Designing oncology clinical trials has become increasingly complex, particularly for biotech sponsors advancing highly targeted assets with lean teams, limited development budgets and compressed timelines. Today’s oncology trials must navigate tumor heterogeneity, evolving diagnostics, biomarker defined populations and continuously shifting standards of care. What was once a relatively standardized approach to protocol development has evolved into a highly nuanced process requiring scientific precision, operational feasibility and real-world relevance.

Biotech sponsors face fragmented patient populations, regional differences in care, diverse and increasing expectations for diversity, patient-centricity and real-world relevance — often with fewer resources and less margin for error. Studies are no longer evaluated solely on scientific rigor; they must also reflect bioethical considerations, generate findings that are generalizable across patient populations and align with real-world care delivery.

These dynamics can lead to overly complex protocols driven by restrictive eligibility criteria, intensive procedures and assumptions that may not reflect real-world care. For biotech sponsors, even small inefficiencies in protocol design can have disproportionate consequences, from slower enrollment and rising costs to delayed inflection points that are critical for funding, partnering or regulatory strategy. Although oncology patients may be more willing to accept unnecessary procedures, intensive visit schedules or logistical burdens, these demands can still hinder patient enrollment and retention and reduce site engagement. For biotech sponsors, reducing avoidable complexity can be an important lever for preserving trial momentum.

In parallel, relying solely on static treatment guidelines without considering real-world evidence introduces additional risk. Clinical practice evolves rapidly and at an unpredictable pace, with guidelines often lagging emerging treatment patterns. As a result, comparator arms or biomarker strategies that appear appropriate in theory may not reflect how patients will be treated in practice. This misalignment can lead to protocol amendments, enrollment delays and increased operational costs once the study is underway. These challenges can be especially difficult for biotech sponsors because their lean teams and limited resources leave little room to absorb avoidable delays.

What Is a Data-Informed Protocol Assessment-Oncology (DIPA-O)?

A Data-Informed Protocol Assessment for Oncology (DIPA-O) is a structured, evidence-based approach that evaluates oncology trial design using real-world clinical data, competitive benchmarks and advanced analytics. Rather than relying on assumptions or fragmented datasets,
DIPA-O enables sponsors to pressure-test protocol decisions against real-world clinical practice before those decisions become costly to change.

By assessing protocol assumptions early in the design process, this approach helps biotech teams make clearer trade-offs between scientific rigor, feasibility, patient access and execution risk. Importantly, it shifts protocol development from a reactive exercise, where issues are addressed after they emerge, to a proactive strategy focused on anticipating and mitigating risk before execution begins.

For biotech sponsors, this early evidence can support more confident internal alignment, investor conversations, partner discussions and regulatory planning by showing that key design choices have been tested against real-world feasibility. This structured assessment also provides a consistent framework for evaluating design decisions across studies, enabling greater transparency and alignment across clinical, operational, executive and strategic stakeholders.

Key Dimensions of Oncology Protocol Optimization

Six key dimensions can help biotech sponsors identify where protocol design choices may create avoidable risk, and where targeted refinements can improve feasibility without compromising scientific intent:

• Design consistency — ensuring study objectives, endpoints and eligibility criteria are clearly aligned and executable, reducing ambiguity during trial conduct
• Patient burden — assessing the demands placed on patients, including procedure frequency, visit schedules and logistical challenges that may affect recruitment and retention.
• Study procedures — evaluating the necessity and frequency of protocol procedures relative to standard clinical practice, highlighting opportunities to reduce non-essential complexity
• Eligibility criteria and patient availability — assessing how inclusion/exclusion criteria shape the addressable patient population, screen failure risk and enrollment timelines
• Comparable oncology trial design — evaluating similar oncology studies to understand how comparable protocols have been designed and executed.
• Real-world treatment patterns in oncology — informing comparator selection based on how patients are actually treated

Together, these dimensions provide a comprehensive, multidimensional view of clinical trial feasibility. Rather than evaluating protocol components in isolation, sponsors can identify how different design elements interact to either support or hinder study success.

For example, tightening eligibility criteria may improve internal validity but significantly reduce patient availability. Similarly, adding study procedures may enhance data collection but increase patient and site burden. Understanding these interdependencies is critical to optimizing protocol design without compromising scientific intent.

