The kidney--the body's playground for drugs: an overview of renal drug handling with selected clinical correlates.
Bibliographic record
Abstract
A greater understanding of transport mechanisms contributing to renal drug handling may be useful in predicting drug clearance and drug interactions. Renal clearance is a dynamic process expressed as the sum of the rates of glomerular filtration and tubular secretion minus the rate of tubular reabsorption. Because the transport of drugs is often against a concentration gradient, renal secretion is mostly an active process involving a variety of transporter mechanisms. Discoveries from molecular biology techniques and gene 'knock-out' experiments have identified a variety of renal tubular proteins responsible for the transport of organic cations, organic anions, neutral and cationic hydrophobic compounds, anionic conjugates and specific agents such as prostaglandins. The discovery of a P-glycoprotein (P-gp) transporter at the apical membrane of renal tubular cells is particularly important. By elucidating compounds that act as substrates, inhibitors or inducers of transport proteins, pharmacologists and clinicians may better understand renal drug clearance. This paper provides a brief overview of several identified renal transport proteins including organic anion transporters, organic cation transporters, ATP-dependent transporters (multidrug resistance [P-gp] and multi-drug resistance associated protein), nucleoside transporters (sodium-dependent purine nucleoside transporter and concentrative nucleoside transporter 1) and peptide transporters. A special focus on known P-gp-mediated drug interactions is included to demonstrate the clinical relevance of transporter protein science. At the patient level, this may lead to novel approaches to alter in vivo pharmacokinetics and improve drug safety through a greater understanding of toxic substrate clearance and drug interactions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".