Patient Selection Strategies to Maximize Therapeutic Index of Antibody–Drug Conjugates: Prior Approaches and Future Directions
Bibliographic record
Abstract
Antibody-drug conjugates (ADC) are targeted agents that have shown promise in treating cancer. A central challenge in development of ADCs is the relatively narrow therapeutic index observed in clinical studies. Patient selection strategies based on expression of the target in tumors have the potential to maximize benefit and provide the best chance of clinical success; however, implementation of biomarker-driven trials can be difficult both practically and scientifically. We conducted a survey of recent clinical experience from early-phase ADC trials completed between 2000 and 2019 to evaluate the different approaches to patient selection currently being used and assess whether there is evidence that target expression is associated with clinical activity. Our analysis of patient selection strategies indicates that optimal trial design for early-stage trials should be based on multiple factors, including prevalence and heterogeneity of target expression among intent-to-treat patients, as well as biological factors influencing expression of cell surface and soluble target. To ensure a high probability of success, early implementation of patient selection strategies centered around target expression are pivotal to development of ADCs. In this review, we propose a strategic approach that can be applied for optimization of trial design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".