The potential protein-mediated hepatic uptake: discussion on the molecular interactions between albumin and the hepatocyte cell surface and their implications for the in vitro-to-in vivo extrapolations of hepatic clearance of drugs
Bibliographic record
Abstract
Introduction: In quantitative modeling, the resolving of underpredictions and overpredictions of hepatic clearance (CLh) makes a top priority for pharmacokinetic modelers. Clearly, the ‘protein-mediated hepatic uptake’ is a violation of ‘the free drug hypothesis’, but the lack of its consideration in CLh-predictive approaches may be one of the reasons to explain the discrepancies between predicted and observed values.Areas covered: We first review the two ‘albumin-facilitated hepatic uptake’ models that were recently challenged to improve the in vitro-to-in vivo extrapolation (IVIVE) of CLh by reducing the underprediction bias, particularly in the absence of albumin (ALB) in vitro compared to the presence of ALB in vivo. Second, we identify three types of interactions related to the ALB-bound drug moiety (i.e., ALB-lipids, ALB-proteins, and ALB-ligand allosteric interactions) that may be behind the ‘ALB-mediated hepatic uptake’ mechanism(s) for highly bound drugs. Main keywords used in our search are IVIVE; albumin; allostery; protein-mediated uptake; hepatic clearance; polarized hepatocytes.Expert opinion: Understanding the implication of these interactions and the enzyme/transporter interplay for each drug would help selecting the appropriate IVIVE model. Therefore, we have proposed a tree of decision for guidance. The next step is to improve the ‘ALB-facilitated hepatic uptake’ models to cover the remaining uncertainties.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".