A Purine Analog Synergizes with Chloroquine (CQ) by Targeting Plasmodium falciparum Hsp90 (PfHsp90)
Bibliographic record
Abstract
BACKGROUND: Drug resistance, absence of an effective vaccine, and inadequate public health measures are major impediments to controlling Plasmodium falciparum malaria worldwide. The development of antimalarials to which resistance is less likely is paramount. To this end, we have exploited the chaperone function of P. falciparum Hsp90 (PfHsp90) that serves to facilitate the expression of resistance determinants. METHODS: The affinity and activity of a purine analogue Hsp90 inhibitor (PU-H71) on PfHsp90 was determined using surface plasmon resonance (SPR) studies and an ATPase activity assay, respectively. In vitro, antimalarial activity was quantified using flow cytometry. Interactors of PfHsp90 were determined by LC-MS/MS. In vivo studies were conducted using the Plasmodium berghei infection mouse model. RESULTS: PU-H71 exhibited antimalarial activity in the nanomolar range, displayed synergistic activity with chloroquine in vitro. Affinity studies reveal that the PfHsp90 interacts either directly or indirectly with the P. falciparum chloroquine resistance transporter (PfCRT) responsible for chloroquine resistance. PU-H71 synergized with chloroquine in the P.berghei mouse model of malaria to reduce parasitemia and improve survival. CONCLUSIONS: We propose that the interaction of PfHsp90 with PfCRT may account for the observed antimalarial synergy and that PU-H71 is an effective adjunct for combination therapy.
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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 | 0.000 | 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) | 0.002 | 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".