Preclinical and clinical evaluation of the LRRK2 inhibitor DNL201 for Parkinson’s disease
Bibliographic record
Abstract
) are the most common genetic risk factors for Parkinson's disease (PD). Increased LRRK2 kinase activity is thought to impair lysosomal function and may contribute to the pathogenesis of PD. Thus, inhibition of LRRK2 is a potential disease-modifying therapeutic strategy for PD. DNL201 is an investigational, first-in-class, CNS-penetrant, selective, ATP-competitive, small-molecule LRRK2 kinase inhibitor. In preclinical models, DNL201 inhibited LRRK2 kinase activity as evidenced by reduced phosphorylation of both LRRK2 at serine-935 (pS935) and Rab10 at threonine-73 (pT73), a direct substrate of LRRK2. Inhibition of LRRK2 by DNL201 demonstrated improved lysosomal function in cellular models of disease, including primary mouse astrocytes and fibroblasts from patients with Gaucher disease. Chronic administration of DNL201 to cynomolgus macaques at pharmacologically relevant doses was not associated with adverse findings. In phase 1 and phase 1b clinical trials in 122 healthy volunteers and in 28 patients with PD, respectively, DNL201 at single and multiple doses inhibited LRRK2 and was well tolerated at doses demonstrating LRRK2 pathway engagement and alteration of downstream lysosomal biomarkers. Robust cerebrospinal fluid penetration of DNL201 was observed in both healthy volunteers and patients with PD. These data support the hypothesis that LRRK2 inhibition has the potential to correct lysosomal dysfunction in patients with PD at doses that are generally safe and well tolerated, warranting further clinical development of LRRK2 inhibitors as a therapeutic modality for PD.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".