Abstract 2514: Refining oncology dose selection: A framework for tailored dose optimization in the era of targeted and immunotherapies
Bibliographic record
Abstract
Abstract Historically, oncology dose selection was driven by the identification of the maximum tolerated dose, often resulting in significant toxicity. The advent of molecular targeted therapies and immunotherapies has created opportunities to achieve similar efficacy at a lower, more tolerable dose. Recent FDA draft guidance highlights the importance of dose optimization in drug development. Building on this, we present a novel framework categorizing drugs into four molecular classes based on their mechanisms of action: (1) small molecule targeted therapies and antibody-drug conjugates, (2) large molecule antagonists, (3) cancer immunotherapy agonists, and (4) molecules with limited or no single-agent activity. Unique considerations and strategies for dose optimization are detailed for each class, supported by case examples. We propose integrating proof of activity as a gating criterion for dose expansion to inform robust dose decisions and maximize the use of patient resources. This perspective underscores the integration of preclinical and clinical data—including pharmacokinetics, pharmacodynamics, and patient-reported outcomes—combined with quantitative pharmacology and statistical modeling, to optimize doses effectively while balancing efficacy and safety. While no one-size-fits-all approach exists, this tailored guidance for specific mechanistic classes provides balanced approaches to dose optimization that ultimately enhance the probability of success in drug development. Citation Format: Neekesh Dharia, Jiawen Zhu, Amy Schroeder, Sabine Frank, Christophe Boetsch, Candice Jamois, Nastya Kassir, Koorosh Korfi, Elizabeth Punnoose, Anjali Vaze, Peter Trask, Pritti Gosai, Jane Fridlyand, Chunze Li. Refining oncology dose selection: A framework for tailored dose optimization in the era of targeted and immunotherapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2514.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".