SARS-CoV-2 infection dysregulates the expression of clinically relevant drug metabolizing enzymes in Vero E6 cells and membrane transporters in human lung tissues
Bibliographic record
Abstract
SARS-CoV-2-mediated interactions with drug metabolizing enzymes and membrane transporters (DMETs) in different tissues, especially lung, the main affected organ may limit the clinical efficacy and safety profile of promising COVID-19 drugs. Herein, we investigated whether SARS-CoV-2 infection could dysregulate the expression of 25 clinically relevant DMETs in Vero E6 cells and postmortem lung tissues from COVID-19 patients. Also, we assessed the role of 2 inflammatory and 4 regulatory proteins in modulating the dysregulation of DMETs in human lung tissues. We showed for the first time that SARS-CoV-2 infection dysregulates CYP3A4 and UGT1A1 at the mRNA level, as well as P-gp and MRP1 at the protein level, in Vero E6 cells and postmortem human lung tissues, respectively. We observed that at the cellular level, DMETs could potentially be dysregulated by SARS-CoV-2-associated inflammatory response and lung injury. We uncovered the pulmonary cellular localization of CYP1A2, CYP2C8, CYP2C9, and CYP2D6, as well as ENT1 and ENT2 in human lung tissues, and observed that the presence of inflammatory cells is the major driving force for the discrepancy in the localization of DMETs between COVID-19 and control human lung tissues. Because alveolar epithelial cells and lymphocytes are both sites of SARS-CoV-2 infection and localization of DMETs, we recommend further investigation of the pulmonary pharmacokinetic profile of current COVID-19 drug dosing regimen to improve clinical outcomes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".