<i>Drosophila melanogaster</i> as a function-based high-throughput screening model for antinephrolithiasis agents in kidney stone patients
Bibliographic record
Abstract
ABSTRACT Kidney stone disease involves the aggregation of stone-forming salts consequent to solute supersaturation in urine. The development of novel therapeutic agents for this predominantly metabolic and biochemical disorder have been hampered by the lack of a practical pre-clinical model amenable to drug screening. Here, Drosophila melanogaster, an emerging model for kidney stone disease research, was adapted as a high-throughput functional drug screening platform independent of the multifactorial nature of mammalian nephrolithiasis. Through functional screening, the therapeutic potential of a novel compound commonly known as arbutin that specifically binds to oxalate, a key component of kidney calculi, was identified. Through isothermal titration calorimetry, high-performance liquid chromatography and atomic force microscopy, arbutin was determined to interact with calcium and oxalate in both free and bound states, disrupting crystal lattice structure, growth and crystallization. When used to treat patient urine samples, arbutin significantly abrogated calculus formation in vivo and outperformed potassium citrate in low pH urine conditions, owing to its oxalate-centric mode of action. The discovery of this novel antilithogenic compound via D. melanogaster, independent of a mammalian model, brings greater recognition to this platform, for which metabolic features are primary outcomes, underscoring the power of D. melanogaster as a high-throughput drug screening platform in similar disorders. This is the first description of the use of D. melanogaster as the model system for a high-throughput chemical library screen. This article has an associated First Person interview with the first authors of the paper.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".