Building Reliable Oncology KOL Data to Accelerate Clinical Development

Clinical development refers to the structured process through which investigational oncology treatments are evaluated before they are approved for widespread use. New drugs, biologics, or medical devices must undergo rigorous testing in clinical trials to demonstrate safety, efficacy, and long-term benefit. These investigational treatments are assessed across multiple phases, each designed to answer specific scientific and regulatory questions.

Across each of the phases, oncology KOLs, including clinical investigators, academic researchers, and experienced practitioners, play a critical role. Their expertise informs trial design, patient selection criteria, endpoint relevance, and interpretation of results, helping ensure that studies are both scientifically sound and clinically meaningful. This makes accurate oncology KOL data essential.

The Growing Need for Accurate KOL Data and Profiles in Oncology

​Oncology clinical development today looks very different from a decade ago. Trials are more specialized, patient populations are narrower, and scientific expectations are higher. At the same time, regulatory scrutiny and competitive pressure have increased. In this environment, oncology KOLs contribute across multiple stages of development:​

• Advising on early scientific strategy
• Shaping protocol design and endpoints
• Leading or participating in clinical trials
• Supporting investigator and site selection
• Influencing treatment guidelines and standards of care

Each of these touchpoints depends on having the right KOLs, not just the most visible ones. This is where accurate and structured oncology KOL data becomes indispensable.

​The Limitations of Single-Dimensional KOL Identification Approaches​

Historically, oncology KOL identification has relied heavily on familiar names such as frequent speakers, prolific publishers, or long-standing investigators. While these individuals remain important, this approach often misses emerging experts, regional influencers, and specialists driving innovation in niche tumor areas.​

Common challenges with these methods include:​

• Fragmented data across teams and geographies
• Static KOL lists that are rarely updated
• Over-reliance on publication counts or conference presence
• Limited visibility into real-world influence and collaboration networks

As oncology research becomes more specialized, these gaps can directly impact trial efficiency and scientific alignment.

Understanding Expert Impact: From KOL Identification to Profiling and Influence Mapping

In oncology clinical development, the value of a key opinion leader is not defined by visibility alone, but by how directly their expertise shapes trial outcomes. Some experts influence early scientific hypotheses, others assess protocol feasibility, and a smaller subset plays a decisive role in trial execution, enrollment quality, and regulatory confidence.

Capturing this impact requires an integrated view of KOL identification, profiling, and influence mapping. Together, they provide a clearer understanding of which experts matter most at different stages of clinical development.

A clinically grounded approach to oncology KOL profiling focuses on attributes that are directly relevant to investigational studies, including:

• Tumor-specific expertise aligned with the therapy under development
• Experience across Phase 1–3 oncology clinical trials
• Familiarity with relevant endpoints, biomarkers, and patient subpopulations
• Site-level influence, enrollment performance, and geographic reach

With this context established, KOL influence can be meaningfully assessed. This involves examining:

• Leadership roles in multicenter trials and cooperative research groups
• Contributions to trial-defining publications and consensus guidelines
• Advisory involvement in study design and regulatory strategy
• Professional collaboration networks across investigators and trial sites

Taken together, these signals provide a realistic picture of who drives oncology clinical development forward, how decisions are formed, and which experts accelerate progress at each stage of a program.

The Role of KOL Management Platforms

​As oncology data volumes grow, manual approaches are no longer scalable. This has driven the adoption of KOL management platforms that centralize, analyze, and contextualize oncology KOL intelligence.

​Modern platforms are designed to:

• Consolidate oncology KOL data into a single source of truth
• Enable dynamic KOL influence mapping by tumor area
• Support consistent oncology KOL identification and profiling
• Align KOL insights with clinical development objectives

konectar, an AI-Powered KOL Management Platform, is built to support this shift, helping organizations move from fragmented data to structured intelligence. By combining scientific activity, clinical involvement, and influence indicators, such platforms enable teams to make decisions grounded in evidence rather than assumptions. Importantly, this approach supports both scientific rigor and operational efficiency without turning KOL engagement into a purely commercial exercise.

​As oncology continues to evolve, KOL engagement will remain a cornerstone of successful clinical development. However, the way organizations identify, understand, and work with KOLs must evolve as well. So reliable oncology KOL data is no longer just a support function; it is a strategic asset.