Pharmacomicrobiomics
Bibliographic record
Abstract
Oral medications encounter gut commensal microbes that participate directly and indirectly in drug effects through metabolism, interactions with drug metabolites, or production of substrates that compete with drugs for drug-metabolizing enzymes, consequently influencing drug pharmacokinetics. The microbiota can also affect drug efficacy or toxicity by modulating the immune system; for example, variability in response to cancer immunotherapy, such as anti-PD-1 and anti-CTLA-4 therapies, is increasingly attributed to differences in gut microbial composition and function. These conditions indicate the need and opportunity to intentionally leverage the microbiome for drug effect; as such, the study of how intra- and inter-individual differences in the microbiome affect drug response has gained a definition termed pharmacomicrobiomics. While the need is clear, clinical studies evaluating pharmacomicrobiomic interactions are challenging due to microbiome variability, multiple potential confounders, no standardization of statistical and bioinformatics methods, and the reluctance of potential clinical study participants. In this review, we make the case for pharmacomicrobiomic clinical studies; for the use of modeling and simulation to provide a quantitative framework for data integration, hypothesis testing, and translational-to-late-stage clinical predictions; and the application of real-world data to support both using a within-subject comparison approach. We argue that an integrated and cohesive approach can address the large "inherent" inter-individual variability in the microbiome, attributed to factors such as age, lifestyle choices, environmental factors, chemical and biological exposures, and disease. In summary, there are many challenges to pharmacomicrobiomics research but also enormous potential to improve the development and utilization of pharmaceutical products.
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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 | 0.030 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".