Oncology Dose Optimization: Tailored Approaches to Different Molecular Classes
Bibliographic record
Abstract
Oncology dose optimization during the era of chemotherapy focused on identifying the maximum tolerated dose (MTD) for registrational trials, often resulting in significant toxicity. The advent of molecular targeted drugs and immunotherapies offers the potential to achieve similar efficacy with lower doses and fewer side effects, as maximal efficacy is often reached at doses below the MTD. Recent FDA guidance outlines expectations for improving dose optimization in oncology drug development. This review presents a framework for tailored dose optimization by categorizing oncology molecules into four distinct classes based on their mechanisms of action and clinical activities: small molecule targeted therapies and antibody-drug conjugates (Class 1), large molecule antagonists (Class 2), cancer immunotherapy agonists (Class 3), and molecules with limited or no single-agent activity (Class 4). Unique dose optimization considerations for each class are discussed, supported by illustrative case examples. To enhance robust dose decision-making and optimize patient resource utilization, we propose using proof of activity as a gate for initiating dose expansion with one or multiple dose levels. This review emphasizes the importance of integrating all relevant preclinical data, disease knowledge, and clinical measurements and highlights the essential role of quantitative pharmacology and statistical modeling in optimizing doses.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".