Minimizing biological risk for novel inhibitory drug targets: One knockout is all you need
Bibliographic record
Abstract
Abstract We argue that biological risk for novel inhibitory drug targets can be minimized, almost eliminated, by a computational analysis of the healthcare records and DNA sequences in resources like UK Biobank or All-of-Us. The key insight is that an inhibitory drug is functionally equivalent to a loss-of-function (LOF) variant in the targeted gene. It is a special case of what has been called an “experiment of nature”. To demonstrate, we considered all available clinical trials (58 in total) and inhibitory drugs (15 in total) for 5 cardiovascular drug targets: PCSK9, APOC3, ANGPTL3, LPA, and ASGR1. The results were shocking. Every biomarker assessed in these clinical trials was successfully predicted, i.e. directionality and proportionality of effect, but not the magnitude since that varies with dosage. This concept has not been widely adopted because geneticists believe that homozygous LOFs, which are exceedingly rare, would be needed to observe a significant phenotypic effect from most genetic knockouts. Our study shows that, to the contrary, given a sufficiently large biobank, counting both carriers and non-carriers, heterozygous LOFs alone can inform drug development.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".