Identifying Antibiotic Effects of Investigational Drugs on Commensal Bacteria with Machine Learning
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Many human-targeted medications have been found to impact patients’ gastrointestinal microbiomes, which has been proposed as an unrecognized source of drug side effects, comorbidities, and reduced treatment efficiencies. However, current methods for detecting such effects, such as patient sample analysis or in vitro high-throughput screening, are both labor- and resource-intensive. To accelerate the discovery of drug effects on the microbiome, we developed machine learning models that predict whether a small, drug-like molecule is likely to inhibit the growth of any of 40 representative human gut commensal microbes. We employed these models to virtually screen thousands of investigational drugs, revealing a strong propensity for human-targeted compounds to potentially modulate commensal microbes. Prospective in vitro validations uncovered two nonantibiotic drugs, the recently approved anti-cancer agent entrectinib and the clinical drug candidate PSI-697, to have previously unknown growth inhibition effects on multiple commensal gut microbes. Furthermore, we show that resistance to the effects of these drugs is mediated by known antibiotic resistance mechanisms BamB and TolC. Additionally, entrectinib significantly reduced microbial richness in a synthetic microbial model community. Taken together, our machine learning-assisted workflow and future extensions can triage microbiome-drug interactions to prioritize experimental testing and validation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 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".