Solubility of Hydrophobic Compounds in Aqueous Solution Using Combinations of Self-assembling Peptide and Amino Acid
Bibliographic record
Abstract
Self-assembling peptides (SAPs) are promising vehicles for the delivery of hydrophobic therapeutics for clinical applications; their amphipathic properties allow them to dissolve hydrophobic compounds in the aqueous environment of the human body. However, self-assembling peptide solutions have poor blood compatibility (e.g., low osmolarity), hindering their clinical application through intravenous administrations. We have recently developed a generalized platform for hydrophobic drug delivery, which combines SAPs with amino acid solutions (SAP-AA) to enhance drug solubility and increase formulation osmolarity to reach the requirements for clinical uses. This formulation strategy was thoroughly tested in the context of three structurally different hydrophobic compounds - PP2, rottlerin, and curcumin - in order to demonstrate its versatility. Furthermore, we examined effects of changing formulation components by analyzing 6 different SAPs, 20 naturally existing amino acids at low and high concentrations, and two different co-solvents dimethyl sulfoxide (DMSO) and ethanol. Our strategy proved to be effective in optimizing components for a given hydrophobic drug, and therapeutic function of the formulated inhibitor, PP2, was observed both in vitro and in vivo. This manuscript outlines our generalized formulation method using SAP-AA combinations for hydrophobic compounds, and analysis of solubility as a first step towards potential use of these formulations in more functional studies. We include representative solubility results for formulation of the hydrophobic compound, curcumin, and discuss how our methodology serves as a platform for future biological studies and disease models.
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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) | 0.000 | 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) | 0.000 | 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".