A generalizable pre-clinical research approach for orphan disease therapy
Bibliographic record
Abstract
With the advent of next-generation DNA sequencing, the pace of inherited orphan disease gene identification has increased dramatically, a situation that will continue for at least the next several years. At present, the numbers of such identified disease genes significantly outstrips the number of laboratories available to investigate a given disorder, an asymmetry that will only increase over time. The hope for any genetic disorder is, where possible and in addition to accurate diagnostic test formulation, the development of therapeutic approaches. To this end, we propose here the development of a strategic toolbox and preclinical research pathway for inherited orphan disease. Taking much of what has been learned from rare genetic disease research over the past two decades, we propose generalizable methods utilizing transcriptomic, system-wide chemical biology datasets combined with chemical informatics and, where possible, repurposing of FDA approved drugs for pre-clinical orphan disease therapies. It is hoped that this approach may be of utility for the broader orphan disease research community and provide funding organizations and patient advocacy groups with suggestions for the optimal path forward. In addition to enabling academic pre-clinical research, strategies such as this may also aid in seeding startup companies, as well as further engaging the pharmaceutical industry in the treatment of rare genetic disease.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".