Population pharmacokinetics and exposure–response of trilaciclib in extensive‐stage small cell lung cancer and triple‐negative breast cancer
Bibliographic record
Abstract
Aims Trilaciclib is a first‐in‐class, intravenous cyclin‐dependent kinase 4/6 inhibitor that provides multilineage protection from chemotherapy‐induced myelosuppression. This analysis aimed to characterize the population pharmacokinetics (PK) of trilaciclib, identify potential covariates influencing trilaciclib PK, and evaluate exposure–response relationships in extensive‐stage small cell lung cancer (ES‐SCLC) and triple‐negative breast cancer (TNBC) trials. Methods Population PK analysis was performed using data from healthy volunteers ( n = 72), patients with ES‐SCLC ( n = 111) and patients with TNBC ( n = 14). Exposure–response analyses were conducted to investigate the impact of trilaciclib exposure (AUC) on myeloprotective efficacy, antitumour efficacy and safety. Logistic regression and Cox regression models were used for binary and time‐to‐event endpoints, respectively. Results Trilaciclib PK was described by a three‐compartment model. Sex, body surface area, baseline albumin concentration and age were identified as significant covariates on trilaciclib PK but did not have clinically relevant impact on exposure. Based on exposure–response analyses, lower and higher exposures of trilaciclib at clinical doses (200–280 mg/m 2 ) were associated with similar myeloprotective effects. Trilaciclib exposure did not impact the antitumour effects of chemotherapy. Higher exposure to trilaciclib was associated with higher probabilities of headache, phlebitis/thrombophlebitis and injection site reactions. Conclusion No dose adjustments are required based on the covariates tested. Trilaciclib resulted in optimal myeloprotective effects with no impact on antitumour effects of chemotherapy. However, higher exposure increased the probabilities of adverse events. The data further support selection of the recommended phase 2 dose (trilaciclib 240 mg/m 2 ).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".