Real-world data (RWD) is gaining importance in the pharma field. The data obtained from the various studies and reports support the regulatory decision essential for getting the approval for the new indication of the approved drugs. This data includes the details regarding the usage of drugs, their potential benefits, risks, etc. RWD applies to the drugs derived from the sources apart from traditional clinical trials.
What is real-world data (RWD)?
The use of modern devices like mobiles, computers, tablets, wearable devices, biosensors, etc., helps gather and store health-related data. This data plays a vital role in designing a better clinical trial study. The collected data allows solving various queries raised in traditional clinical trials. In addition, the newer sophisticated analytical capabilities help in better analysis and its application to product development.
The real-world data include patient-related data associated with their health status, medication, and routine analysis from different sources. RWD is collected from various sources like:
- Electronic health records (EHRs)
- Claims and billing activities
- Disease and product registries
- Patient generated data
- Data collected from different sources like home patients, health status, mobile devices, biosensor reports, etc
RWD includes the data generated from different study designs, analyses that include randomized trials, pragmatic trials, more extensive simple trials, observations studies, retrospective studies, etc.
What are the drivers of RWD requirements?
The different factors that contribute to the importance of the RWD in the pharma industry include:
- The regulatory authorities consider the RWD as having the potential impact on getting the approvals and accelerating the drug development process. This saves money and time as these factors play an essential role in drug development and market time.
- The RWD complements the clinical trial data as it provides insights for the trial details like the safety, efficacy, and effectiveness data from the patients. These RWD helps gain some significant findings, like in the case of narrower constraints. It helps in understanding the data from real-world studies.
- The RWD plays a vital role for the insurance companies and other stakeholders in the solid understanding of the benefits gained by the drug to the patients and the effectiveness of the treatment.
How is RWD changing the pharmaceutical industry?
Using the RWD helps the pharma companies identify the potential newer candidates and their promotion for getting an approved, effective, and reimbursable drug candidate. This helps lower the cost of drug development if taken ahead traditionally. The RWD helps in taking important decisions for the clinical trials studies. The companies need to take the approval and check with the local authorities before considering the RWD for planning their study based on the completeness of the data and its accuracy.
How does RWD change the way drugs are developed, prescribed, and monitored?
The RWD helps the pharma industries in different ways like:
- Drug repurposing: In the case of the drugs already present in the market for a particular indication, they can be tested for their application for some other function. This repurposing of the drugs needs to undergo the approval process for the new intended purpose. For this repurpose type of study, the companies can use the RWD for the drug in consideration of its data for safety and effectiveness as in their original indication.
- Drug development: The RWD provides the details of the patient information, including the treatment outcomes, safety, therapeutic development, effectiveness, etc. The FDA allows using the HER data and the RWD for drug development.
Limitations and challenges with RWD and how to overcome them?
The different challenges and limitations while using the RWD and the methods to overcome them include:
- Data quality: The RWD includes a massive collection of data, and it is essential to use good quality data. The data from the low-quality patient registry is the cause of concern. The RWD from the various observational studies may have low-quality data and may affect the study’s design. It is essential to ensure that the data used is of good quality as it will affect the study outcome.
- Standards: Standardization is essential for different levels like data collection, quality, processing, designing, reporting, etc. FDA recommends using the data standards for improved accuracy.
- Coordination: Lack of coordination within the different organizations can lead to low-quality data due to insufficient interactions. There should be harmonization between the healthcare providers for better results.
The DforD services, with its novel artificial intelligence (AI), machine learning (ML) and other innovative techniques, can help in deriving insights from the real-world data that are essential for the pharma industry. The insights based on real-world evidence allow the pharma companies to make crucial decisions while designing, planning and executing their clinical trial studies. This helps you get faster approvals and achieve quicker development. Furthermore, this insight allows developing the guidelines and support tools for their use in the clinical practice. In addition, it will enable collecting and understanding the potential benefits and risks of the medicinal product or devices.
