Modeling Gadoxetate Liver Uptake and Efflux Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Enables Preclinical Quantification of Transporter Drug-Drug Interactions
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to model the in vivo transporter-mediated uptake and efflux of the hepatobiliary contrast agent gadoxetate in the liver. The efficacy of the proposed technique was assessed for its ability to provide quantitative insights into drug-drug interactions (DDIs), using rifampicin as inhibitor. MATERIALS AND METHODS: Three groups of C57 mice were scanned twice with a dynamic gadoxetate-enhanced magnetic resonance imaging protocol, using a 3-dimensional spoiled gradient-echo sequence for approximately 72 minutes. Before the second magnetic resonance imaging session, 2 of the groups received a rifampicin dose of 20 (n = 7) or 40 (n = 7) mg/kg, respectively. Data from regions of interest in the liver were analyzed using 2 simplifications of a 2-compartment uptake and efflux model to provide estimates for the gadoxetate uptake rate (ki) into the hepatocytes and its efflux rate (kef) into the bile. Both models were assessed for goodness-of-fit in the group without rifampicin (n = 9), and the appropriate model was selected for assessing the ability to monitor DDIs in vivo. RESULTS: Seven of 9 mice from the group without rifampicin were assessed for model implementation and reproducibility. A simple 3 parameter model (ki, kef, and extracellular space, vecs) adequately described the observed liver concentration time series with mean ki = 0.47 ± 0.11 min and mean kef = 0.039 ± 0.016 min. Visually, the area under the liver concentration time profile was reduced for the groups receiving rifampicin. Furthermore, tracer kinetic modeling demonstrated a significant dose-dependent decrease in the uptake (5.9- and 17.3-fold decrease for 20 mg/kg and 40 mg/kg, respectively) and efflux rates (2.2- and 7.9-fold decrease) compared with the first scan for each group. CONCLUSIONS: This study presents the first in vivo implementation of a 2-compartment uptake and efflux model to monitor DDIs at the transporter-protein level, using the clinically relevant organic anion transporting polypeptide inhibitor rifampicin. The technique has the potential to be a novel alternative to other methods, allowing real-time changes in transporter DDIs to be measured directly in vivo.
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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 | 0.000 | 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 | 0.002 |
| 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 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".