Critical assessment of purification processes for the robust production of polymeric nanomedicine
Bibliographic record
Abstract
Polymeric nanoparticles are among the most widely used nanocarriers for delivering therapeutic molecules. However, their synthesis processes often generate undesirable impurities that could be toxic and challenging to eliminate. In this study, we compared three purification techniques - centrifugation, dialysis, and tangential flow filtration (TFF) - to evaluate their efficacy in removing residual drug, surfactant, and solvent while preserving the nanoparticles' physicochemical features (hydrodynamic size, zeta potential, polydispersity index). Centrifugation excels in eliminating unencapsulated drug and residual surfactant but significantly affects the nanoparticles' physicochemical properties, such as colloidal stability and size homogeneity. On the other hand, dialysis is a gentler technique effective in removing residual solvent but less so for residual surfactant and unencapsulated drug. TFF emerges as a balanced approach, offering a compromise between the two but none of these techniques achieves satisfactory purification at lab-scale alone. While each technique has its merits, none can meet all requirements independently. The optimal purification strategy often involves a combination of techniques, determined on a case-by-case basis considering factors like purity levels, time, costs, and the preservation of critical properties such as drug loading and colloidal stability. This study underscores the need for a nuanced approach in selecting purification strategies for polymeric nanoparticles in drug delivery applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".