Drug-disease interaction: Clinical consequences of inflammation on drugs action and disposition
Bibliographic record
Abstract
Inflammation is a culprit in many conditions affecting millions of people worldwide. A plethora of studies has revealed that inflammation and inflammatory mediators such as cytokines and chemokines are associated with altered expression and activity of various proteins such as those involved in drug metabolism, specifically cytochrome P450 enzymes (CYPs). Emphasis of most available reports is on the inflammation-induced downregulation of CYPs, subsequently an increase in their substrate concentrations, and the link between the condition and the inflammatory mediators such as interleukin-6 and tumor necrosis factor alpha. However, reports also suggest that inflammation influences expression and/or activity of other proteins such as those involved in the drug-receptor interaction. These multifaced involvements render the clinical consequence of the inflammation unexpected. Such changes are shown in many inflammatory conditions including rheumatoid arthritis, Crohn's disease, acute respiratory illnesses as well as natural processes such as aging, among others. For example, some commonly used cardiovascular drugs lose their efficacy when patients get afflicted with inflammatory conditions such as rheumatoid arthritis and Crohn's disease. Interestingly, this is despite increased concentration subsequent to reduced clearance. The observation is attributed to a simultaneous reduction in the expression of target receptor proteins such as the calcium and potassium channel and β-adrenergic receptor as well as the metabolic enzymes. This narrative review summarizes the current understanding and clinical implications of the inflammatory effects on both CYPs and drug-receptor target proteins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".