Membrane-water partitioning – Tackling the challenges of poorly soluble drugs using chaotropic co-solvents
Bibliographic record
Abstract
Many newly developed drugs suffer from poor water solubility and low bioavailability and hence, need special formulation vehicles like vesicular or micellar drug delivery systems. The knowledge of their membrane-water partition coefficient K becomes critical as is governs drug loading and release from the vehicle, as well as absorption into the body. The dilemma is that measuring K is particularly challenging for these very compounds. Here we establish a strategy to resolve this problem. We added DMSO to shift K and solubility into a convenient range and extrapolated these results back to zero-DMSO. Isothermal titration calorimetry revealed that logK of the kinase inhibitor Lapatinib decreased proportionally to DMSO content (2.5 – 20v%) with a slope of −1/20v% (m value = 28 kJ/mol). This implies a K of 84 mM−1 in DMSO-free buffer. This strategy should be transferable to other poorly soluble drugs and further detection methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".