Predicting disruptions to drug pharmacokinetics and the risk of adverse drug reactions in non-alcoholic steatohepatitis patients
Bibliographic record
Abstract
The liver plays a central role in the pharmacokinetics of drugs through drug metabolizing enzymes and transporters. Non-alcoholic steatohepatitis (NASH) causes disease-specific alterations to the absorption, distribution, metabolism, and excretion (ADME) processes, including a decrease in protein expression of basolateral uptake transporters, an increase in efflux transporters, and modifications to enzyme activity. This can result in increased drug exposure and adverse drug reactions (ADRs). Our goal was to predict drugs that pose increased risks for ADRs in NASH patients. Bibliographic research identified 71 drugs with reported ADRs in patients with liver disease, mainly non-alcoholic fatty liver disease (NAFLD), 54 of which are known substrates of transporters and/or metabolizing enzymes. Since NASH is the progressive form of NAFLD but is most frequently undiagnosed, we identified other drugs at risk based on NASH-specific alterations to ADME processes. Here, we present another list of 71 drugs at risk of pharmacokinetic disruption in NASH, based on their transport and/or metabolism processes. It encompasses drugs from various pharmacological classes for which ADRs may occur when used in NASH patients, especially when eliminated through multiple pathways altered by the disease. Therefore, these results may inform clinicians regarding the selection of drugs for use in NASH patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".