Polycystic Kidney Disease Drug Development: A Conference Report
Bibliographic record
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is part of a spectrum of inherited diseases that also includes autosomal recessive polycystic kidney disease, autosomal dominant polycystic liver disease, and an expanding group of recessively inherited disorders collectively termed hepatorenal fibrocystic disorders. ADPKD is the most common monogenic disorder frequently leading to chronic kidney failure with an estimated prevalence of 12 million people worldwide. Currently, only one drug (tolvaptan) has been approved by regulatory agencies as disease-modifying therapy for ADPKD, but, given its mechanism of action and side effect profile, the need for an improved therapy for ADPKD remains a priority. Although significant regulatory progress has been made, with qualification of total kidney volume as a prognostic enrichment biomarker and its later designation as a reasonably likely surrogate endpoint for progression of ADPKD within clinical trials, further work is needed to accelerate drug development efforts for all forms of PKD. In May 2021, the PKD Outcomes Consortium at the Critical Path Institute and the PKD Foundation organized a PKD Regulatory Summit to spur conversations among patients, industry, academic, and regulatory stakeholders regarding future development of tools and drugs for ADPKD and autosomal recessive polycystic kidney disease. This Special Report reviews the key points discussed during the summit and provides future direction related to PKD drug development tools.
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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".