Initial characterization of the toxicity and efficacy of elacytarabine (CP-4055), a novel antileukemic agent, using a multidimensional exposure-response relationship model.
Bibliographic record
Abstract
6619^ Background: Elacytarabine (CP-4055), an elaidic acid ester of ara-C, is a novel antineoplastic agent developed to treat hematologic malignancies (HM). It is metabolized to active ara-CTP and inactive ara-U. In 3 studies, diverse elacytarabine monotherapy doses and regimens were given to patients with advanced HM and solid tumours. This analysis aimed to 1) describe the pharmacokinetics (PK) of elacytarabine in these patients and 2) investigate the relationship between PK and toxicity and efficacy in HM. Methods: Population PK analyses were run with ADAPT 5 (ITS) and included 146 patients given a 2h, 4h or 120h continuous infusion (CIV) of elacytarabine. Elacytarabine, ara-C and ara-U plasma concentrations (Cp) were simultaneously modeled and explained. Standard model discrimination criteria were used to select the best model. With the model, different dosing regimens were simulated to find average exposure and % of patients with efficacious or toxic exposure. A multi-dimensional exposure-response relationship (MDERR) was developed with pre-clinical and clinical data. Results: The best model was a 2-compartment (CPT) model with linear elimination and formation rates for the metabolism of elacytarabine to ara-C. Mean PK parameters were Vc = 4.12 L/m2, CL= 5.27 L/h/m2 and T1/2 = 7.57 h. Elacytarabine PK included a peripheral CPT with a non-linear distribution process to reflect saturable binding to red blood cells, otherwise its PK was linear. Ara-C and ara-U PK were described by 2- and 1-CPT linear models, respectively. The MDERR model related efficacy and toxicity thresholds to elacytarabine dosing regimens, and indicated that a 120 h CIV dosing regimen is preferable, allowing elacytarabine to exceed efficacious Cp for longer than the other investigated regimens. A minimum dose of 1000 mg/m2/day is needed for most patients to receive efficacious exposure. Conclusions: Elacytarabine is a promising anti-leukemic agent for patients with advanced HM. Its PK, toxicity and efficacy were characterized in patients given diverse dosing regimens. An MDERR model, which will be further refined or confirmed in upcoming clinical trials, was proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".