A method for systematically ranking therapeutic drug candidates using multiple uncertain screening criteria
Bibliographic record
Abstract
Multiple different screening tests for candidate leads in drug development may often yield conflicting or ambiguous results, sometimes making the selection of leads a nontrivial maximum-likelihood ranking problem. Here, we employ methods from the field of multiple criteria decision making (MCDM) to the problem of screening candidate antibody therapeutics. We employ the SMAA-TOPSIS method to rank a large cohort of antibodies using up to eight weighted screening criteria, in order to find lead candidate therapeutics for Alzheimer's disease, and determine their robustness to both uncertainty in screening measurements, as well as uncertainty in the user-defined weights of importance attributed to each screening criterion. To choose lead candidates and measure the confidence in their ranking, we propose two new quantities, the Retention Probability and the Topness, as robust measures for ranking. This method may enable more systematic screening of candidate therapeutics when it becomes difficult intuitively to process multi-variate screening data that distinguishes candidates, so that additional candidates may be exposed as potential leads, increasing the likelihood of success in downstream clinical trials. The method properly identifies true positives and true negatives from synthetic data, its predictions correlate well with known clinically approved antibodies vs. those still in trials, and it allows for ranking analyses using antibody developability profiles in the literature. We provide a webserver where users can apply the method to their own data: http://bjork.phas.ubc.ca.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".