Pharmacodynamic Markers of LRRK2 Inhibition in Biofluids
Bibliographic record
Abstract
Abstract Hyper-activated LRRK2 is linked to Parkinson’s disease susceptibility and progression. Quantitative measures of LRRK2 inhibition, especially in the brain, may be critical in the clinical development of successful LRRK2-targeting therapeutics. In this study, three different brain-penetrant and selective LRRK2 small-molecule kinase inhibitors (PFE-360, MLi2, and RA283) were orally administered to groups of cynomolgus macaques at different doses. Biofluid markers with proposed pharmacodynamic properties for assessing LRRK2 inhibition were measured from samples of blood, urine, and cerebral-spinal fluid (CSF). LRRK2 kinase inhibition led to consistent reduced pS935-LRRK2 and pRab10 proteins in blood mononuclear cells, reduced exosome LRRK2 protein and di-docosahexaenoyl (22:6) bis (monoacylglycerol) phosphate in urine, and reduced exosome LRRK2 and autophosphorylated pS1292-LRRK2 protein in CSF. Incomplete LRRK2 kinase inhibition reduced LRRK2 protein secretion in exosomes whereas high drug exposures may reduce both exosome and tissue levels of LRRK2 protein. These orthogonal markers for LRRK2 inhibition in urine and CSF can be used in combination with blood markers to non-invasively monitor the potency of LRRK2-targeting therapeutics in the brain and periphery.
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) | 0.001 | 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".