Abstract 5042: Defining an optimal single time point sampling strategy representative of overall capecitabine pharmacokinetics
Bibliographic record
Abstract
Abstract Background: Capecitabine is an oral chemotherapy pro-drug used to treat advanced colorectal cancer. Patients may experience hand-foot syndrome and diarrhea, among other side effects, that affect quality of life and may necessitate dose modification or discontinuation. There is significant regional variation in capecitabine tolerability, related to a myriad of factors including pharmacogenomics, dietary and cultural differences. Capecitabine dose modification, when necessary, is empirical based on toxicity suggesting a personalized dosing approach might better optimize therapy. Objective: In phase I of a personalized dosing approach, our objective was to define an optimal time point for blood sampling that best represented overall exposure of capecitabine and its metabolites. Methods: A single-arm prospective pharmacokinetic cohort study of patients with advanced or metastatic colorectal cancer prescribed capecitabine monotherapy was done. Blood samples were collected pre-dose and at timed intervals between 0 and 8 hours post-dose. Plasma concentration of capecitabine and its major metabolites, 5'-deoxy-5-fluorocytidine (5'-DFCR) and 5'-deoxy-5-fluorouracil (5'-DFUR), were measured by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Results: 26 patients were enrolled; 65% were male and 42.3% had metastatic disease. Mean capecitabine dose was 2854 ± 944 mg. Hand-foot symptoms (60%), fatigue (53%) and diarrhea (30%) were the most common adverse drug reactions. Dose normalized mean (SD) AUC0-8h for capecitabine, 5’-DFCR and 5’-DFUR were 6.74 (3.0), 4.19 (1.5) and 6.33 (2.8) ng/ml*h, respectively. Spearman correlation between dose normalized concentrations and AUC at each blood draw was performed. The best estimated time points for capecitabine, 5’-DFCR and 5’-DFUR were 1.5, 2 and 2 hours with r2 values of 0.6 (p <0.01), 0.64 (p <0.001) and 0.51 (p <0.01), respectively. There was a significant correlation seen between capecitabine AUC and need for subsequent dose reduction (p<0.05). Conclusions: Blood samples obtained between 1.5 and 2 hours post-dose provide the best estimate of capecitabine exposure. Further pharmacokinetic analysis in this cohort is ongoing. This blood draw strategy will be used in a larger trial intended to develop a personalized capecitabine dosing algorithm. Citation Format: Stephen Welch, Wendy Teft, John Lenehan, Rommel Tirona, Karen Lumsden, Eric Winquist, Richard B. Kim. Defining an optimal single time point sampling strategy representative of overall capecitabine pharmacokinetics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5042. doi:10.1158/1538-7445.AM2017-5042
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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 | 0.003 | 0.005 |
| 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 | 0.001 | 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".