Development of a Practical Nomogram for Personalized Anemia Management in Patients Treated with Ataxia Telangiectasia and Rad3-related Inhibitor Camonsertib
Bibliographic record
Abstract
PURPOSE: Camonsertib is a highly selective and potent inhibitor of ataxia telangiectasia and Rad3-related (ATR) kinase. Dose-dependent anemia is a class-related on-target adverse event often requiring dose modifications. Individual patient risk factors for the development of significant anemia complicate the selection of a "one-size-fits-all" ATR inhibitor (ATRi) dose and schedule, possibly leading to suboptimal therapeutic doses in patients at low risk of anemia. We evaluated whether early predictors of anemia could be identified to ultimately inform a personalized dose-modification approach. PATIENTS AND METHODS: On the basis of preclinical observations and a mechanistic understanding of ATRi-related anemia, we identified several potential factors to explore in a multivariable linear regression modeling tool for predicting hemoglobin level ahead of day 22 (cycle 2) of treatment. RESULTS: In patients treated with camonsertib monotherapy (NCT04497116), we observed that hemoglobin decline is consistently preceded by reticulocytopenia, and dose- and exposure-dependent decreases in monocytes. We developed a nomogram incorporating baseline and day 8 hemoglobin and reticulocyte values that predicted the day 22 hemoglobin values of patients with clinically valuable concordance (within 7.5% of observations) 80% of the time in a cross-validation performance test of data from 60 patients. CONCLUSIONS: The prediction of future hemoglobin decrease, after a week of treatment, may enable a personalized, early dose modification to prevent development of clinically significant anemia and resulting unscheduled dose holds or transfusions.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".