The Role of Advanced Oncology Analytics in Trial Design

While the core DIPA-O framework addresses foundational design elements, advanced oncology analytics introduce a deeper level of precision. These capabilities help biotech sponsors move beyond generalized assumptions and incorporate targeted, context-specific insights which are particularly important when a single asset, indication or study can carry significant strategic and financial weight.

Key capabilities include:

• Biomarker prevalence and testing patterns, supporting more realistic eligibility criteria and stratification strategies
• Global treatment patterns and health technology assessment dynamics, helping ensure comparator arms align with regional standards of care, treatment access and reimbursement realities that may affect enrollment and acceptance
• Racial and ethnic diversity insights, improving patient representativeness and supporting evolving regulatory expectations
• Expanded oncology real-world evidence, providing deeper visibility into treatment pathways and clinical practice nuances to support feasibility assessment, comparator selection and overall protocol robustness

These analytics are particularly valuable in global oncology studies, where variability in care delivery, treatment access and diagnostic infrastructure can significantly impact feasibility. By incorporating these insights early, biotech sponsors can design protocols that are both globally consistent and locally executable.

Applying Data-Informed Insights to Protocol Design

The value of a data-informed approach becomes most apparent when examining how insights translate into concrete design improvements.

Optimizing eligibility criteria:

A biotech sponsor designing an oncology study for a targeted therapy may initially restrict eligibility to patients with a specific biomarker and limited prior therapy exposure. Data-informed assessment may reveal that only a small proportion of patients meet these combined criteria in target geographies. By refining thresholds while maintaining scientific intent, the sponsor may expand the addressable population, reduce screen failure risk and improve the likelihood of meeting enrollment milestones.

Reducing unnecessary procedures:

Protocols often include frequent imaging or invasive procedures based on conservative assumptions. Analysis of real-world clinical practice may show that these interventions occur less frequently without compromising patient management. Reducing non-essential procedures can decrease patient burden, improve retention, simplify site participation and help lean sponsor teams manage study execution more efficiently.

Improving comparator selection:

A comparator arm based on historical guidelines may not reflect current clinical practice. Real-world treatment pattern analysis can reveal shifts toward newer therapies or regional variability in standard of care. Incorporating these insights can help biotech sponsors design comparator strategies that are more relevant, feasible and defensible — supporting enrollment, investigator confidence and regulatory discussions.

These examples illustrate how data-informed insights enable sponsors to refine protocol design in ways that are practical, impactful and aligned with real-world clinical realities.

Balancing Trade-Offs in Oncology Trial Design

Oncology protocol design is inherently a process of managing trade-offs. Decisions related to eligibility, procedures, comparator selection and biomarker inclusion must balance competing priorities, including:

• Scientific rigor vs. patient accessibility
• Data richness vs. operational simplicity
• Global consistency vs. local feasibility
• Protocol ambition vs. execution risk

These decisions are highly interconnected and can significantly influence trial outcomes. For example, expanding eligibility criteria may increase enrollment potential but introduce variability in patient populations. Conversely, narrowing criteria may improve study precision but reduce feasibility.

IQVIA combines proprietary real-world evidence, advanced analytics and deep oncology expertise to help biotech sponsors evaluate these trade-offs early, objectively and holistically. Rather than assessing individual design elements in isolation, this integrated approach enables a comprehensive understanding of how each decision impacts recruitment timelines, operational complexity, patient experience and overall trial success.

This perspective helps transform protocol design from a document-development exercise into a strategic decision point by enabling biotech sponsors to move forward with greater confidence, clearer rationale and stronger alignment across clinical, operational and executive stakeholders.

Why Data-Informed Oncology Trial Design Improves Outcomes

Protocols that are aligned with real-world clinical practice, patient realities and site capabilities are more likely to succeed operationally — an important advantage for biotech sponsors that may have fewer opportunities to absorb delays, amendments or enrollment setbacks. By identifying risks early and optimizing design decisions before execution, data-informed approaches can help reduce the need for costly amendments and mitigate potential delays.

Tailored eligibility criteria can improve patient access and recruitment rates. Streamlined procedures reduce burden on patients and sites. More representative comparator arms can enhance trial relevance and acceptance among investigators and regulators.

Collectively, these improvements contribute to more efficient study execution, stronger decision-making and greater confidence at critical development milestones.

In an environment where oncology trials are becoming increasingly complex and resource-intensive, data-informed protocol optimization is no longer optional — it is essential to achieving both scientific and operational objectives.

Learn how IQVIA Biotech can support your oncology drug development journey from strategy through execution.