The advantages of frontotemporal degeneration drug development (part 2 of frontotemporal degeneration: The next therapeutic frontier)
Bibliographic record
Abstract
Frontotemporal degeneration (FTD) encompasses a spectrum of related neurodegenerative disorders with behavioral, language, and motor phenotypes for which there are currently no effective therapies. This is the second of two articles that summarize the presentations and discussions that occurred at two symposia in 2011 sponsored by the Frontotemporal Degeneration Treatment Study Group, a collaborative group of academic and industry researchers that is devoted to developing treatments for FTD. This article discusses the current status of FTD clinical research that is relevant to the conduct of clinical trials, and why FTD research may be an attractive pathway for developing therapies for neurodegenerative disorders. The clinical and molecular features of FTD, including rapid disease progression and relatively pure molecular pathology, suggest that there are advantages to developing drugs for FTD as compared with other dementias. FTD qualifies as orphan indication, providing additional advantages for drug development. Two recent sets of consensus diagnostic criteria will facilitate the identification of patients with FTD, and a variety of neuropsychological, functional, and behavioral scales have been shown to be sensitive to disease progression. Moreover, quantitative neuroimaging measurements demonstrate progressive brain atrophy in FTD at rates that may surpass Alzheimer's disease. Finally, the similarities between FTD and other neurodegenerative diseases with drug development efforts already underway suggest that FTD researchers will be able to draw on this experience to create a road map for FTD drug development. We conclude that FTD research has reached sufficient maturity to pursue clinical development of specific FTD therapies.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".