Mining the transcriptome for rare disease therapies: a comparison of the efficiencies of two data mining approaches and a targeted cell-based drug screen
Bibliographic record
Abstract
Most monogenic diseases can be viewed as conditions caused by dysregulated protein activity; therefore, drugs can be used to modulate gene expression, and thus protein level, possibly conferring clinical benefit. When considering repurposing drugs for loss of function diseases, there are three classes of genetic disease amenable to an increase of function; haploinsufficient dominant diseases, those secondary to hypomorphic recessive alleles, and conditions with rescuing paralogs. This therapeutic model then brings the questions: how frequently do such clinically useful drug-gene interactions occur and what is the most rapid and efficient route by which to identify them. Here we compare three approaches: (1) mining of pre-existing system-wide transcriptomal datasets such as Connectivity Map; (2) utilization of a proprietary causal reasoning engine knowledge base; and, (3) a targeted drug screen using clinically accepted agents tested against normal human fibroblasts. We have determined the validation rate of these approaches for 76 diseases (i.e., in vitro fibroblast mRNA increase); for the Connectivity Map, approximately 5% of tested putative drug-gene interactions validated, for causal reasoning engine knowledge base the rate was 10%, and for the targeted drug screen 9%. The degree of overlap between these methodologies was low suggesting they are complementary not redundant approaches to identify putative drug-gene interactions. Although the validation rate was low, a number of drug-gene interactions were successfully identified and are now being investigated for protein induction and in vivo effect. This analysis establishes potentially valuable therapeutic leads as well as useful benchmarks for the thousands of currently untreatable rare genetic conditions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".