Uncovering the impact of COVID-19–mediated bidirectional dysregulation of cytochrome P450 3A4 on systemic and pulmonary drug concentrations using physiologically based pharmacokinetic modeling
Bibliographic record
Abstract
Several clinical studies have shown that COVID-19 increases the systemic concentration of drugs in hospitalized patients with COVID-19. However, it is unclear how COVID-19-mediated bidirectional dysregulation of hepatic and pulmonary cytochrome P450 (CYP) 3A4 affects drug concentrations, especially in the lung tissue, which is most affected by the disease. Herein, physiologically based pharmacokinetic modeling was used to demonstrate the differences in systemic and pulmonary concentrations of 4 respiratory infectious disease drugs when CYP3A4 is concurrently downregulated in the liver and upregulated in the lung based on existing clinical data on COVID-19-CYP3A4 interactions at varying severity levels including outpatients, non-intensive care unit (ICU), and ICU patients. The study showed that hepatic metabolism is the primary determinant of both systemic and pulmonary drug concentrations despite the concurrent bidirectional dysregulation of liver and lung CYP3A4. ICU patients had the most systemic and pulmonary drug exposure, with a percentage increase in the area under the concentration-time curve in the plasma compartment of approximately 44%, 56%, 114%, and 196% for clarithromycin, nirmatrelvir, dexamethasone, and itraconazole, respectively, relative to the healthy group. Within the ICU cohort, clarithromycin exhibited its highest exposure in lung tissue mass with a fold change of 1189, whereas nirmatrelvir and dexamethasone showed their highest exposure in the plasma compartment, with fold changes of about 126 and 5, respectively, compared with the maximum therapeutic concentrations for their target pathogens. Itraconazole was significantly underexposed in the lung fluid compartment, potentially explaining its limited efficacy for the treatment of COVID-19. These findings underscore the importance of optimizing dosing regimens in at risk ICU patients to enhance both efficacy and safety profiles. SIGNIFICANCE STATEMENT: This study investigated whether COVID-19-mediated concurrent hepatic downregulation and pulmonary upregulation of cytochrome P450 (CYP) 3A4 leads to differences in the systemic and pulmonary concentrations of 4 respiratory medicines. The study demonstrated that intercompartmental differences in drug concentrations were driven by only hepatic CYP3A4 expression. This work suggests that ICU patients with significant COVID-19-CYP3A4 interactions may be at risk of clinically relevant COVID-19-drug interactions, highlighting the need for optimizing dosing regimens in this patient group to improve safety and efficacy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".