Frontotemporal degeneration, the next therapeutic frontier: Molecules and animal models for frontotemporal degeneration drug development
Bibliographic record
Abstract
Frontotemporal degeneration (FTD) is a common cause of dementia for which there are currently no approved therapies. Over the past decade, there has been an explosion of knowledge about the biology and clinical features of FTD that has identified a number of promising therapeutic targets as well as animal models in which to develop drugs. The close association of some forms of FTD with neuropathological accumulation of tau protein or increased neuroinflammation due to progranulin protein deficiency suggests that a drug's success in treating FTD may predict efficacy in more common diseases such as Alzheimer's disease. A variety of regulatory incentives, clinical features of FTD such as rapid disease progression, and relatively pure molecular pathology suggest that there are advantages to developing drugs for FTD as compared with other more common neurodegenerative diseases such as Alzheimer's disease. In March 2011, the Frontotemporal Degeneration Treatment Study Group sponsored a conference entitled "FTD, the Next Therapeutic Frontier," which focused on preclinical aspects of FTD drug development. The goal of the meeting was to promote collaborations between academic researchers and biotechnology and pharmaceutical researchers to accelerate the development of new treatments for FTD. Here we report the key findings from the conference, including the rationale for FTD drug development; epidemiological, genetic, and neuropathological features of FTD; FTD animal models and how best to use them; and examples of successful drug development collaborations in other neurodegenerative diseases.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".