This white paper was prepared in conjunction with our partners at Intelligencia. Read the full white paper at this link.

Innovation in oncology R&D has been driven both by advances in basic science (e.g., improved understanding of hallmarks of cancer) and steadily increasing biopharma R&D investment. This growing investment has been justified by oncology’s position as a therapeutic area that offers an efficient path to marketable drugs, driven by high unmet need and relatively short development times. However, the efficiency of innovation in oncology R&D is becoming a challenge. Success rates for oncology clinical trials (Phases 1-3) have declined steadily from 2015 to 2023. Notably, the distribution of clinical trials by phase has remained relatively consistent over this period, meaning this decline can be attributed to declining clinical trial success rates.

There’s no debate regarding accelerating treatments and addressing unmet patient needs for cancer medicines. The more quickly and efficiently new cancer treatments are commercialized, the better the industry can address patients’ medical needs, as well as the financial and operational goals of optimizing R&D investment and return on investment. As an industry, we achieve these goals through strategic decisions that drive clinical trial design or sequencing, or operational decisions that define clinical trial execution once designed.

This analysis focuses on the former, specifically on the following strategic decisions:

1. Pursuit of accelerated approval: Seeking approval for drugs that target serious conditions and fill unmet medical needs based on surrogate endpoints.
2. Use of surrogate endpoints (for regular approvals): Employing markers that have been proven (or are likely) to predict clinical benefits.
3. Front-loading indication expansion: Initiating clinical programs to expand approved indications for drugs before they demonstrate clinical benefit in their initial registrational clinical trials.
4. Study of patient subpopulations (as opposed to all-comers): Targeting focused subgroups (e.g., those defined by biomarker, risk status and age) rather than entire tumor types.
5. Innovative program design—aggregation of trial phases (for regular approvals): Designing and running dual phase trials (e.g., Phase 1/2, Phase 2/3).
6. Innovative program design—skipping trial phases (for regular approvals): Designing and running programs that omit a phase (e.g., initiating a Phase 2 trial in a specific tumor type without a Phase 1 trial dedicated to that tumor type).
7. Choice of novel (rather than non-novel) clinical trial comparators: Comparing against a novel medicine (i.e., medicines other than chemotherapy and other generic cytotoxic regimens, radiotherapy, generic hormonal therapies, surgery and placebo/observation/best supportive care).

Read the full white paper here.