Synthetic Ionizable Colloidal Drug Aggregates Enable Endosomal Disruption
Bibliographic record
Abstract
Abstract Colloidal drug aggregates enable the design of drug‐rich nanoparticles; however, the efficacy of stabilized colloidal drug aggregates is limited by entrapment in the endo‐lysosomal pathway. Although ionizable drugs are used to elicit lysosomal escape, this approach is hindered by toxicity associated with phospholipidosis. It is hypothesized that tuning the pKa of the drug would enable endosomal disruption while avoiding phospholipidosis and minimizing toxicity. To test this idea, 12 analogs of the nonionizable colloidal drug fulvestrant are synthesized with ionizable groups to enable pH‐dependent endosomal disruption while maintaining bioactivity. Lipid‐stabilized fulvestrant analog colloids are endocytosed by cancer cells, and the pKa of these ionizable colloids influenced the mechanism of endosomal and lysosomal disruption. Four fulvestrant analogs—those with pKa values between 5.1 and 5.7—disrupted endo‐lysosomes without measurable phospholipidosis. Thus, by manipulating the pKa of colloid‐forming drugs, a tunable and generalizable strategy for endosomal disruption is established.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".