Bis(monoacylglycerol)phosphate as a non-invasive biomarker to monitor the onset and time-course of phospholipidosis with drug-induced toxicities
Bibliographic record
Abstract
IMPORTANCE TO THE FIELD: Drug-induced phospholipidosis (PL) is a phospholipid storage disorder characterized by the accumulation of multi-lamellar bodies (myeloid bodies) in tissues. A major unanswered question is whether PL represents a benign adaptive response, symptom or early event in drug toxicity. The absence of a non-invasive biomarker to monitor tissue PL has made it difficult to determine the prevalence and implications of PL in the clinic. As a result, the interpretation of PL in risk assessment remains uncertain in preclinical and clinical drug development. AREAS COVERED IN THIS REVIEW: This review describes the rationale for bis(monoacylglycerol)phosphate (BMP) as a biomarker of PL and explores the potential links between PL and the toxicities of drugs. WHAT THE READER WILL GAIN: The similarities between the hypothesized roles of BMP in PL and Niemann-Pick type C disease are discussed. The potential implications of PL for cellular function are described in the context of drug-induced QT prolongation, myopathy and renal toxicity. TAKE HOME MESSAGE: A specific species of BMP, di-docosahexaenoyl-BMP, should be investigated further as a non-invasive biomarker to monitor the onset and time course of PL and to better understand the functional consequences which could contribute to the toxicities of drugs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".