A Repurposing Strategy Identifies Novel Synergistic Inhibitors of <i>Plasmodium falciparum</i> Heat Shock Protein 90
Bibliographic record
Abstract
Malaria is responsible for 3 million deaths annually. Antimalarial drug resistance is widespread, and few novel, well-defined targets exist. A robotic high throughput screen (HTS) was performed using 4000 small molecules from a natural compound (Spectrum), pharmacologically active (Lopac), and Food and Drug Administration (FDA) approved drug library (Prestwick) for competitive inhibition of the ATP-binding (GHKL) domain of Plasmodium falciparum (Pf) Hsp90, a highly conserved chaperone. Hits were further screened for specificity based on differential inhibition of PfHsp90 in comparison to human (Hs) Hsp90. PfHsp90-specific inhibitors showed 50% inhibitory concentrations (IC(50)) in the nanomolar range when tested using a cell-based antimalarial validation assay. Three hits, identified as selective PfHsp90 inhibitors in the HTS, also demonstrated synergistic activity in the presence of the known antimalarial drug chloroquine. These data support PfHsp90 as a specific antimalarial target with potential for synergy with known antimalarials.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".