Potential Application of Ionic Liquids in Pharmaceutical Dosage Forms for Small Molecule Drug and Vaccine Delivery System
Bibliographic record
Abstract
Ionic liquids are salts in which the ions are poorly coordinated, which causes them to exist in liquid form below 100°C, or at room temperature. Therefore, these are also defined as room temperature ionic liquids (RTILs). In ionic liquids, at least one ion has a delocalized charge and one component is organic, which prevents the formation of a stable solid form of crystal lattice. Physical properties of ionic liquids, such as melting point, viscosity, and solubility of starting materials and other solvents, are impacted by the substituents on the organic component and by the counterions. Many ionic liquids have even been developed to address specific synthetic problems and that is the reason these are also termed as "designer solvents". Ionic liquids are considered as "green solvents" that exhibit several unique characteristics such as high ionic conductivity, high solvation power, thermal stability, low volatility, and recyclability. Although very useful with several advantages, ionic liquids have some limitations that include high cost and ease of recycling. Moreover, the toxicity and biodegradability of ionic liquids are not yet well understood. Nonetheless, ionic liquids can potentially be used in the field of pharmacy in drug design and formulation development. In drug or vaccine dosage formulation development, ionic liquids can be used as a solubility enhancer, permeability enhancer, stabilizer, targeted delivery inducer, stealth property provider or bioavailability enhancer. In this article we reviewed the physical properties of ionic liquids and potential application of ionic liquids in developing formulations for vaccines and small molecule drugs (A table has been added).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".