Real-world Evidence’s Revolution to Innovation

As we kick off 2024, It’s important to shed light on one of the most crucial topics in the biopharmaceutical industry.

Most biopharmaceutical industry enterprises are attempting to leverage the use of RWD & RWE for decision-making across the product life cycle. This edition will provide key insights on embedding RWD & RWE across the enterprise advancing the future of life-saving medicines to innovate faster than ever.

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Real-world data (RWD) is data that relates to patient health status and/or the delivery of health care routinely collected from a variety of sources.

Real-world evidence (RWE) is the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD.

RWE use & regulations are changing

Historically, biopharma’s health economics and outcomes research (HEOR), epidemiology, and medical affairs experts have used RWE to understand disease progression, monitor patient safety, and assess clinical and cost-effectiveness. Sponsors are even increasingly including RWE in regulatory submissions nowadays.

In parallel, the pandemic highlighted the utility of RWD/E in understanding and combating a novel disease. Lately, we have seen how Evidence Accelerator brought together multiple stakeholders to advance methods to convert RWD into actionable disease-oriented insights to be applied to other conditions such as cancer & Alzheimer’s disease.

R&D and commercial are the top two functions that organizations generate RWE for. Top use cases include:

1. Informing the clinical trial design & site selection
2. Understanding the heterogeneity of treatment effects
3. Informing pricing and forecasting assumptions

Beyond new vaccines and therapies, biopharma companies turned to RWD/E to understand how the pandemic impacted care or treatment patterns for patients suffering from other diseases, such as cancer.

Technology investments have enabled scaling the use of RWD/E

AI/ML workbench to help data scientists collaborate on developing training and deploying AI & ML models creating efficiencies in RWE generation by, reducing time to insight and enabling the use of new analysis methodologies (e.g., the use of AI).

Adoption of RWD/E in R&D has accelerated as the application in commercial expands

RWD/E can be used to enable a data-driven understanding of disease progression in populations of interest, support label revisions related to safety, and make better decisions on development strategy.

Doing so requires collaboration between industry stakeholders to help streamline RWE use for regulatory submissions. Regulatory openness and acceptance have greatly accelerated the generation and use of RWE for drug development.

Biopharma is leveraging RWD/E in two ways:

1.?Building an external control arm to expedite access to a lifesaving therapy

2.?Using RWE to support label revisions for some drugs (e.g. Updating cancer drugs label revisions can reduce the number of patient visits to infusion centers).

This will create a more robust long-term view of the patient.

Post-launch, organizations have been expanding the use of RWD/E to better understand how therapies perform in the real world and their impact on patient health outcomes.

Some biopharma companies are already analyzing RWD to determine the impact of their marketing outreach to patients and HCPs, allowing the brand to grow more efficiently.

AI-enabled RWE generation is picking up pace

Given the expanding volume and increasing access to RWD, companies are exploring advanced analytics, including machine learning (ML), deep learning, and natural language processing, to generate RWE. Top use cases for AI include:

Understanding patient behaviors Segmenting patients for trial matching Enabling a data-driven understanding of disease progression

More concerted efforts to apply AI for RWE generation require a strong leadership commitment to AI as a long-term strategic priority. Expanding access to high-quality RWD Through a thoughtful strategy, companies could create a cost-effective mix of RWD assets to support their evidence-generation needs

Taking full advantage of the next wave of RWD entails managing RWD as a strategic asset by proactively assessing opportunities and identifying potential partners to access data. A data strategy coupled with the right governance and infrastructure to support partnerships will be key.

Opening the proverbial AI black box or at least creating an understanding of how AI arrived at a particular decision can also help change perceptions around AI use.

As RWE becomes a key future capability, now is the time for organizations to consider steps to embed its use more extensively across the enterprise. Organizations that do so are likely to emerge as leaders and differentiate themselves as they win with RWE as an end-to-end capability.

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Here's to a year of innovation, collaboration, and progress!