Targeting BMP signaling for the treatment of neovascular age‐related macular degeneration
Bibliographic record
Abstract
The majority of current vascular-directed treatments for pathologies such as Age-Related Macular Degeneration (AMD) or cancer target the VEGFR pathway to induce the quiescence of the vasculature network. However, they use frequent or high doses of drugs resulting in local or systemic secondary effects and are only partially effective in some patients. Therefore, treatments targeting additional anti-angiogenic pathways are presently needed to improve conventional therapies and efficiently treat vascular-related pathologies. We hypothesize that current AMD treatments may be improved by combining anti-VEGF treatment with therapies that target additional signaling pathways that promote vessel quiescence. We are therefore investigating the therapeutic potential of BMP9 and its receptor Alk1 for AMD. We have observed an up-regulation of the BMP9 receptors, Alk1 and Endoglin in the endothelium of mice subjected to oxygen-induced retinopathy (OIR) or laser-induced choroidal neovascularization (CNV). In addition, multiple genes associated with BMP signaling were found to be dysregulated in these models. To study the effects of BMP9/Alk1 in retinal pathological angiogenesis, we have generated adenoviral constructs that can either activate or block Alk1 signaling in vivo. We have observed that pathological neovascularization was reduced in mice receiving BMP9, while mice receiving an Alk1 antagonist (Alk1Fc) displayed a higher frequency of vascular abnormalities such as tufts. This study suggests an impairment in BMP signaling in the endothelium of mice subjected to OIR or CVN and that modulation of BMP9 signaling can prevent pathological neovascularization in these models.
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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 | 0.001 |
| 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".