Lipid Bilayer Properties Control Membrane Partitioning, Binding, and Transport of P-Glycoprotein Substrates
Bibliographic record
Abstract
The ABC protein P-glycoprotein (Pgp or ABCB1) is a multidrug efflux pump capable of transporting many structurally diverse substrates from within the lipid bilayer. Previous studies have demonstrated the importance of the membrane in modulating Pgp function, but few have quantified these effects. We employed purified Pgp reconstituted into phospholipid bilayers with defined gel to liquid-crystalline melting transitions to investigate the effect of membrane environment on the transporter and three of its substrates. Equilibrium dialysis measurements indicated that Hoechst 33342, LDS-751, and MK-571 partitioned much more readily into liquid-crystalline phase bilayers than into gel phase bilayers. However, drug binding affinities revealed that Pgp bound the three substrates more tightly when the lipid bilayer was in the gel phase. The binding affinity of the transporter for substrates within the bilayer was low, in the millimolar range, suggesting that it interacts with them weakly. Thermodynamic analysis revealed that both drug-Pgp and drug-lipid interactions contribute to binding affinity. The kinetics of LDS-751 and Hoechst 33342 transport by reconstituted Pgp was monitored using a real-time fluorescence-based assay to obtain apparent turnover frequencies. Transport rates were found to be sensitive to both drug structure and lipid environment. Arrhenius and transition state analysis of transport rates suggested that the rate of drug transport depends on both the affinity of Pgp for substrate and protein conformational changes. Transport rates did not appear to be limited exclusively by the rate of ATP hydrolysis and may be partially controlled by the rate of drug dissociation.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".