Prospects for mTOR Inhibitor Use in Patients with Polycystic Kidney Disease and Hamartomatous Diseases
Bibliographic record
Abstract
Mammalian target of rapamycin (mTOR) is the core component of two complexes, mTORC1 and mTORC2. mTORC1 is inhibited by rapamycin and analogues. mTORC2 is impeded only in some cell types by prolonged exposure to these compounds. mTOR activation is linked to tubular cell proliferation in animal models and human autosomal dominant polycystic kidney disease (ADPKD). mTOR inhibitors impede cell proliferation and cyst growth in polycystic kidney disease (PKD) models. After renal transplantation, two small retrospective studies suggested that mTOR was more effective than calcineurin inhibitor-based immunosuppression in limiting kidney and/or liver enlargement. By inhibiting vascular remodeling, angiogenesis, and fibrogenesis, mTOR inhibitors may attenuate nephroangiosclerosis, cyst growth, and interstitial fibrosis. Thus, they may benefit ADPKD at multiple levels. However, mTOR inhibition is not without risks and side effects, mostly dose-dependent. Under certain conditions, mTOR inhibition interferes with adaptive increases in renal proliferation necessary for recovery from injury. They restrict Akt activation, nitric oxide synthesis, and endothelial cell survival (downstream from mTORC2) and potentially increase the risk for glomerular and peritubular capillary loss, vasospasm, and hypertension. They impair podocyte integrity pathways and may predispose to glomerular injury. Administration of mTOR inhibitors is discontinued because of side effects in up to 40% of transplant recipients. Currently, treatment with mTOR inhibitors should not be recommended to treat ADPKD. Results of ongoing studies must be awaited and patients informed accordingly. If effective, lower dosages than those used to prevent rejection would minimize side effects. Combination therapy with other effective drugs could improve tolerability and results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".