What is the clinical landscape?
The clinical landscape is a comprehensive view of all clinical research and therapeutic development activity within a particular disease area or indication. It encompasses ongoing, completed, and planned clinical trials, as well as investigational and approved therapies. Key elements of the clinical landscape include study design, endpoints, comparators, patient populations, and trial sponsors. Understanding this landscape provides critical insights into the therapeutic strategies being pursued and the evidence base supporting them.
Why is the clinical landscape important?
Mapping the clinical landscape helps pharmaceutical and biotech companies identify gaps in evidence, differentiate their development programmes, and anticipate regulatory and competitive challenges. It informs clinical trial design, comparator selection, and indication prioritization, and is essential for positioning products within a rapidly evolving treatment environment. For medical and market access teams, understanding the clinical landscape supports engagement with stakeholders, development of value narratives, and evidence generation planning.
What is included in a clinical landscape analysis?
A clinical landscape analysis typically includes all ongoing, completed, and planned clinical trials for a given disease area. It examines trial design, endpoints, comparators, eligibility criteria, study sponsors, geographic spread, and investigational vs. approved therapies.
Why is understanding the clinical landscape important for drug development?
Understanding the clinical landscape helps identify therapeutic gaps, competitor activity, and regulatory precedents. It supports informed decisions around trial design, indication selection, and differentiation strategy.
How does the clinical landscape inform clinical trial design?
The clinical landscape provides insights into common endpoints, inclusion/exclusion criteria, and comparator choices. These inform trial feasibility, regulatory expectations, and opportunities for differentiation.
How often should companies update their clinical landscape analysis?
Clinical landscapes should be reviewed and updated regularly, especially before key decisions such as study protocol finalization, indication expansion, or regulatory submission. Updates are typically aligned with major congresses or quarterly review cycles.
What is the difference between clinical landscape and scientific landscape?
The clinical landscape focuses on trial activity and therapeutic development, while the scientific landscape includes broader academic research, including mechanistic studies, reviews, and preclinical work. Both are complementary for holistic strategic planning.
How do sponsors use the clinical landscape to select comparators?
By identifying what comparators are used in ongoing or recently completed trials, sponsors can ensure their own study design aligns with or improves upon industry norms and regulatory preferences.
What tools are used to assess the clinical landscape?
Common tools include clinical trial databases (e.g., ClinicalTrials.gov, EU Clinical Trials Register), proprietary platforms, literature analysis, and AI-powered landscaping tools. These help track trends and map gaps in real time.
Can the clinical landscape reveal unmet needs?
Yes. A sparse or uneven clinical landscape can highlight areas of low research activity, suggesting potential unmet needs or opportunities for differentiation, especially when triangulated with epidemiological and real-world data.
What role does the clinical landscape play in market access planning?
Market access teams use the clinical landscape to understand the evolving standard of care, anticipate payer questions, and develop value narratives that reflect current treatment options and gaps.
How does clinical landscape analysis support cross-functional collaboration?
It provides a common evidence base for clinical, regulatory, medical, and commercial teams to align around strategy, messaging, and planning. This shared understanding improves efficiency and coherence across functions.