Drug-Drug Interactions and the Clinical Tolerability of Colchicine Among Patients With COVID-19
Bibliographic record
Abstract
Importance: Colchicine has many drug-drug interactions with commonly prescribed medications. Only pharmacokinetic studies have provided data on colchicine drug-drug interactions. Objective: To evaluate the clinical tolerability of colchicine according to the presence or absence of a drug-drug interaction. Design, Setting, and Participants: A secondary analysis of the COLCORONA trial was performed. The COLCORONA trial was a randomized, double-blind, placebo-controlled trial conducted in Brazil, Canada, Greece, South Africa, Spain, and the US between March 23, 2020, and January 20, 2021. The COLCORONA trial included ambulatory patients with COVID-19 with at least 1 high-risk characteristic and compared the effects of colchicine (0.5 mg twice daily for 3 days, then 0.5 mg daily thereafter) with placebo for 27 days. Data analysis was performed from February 24, 2023, to June 20, 2024. Exposure: In this secondary analysis, baseline medications that had interactions with colchicine were identified using a previously published expert classification. Main Outcomes and Measures: The primary outcome for this analysis was the composite of serious and nonserious treatment-related and treatment-unrelated gastrointestinal adverse events. The secondary outcomes were other adverse events and the composite of death or hospital admission due to COVID-19 infection. Logistic regression models adjusted for age, sex, estimated glomerular filtration rate, diabetes, heart failure, and myocardial infarction were assessed for effect modification of the association between the randomization arm and the outcomes of interest by drug-drug interaction status. Results: The cohort included 2205 participants in the colchicine arm and 2227 in the placebo arm (median age, 54 [IQR, 47-61] years; 2389 [54%] women). The most common colchicine drug-drug interactions were rosuvastatin (12%) and atorvastatin (10%). In fully adjusted models, the odds of any gastrointestinal adverse event were 1.80 (95% CI, 1.51-2.15) times higher in the colchicine arm than the placebo arm among people without a drug-drug interaction and 1.68 (95% CI, 1.24-2.26) times higher in the colchicine arm than the placebo arm among people with a drug-drug interaction (P = .69 for interaction). Drug-drug interaction status did not significantly modify the effect of colchicine on the composite of COVID-19 hospitalization or death (odds ratio, 0.91; 95% CI, 0.59-1.40 for drug-drug interaction and 0.84; 95% CI, 0.60-1.19 for no drug-drug interaction; P = .80 for interaction). Conclusions and Relevance: In this secondary analysis of the COLCORONA trial, operational classification of drug interactions system class 3 or 4 drug-drug interactions did not appear to significantly increase the risk of colchicine-related adverse effects. Trial Registration: ClinicalTrials.gov Identifier: NCT04322682.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".