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Record W4407190420 · doi:10.1093/ndt/gfae216

Upcoming drug targets for kidney protective effects in chronic kidney disease

2024· review· en· W4407190420 on OpenAlexafffund
Massimo Nardone, Kevin Yau, Luxcia Kugathasan, Ayodele Odutayo, Mai Mohsen, Vikas S. Sridhar, David Z.I. Cherney

Bibliographic record

VenueNephrology Dialysis Transplantation · 2024
Typereview
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersBanting and Best Diabetes Centre, University of TorontoCanadian Institutes of Health ResearchDepartment of Medicine, Georgetown UniversityAstellas PharmaUniversity of TorontoSanofiKidney Foundation of CanadaBristol-Myers SquibbEli Lilly and CompanyAstraZenecaCanadian Society of NephrologyDiabetes CanadaNovo NordiskBayer
KeywordsMedicineKidney diseaseIntensive care medicineCardiorenal syndromeKidneyDiabetes mellitusInternal medicineDiseaseEndocrinology

Abstract

fetched live from OpenAlex

People with chronic kidney disease (CKD) are at a high risk of heart disease and end-stage kidney disease. This review describes how new medications, such as glucagon-like peptide-1 receptor agonists (GLP1RA), aldosterone synthase inhibitors (ASi), soluble guanylate cyclase (sGC) and endothelin receptor antagonists (ERA), can lower heart-kidney risk in people with CKD. GLP1RA are already recommended for managing blood sugar in people with CKD and type 2 diabetes and have been shown to lower the risk of developing end-stage kidney disease. GLP1RA will likely soon be included in clinical guidelines, but further research is needed to understand how these medications protect the kidneys. ASi are another new medication that lower the protein found in urine. Larger trials are being done to see how well these medications work in slowing CKD. Lastly, both sGC agonists and ERAs have been shown to relax blood vessels to improve blood flow in the kidney, and reduce the amount of protein found in urine, both of which are critical to protecting kidneys. Larger clinical trials are being done to see if these medications prevent CKD from getting worse. In summary, this review describes the new and promising treatments for CKD. These therapies hold the potential to slow kidney disease and improve the wellbeing of patients. Further research of these new treatments is important for improving CKD care. ABSTRACT: Despite recent advancements in the treatment of chronic kidney disease (CKD), identifying novel therapies beyond guideline-directed therapies that reduce residual cardiorenal risk remains imperative. In this review, we highlight the clinical evidence supporting emerging therapies for CKD, including glucagon-like peptide-1 receptor agonists (GLP1RA) and other incretin-based therapies, aldosterone synthase inhibitors (ASI), endothelin receptor antagonists (ERA), soluble guanylate cyclase (sGC) agonists and anti-inflammatory drugs. Long-acting GLP1RA are already recommended for glycemic control in patients with CKD and type 2 diabetes and the large, dedicated kidney outcome trial FLOW was recently stopped early for efficacy. Emerging clinical trial evidence supports the concept that ASI also provide additional benefit on top of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, which remain a cornerstone of CKD treatment. Next, we consider the use of sGC agonists, which target nitric oxide bioavailability and thereby reduce albuminuria. Finally, we explore the therapeutic potential of ERA, which act through hemodynamic and anti-fibrotic mechanisms, thereby addressing a common final pathway in the development of CKD. Accordingly, our review highlights the changing therapeutic landscape for CKD with promising agents to further prevent the progression of kidney disease.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.312
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations13
Published2024
Admission routes2
Has abstractyes

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