Identification and Validation of Hits from High Throughput Screens for CFTR Modulators
Bibliographic record
Abstract
These are exciting times with the appearance of small molecule compounds in clinical trials which target the basic defects caused by mutation in the CFTR gene. This progress was enabled by years of basic research probing the molecular and cellular consequences caused by mutation and the development of methods by which to study the primary anion transport defect in a high-throughput manner by robotics. Future progress with the development of new, more effective corrector compounds is needed. Such discovery will require further progress in defining the molecular targets for effective intervention using a multidisciplinary approach, merging computational, molecular, proteomic and cell biological methods. There is also an urgent need to develop means to link the right therapeutic compound to the right patients given the heterogeneity of the CF patient population. We envision a time when mid to high-throughput methods will be married with stem cell biology to enable testing a compendium of compounds on cells derived from each individual patient. Given the rate of progress in this field- this scenario may exist in the not too distant future. Keywords: High-throughput screening, in-silico screening, biological validation, selection of appropriate animal and cellular models, mutation, CFTR gene, CF patient population, Cystic fibrosis, ATP-Binding Cassette (ABC) Superfamily, airway
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".