments work in different patient populations which results in better patient response and more treatment options. For instance, Google Health AI increases disease detection rates by utilizing sophisticated imaging techniques. This technology uses deep learning algorithms to analyse medical images with remarkable accuracy, that makes it difficult for humans to detect early signs of conditions, such as cancer.

Strong post-marketing surveillance: RWD continuously monitors the safety & efficacy of the drugs, once they are available in the market, for long-term effects or any uncommon side effects, that may not be noticed in controlled trials.

What are the Regulatory Guidelines that are followed in RWD Analysis?

The European Medicines Agency (EMA) and the Food and Drug Administration (FDA) have issued several guidance documents and created frameworks, for easy use of real-world data (RWD) and real-world evidence (RWE) in the drug development process.

Role of U.S. Food & Drug Administration: The FDA, by the 21st Century Cures Act of 2016, has set up a program to assess the use of RWE for the approval of new drug or medications, during post-approval study, that have some role during the regulatory decision-making, which involve the following process:

FDA’s Real-World Evidence Program Framework: This framework, established in 2018, describes the process of how FDA will assess RWD, as per the clauses related to internal methods, stakeholder engagement and pilot projects.

Some guidance documents are released by the FDA, that describes various uses of RWD/RWE in drug discovery, which are as follows:

Data Sources: Registries or medical claim data and electronic health records.
Data Standards: Instructions followed while applying for FDA-backed data standards in RWD submission.
Regulatory Aspects of Non-Interventional Research: This guideline deals with the application of RWD in observational study that involves important topics such as protocol drafting, analysis techniques, data transparency and access to source data for verification.

The European Medical Agency: The EMA states that RWD/RWE actively supports the timely access to medications, that provides evidence that support benefit-risk assessments. Following are some important EMA initiatives and guidelines:

Real-World Evidence to Support EU Regulatory Decision-Making: This framework outlines how EMA can assist stakeholders in producing and submitting RWD-based studies and directs the incorporation of RWD/RWE in the regulatory process.
Data Analysis and Real-World Interrogation Network (DARWIN EU®): It is a coordination centre that makes real-world healthcare databases available throughout the EU so that evidence regarding the efficacy safety and use of medications can be produced.
HMA-EMA Data Quality Framework: This network deals with certain aspects such as data linkage relevance and dependability and offers an organized method to evaluate RWD quality.
HMA-EMA RWD Catalogue: These resources make it easier to find relevant RWD sources that deals with the safe application of best practices in RWD-based research or study.

Conclusion

Real-world data integration has brought a major change in the way; we view drug development and regulatory oversight in the healthcare system, from improving patient care to streamline clinical trials.

More effective analysis of RWD is possible due to the technological advancements in fields like artificial intelligence (AI) and machine learning (ML) that results in innovations like personalized medicine and quicker drug discoveries. However, for RWD to realize its full potential to improve patient outcomes and drug delivery, collaboration between regulators, industry stakeholders and healthcare providers along with technology partners is crucial that can lead to better healthcare in future. RWD is thus predicted to become a key player in drug development and regulatory decision-making

How DDReg Can Help You?

With its extensive custom solutions and expert PV knowledge team to utilize real-world data in varying pharmaceutical aspects, DDReg is well-positioned to assist your company on this journey that can help you use RWD for better results. It thereby ensures that the real-world data should reach global markets faster, remain compliant across jurisdictions, and deliver measurable health impact.