The Effect of the Lipid Environment on Drug Binding and Transport by the P‐Glycoprotein Multidrug Transporter
Bibliographic record
Abstract
The P‐glycoprotein multidrug transporter (Pgp, ABCB1) is an ABC protein that effluxes hundreds of structurally unrelated compounds from cells, and is implicated in multidrug resistance in many human cancers. The membrane environment is critically important for Pgp function since lipophilic substrates partition into, and are pumped out of, the membrane. Using purified Pgp reconstituted into proteoliposomes of defined lipids, the effect of the bilayer melting transition on the lipid partition coefficient, binding affinity, and initial rate of transport was measured for Hoechst 33342, LDS‐751 and MK‐571. All drugs partitioned best into liquid crystalline lipid, and bound to Pgp with millimolar affinities within the membrane, suggesting that they interact with the protein weakly. Pgp drug binding affinity was modulated by both the lipid phase state and acyl chain length, and was higher for all drugs in the rigid gel phase. The k cat values for Pgp‐mediated drug transport were also sensitive to lipid melting, however, they were higher in the liquid crystalline phase. Transition state analysis showed the existence of entropy‐enthalpy compensation and an isokinetic relationship between ΔH ‡ and ΔS ‡ . The transport rate is proposed to be controlled by the drug binding affinity and a large conformational change, rather than the rate of ATP hydrolysis. This research is funded by the Canadian Cancer Society (grant #700248).
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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) | 0.001 | 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".