The metabolic fate of oxaliplatin in the biological milieu investigated during in vivo lung perfusion using a unique miniaturized sampling approach based on solid-phase microextraction coupled with liquid chromatography-mass spectrometry
Bibliographic record
Abstract
Adjuvant chemotherapy after pulmonary metastasectomy for colorectal cancer may reduce recurrence and improve survival rates; however, the benefits of this treatment are limited by the significant side effects that accompany it. The development of a novel in vivo lung perfusion (IVLP) platform would permit the localized delivery of high doses of chemotherapeutic drugs to target residual micrometastatic disease. Nonetheless, it is critical to continuously monitor the levels of such drugs during IVLP administration, as lung injury can occur if tissue concentrations are not maintained within the therapeutic window. This paper presents a simple chemical-biopsy approach based on sampling with a small nitinol wire coated with a sorbent of biocompatible morphology and evaluates its applicability for the near-real-time in vivo determination of oxaliplatin (OxPt) in a 72-h porcine IVLP survival model. To this end, the pigs underwent a 3-h left lung IVLP with 3 doses of the tested drug (5, 7.5, and 40 mg/L), which were administered to the perfusion circuit reservoir as a bolus after a full perfusion flow had been established. Along with OxPt levels, the biocompatible solid-phase microextraction (SPME) probes were employed to profile other low-molecular-weight compounds to provide spatial and temporal information about the toxicity of chemotherapy or lung injury. The resultant measurements revealed a rather heterogeneous distribution of OxPt (over the course of IVLP) in the two sampled sections of the lung. In most cases, the OxPt concentration in the lung tissue peaked during the second hour of IVLP, with this trend being more evident in the upper section. In turn, OxPt in supernatant samples represented ∼25% of the entire drug after the first hour of perfusion, which may be attributable to the binding of OxPt to albumin, its sequestration into erythrocytes, or its rapid nonenzymatic biotransformation. Additionally, the Bio-SPME probes also facilitated the extraction of various endogenous molecules for the purpose of screening biochemical pathways affected during IVLP (i.e., lipid and amino acid metabolism, steroidogenesis, or purine metabolism). Overall, the results of this study demonstrate that the minimally invasive SPME-based sampling approach presented in this work can serve as (pre)clinical and precise bedside medical tool.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".